Flume 1.11.0 User Guide

Introduction

Overview

Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store.

The use of Apache Flume is not only restricted to log data aggregation. Since data sources are customizable, Flume can be used to transport massive quantities of event data including but not limited to network traffic data, social-media-generated data, email messages and pretty much any data source possible.

Apache Flume is a top level project at the Apache Software Foundation.

System Requirements

1. Java Runtime Environment - Java 1.8 or later
2. Memory - Sufficient memory for configurations used by sources, channels or sinks
3. Disk Space - Sufficient disk space for configurations used by channels or sinks
4. Directory Permissions - Read/Write permissions for directories used by agent

Architecture

Data flow model

A Flume event is defined as a unit of data flow having a byte payload and an optional set of string attributes. A Flume agent is a (JVM) process that hosts the components through which events flow from an external source to the next destination (hop).

A Flume source consumes events delivered to it by an external source like a web server. The external source sends events to Flume in a format that is recognized by the target Flume source. For example, an Avro Flume source can be used to receive Avro events from Avro clients or other Flume agents in the flow that send events from an Avro sink. A similar flow can be defined using a Thrift Flume Source to receive events from a Thrift Sink or a Flume Thrift Rpc Client or Thrift clients written in any language generated from the Flume thrift protocol.When a Flume source receives an event, it stores it into one or more channels. The channel is a passive store that keeps the event until it’s consumed by a Flume sink. The file channel is one example – it is backed by the local filesystem. The sink removes the event from the channel and puts it into an external repository like HDFS (via Flume HDFS sink) or forwards it to the Flume source of the next Flume agent (next hop) in the flow. The source and sink within the given agent run asynchronously with the events staged in the channel.

Complex flows

Flume allows a user to build multi-hop flows where events travel through multiple agents before reaching the final destination. It also allows fan-in and fan-out flows, contextual routing and backup routes (fail-over) for failed hops.

Reliability

The events are staged in a channel on each agent. The events are then delivered to the next agent or terminal repository (like HDFS) in the flow. The events are removed from a channel only after they are stored in the channel of next agent or in the terminal repository. This is a how the single-hop message delivery semantics in Flume provide end-to-end reliability of the flow.

Flume uses a transactional approach to guarantee the reliable delivery of the events. The sources and sinks encapsulate in a transaction the storage/retrieval, respectively, of the events placed in or provided by a transaction provided by the channel. This ensures that the set of events are reliably passed from point to point in the flow. In the case of a multi-hop flow, the sink from the previous hop and the source from the next hop both have their transactions running to ensure that the data is safely stored in the channel of the next hop.

Recoverability

The events are staged in the channel, which manages recovery from failure. Flume supports a durable file channel which is backed by the local file system. There’s also a memory channel which simply stores the events in an in-memory queue, which is faster but any events still left in the memory channel when an agent process dies can’t be recovered.

Flume’s KafkaChannel uses Apache Kafka to stage events. Using a replicated Kafka topic as a channel helps avoiding event loss in case of a disk failure.

Standard Setup

This section documents how to configure and wire Flume components using Flume’s long-standing configuration techniques using properties files. See the following section for creating a Flume application using Spring Boot.

Setting up an agent

Flume agent configuration is stored in one or more configuration files that follow the Java properties file format. Configurations for one or more agents can be specified in these configuration files. The configuration includes properties of each source, sink and channel in an agent and how they are wired together to form data flows.

Configuring individual components

Each component (source, sink or channel) in the flow has a name, type, and set of properties that are specific to the type and instantiation. For example, an Avro source needs a hostname (or IP address) and a port number to receive data from. A memory channel can have max queue size (“capacity”), and an HDFS sink needs to know the file system URI, path to create files, frequency of file rotation (“hdfs.rollInterval”) etc. All such attributes of a component needs to be set in the properties file of the hosting Flume agent.

Wiring the pieces together

The agent needs to know what individual components to load and how they are connected in order to constitute the flow. This is done by listing the names of each of the sources, sinks and channels in the agent, and then specifying the connecting channel for each sink and source. For example, an agent flows events from an Avro source called avroWeb to HDFS sink hdfs-cluster1 via a file channel called file-channel. The configuration file will contain names of these components and file-channel as a shared channel for both avroWeb source and hdfs-cluster1 sink.

Starting an agent

An agent is started using a shell script called flume-ng which is located in the bin directory of the Flume distribution. You need to specify the agent name, the config directory, and the config file on the command line:

$ bin/flume-ng agent -n $agent_name -c conf -f conf/flume-conf.properties.template

Now the agent will start running source and sinks configured in the given properties file.

A simple example

Here, we give an example configuration file, describing a single-node Flume deployment. This configuration lets a user generate events and subsequently logs them to the console.

# example.conf: A single-node Flume configuration

# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444

# Describe the sink
a1.sinks.k1.type = logger

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

This configuration defines a single agent named a1. a1 has a source that listens for data on port 44444, a channel that buffers event data in memory, and a sink that logs event data to the console. The configuration file names the various components, then describes their types and configuration parameters. A given configuration file might define several named agents; when a given Flume process is launched a flag is passed telling it which named agent to manifest.

Given this configuration file, we can start Flume as follows:

$ bin/flume-ng agent --conf conf --conf-file example.conf --name a1

Note that in a full deployment we would typically include one more option: --conf=<conf-dir>. The <conf-dir> directory would include a shell script flume-env.sh and potentially a log4j configuration file.

From a separate terminal, we can then telnet port 44444 and send Flume an event:

$ telnet localhost 44444
Trying 127.0.0.1...
Connected to localhost.localdomain (127.0.0.1).
Escape character is '^]'.
Hello world! <ENTER>
OK

The original Flume terminal will output the event in a log message.

12/06/19 15:32:19 INFO source.NetcatSource: Source starting
12/06/19 15:32:19 INFO source.NetcatSource: Created serverSocket:sun.nio.ch.ServerSocketChannelImpl[/127.0.0.1:44444]
12/06/19 15:32:34 INFO sink.LoggerSink: Event: { headers:{} body: 48 65 6C 6C 6F 20 77 6F 72 6C 64 21 0D          Hello world!. }

Congratulations - you’ve successfully configured and deployed a Flume agent! Subsequent sections cover agent configuration in much more detail.

Configuration from URIs

As of version 1.10.0 Flume supports being configured using URIs instead of just from local files. Direct support for HTTP(S), file, and classpath URIs is included. The HTTP support includes support for authentication using basic authorization but other authorization mechanisms may be supported by specifying the fully qualified name of the class that implements the AuthorizationProvider interface using the –auth-provider option. HTTP also supports reloading of configuration files using polling if the target server properly responds to the If-Modified-Since header.

To specify credentials for HTTP authentication add:

--conf-user userid --conf-password password

to the startup command.

Multiple Configuration Files

As of version 1.10.0 Flume supports being configured from multiple configuration files instead of just one. This more easily allows values to be overridden or added based on specific environments. Each file should be configured using its own –conf-file or –conf-uri option. However, all files should either be provided with –conf-file or with –conf-uri. If –conf-file and –conf-uri appear together as options all –conf-uri configurations will be processed before any of the –conf-file configurations are merged.

For example, a configuration of:

$ bin/flume-ng agent --conf conf --conf-file example.conf --conf-uri http://localhost:80/flume.conf --conf-uri http://localhost:80/override.conf --name a1

will cause flume.conf to be read first, override.conf to be merged with it and finally example.conf would be merged last. If it is desirec to have example.conf be the base configuration it should be specified using the –conf-uri option either as:

--conf-uri classpath://example.conf
or
--conf-uri file:///example.conf

depending on how it should be accessed.

Using environment variables, system properies, or other properties configuration files

Flume has the ability to substitute environment variables in the configuration. For example:

a1.sources = r1
a1.sources.r1.type = netcat
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = ${env:NC_PORT}
a1.sources.r1.channels = c1

NB: it currently works for values only, not for keys. (Ie. only on the “right side” of the = mark of the config lines.)

As of version 1.10.0 Flume resolves configuration values using Apache Commons Text’s StringSubstitutor class using the default set of Lookups along with a lookup that uses the configuration files as a source for replacement values.

For example::

$ NC_PORT=44444 bin/flume-ng agent –conf conf –conf-file example.conf –name a1

Note the above is just an example, environment variables can be configured in other ways, including being set in conf/flume-env.sh.

As noted, system properties are also supported, so the configuration:

a1.sources = r1
a1.sources.r1.type = netcat
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = ${sys:NC_PORT}
a1.sources.r1.channels = c1

could be used and the startup command could be:

$ bin/flume-ng agent --conf conf --conf-file example.conf --name a1 -DNC_PORT=44444

Furthermore, because multiple configuration files are allowed the first file could contain:

a1.sources = r1
a1.sources.r1.type = netcat
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = ${NC_PORT}
a1.sources.r1.channels = c1

and the override file could contain:

NC_PORT = 44444

In this case the startup command could be:

$ bin/flume-ng agent --conf conf --conf-file example.conf --conf-file override.conf --name a1

Note that the method for specifying environment variables as was done in prior versions will stil work but has been deprecated in favor of using ${env:varName}.

Using a command options file

Instead of specifying all the command options on the command line as of version 1.10.0 command options may be placed in either /etc/flume/flume.opts or flume.opts on the classpath. An example might be:

conf-file = example.conf
conf-file = override.conf
name = a1

Logging raw data

Logging the raw stream of data flowing through the ingest pipeline is not desired behavior in many production environments because this may result in leaking sensitive data or security related configurations, such as secret keys, to Flume log files. By default, Flume will not log such information. On the other hand, if the data pipeline is broken, Flume will attempt to provide clues for debugging the problem.

One way to debug problems with event pipelines is to set up an additional Memory Channel connected to a Logger Sink, which will output all event data to the Flume logs. In some situations, however, this approach is insufficient.

In order to enable logging of event- and configuration-related data, some Java system properties must be set in addition to log4j properties.

To enable configuration-related logging, set the Java system property -Dorg.apache.flume.log.printconfig=true. This can either be passed on the command line or by setting this in the JAVA_OPTS variable in flume-env.sh.

To enable data logging, set the Java system property -Dorg.apache.flume.log.rawdata=true in the same way described above. For most components, the log4j logging level must also be set to DEBUG or TRACE to make event-specific logging appear in the Flume logs.

Here is an example of enabling both configuration logging and raw data logging:

$ bin/flume-ng agent --conf conf --conf-file example.conf --name a1 -Dorg.apache.flume.log.printconfig=true -Dorg.apache.flume.log.rawdata=true

Zookeeper based Configuration

Flume supports Agent configurations via Zookeeper. This is an experimental feature. The configuration file needs to be uploaded in the Zookeeper, under a configurable prefix. The configuration file is stored in Zookeeper Node data. Following is how the Zookeeper Node tree would look like for agents a1 and a2

- /flume
 |- /a1 [Agent config file]
 |- /a2 [Agent config file]

Once the configuration file is uploaded, start the agent with following options

$ bin/flume-ng agent –conf conf -z zkhost:2181,zkhost1:2181 -p /flume –name a1

Argument Name	Default	Description
z	–	Zookeeper connection string. Comma separated list of hostname:port
p	/flume	Base Path in Zookeeper to store Agent configurations

Installing third-party plugins

Flume has a fully plugin-based architecture. While Flume ships with many out-of-the-box sources, channels, sinks, serializers, and the like, many implementations exist which ship separately from Flume.

While it has always been possible to include custom Flume components by adding their jars to the FLUME_CLASSPATH variable in the flume-env.sh file, Flume now supports a special directory called plugins.d which automatically picks up plugins that are packaged in a specific format. This allows for easier management of plugin packaging issues as well as simpler debugging and troubleshooting of several classes of issues, especially library dependency conflicts.

The plugins.d directory

The plugins.d directory is located at $FLUME_HOME/plugins.d. At startup time, the flume-ng start script looks in the plugins.d directory for plugins that conform to the below format and includes them in proper paths when starting up java.

Directory layout for plugins

Each plugin (subdirectory) within plugins.d can have up to three sub-directories:

1. lib - the plugin’s jar(s)
2. libext - the plugin’s dependency jar(s)
3. native - any required native libraries, such as .so files

Example of two plugins within the plugins.d directory:

plugins.d/
plugins.d/custom-source-1/
plugins.d/custom-source-1/lib/my-source.jar
plugins.d/custom-source-1/libext/spring-core-2.5.6.jar
plugins.d/custom-source-2/
plugins.d/custom-source-2/lib/custom.jar
plugins.d/custom-source-2/native/gettext.so

Data ingestion

Flume supports a number of mechanisms to ingest data from external sources.

RPC

An Avro client included in the Flume distribution can send a given file to Flume Avro source using avro RPC mechanism:

$ bin/flume-ng avro-client -H localhost -p 41414 -F /usr/logs/log.10

The above command will send the contents of /usr/logs/log.10 to to the Flume source listening on that ports.

Executing commands

There’s an exec source that executes a given command and consumes the output. A single ‘line’ of output ie. text followed by carriage return (‘\r’) or line feed (‘\n’) or both together.

Network streams

Flume supports the following mechanisms to read data from popular log stream types, such as:

1. Avro
2. Thrift
3. Syslog
4. Netcat

Setting multi-agent flow

In order to flow the data across multiple agents or hops, the sink of the previous agent and source of the current hop need to be avro type with the sink pointing to the hostname (or IP address) and port of the source.

Consolidation

A very common scenario in log collection is a large number of log producing clients sending data to a few consumer agents that are attached to the storage subsystem. For example, logs collected from hundreds of web servers sent to a dozen of agents that write to HDFS cluster.

This can be achieved in Flume by configuring a number of first tier agents with an avro sink, all pointing to an avro source of single agent (Again you could use the thrift sources/sinks/clients in such a scenario). This source on the second tier agent consolidates the received events into a single channel which is consumed by a sink to its final destination.

Multiplexing the flow

Flume supports multiplexing the event flow to one or more destinations. This is achieved by defining a flow multiplexer that can replicate or selectively route an event to one or more channels.

The above example shows a source from agent “foo” fanning out the flow to three different channels. This fan out can be replicating or multiplexing. In case of replicating flow, each event is sent to all three channels. For the multiplexing case, an event is delivered to a subset of available channels when an event’s attribute matches a preconfigured value. For example, if an event attribute called “txnType” is set to “customer”, then it should go to channel1 and channel3, if it’s “vendor” then it should go to channel2, otherwise channel3. The mapping can be set in the agent’s configuration file.

Spring Boot Setup

Apache Flume provides the flume-spring-boot module to provide support for packaging and configuring an application using Spring Boot. Version 2.0.0 or greater of flume-spring-boot should be used.

Flume “normally” follows a paradigm where each component implements the Configurable interface and must implement the configure method to configure itself by retrieving the configuration attributes from its Context’s properties. In contrast, Spring and Spring Boot normally rely on dependency injection. where the configuration values are set into the object being configured either as constructor parameters or via setter methods.

Flume’s integration with Spring Boot provides support to configure Flume components in the application’s properties via the normal application.yml. Unlike Flume’s default method of configuring however, only component attributes are specified in the application.yml. Wiring components together, and thus defining the flows, is handled via Spring’s Java configuration.

Creating the application

Flume’s Spring Boot support provides the main class to be configured with spring as org.apache.flume.spring.boot.FlumeApplication. Flume applications that use Spring Boot should configure the Spring Boot Maven plugin with that as the main class as in:

<execution>
  <id>repackage</id>
  <goals>
    <goal>repackage</goal>
  </goals>
  <configuration>
    <executable>true</executable>
    <mainClass>org.apache.flume.spring.boot.FlumeApplication</mainClass>
  </configuration>
</execution>

Component Scanning

Spring Boot will automatically locate all the Spring components provided by Flume. However, in order for the Flume application to be configured Spring needs the configuration and package names used by the application in order for Spring to locate these components.This is accomplished in the application by providing a META-INF/spring.factories file that enables auto configuration for one class that will then provide the component scanning information for the rest of the application. For example:

META-INF/spring.factories:

org.springframework.boot.autoconfigure.EnableAutoConfiguration=com.sample.myapp.config.AppConfig

com.sample.config.AppConfig.java:

package com.sample.myapp.config;

import org.springframework.context.annotation.ComponentScan;
import org.springframework.context.annotation.Configuration;

@Configuration
@ComponentScan(basePackages="com.sample.myapp")
public class MyConfiguration {

}

This will result in all classes in the com.sample.myapp package and it`s sub-packages being scanned by Spring for components to be included. Note that classes found there may also use Spring’s @Import annotation to include classes in other packages.

Component Wiring

Flume’s Spring Boot support will automatically gather all the defined Channels, SourceRunners, and SinkRunners and start them. To do that they all must first be created as Spring Singletons using the Spring @Bean annotation in a class containing the @Configuration annotation on the class declaration and then initialize them just as the “normal” FlumeApplication class does. To define these components an application should provide a Configuration class that creates these Flume components. An example configuration that generates sequence numbers, writes them to a MemoryChannel and then consumes these events without publishing them anywhere would look like:

@Configuration
@ComponentScan(basePackages="com.sample.myapp")
public class AppConfig extends AbstractFlumeConfiguration {

  @Bean
  @ConfigurationProperties(prefix = "flume.sources.source1")
  public Map<String, String> source1Properties() {
    return new HashMap<>();
  }

  @Bean
  @ConfigurationProperties(prefix = "flume.channels.channel1")
  public Map<String, String> channel1Properties() {
    return new HashMap<>();
  }

  @Bean
  public Channel memoryChannel(Map<String, String> channel1Properties) {
    return configureChannel("channel1", MemoryChannel.class, channel1Properties);
  }

  @Bean
  public SourceRunner seqSource(Channel memoryChannel, Map<String, String> source1Properties) {
    ChannelSelector selector = new ReplicatingChannelSelector();
    selector.setChannels(listOf(memoryChannel));
    return configureSource("source1", SequenceGeneratorSource.class, selector,
        source1Properties);
  }

  @Bean
  public SinkRunner nullSink(Channel memoryChannel) {
    Sink sink = configureSink("null", NullSink.class, memoryChannel,null);
    return createSinkRunner(configureSinkProcessor(null, DefaultSinkProcessor.class,
        listOf(sink)));
  }
}

The configuration for this configuration might look like:

spring:
  application:
    name: flume-test

server:
  port: 41414

flume:
  metrics: http
  sources:
    source1:
      totalEvents: 10000
  channels:
    channel1:
      capacity: 10000

This would result in an application named “flume-test” that listens on port 41414 for the /metrics endpoint. 10,000 events would be written to the channel. These events would be consumed by the NullSink. The Configuration class should extend AbstractFlumeConfiguration, as shown here, to be able to use the helper classes that construct the appropriate Flume components.

Note that all Spring Boot facilities are available to Flume applications configured this way.

SinkGroups and Sinks may also be configured in a similar fashion as in:

flume:
  sinkGroups:
    rrobin:
       backoff: true
       selector: round_robin
       "selector.maxTimeOut": 30000

  sinks:
    avroSinks:
      avroSink1:
        hostname: 192.168.10.10
        port: 4141
        batch-size: 100
        compression-type: deflate
      avroSink2:
        hostname: 192.168.10.11
        port: 4141
        batch-size: 100
        compression-type: deflate

These would be configured in the Java config with:

@Bean
@ConfigurationProperties(prefix = "flume.sink-groups.rrobin")
public Map<String, String> rrobinProperties() {
    return new HashMap<>();
}

@Bean
@ConfigurationProperties(prefix = "flume.sinks.avro-sinks")
public Map<String, AvroSinkConfiguration> avroSinksProperties() {
    return new HashMap<>();
}

@Bean
public List<Sink> avroSinks(final Channel avroFileChannel,
    final Map<String, AvroSinkConfiguration> avroSinksProperties) {
    List<Sink> sinks = new ArrayList<>();
    for (Map.Entry<String, AvroSinkConfiguration> entry : avroSinksProperties.entrySet()) {
        sinks.add(configureSink(entry.getKey(), AvroSink.class, avroFileChannel,
            entry.getValue().getProperties()));
    }
    return sinks;
}

@Bean
public SinkRunner avroSinkRunner(final Map<String, String> rrobinProperties, final List<Sink> avroSinks) {
    return createSinkRunner(
        configureSinkProcessor(rrobinProperties, LoadBalancingSinkProcessor.class, avroSinks));
}

Note that the attribute names specified for the sources, channels, and sink groups must match the attribute names specified for the components in other portions of this documentation.

It is important to note that a concrete class was used to capture the data for the Avro Sinks. When a simple Map is used Spring seems to get confused with the nested Maps. The AvroSinkConfiguration class would look like:

public class AvroSinkConfiguration {

   private String hostName;
   private int port;
   private int batchSize;
   private String compressionType;

   public String getHostName() {
      return hostName;
   }

   public void setHostName(String hostName) {
      this.hostName = hostName;
   }

   public int getPort() {
      return port;
   }

   public void setPort(int port) {
      this.port = port;
   }

   public int getBatchSize() {
      return batchSize;
   }

   public void setBatchSize(int batchSize) {
      this.batchSize = batchSize;
   }

   public String getCompressionType() {
      return compressionType;
   }

   public void setCompressionType(String compressionType) {
      this.compressionType = compressionType;
   }

   public Map<String, String> getProperties() {
      Map<String, String> map = new HashMap<>();
      map.put("hostname", hostName);
      map.put("port", Integer.toString(port));
      map.put("batchSize", Integer.toString(batchSize));
      map.put(compressionType, compressionType);
      return map;
   }
}

Configuration

As mentioned in the earlier section, standard Flume agent configuration is read from a file that resembles a Java property file format with hierarchical property settings.

Defining the flow

To define the flow within a single agent, you need to link the sources and sinks via a channel. You need to list the sources, sinks and channels for the given agent, and then point the source and sink to a channel. A source instance can specify multiple channels, but a sink instance can only specify one channel. The format is as follows:

# list the sources, sinks and channels for the agent
<Agent>.sources = <Source>
<Agent>.sinks = <Sink>
<Agent>.channels = <Channel1> <Channel2>

# set channel for source
<Agent>.sources.<Source>.channels = <Channel1> <Channel2> ...

# set channel for sink
<Agent>.sinks.<Sink>.channel = <Channel1>

For example, an agent named agent_foo is reading data from an external avro client and sending it to HDFS via a memory channel. The config file weblog.config could look like:

# list the sources, sinks and channels for the agent
agent_foo.sources = avro-appserver-src-1
agent_foo.sinks = hdfs-sink-1
agent_foo.channels = mem-channel-1

# set channel for source
agent_foo.sources.avro-appserver-src-1.channels = mem-channel-1

# set channel for sink
agent_foo.sinks.hdfs-sink-1.channel = mem-channel-1

This will make the events flow from avro-AppSrv-source to hdfs-Cluster1-sink through the memory channel mem-channel-1. When the agent is started with the weblog.config as its config file, it will instantiate that flow.

Configuring individual components

After defining the flow, you need to set properties of each source, sink and channel. This is done in the same hierarchical namespace fashion where you set the component type and other values for the properties specific to each component:

# properties for sources
<Agent>.sources.<Source>.<someProperty> = <someValue>

# properties for channels
<Agent>.channels.<Channel>.<someProperty> = <someValue>

# properties for sinks
<Agent>.sinks.<Sink>.<someProperty> = <someValue>

The property “type” needs to be set for each component for Flume to understand what kind of object it needs to be. Each source, sink and channel type has its own set of properties required for it to function as intended. All those need to be set as needed. In the previous example, we have a flow from avro-AppSrv-source to hdfs-Cluster1-sink through the memory channel mem-channel-1. Here’s an example that shows configuration of each of those components:

agent_foo.sources = avro-AppSrv-source
agent_foo.sinks = hdfs-Cluster1-sink
agent_foo.channels = mem-channel-1

# set channel for sources, sinks

# properties of avro-AppSrv-source
agent_foo.sources.avro-AppSrv-source.type = avro
agent_foo.sources.avro-AppSrv-source.bind = localhost
agent_foo.sources.avro-AppSrv-source.port = 10000

# properties of mem-channel-1
agent_foo.channels.mem-channel-1.type = memory
agent_foo.channels.mem-channel-1.capacity = 1000
agent_foo.channels.mem-channel-1.transactionCapacity = 100

# properties of hdfs-Cluster1-sink
agent_foo.sinks.hdfs-Cluster1-sink.type = hdfs
agent_foo.sinks.hdfs-Cluster1-sink.hdfs.path = hdfs://namenode/flume/webdata

#...

Adding multiple flows in an agent

A single Flume agent can contain several independent flows. You can list multiple sources, sinks and channels in a config. These components can be linked to form multiple flows:

# list the sources, sinks and channels for the agent
<Agent>.sources = <Source1> <Source2>
<Agent>.sinks = <Sink1> <Sink2>
<Agent>.channels = <Channel1> <Channel2>

Then you can link the sources and sinks to their corresponding channels (for sources) or channel (for sinks) to setup two different flows. For example, if you need to setup two flows in an agent, one going from an external avro client to external HDFS and another from output of a tail to avro sink, then here’s a config to do that:

# list the sources, sinks and channels in the agent
agent_foo.sources = avro-AppSrv-source1 exec-tail-source2
agent_foo.sinks = hdfs-Cluster1-sink1 avro-forward-sink2
agent_foo.channels = mem-channel-1 file-channel-2

# flow #1 configuration
agent_foo.sources.avro-AppSrv-source1.channels = mem-channel-1
agent_foo.sinks.hdfs-Cluster1-sink1.channel = mem-channel-1

# flow #2 configuration
agent_foo.sources.exec-tail-source2.channels = file-channel-2
agent_foo.sinks.avro-forward-sink2.channel = file-channel-2

Configuring a multi agent flow

To setup a multi-tier flow, you need to have an avro/thrift sink of first hop pointing to avro/thrift source of the next hop. This will result in the first Flume agent forwarding events to the next Flume agent. For example, if you are periodically sending files (1 file per event) using avro client to a local Flume agent, then this local agent can forward it to another agent that has the mounted for storage.

Weblog agent config:

# list sources, sinks and channels in the agent
agent_foo.sources = avro-AppSrv-source
agent_foo.sinks = avro-forward-sink
agent_foo.channels = file-channel

# define the flow
agent_foo.sources.avro-AppSrv-source.channels = file-channel
agent_foo.sinks.avro-forward-sink.channel = file-channel

# avro sink properties
agent_foo.sinks.avro-forward-sink.type = avro
agent_foo.sinks.avro-forward-sink.hostname = 10.1.1.100
agent_foo.sinks.avro-forward-sink.port = 10000

# configure other pieces
#...

HDFS agent config:

# list sources, sinks and channels in the agent
agent_foo.sources = avro-collection-source
agent_foo.sinks = hdfs-sink
agent_foo.channels = mem-channel

# define the flow
agent_foo.sources.avro-collection-source.channels = mem-channel
agent_foo.sinks.hdfs-sink.channel = mem-channel

# avro source properties
agent_foo.sources.avro-collection-source.type = avro
agent_foo.sources.avro-collection-source.bind = 10.1.1.100
agent_foo.sources.avro-collection-source.port = 10000

# configure other pieces
#...

Here we link the avro-forward-sink from the weblog agent to the avro-collection-source of the hdfs agent. This will result in the events coming from the external appserver source eventually getting stored in HDFS.

Fan out flow

As discussed in previous section, Flume supports fanning out the flow from one source to multiple channels. There are two modes of fan out, replicating and multiplexing. In the replicating flow, the event is sent to all the configured channels. In case of multiplexing, the event is sent to only a subset of qualifying channels. To fan out the flow, one needs to specify a list of channels for a source and the policy for the fanning it out. This is done by adding a channel “selector” that can be replicating or multiplexing. Then further specify the selection rules if it’s a multiplexer. If you don’t specify a selector, then by default it’s replicating:

# List the sources, sinks and channels for the agent
<Agent>.sources = <Source1>
<Agent>.sinks = <Sink1> <Sink2>
<Agent>.channels = <Channel1> <Channel2>

# set list of channels for source (separated by space)
<Agent>.sources.<Source1>.channels = <Channel1> <Channel2>

# set channel for sinks
<Agent>.sinks.<Sink1>.channel = <Channel1>
<Agent>.sinks.<Sink2>.channel = <Channel2>

<Agent>.sources.<Source1>.selector.type = replicating

The multiplexing select has a further set of properties to bifurcate the flow. This requires specifying a mapping of an event attribute to a set for channel. The selector checks for each configured attribute in the event header. If it matches the specified value, then that event is sent to all the channels mapped to that value. If there’s no match, then the event is sent to set of channels configured as default:

# Mapping for multiplexing selector
<Agent>.sources.<Source1>.selector.type = multiplexing
<Agent>.sources.<Source1>.selector.header = <someHeader>
<Agent>.sources.<Source1>.selector.mapping.<Value1> = <Channel1>
<Agent>.sources.<Source1>.selector.mapping.<Value2> = <Channel1> <Channel2>
<Agent>.sources.<Source1>.selector.mapping.<Value3> = <Channel2>
#...

<Agent>.sources.<Source1>.selector.default = <Channel2>

The mapping allows overlapping the channels for each value.

The following example has a single flow that multiplexed to two paths. The agent named agent_foo has a single avro source and two channels linked to two sinks:

# list the sources, sinks and channels in the agent
agent_foo.sources = avro-AppSrv-source1
agent_foo.sinks = hdfs-Cluster1-sink1 avro-forward-sink2
agent_foo.channels = mem-channel-1 file-channel-2

# set channels for source
agent_foo.sources.avro-AppSrv-source1.channels = mem-channel-1 file-channel-2

# set channel for sinks
agent_foo.sinks.hdfs-Cluster1-sink1.channel = mem-channel-1
agent_foo.sinks.avro-forward-sink2.channel = file-channel-2

# channel selector configuration
agent_foo.sources.avro-AppSrv-source1.selector.type = multiplexing
agent_foo.sources.avro-AppSrv-source1.selector.header = State
agent_foo.sources.avro-AppSrv-source1.selector.mapping.CA = mem-channel-1
agent_foo.sources.avro-AppSrv-source1.selector.mapping.AZ = file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.mapping.NY = mem-channel-1 file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.default = mem-channel-1

The selector checks for a header called “State”. If the value is “CA” then its sent to mem-channel-1, if its “AZ” then it goes to file-channel-2 or if its “NY” then both. If the “State” header is not set or doesn’t match any of the three, then it goes to mem-channel-1 which is designated as ‘default’.

The selector also supports optional channels. To specify optional channels for a header, the config parameter ‘optional’ is used in the following way:

# channel selector configuration
agent_foo.sources.avro-AppSrv-source1.selector.type = multiplexing
agent_foo.sources.avro-AppSrv-source1.selector.header = State
agent_foo.sources.avro-AppSrv-source1.selector.mapping.CA = mem-channel-1
agent_foo.sources.avro-AppSrv-source1.selector.mapping.AZ = file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.mapping.NY = mem-channel-1 file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.optional.CA = mem-channel-1 file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.mapping.AZ = file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.default = mem-channel-1

The selector will attempt to write to the required channels first and will fail the transaction if even one of these channels fails to consume the events. The transaction is reattempted on all of the channels. Once all required channels have consumed the events, then the selector will attempt to write to the optional channels. A failure by any of the optional channels to consume the event is simply ignored and not retried.

If there is an overlap between the optional channels and required channels for a specific header, the channel is considered to be required, and a failure in the channel will cause the entire set of required channels to be retried. For instance, in the above example, for the header “CA” mem-channel-1 is considered to be a required channel even though it is marked both as required and optional, and a failure to write to this channel will cause that event to be retried on all channels configured for the selector.

Note that if a header does not have any required channels, then the event will be written to the default channels and will be attempted to be written to the optional channels for that header. Specifying optional channels will still cause the event to be written to the default channels, if no required channels are specified. If no channels are designated as default and there are no required, the selector will attempt to write the events to the optional channels. Any failures are simply ignored in that case.

SSL/TLS support

Several Flume components support the SSL/TLS protocols in order to communicate with other systems securely.

Component	SSL server or client
Avro Source	server
Avro Sink	client
Thrift Source	server
Thrift Sink	client
Kafka Source	client
Kafka Channel	client
Kafka Sink	client
HTTP Source	server
JMS Source	client
Syslog TCP Source	server
Multiport Syslog TCP Source	server

The SSL compatible components have several configuration parameters to set up SSL, like enable SSL flag, keystore / truststore parameters (location, password, type) and additional SSL parameters (eg. disabled protocols).

Enabling SSL for a component is always specified at component level in the agent configuration file. So some components may be configured to use SSL while others not (even with the same component type).

The keystore / truststore setup can be specified at component level or globally.

In case of the component level setup, the keystore / truststore is configured in the agent configuration file through component specific parameters. The advantage of this method is that the components can use different keystores (if this would be needed). The disadvantage is that the keystore parameters must be copied for each component in the agent configuration file. The component level setup is optional, but if it is defined, it has higher precedence than the global parameters.

With the global setup, it is enough to define the keystore / truststore parameters once and use the same settings for all components, which means less and more centralized configuration.

The global setup can be configured either through system properties or through environment variables.

System property	Environment variable	Description
javax.net.ssl.keyStore	FLUME_SSL_KEYSTORE_PATH	Keystore location
javax.net.ssl.keyStorePassword	FLUME_SSL_KEYSTORE_PASSWORD	Keystore password
javax.net.ssl.keyStoreType	FLUME_SSL_KEYSTORE_TYPE	Keystore type (by default JKS)
javax.net.ssl.trustStore	FLUME_SSL_TRUSTSTORE_PATH	Truststore location
javax.net.ssl.trustStorePassword	FLUME_SSL_TRUSTSTORE_PASSWORD	Truststore password
javax.net.ssl.trustStoreType	FLUME_SSL_TRUSTSTORE_TYPE	Truststore type (by default JKS)
flume.ssl.include.protocols	FLUME_SSL_INCLUDE_PROTOCOLS	Protocols to include when calculating enabled protocols. A comma (,) separated list. Excluded protocols will be excluded from this list if provided.
flume.ssl.exclude.protocols	FLUME_SSL_EXCLUDE_PROTOCOLS	Protocols to exclude when calculating enabled protocols. A comma (,) separated list.
flume.ssl.include.cipherSuites	FLUME_SSL_INCLUDE_CIPHERSUITES	Cipher suites to include when calculating enabled cipher suites. A comma (,) separated list. Excluded cipher suites will be excluded from this list if provided.
flume.ssl.exclude.cipherSuites	FLUME_SSL_EXCLUDE_CIPHERSUITES	Cipher suites to exclude when calculating enabled cipher suites. A comma (,) separated list.

The SSL system properties can either be passed on the command line or by setting the JAVA_OPTS environment variable in conf/flume-env.sh. (Although, using the command line is inadvisable because the commands including the passwords will be saved to the command history.)

export JAVA_OPTS="$JAVA_OPTS -Djavax.net.ssl.keyStore=/path/to/keystore.jks"
export JAVA_OPTS="$JAVA_OPTS -Djavax.net.ssl.keyStorePassword=password"

Flume uses the system properties defined in JSSE (Java Secure Socket Extension), so this is a standard way for setting up SSL. On the other hand, specifying passwords in system properties means that the passwords can be seen in the process list. For cases where it is not acceptable, it is also be possible to define the parameters in environment variables. Flume initializes the JSSE system properties from the corresponding environment variables internally in this case.

The SSL environment variables can either be set in the shell environment before starting Flume or in conf/flume-env.sh. (Although, using the command line is inadvisable because the commands including the passwords will be saved to the command history.)

export FLUME_SSL_KEYSTORE_PATH=/path/to/keystore.jks
export FLUME_SSL_KEYSTORE_PASSWORD=password

Please note:

• SSL must be enabled at component level. Specifying the global SSL parameters alone will not have any effect.
• If the global SSL parameters are specified at multiple levels, the priority is the following (from higher to lower):
  • component parameters in agent config
  • system properties
  • environment variables
• If SSL is enabled for a component, but the SSL parameters are not specified in any of the ways described above, then
  • in case of keystores: configuration error
  • in case of truststores: the default truststore will be used (jssecacerts / cacerts in Oracle JDK)
• The trustore password is optional in all cases. If not specified, then no integrity check will be performed on the truststore when it is opened by the JDK.

Source and sink batch sizes and channel transaction capacities

Sources and sinks can have a batch size parameter that determines the maximum number of events they process in one batch. This happens within a channel transaction that has an upper limit called transaction capacity. Batch size must be smaller than the channel’s transaction capacity. There is an explicit check to prevent incompatible settings. This check happens whenever the configuration is read.

Flume Sources

Avro Source

Listens on Avro port and receives events from external Avro client streams. When paired with the built-in Avro Sink on another (previous hop) Flume agent, it can create tiered collection topologies. Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be avro
bind	–	hostname or IP address to listen on
port	–	Port # to bind to
threads	–	Maximum number of worker threads to spawn
selector.type
selector.*
interceptors	–	Space-separated list of interceptors
interceptors.*
compression-type	none	This can be “none” or “deflate”. The compression-type must match the compression-type of matching AvroSource
ssl	false	Set this to true to enable SSL encryption. If SSL is enabled, you must also specify a “keystore” and a “keystore-password”, either through component level parameters (see below) or as global SSL parameters (see SSL/TLS support section).
keystore	–	This is the path to a Java keystore file. If not specified here, then the global keystore will be used (if defined, otherwise configuration error).
keystore-password	–	The password for the Java keystore. If not specified here, then the global keystore password will be used (if defined, otherwise configuration error).
keystore-type	JKS	The type of the Java keystore. This can be “JKS” or “PKCS12”. If not specified here, then the global keystore type will be used (if defined, otherwise the default is JKS).
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude. SSLv3 will always be excluded in addition to the protocols specified.
include-protocols	–	Space-separated list of SSL/TLS protocols to include. The enabled protocols will be the included protocols without the excluded protocols. If included-protocols is empty, it includes every supported protocols.
exclude-cipher-suites	–	Space-separated list of cipher suites to exclude.
include-cipher-suites	–	Space-separated list of cipher suites to include. The enabled cipher suites will be the included cipher suites without the excluded cipher suites. If included-cipher-suites is empty, it includes every supported cipher suites.
ipFilter	false	Set this to true to enable ipFiltering for netty
ipFilterRules	–	Define N netty ipFilter pattern rules with this config.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = avro
a1.sources.r1.channels = c1
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = 4141

Example of ipFilterRules

ipFilterRules defines N netty ipFilters separated by a comma a pattern rule must be in this format.

<’allow’ or deny>:<’ip’ or ‘name’ for computer name>:<pattern> or allow/deny:ip/name:pattern

example: ipFilterRules=allow:ip:127.*,allow:name:localhost,deny:ip:*

Note that the first rule to match will apply as the example below shows from a client on the localhost

This will Allow the client on localhost be deny clients from any other ip “allow:name:localhost,deny:ip:” This will deny the client on localhost be allow clients from any other ip “deny:name:localhost,allow:ip:“

Thrift Source

Listens on Thrift port and receives events from external Thrift client streams. When paired with the built-in ThriftSink on another (previous hop) Flume agent, it can create tiered collection topologies. Thrift source can be configured to start in secure mode by enabling kerberos authentication. agent-principal and agent-keytab are the properties used by the Thrift source to authenticate to the kerberos KDC. Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be thrift
bind	–	hostname or IP address to listen on
port	–	Port # to bind to
threads	–	Maximum number of worker threads to spawn
selector.type
selector.*
interceptors	–	Space separated list of interceptors
interceptors.*
ssl	false	Set this to true to enable SSL encryption. If SSL is enabled, you must also specify a “keystore” and a “keystore-password”, either through component level parameters (see below) or as global SSL parameters (see SSL/TLS support section)
keystore	–	This is the path to a Java keystore file. If not specified here, then the global keystore will be used (if defined, otherwise configuration error).
keystore-password	–	The password for the Java keystore. If not specified here, then the global keystore password will be used (if defined, otherwise configuration error).
keystore-type	JKS	The type of the Java keystore. This can be “JKS” or “PKCS12”. If not specified here, then the global keystore type will be used (if defined, otherwise the default is JKS).
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude. SSLv3 will always be excluded in addition to the protocols specified.
include-protocols	–	Space-separated list of SSL/TLS protocols to include. The enabled protocols will be the included protocols without the excluded protocols. If included-protocols is empty, it includes every supported protocols.
exclude-cipher-suites	–	Space-separated list of cipher suites to exclude.
include-cipher-suites	–	Space-separated list of cipher suites to include. The enabled cipher suites will be the included cipher suites without the excluded cipher suites.
kerberos	false	Set to true to enable kerberos authentication. In kerberos mode, agent-principal and agent-keytab are required for successful authentication. The Thrift source in secure mode, will accept connections only from Thrift clients that have kerberos enabled and are successfully authenticated to the kerberos KDC.
agent-principal	–	The kerberos principal used by the Thrift Source to authenticate to the kerberos KDC.
agent-keytab	—-	The keytab location used by the Thrift Source in combination with the agent-principal to authenticate to the kerberos KDC.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = thrift
a1.sources.r1.channels = c1
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = 4141

Exec Source

Exec source runs a given Unix command on start-up and expects that process to continuously produce data on standard out (stderr is simply discarded, unless property logStdErr is set to true). If the process exits for any reason, the source also exits and will produce no further data. This means configurations such as cat [named pipe] or tail -F [file] are going to produce the desired results where as date will probably not - the former two commands produce streams of data where as the latter produces a single event and exits.

Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be exec
command	–	The command to execute
shell	–	A shell invocation used to run the command. e.g. /bin/sh -c. Required only for commands relying on shell features like wildcards, back ticks, pipes etc.
restartThrottle	10000	Amount of time (in millis) to wait before attempting a restart
restart	false	Whether the executed cmd should be restarted if it dies
logStdErr	false	Whether the command’s stderr should be logged
batchSize	20	The max number of lines to read and send to the channel at a time
batchTimeout	3000	Amount of time (in milliseconds) to wait, if the buffer size was not reached, before data is pushed downstream
selector.type	replicating	replicating or multiplexing
selector.*		Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Warning

The problem with ExecSource and other asynchronous sources is that the source can not guarantee that if there is a failure to put the event into the Channel the client knows about it. In such cases, the data will be lost. As a for instance, one of the most commonly requested features is the tail -F [file]-like use case where an application writes to a log file on disk and Flume tails the file, sending each line as an event. While this is possible, there’s an obvious problem; what happens if the channel fills up and Flume can’t send an event? Flume has no way of indicating to the application writing the log file that it needs to retain the log or that the event hasn’t been sent, for some reason. If this doesn’t make sense, you need only know this: Your application can never guarantee data has been received when using a unidirectional asynchronous interface such as ExecSource! As an extension of this warning - and to be completely clear - there is absolutely zero guarantee of event delivery when using this source. For stronger reliability guarantees, consider the Spooling Directory Source, Taildir Source or direct integration with Flume via the SDK.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /var/log/secure
a1.sources.r1.channels = c1

The ‘shell’ config is used to invoke the ‘command’ through a command shell (such as Bash or Powershell). The ‘command’ is passed as an argument to ‘shell’ for execution. This allows the ‘command’ to use features from the shell such as wildcards, back ticks, pipes, loops, conditionals etc. In the absence of the ‘shell’ config, the ‘command’ will be invoked directly. Common values for ‘shell’ : ‘/bin/sh -c’, ‘/bin/ksh -c’, ‘cmd /c’, ‘powershell -Command’, etc.

a1.sources.tailsource-1.type = exec
a1.sources.tailsource-1.shell = /bin/bash -c
a1.sources.tailsource-1.command = for i in /path/*.txt; do cat $i; done

JMS Source

JMS Source reads messages from a JMS destination such as a queue or topic. Being a JMS application it should work with any JMS provider but has only been tested with ActiveMQ. The JMS source provides configurable batch size, message selector, user/pass, and message to flume event converter. Note that the vendor provided JMS jars should be included in the Flume classpath using plugins.d directory (preferred), –classpath on command line, or via FLUME_CLASSPATH variable in flume-env.sh.

Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be jms
initialContextFactory	–	Inital Context Factory, e.g: org.apache.activemq.jndi.ActiveMQInitialContextFactory
connectionFactory	–	The JNDI name the connection factory should appear as
providerURL	–	The JMS provider URL
destinationName	–	Destination name
destinationType	–	Destination type (queue or topic)
messageSelector	–	Message selector to use when creating the consumer
userName	–	Username for the destination/provider
passwordFile	–	File containing the password for the destination/provider
batchSize	100	Number of messages to consume in one batch
converter.type	DEFAULT	Class to use to convert messages to flume events. See below.
converter.*	–	Converter properties.
converter.charset	UTF-8	Default converter only. Charset to use when converting JMS TextMessages to byte arrays.
createDurableSubscription	false	Whether to create durable subscription. Durable subscription can only be used with destinationType topic. If true, “clientId” and “durableSubscriptionName” have to be specified.
clientId	–	JMS client identifier set on Connection right after it is created. Required for durable subscriptions.
durableSubscriptionName	–	Name used to identify the durable subscription. Required for durable subscriptions.

JMS message converter

The JMS source allows pluggable converters, though it’s likely the default converter will work for most purposes. The default converter is able to convert Bytes, Text, and Object messages to FlumeEvents. In all cases, the properties in the message are added as headers to the FlumeEvent.

BytesMessage:

Bytes of message are copied to body of the FlumeEvent. Cannot convert more than 2GB of data per message.

TextMessage:

Text of message is converted to a byte array and copied to the body of the FlumeEvent. The default converter uses UTF-8 by default but this is configurable.

ObjectMessage:

Object is written out to a ByteArrayOutputStream wrapped in an ObjectOutputStream and the resulting array is copied to the body of the FlumeEvent.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = jms
a1.sources.r1.channels = c1
a1.sources.r1.initialContextFactory = org.apache.activemq.jndi.ActiveMQInitialContextFactory
a1.sources.r1.connectionFactory = GenericConnectionFactory
a1.sources.r1.providerURL = tcp://mqserver:61616
a1.sources.r1.destinationName = BUSINESS_DATA
a1.sources.r1.destinationType = QUEUE

SSL and JMS Source

JMS client implementations typically support to configure SSL/TLS via some Java system properties defined by JSSE (Java Secure Socket Extension). Specifying these system properties for Flume’s JVM, JMS Source (or more precisely the JMS client implementation used by the JMS Source) can connect to the JMS server through SSL (of course only when the JMS server has also been set up to use SSL). It should work with any JMS provider and has been tested with ActiveMQ, IBM MQ and Oracle WebLogic.

The following sections describe the SSL configuration steps needed on the Flume side only. You can find more detailed descriptions about the server side setup of the different JMS providers and also full working configuration examples on Flume Wiki.

SSL transport / server authentication:

If the JMS server uses self-signed certificate or its certificate is signed by a non-trusted CA (eg. the company’s own CA), then a truststore (containing the right certificate) needs to be set up and passed to Flume. It can be done via the global SSL parameters. For more details about the global SSL setup, see the SSL/TLS support section.

Some JMS providers require SSL specific JNDI Initial Context Factory and/or Provider URL settings when using SSL (eg. ActiveMQ uses ssl:// URL prefix instead of tcp://). In this case the source properties (initialContextFactory and/or providerURL) have to be adjusted in the agent config file.

Client certificate authentication (two-way SSL):

JMS Source can authenticate to the JMS server through client certificate authentication instead of the usual user/password login (when SSL is used and the JMS server is configured to accept this kind of authentication).

The keystore containing Flume’s key used for the authentication needs to be configured via the global SSL parameters again. For more details about the global SSL setup, see the SSL/TLS support section.

The keystore should contain only one key (if multiple keys are present, then the first one will be used). The key password must be the same as the keystore password.

In case of client certificate authentication, it is not needed to specify the userName / passwordFile properties for the JMS Source in the Flume agent config file.

Please note:

There are no component level configuration parameters for JMS Source unlike in case of other components. No enable SSL flag either. SSL setup is controlled by JNDI/Provider URL settings (ultimately the JMS server settings) and by the presence / absence of the truststore / keystore.

Spooling Directory Source

This source lets you ingest data by placing files to be ingested into a “spooling” directory on disk. This source will watch the specified directory for new files, and will parse events out of new files as they appear. The event parsing logic is pluggable. After a given file has been fully read into the channel, completion by default is indicated by renaming the file or it can be deleted or the trackerDir is used to keep track of processed files.

Unlike the Exec source, this source is reliable and will not miss data, even if Flume is restarted or killed. In exchange for this reliability, only immutable, uniquely-named files must be dropped into the spooling directory. Flume tries to detect these problem conditions and will fail loudly if they are violated:

1. If a file is written to after being placed into the spooling directory, Flume will print an error to its log file and stop processing.
2. If a file name is reused at a later time, Flume will print an error to its log file and stop processing.

To avoid the above issues, it may be useful to add a unique identifier (such as a timestamp) to log file names when they are moved into the spooling directory.

Despite the reliability guarantees of this source, there are still cases in which events may be duplicated if certain downstream failures occur. This is consistent with the guarantees offered by other Flume components.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be spooldir.
spoolDir	–	The directory from which to read files from.
fileSuffix	.COMPLETED	Suffix to append to completely ingested files
deletePolicy	never	When to delete completed files: never or immediate
fileHeader	false	Whether to add a header storing the absolute path filename.
fileHeaderKey	file	Header key to use when appending absolute path filename to event header.
basenameHeader	false	Whether to add a header storing the basename of the file.
basenameHeaderKey	basename	Header Key to use when appending basename of file to event header.
includePattern	^.*$	Regular expression specifying which files to include. It can used together with ignorePattern. If a file matches both ignorePattern and includePattern regex, the file is ignored.
ignorePattern	^$	Regular expression specifying which files to ignore (skip). It can used together with includePattern. If a file matches both ignorePattern and includePattern regex, the file is ignored.
trackerDir	.flumespool	Directory to store metadata related to processing of files. If this path is not an absolute path, then it is interpreted as relative to the spoolDir.
trackingPolicy	rename	The tracking policy defines how file processing is tracked. It can be “rename” or “tracker_dir”. This parameter is only effective if the deletePolicy is “never”. “rename” - After processing files they get renamed according to the fileSuffix parameter. “tracker_dir” - Files are not renamed but a new empty file is created in the trackerDir. The new tracker file name is derived from the ingested one plus the fileSuffix.
consumeOrder	oldest	In which order files in the spooling directory will be consumed oldest, youngest and random. In case of oldest and youngest, the last modified time of the files will be used to compare the files. In case of a tie, the file with smallest lexicographical order will be consumed first. In case of random any file will be picked randomly. When using oldest and youngest the whole directory will be scanned to pick the oldest/youngest file, which might be slow if there are a large number of files, while using random may cause old files to be consumed very late if new files keep coming in the spooling directory.
pollDelay	500	Delay (in milliseconds) used when polling for new files.
recursiveDirectorySearch	false	Whether to monitor sub directories for new files to read.
maxBackoff	4000	The maximum time (in millis) to wait between consecutive attempts to write to the channel(s) if the channel is full. The source will start at a low backoff and increase it exponentially each time the channel throws a ChannelException, up to the value specified by this parameter.
batchSize	100	Granularity at which to batch transfer to the channel
inputCharset	UTF-8	Character set used by deserializers that treat the input file as text.
decodeErrorPolicy	FAIL	What to do when we see a non-decodable character in the input file. FAIL: Throw an exception and fail to parse the file. REPLACE: Replace the unparseable character with the “replacement character” char, typically Unicode U+FFFD. IGNORE: Drop the unparseable character sequence.
deserializer	LINE	Specify the deserializer used to parse the file into events. Defaults to parsing each line as an event. The class specified must implement EventDeserializer.Builder.
deserializer.*		Varies per event deserializer.
bufferMaxLines	–	(Obsolete) This option is now ignored.
bufferMaxLineLength	5000	(Deprecated) Maximum length of a line in the commit buffer. Use deserializer.maxLineLength instead.
selector.type	replicating	replicating or multiplexing
selector.*		Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Example for an agent named agent-1:

a1.channels = ch-1
a1.sources = src-1

a1.sources.src-1.type = spooldir
a1.sources.src-1.channels = ch-1
a1.sources.src-1.spoolDir = /var/log/apache/flumeSpool
a1.sources.src-1.fileHeader = true

Event Deserializers

The following event deserializers ship with Flume.

LINE

This deserializer generates one event per line of text input.

Property Name	Default	Description
deserializer.maxLineLength	2048	Maximum number of characters to include in a single event. If a line exceeds this length, it is truncated, and the remaining characters on the line will appear in a subsequent event.
deserializer.outputCharset	UTF-8	Charset to use for encoding events put into the channel.

AVRO

This deserializer is able to read an Avro container file, and it generates one event per Avro record in the file. Each event is annotated with a header that indicates the schema used. The body of the event is the binary Avro record data, not including the schema or the rest of the container file elements.

Note that if the spool directory source must retry putting one of these events onto a channel (for example, because the channel is full), then it will reset and retry from the most recent Avro container file sync point. To reduce potential event duplication in such a failure scenario, write sync markers more frequently in your Avro input files.

Property Name	Default	Description
deserializer.schemaType	HASH	How the schema is represented. By default, or when the value HASH is specified, the Avro schema is hashed and the hash is stored in every event in the event header “flume.avro.schema.hash”. If LITERAL is specified, the JSON-encoded schema itself is stored in every event in the event header “flume.avro.schema.literal”. Using LITERAL mode is relatively inefficient compared to HASH mode.

BlobDeserializer

This deserializer reads a Binary Large Object (BLOB) per event, typically one BLOB per file. For example a PDF or JPG file. Note that this approach is not suitable for very large objects because the entire BLOB is buffered in RAM.

Property Name	Default	Description
deserializer	–	The FQCN of this class: org.apache.flume.sink.solr.morphline.BlobDeserializer$Builder
deserializer.maxBlobLength	100000000	The maximum number of bytes to read and buffer for a given request

Taildir Source

Note

This source is provided as a preview feature. It does not work on Windows.

Watch the specified files, and tail them in nearly real-time once detected new lines appended to the each files. If the new lines are being written, this source will retry reading them in wait for the completion of the write.

This source is reliable and will not miss data even when the tailing files rotate. It periodically writes the last read position of each files on the given position file in JSON format. If Flume is stopped or down for some reason, it can restart tailing from the position written on the existing position file.

In other use case, this source can also start tailing from the arbitrary position for each files using the given position file. When there is no position file on the specified path, it will start tailing from the first line of each files by default.

Files will be consumed in order of their modification time. File with the oldest modification time will be consumed first.

This source does not rename or delete or do any modifications to the file being tailed. Currently this source does not support tailing binary files. It reads text files line by line.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be TAILDIR.
filegroups	–	Space-separated list of file groups. Each file group indicates a set of files to be tailed.
filegroups.<filegroupName>	–	Absolute path of the file group. Regular expression (and not file system patterns) can be used for filename only.
positionFile	~/.flume/taildir_position.json	File in JSON format to record the inode, the absolute path and the last position of each tailing file.
headers.<filegroupName>.<headerKey>	–	Header value which is the set with header key. Multiple headers can be specified for one file group.
byteOffsetHeader	false	Whether to add the byte offset of a tailed line to a header called ‘byteoffset’.
skipToEnd	false	Whether to skip the position to EOF in the case of files not written on the position file.
idleTimeout	120000	Time (ms) to close inactive files. If the closed file is appended new lines to, this source will automatically re-open it.
writePosInterval	3000	Interval time (ms) to write the last position of each file on the position file.
batchSize	100	Max number of lines to read and send to the channel at a time. Using the default is usually fine.
maxBatchCount	Long.MAX_VALUE	Controls the number of batches being read consecutively from the same file. If the source is tailing multiple files and one of them is written at a fast rate, it can prevent other files to be processed, because the busy file would be read in an endless loop. In this case lower this value.
backoffSleepIncrement	1000	The increment for time delay before reattempting to poll for new data, when the last attempt did not find any new data.
maxBackoffSleep	5000	The max time delay between each reattempt to poll for new data, when the last attempt did not find any new data.
cachePatternMatching	true	Listing directories and applying the filename regex pattern may be time consuming for directories containing thousands of files. Caching the list of matching files can improve performance. The order in which files are consumed will also be cached. Requires that the file system keeps track of modification times with at least a 1-second granularity.
fileHeader	false	Whether to add a header storing the absolute path filename.
fileHeaderKey	file	Header key to use when appending absolute path filename to event header.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = TAILDIR
a1.sources.r1.channels = c1
a1.sources.r1.positionFile = /var/log/flume/taildir_position.json
a1.sources.r1.filegroups = f1 f2
a1.sources.r1.filegroups.f1 = /var/log/test1/example.log
a1.sources.r1.headers.f1.headerKey1 = value1
a1.sources.r1.filegroups.f2 = /var/log/test2/.*log.*
a1.sources.r1.headers.f2.headerKey1 = value2
a1.sources.r1.headers.f2.headerKey2 = value2-2
a1.sources.r1.fileHeader = true
a1.sources.ri.maxBatchCount = 1000

Twitter 1% firehose Source (experimental)

Warning

This source is highly experimental and may change between minor versions of Flume. Use at your own risk.

Experimental source that connects via Streaming API to the 1% sample twitter firehose, continuously downloads tweets, converts them to Avro format and sends Avro events to a downstream Flume sink. Requires the consumer and access tokens and secrets of a Twitter developer account. Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be org.apache.flume.source.twitter.TwitterSource
consumerKey	–	OAuth consumer key
consumerSecret	–	OAuth consumer secret
accessToken	–	OAuth access token
accessTokenSecret	–	OAuth token secret
maxBatchSize	1000	Maximum number of twitter messages to put in a single batch
maxBatchDurationMillis	1000	Maximum number of milliseconds to wait before closing a batch

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = org.apache.flume.source.twitter.TwitterSource
a1.sources.r1.channels = c1
a1.sources.r1.consumerKey = YOUR_TWITTER_CONSUMER_KEY
a1.sources.r1.consumerSecret = YOUR_TWITTER_CONSUMER_SECRET
a1.sources.r1.accessToken = YOUR_TWITTER_ACCESS_TOKEN
a1.sources.r1.accessTokenSecret = YOUR_TWITTER_ACCESS_TOKEN_SECRET
a1.sources.r1.maxBatchSize = 10
a1.sources.r1.maxBatchDurationMillis = 200

Kafka Source

Kafka Source is an Apache Kafka consumer that reads messages from Kafka topics. If you have multiple Kafka sources running, you can configure them with the same Consumer Group so each will read a unique set of partitions for the topics. This currently supports Kafka server releases 0.10.1.0 or higher. Testing was done up to 2.0.1 that was the highest avilable version at the time of the release.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be org.apache.flume.source.kafka.KafkaSource
kafka.bootstrap.servers	–	List of brokers in the Kafka cluster used by the source
kafka.consumer.group.id	flume	Unique identified of consumer group. Setting the same id in multiple sources or agents indicates that they are part of the same consumer group
kafka.topics	–	Comma-separated list of topics the Kafka consumer will read messages from.
kafka.topics.regex	–	Regex that defines set of topics the source is subscribed on. This property has higher priority than kafka.topics and overrides kafka.topics if exists.
batchSize	1000	Maximum number of messages written to Channel in one batch
batchDurationMillis	1000	Maximum time (in ms) before a batch will be written to Channel The batch will be written whenever the first of size and time will be reached.
backoffSleepIncrement	1000	Initial and incremental wait time that is triggered when a Kafka Topic appears to be empty. Wait period will reduce aggressive pinging of an empty Kafka Topic. One second is ideal for ingestion use cases but a lower value may be required for low latency operations with interceptors.
maxBackoffSleep	5000	Maximum wait time that is triggered when a Kafka Topic appears to be empty. Five seconds is ideal for ingestion use cases but a lower value may be required for low latency operations with interceptors.
useFlumeEventFormat	false	By default events are taken as bytes from the Kafka topic directly into the event body. Set to true to read events as the Flume Avro binary format. Used in conjunction with the same property on the KafkaSink or with the parseAsFlumeEvent property on the Kafka Channel this will preserve any Flume headers sent on the producing side.
setTopicHeader	true	When set to true, stores the topic of the retrieved message into a header, defined by the topicHeader property.
topicHeader	topic	Defines the name of the header in which to store the name of the topic the message was received from, if the setTopicHeader property is set to true. Care should be taken if combining with the Kafka Sink topicHeader property so as to avoid sending the message back to the same topic in a loop.
timestampHeader	–	If present the Kafka message timestamp value will be copied into the specified Flume header name.
header.NAME	–	Used to identify which headers from the Kafka message should be added as a FLume header. The value of NAME should match the Flume header name and the value should be the name of the header to be used as the Kafka header name.
kafka.consumer.security.protocol	PLAINTEXT	Set to SASL_PLAINTEXT, SASL_SSL or SSL if writing to Kafka using some level of security. See below for additional info on secure setup.
more consumer security props		If using SASL_PLAINTEXT, SASL_SSL or SSL refer to Kafka security for additional properties that need to be set on consumer.
Other Kafka Consumer Properties	–	These properties are used to configure the Kafka Consumer. Any consumer property supported by Kafka can be used. The only requirement is to prepend the property name with the prefix kafka.consumer. For example: kafka.consumer.auto.offset.reset

Note

The Kafka Source overrides two Kafka consumer parameters: auto.commit.enable is set to “false” by the source and every batch is committed. Kafka source guarantees at least once strategy of messages retrieval. The duplicates can be present when the source starts. The Kafka Source also provides defaults for the key.deserializer(org.apache.kafka.common.serialization.StringSerializer) and value.deserializer(org.apache.kafka.common.serialization.ByteArraySerializer). Modification of these parameters is not recommended.

Deprecated Properties

Property Name	Default	Description
topic	–	Use kafka.topics
groupId	flume	Use kafka.consumer.group.id
zookeeperConnect	–	Is no longer supported by Kafka consumer client since 0.9.x. Use kafka.bootstrap.servers to establish connection with Kafka cluster
migrateZookeeperOffsets	true	When no Kafka stored offset is found, look up the offsets in Zookeeper and commit them to Kafka. This should be true to support seamless Kafka client migration from older versions of Flume. Once migrated this can be set to false, though that should generally not be required. If no Zookeeper offset is found, the Kafka configuration kafka.consumer.auto.offset.reset defines how offsets are handled. Check Kafka documentation for details

Example for topic subscription by comma-separated topic list.

tier1.sources.source1.type = org.apache.flume.source.kafka.KafkaSource
tier1.sources.source1.channels = channel1
tier1.sources.source1.batchSize = 5000
tier1.sources.source1.batchDurationMillis = 2000
tier1.sources.source1.kafka.bootstrap.servers = localhost:9092
tier1.sources.source1.kafka.topics = test1, test2
tier1.sources.source1.kafka.consumer.group.id = custom.g.id

Example for topic subscription by regex

tier1.sources.source1.type = org.apache.flume.source.kafka.KafkaSource
tier1.sources.source1.channels = channel1
tier1.sources.source1.kafka.bootstrap.servers = localhost:9092
tier1.sources.source1.kafka.topics.regex = ^topic[0-9]$
# the default kafka.consumer.group.id=flume is used

Security and Kafka Source:

Secure authentication as well as data encryption is supported on the communication channel between Flume and Kafka. For secure authentication SASL/GSSAPI (Kerberos V5) or SSL (even though the parameter is named SSL, the actual protocol is a TLS implementation) can be used from Kafka version 0.9.0.

As of now data encryption is solely provided by SSL/TLS.

Setting kafka.consumer.security.protocol to any of the following value means:

• SASL_PLAINTEXT - Kerberos or plaintext authentication with no data encryption
• SASL_SSL - Kerberos or plaintext authentication with data encryption
• SSL - TLS based encryption with optional authentication.

Warning

There is a performance degradation when SSL is enabled, the magnitude of which depends on the CPU type and the JVM implementation. Reference: Kafka security overview and the jira for tracking this issue: KAFKA-2561

TLS and Kafka Source:

Please read the steps described in Configuring Kafka Clients SSL to learn about additional configuration settings for fine tuning for example any of the following: security provider, cipher suites, enabled protocols, truststore or keystore types.

Example configuration with server side authentication and data encryption.

a1.sources.source1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.source1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.sources.source1.kafka.topics = mytopic
a1.sources.source1.kafka.consumer.group.id = flume-consumer
a1.sources.source1.kafka.consumer.security.protocol = SSL
# optional, the global truststore can be used alternatively
a1.sources.source1.kafka.consumer.ssl.truststore.location=/path/to/truststore.jks
a1.sources.source1.kafka.consumer.ssl.truststore.password=<password to access the truststore>

Specifying the truststore is optional here, the global truststore can be used instead. For more details about the global SSL setup, see the SSL/TLS support section.

Note: By default the property ssl.endpoint.identification.algorithm is not defined, so hostname verification is not performed. In order to enable hostname verification, set the following properties

a1.sources.source1.kafka.consumer.ssl.endpoint.identification.algorithm=HTTPS

Once enabled, clients will verify the server’s fully qualified domain name (FQDN) against one of the following two fields:

1. Common Name (CN) https://tools.ietf.org/html/rfc6125#section-2.3
2. Subject Alternative Name (SAN) https://tools.ietf.org/html/rfc5280#section-4.2.1.6

If client side authentication is also required then additionally the following needs to be added to Flume agent configuration or the global SSL setup can be used (see SSL/TLS support section). Each Flume agent has to have its client certificate which has to be trusted by Kafka brokers either individually or by their signature chain. Common example is to sign each client certificate by a single Root CA which in turn is trusted by Kafka brokers.

# optional, the global keystore can be used alternatively
a1.sources.source1.kafka.consumer.ssl.keystore.location=/path/to/client.keystore.jks
a1.sources.source1.kafka.consumer.ssl.keystore.password=<password to access the keystore>

If keystore and key use different password protection then ssl.key.password property will provide the required additional secret for both consumer keystores:

a1.sources.source1.kafka.consumer.ssl.key.password=<password to access the key>

Kerberos and Kafka Source:

To use Kafka source with a Kafka cluster secured with Kerberos, set the consumer.security.protocol properties noted above for consumer. The Kerberos keytab and principal to be used with Kafka brokers is specified in a JAAS file’s “KafkaClient” section. “Client” section describes the Zookeeper connection if needed. See Kafka doc for information on the JAAS file contents. The location of this JAAS file and optionally the system wide kerberos configuration can be specified via JAVA_OPTS in flume-env.sh:

JAVA_OPTS="$JAVA_OPTS -Djava.security.krb5.conf=/path/to/krb5.conf"
JAVA_OPTS="$JAVA_OPTS -Djava.security.auth.login.config=/path/to/flume_jaas.conf"

Example secure configuration using SASL_PLAINTEXT:

a1.sources.source1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.source1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.sources.source1.kafka.topics = mytopic
a1.sources.source1.kafka.consumer.group.id = flume-consumer
a1.sources.source1.kafka.consumer.security.protocol = SASL_PLAINTEXT
a1.sources.source1.kafka.consumer.sasl.mechanism = GSSAPI
a1.sources.source1.kafka.consumer.sasl.kerberos.service.name = kafka

Example secure configuration using SASL_SSL:

a1.sources.source1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.source1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.sources.source1.kafka.topics = mytopic
a1.sources.source1.kafka.consumer.group.id = flume-consumer
a1.sources.source1.kafka.consumer.security.protocol = SASL_SSL
a1.sources.source1.kafka.consumer.sasl.mechanism = GSSAPI
a1.sources.source1.kafka.consumer.sasl.kerberos.service.name = kafka
# optional, the global truststore can be used alternatively
a1.sources.source1.kafka.consumer.ssl.truststore.location=/path/to/truststore.jks
a1.sources.source1.kafka.consumer.ssl.truststore.password=<password to access the truststore>

Sample JAAS file. For reference of its content please see client config sections of the desired authentication mechanism (GSSAPI/PLAIN) in Kafka documentation of SASL configuration. Since the Kafka Source may also connect to Zookeeper for offset migration, the “Client” section was also added to this example. This won’t be needed unless you require offset migration, or you require this section for other secure components. Also please make sure that the operating system user of the Flume processes has read privileges on the jaas and keytab files.

Client {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  storeKey=true
  keyTab="/path/to/keytabs/flume.keytab"
  principal="flume/flumehost1.example.com@YOURKERBEROSREALM";
};

KafkaClient {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  storeKey=true
  keyTab="/path/to/keytabs/flume.keytab"
  principal="flume/flumehost1.example.com@YOURKERBEROSREALM";
};

NetCat TCP Source

A netcat-like source that listens on a given port and turns each line of text into an event. Acts like nc -k -l [host] [port]. In other words, it opens a specified port and listens for data. The expectation is that the supplied data is newline separated text. Each line of text is turned into a Flume event and sent via the connected channel.

Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be netcat
bind	–	Host name or IP address to bind to
port	–	Port # to bind to
max-line-length	512	Max line length per event body (in bytes)
ack-every-event	true	Respond with an “OK” for every event received
selector.type	replicating	replicating or multiplexing
selector.*		Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = netcat
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = 6666
a1.sources.r1.channels = c1

NetCat UDP Source

As per the original Netcat (TCP) source, this source that listens on a given port and turns each line of text into an event and sent via the connected channel. Acts like nc -u -k -l [host] [port].

Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be netcatudp
bind	–	Host name or IP address to bind to
port	–	Port # to bind to
remoteAddressHeader	–
selector.type	replicating	replicating or multiplexing
selector.*		Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = netcatudp
a1.sources.r1.bind = 0.0.0.0
a1.sources.r1.port = 6666
a1.sources.r1.channels = c1

Sequence Generator Source

A simple sequence generator that continuously generates events with a counter that starts from 0, increments by 1 and stops at totalEvents. Retries when it can’t send events to the channel. Useful mainly for testing. During retries it keeps the body of the retried messages the same as before so that the number of unique events - after de-duplication at destination - is expected to be equal to the specified totalEvents. Required properties are in bold.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be seq
selector.type		replicating or multiplexing
selector.*	replicating	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*
batchSize	1	Number of events to attempt to process per request loop.
totalEvents	Long.MAX_VALUE	Number of unique events sent by the source.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = seq
a1.sources.r1.channels = c1

Syslog Sources

Reads syslog data and generate Flume events. The UDP source treats an entire message as a single event. The TCP sources create a new event for each string of characters separated by a newline (‘n’).

Required properties are in bold.

Syslog TCP Source

The original, tried-and-true syslog TCP source.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be syslogtcp
host	–	Host name or IP address to bind to
port	–	Port # to bind to
eventSize	2500	Maximum size of a single event line, in bytes
keepFields	none	Setting this to ‘all’ will preserve the Priority, Timestamp and Hostname in the body of the event. A spaced separated list of fields to include is allowed as well. Currently, the following fields can be included: priority, version, timestamp, hostname. The values ‘true’ and ‘false’ have been deprecated in favor of ‘all’ and ‘none’.
clientIPHeader	–	If specified, the IP address of the client will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the IP address of the client. Do not use the standard Syslog header names here (like _host_) because the event header will be overridden in that case.
clientHostnameHeader	–	If specified, the host name of the client will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the host name of the client. Retrieving the host name may involve a name service reverse lookup which may affect the performance. Do not use the standard Syslog header names here (like _host_) because the event header will be overridden in that case.
selector.type		replicating or multiplexing
selector.*	replicating	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*
ssl	false	Set this to true to enable SSL encryption. If SSL is enabled, you must also specify a “keystore” and a “keystore-password”, either through component level parameters (see below) or as global SSL parameters (see SSL/TLS support section).
keystore	–	This is the path to a Java keystore file. If not specified here, then the global keystore will be used (if defined, otherwise configuration error).
keystore-password	–	The password for the Java keystore. If not specified here, then the global keystore password will be used (if defined, otherwise configuration error).
keystore-type	JKS	The type of the Java keystore. This can be “JKS” or “PKCS12”. If not specified here, then the global keystore type will be used (if defined, otherwise the default is JKS).
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude. SSLv3 will always be excluded in addition to the protocols specified.
include-protocols	–	Space-separated list of SSL/TLS protocols to include. The enabled protocols will be the included protocols without the excluded protocols. If included-protocols is empty, it includes every supported protocols.
exclude-cipher-suites	–	Space-separated list of cipher suites to exclude.
include-cipher-suites	–	Space-separated list of cipher suites to include. The enabled cipher suites will be the included cipher suites without the excluded cipher suites. If included-cipher-suites is empty, it includes every supported cipher suites.

For example, a syslog TCP source for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = syslogtcp
a1.sources.r1.port = 5140
a1.sources.r1.host = localhost
a1.sources.r1.channels = c1

Multiport Syslog TCP Source

This is a newer, faster, multi-port capable version of the Syslog TCP source. Note that the ports configuration setting has replaced port. Multi-port capability means that it can listen on many ports at once in an efficient manner. This source uses the Apache Mina library to do that. Provides support for RFC-3164 and many common RFC-5424 formatted messages. Also provides the capability to configure the character set used on a per-port basis.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be multiport_syslogtcp
host	–	Host name or IP address to bind to.
ports	–	Space-separated list (one or more) of ports to bind to.
eventSize	2500	Maximum size of a single event line, in bytes.
keepFields	none	Setting this to ‘all’ will preserve the Priority, Timestamp and Hostname in the body of the event. A spaced separated list of fields to include is allowed as well. Currently, the following fields can be included: priority, version, timestamp, hostname. The values ‘true’ and ‘false’ have been deprecated in favor of ‘all’ and ‘none’.
portHeader	–	If specified, the port number will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the incoming port.
clientIPHeader	–	If specified, the IP address of the client will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the IP address of the client. Do not use the standard Syslog header names here (like _host_) because the event header will be overridden in that case.
clientHostnameHeader	–	If specified, the host name of the client will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the host name of the client. Retrieving the host name may involve a name service reverse lookup which may affect the performance. Do not use the standard Syslog header names here (like _host_) because the event header will be overridden in that case.
charset.default	UTF-8	Default character set used while parsing syslog events into strings.
charset.port.<port>	–	Character set is configurable on a per-port basis.
batchSize	100	Maximum number of events to attempt to process per request loop. Using the default is usually fine.
readBufferSize	1024	Size of the internal Mina read buffer. Provided for performance tuning. Using the default is usually fine.
numProcessors	(auto-detected)	Number of processors available on the system for use while processing messages. Default is to auto-detect # of CPUs using the Java Runtime API. Mina will spawn 2 request-processing threads per detected CPU, which is often reasonable.
selector.type	replicating	replicating, multiplexing, or custom
selector.*	–	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors.
interceptors.*
ssl	false	Set this to true to enable SSL encryption. If SSL is enabled, you must also specify a “keystore” and a “keystore-password”, either through component level parameters (see below) or as global SSL parameters (see SSL/TLS support section).
keystore	–	This is the path to a Java keystore file. If not specified here, then the global keystore will be used (if defined, otherwise configuration error).
keystore-password	–	The password for the Java keystore. If not specified here, then the global keystore password will be used (if defined, otherwise configuration error).
keystore-type	JKS	The type of the Java keystore. This can be “JKS” or “PKCS12”. If not specified here, then the global keystore type will be used (if defined, otherwise the default is JKS).
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude. SSLv3 will always be excluded in addition to the protocols specified.
include-protocols	–	Space-separated list of SSL/TLS protocols to include. The enabled protocols will be the included protocols without the excluded protocols. If included-protocols is empty, it includes every supported protocols.
exclude-cipher-suites	–	Space-separated list of cipher suites to exclude.
include-cipher-suites	–	Space-separated list of cipher suites to include. The enabled cipher suites will be the included cipher suites without the excluded cipher suites. If included-cipher-suites is empty, it includes every supported cipher suites.

For example, a multiport syslog TCP source for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = multiport_syslogtcp
a1.sources.r1.channels = c1
a1.sources.r1.host = 0.0.0.0
a1.sources.r1.ports = 10001 10002 10003
a1.sources.r1.portHeader = port

Syslog UDP Source

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be syslogudp
host	–	Host name or IP address to bind to
port	–	Port # to bind to
keepFields	false	Setting this to true will preserve the Priority, Timestamp and Hostname in the body of the event.
clientIPHeader	–	If specified, the IP address of the client will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the IP address of the client. Do not use the standard Syslog header names here (like _host_) because the event header will be overridden in that case.
clientHostnameHeader	–	If specified, the host name of the client will be stored in the header of each event using the header name specified here. This allows for interceptors and channel selectors to customize routing logic based on the host name of the client. Retrieving the host name may involve a name service reverse lookup which may affect the performance. Do not use the standard Syslog header names here (like _host_) because the event header will be overridden in that case.
selector.type		replicating or multiplexing
selector.*	replicating	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

For example, a syslog UDP source for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = syslogudp
a1.sources.r1.port = 5140
a1.sources.r1.host = localhost
a1.sources.r1.channels = c1

HTTP Source

A source which accepts Flume Events by HTTP POST and GET. GET should be used for experimentation only. HTTP requests are converted into flume events by a pluggable “handler” which must implement the HTTPSourceHandler interface. This handler takes a HttpServletRequest and returns a list of flume events. All events handled from one Http request are committed to the channel in one transaction, thus allowing for increased efficiency on channels like the file channel. If the handler throws an exception, this source will return a HTTP status of 400. If the channel is full, or the source is unable to append events to the channel, the source will return a HTTP 503 - Temporarily unavailable status.

All events sent in one post request are considered to be one batch and inserted into the channel in one transaction.

This source is based on Jetty 9.4 and offers the ability to set additional Jetty-specific parameters which will be passed directly to the Jetty components.

Property Name	Default	Description
type		The component type name, needs to be http
port	–	The port the source should bind to.
bind	0.0.0.0	The hostname or IP address to listen on
handler	org.apache.flume.source.http.JSONHandler	The FQCN of the handler class.
handler.*	–	Config parameters for the handler
selector.type	replicating	replicating or multiplexing
selector.*		Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*
ssl	false	Set the property true, to enable SSL. HTTP Source does not support SSLv3.
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude. SSLv3 will always be excluded in addition to the protocols specified.
include-protocols	–	Space-separated list of SSL/TLS protocols to include. The enabled protocols will be the included protocols without the excluded protocols. If included-protocols is empty, it includes every supported protocols.
exclude-cipher-suites	–	Space-separated list of cipher suites to exclude.
include-cipher-suites	–	Space-separated list of cipher suites to include. The enabled cipher suites will be the included cipher suites without the excluded cipher suites.
keystore		Location of the keystore including keystore file name. If SSL is enabled but the keystore is not specified here, then the global keystore will be used (if defined, otherwise configuration error).
keystore-password		Keystore password. If SSL is enabled but the keystore password is not specified here, then the global keystore password will be used (if defined, otherwise configuration error).
keystore-type	JKS	Keystore type. This can be “JKS” or “PKCS12”.
QueuedThreadPool.*		Jetty specific settings to be set on org.eclipse.jetty.util.thread.QueuedThreadPool. N.B. QueuedThreadPool will only be used if at least one property of this class is set.
HttpConfiguration.*		Jetty specific settings to be set on org.eclipse.jetty.server.HttpConfiguration
SslContextFactory.*		Jetty specific settings to be set on org.eclipse.jetty.util.ssl.SslContextFactory (only applicable when ssl is set to true).
ServerConnector.*		Jetty specific settings to be set on org.eclipse.jetty.server.ServerConnector

Deprecated Properties

Property Name	Default	Description
keystorePassword	–	Use keystore-password. Deprecated value will be overwritten with the new one.
excludeProtocols	SSLv3	Use exclude-protocols. Deprecated value will be overwritten with the new one.
enableSSL	false	Use ssl. Deprecated value will be overwritten with the new one.

N.B. Jetty-specific settings are set using the setter-methods on the objects listed above. For full details see the Javadoc for these classes (QueuedThreadPool, HttpConfiguration, SslContextFactory and ServerConnector).

When using Jetty-specific setings, named properites above will take precedence (for example excludeProtocols will take precedence over SslContextFactory.ExcludeProtocols). All properties will be inital lower case.

An example http source for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = http
a1.sources.r1.port = 5140
a1.sources.r1.channels = c1
a1.sources.r1.handler = org.example.rest.RestHandler
a1.sources.r1.handler.nickname = random props
a1.sources.r1.HttpConfiguration.sendServerVersion = false
a1.sources.r1.ServerConnector.idleTimeout = 300

JSONHandler

A handler is provided out of the box which can handle events represented in JSON format, and supports UTF-8, UTF-16 and UTF-32 character sets. The handler accepts an array of events (even if there is only one event, the event has to be sent in an array) and converts them to a Flume event based on the encoding specified in the request. If no encoding is specified, UTF-8 is assumed. The JSON handler supports UTF-8, UTF-16 and UTF-32. Events are represented as follows.

[{
  "headers" : {
             "timestamp" : "434324343",
             "host" : "random_host.example.com"
             },
  "body" : "random_body"
  },
  {
  "headers" : {
             "namenode" : "namenode.example.com",
             "datanode" : "random_datanode.example.com"
             },
  "body" : "really_random_body"
  }]

To set the charset, the request must have content type specified as application/json; charset=UTF-8 (replace UTF-8 with UTF-16 or UTF-32 as required).

One way to create an event in the format expected by this handler is to use JSONEvent provided in the Flume SDK and use Google Gson to create the JSON string using the Gson#fromJson(Object, Type) method. The type token to pass as the 2nd argument of this method for list of events can be created by:

Type type = new TypeToken<List<JSONEvent>>() {}.getType();

BlobHandler

By default HTTPSource splits JSON input into Flume events. As an alternative, BlobHandler is a handler for HTTPSource that returns an event that contains the request parameters as well as the Binary Large Object (BLOB) uploaded with this request. For example a PDF or JPG file. Note that this approach is not suitable for very large objects because it buffers up the entire BLOB in RAM.

Property Name	Default	Description
handler	–	The FQCN of this class: org.apache.flume.sink.solr.morphline.BlobHandler
handler.maxBlobLength	100000000	The maximum number of bytes to read and buffer for a given request

Stress Source

StressSource is an internal load-generating source implementation which is very useful for stress tests. It allows User to configure the size of Event payload, with empty headers. User can configure total number of events to be sent as well maximum number of Successful Event to be delivered.

Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be org.apache.flume.source.StressSource
size	500	Payload size of each Event. Unit:byte
maxTotalEvents	-1	Maximum number of Events to be sent
maxSuccessfulEvents	-1	Maximum number of Events successfully sent
batchSize	1	Number of Events to be sent in one batch
maxEventsPerSecond	0	When set to an integer greater than zero, enforces a rate limiter onto the source.

Example for agent named a1:

a1.sources = stresssource-1
a1.channels = memoryChannel-1
a1.sources.stresssource-1.type = org.apache.flume.source.StressSource
a1.sources.stresssource-1.size = 10240
a1.sources.stresssource-1.maxTotalEvents = 1000000
a1.sources.stresssource-1.channels = memoryChannel-1

Legacy Sources

The legacy sources allow a Flume 1.x agent to receive events from Flume 0.9.4 agents. It accepts events in the Flume 0.9.4 format, converts them to the Flume 1.0 format, and stores them in the connected channel. The 0.9.4 event properties like timestamp, pri, host, nanos, etc get converted to 1.x event header attributes. The legacy source supports both Avro and Thrift RPC connections. To use this bridge between two Flume versions, you need to start a Flume 1.x agent with the avroLegacy or thriftLegacy source. The 0.9.4 agent should have the agent Sink pointing to the host/port of the 1.x agent.

Note

The reliability semantics of Flume 1.x are different from that of Flume 0.9.x. The E2E or DFO mode of a Flume 0.9.x agent will not be supported by the legacy source. The only supported 0.9.x mode is the best effort, though the reliability setting of the 1.x flow will be applicable to the events once they are saved into the Flume 1.x channel by the legacy source.

Required properties are in bold.

Avro Legacy Source

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be org.apache.flume.source.avroLegacy.AvroLegacySource
host	–	The hostname or IP address to bind to
port	–	The port # to listen on
selector.type		replicating or multiplexing
selector.*	replicating	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = org.apache.flume.source.avroLegacy.AvroLegacySource
a1.sources.r1.host = 0.0.0.0
a1.sources.r1.bind = 6666
a1.sources.r1.channels = c1

Thrift Legacy Source

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be org.apache.flume.source.thriftLegacy.ThriftLegacySource
host	–	The hostname or IP address to bind to
port	–	The port # to listen on
selector.type		replicating or multiplexing
selector.*	replicating	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = org.apache.flume.source.thriftLegacy.ThriftLegacySource
a1.sources.r1.host = 0.0.0.0
a1.sources.r1.bind = 6666
a1.sources.r1.channels = c1

Custom Source

A custom source is your own implementation of the Source interface. A custom source’s class and its dependencies must be included in the agent’s classpath when starting the Flume agent. The type of the custom source is its FQCN.

Property Name	Default	Description
channels	–
type	–	The component type name, needs to be your FQCN
selector.type		replicating or multiplexing
selector.*	replicating	Depends on the selector.type value
interceptors	–	Space-separated list of interceptors
interceptors.*

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = org.example.MySource
a1.sources.r1.channels = c1

Scribe Source

Scribe is another type of ingest system. To adopt existing Scribe ingest system, Flume should use ScribeSource based on Thrift with compatible transfering protocol. For deployment of Scribe please follow the guide from Facebook. Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be org.apache.flume.source.scribe.ScribeSource
port	1499	Port that Scribe should be connected
maxReadBufferBytes	16384000	Thrift Default FrameBuffer Size
workerThreads	5	Handing threads number in Thrift
selector.type
selector.*

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.type = org.apache.flume.source.scribe.ScribeSource
a1.sources.r1.port = 1463
a1.sources.r1.workerThreads = 5
a1.sources.r1.channels = c1

Flume Sinks

HDFS Sink

This sink writes events into the Hadoop Distributed File System (HDFS). It currently supports creating text and sequence files. It supports compression in both file types. The files can be rolled (close current file and create a new one) periodically based on the elapsed time or size of data or number of events. It also buckets/partitions data by attributes like timestamp or machine where the event originated. The HDFS directory path may contain formatting escape sequences that will replaced by the HDFS sink to generate a directory/file name to store the events. Using this sink requires hadoop to be installed so that Flume can use the Hadoop jars to communicate with the HDFS cluster. Note that a version of Hadoop that supports the sync() call is required.

The following are the escape sequences supported:

Alias	Description
%{host}	Substitute value of event header named “host”. Arbitrary header names are supported.
%t	Unix time in milliseconds
%a	locale’s short weekday name (Mon, Tue, ...)
%A	locale’s full weekday name (Monday, Tuesday, ...)
%b	locale’s short month name (Jan, Feb, ...)
%B	locale’s long month name (January, February, ...)
%c	locale’s date and time (Thu Mar 3 23:05:25 2005)
%d	day of month (01)
%e	day of month without padding (1)
%D	date; same as %m/%d/%y
%H	hour (00..23)
%I	hour (01..12)
%j	day of year (001..366)
%k	hour ( 0..23)
%m	month (01..12)
%n	month without padding (1..12)
%M	minute (00..59)
%p	locale’s equivalent of am or pm
%s	seconds since 1970-01-01 00:00:00 UTC
%S	second (00..59)
%y	last two digits of year (00..99)
%Y	year (2010)
%z	+hhmm numeric timezone (for example, -0400)
%[localhost]	Substitute the hostname of the host where the agent is running
%[IP]	Substitute the IP address of the host where the agent is running
%[FQDN]	Substitute the canonical hostname of the host where the agent is running

Note: The escape strings %[localhost], %[IP] and %[FQDN] all rely on Java’s ability to obtain the hostname, which may fail in some networking environments.

The file in use will have the name mangled to include ”.tmp” at the end. Once the file is closed, this extension is removed. This allows excluding partially complete files in the directory. Required properties are in bold.

Note

For all of the time related escape sequences, a header with the key “timestamp” must exist among the headers of the event (unless hdfs.useLocalTimeStamp is set to true). One way to add this automatically is to use the TimestampInterceptor.

Name	Default	Description
channel	–
type	–	The component type name, needs to be hdfs
hdfs.path	–	HDFS directory path (eg hdfs://namenode/flume/webdata/)
hdfs.filePrefix	FlumeData	Name prefixed to files created by Flume in hdfs directory
hdfs.fileSuffix	–	Suffix to append to file (eg .avro - NOTE: period is not automatically added)
hdfs.inUsePrefix	–	Prefix that is used for temporal files that flume actively writes into
hdfs.inUseSuffix	.tmp	Suffix that is used for temporal files that flume actively writes into
hdfs.emptyInUseSuffix	false	If false an hdfs.inUseSuffix is used while writing the output. After closing the output hdfs.inUseSuffix is removed from the output file name. If true the hdfs.inUseSuffix parameter is ignored an empty string is used instead.
hdfs.rollInterval	30	Number of seconds to wait before rolling current file (0 = never roll based on time interval)
hdfs.rollSize	1024	File size to trigger roll, in bytes (0: never roll based on file size)
hdfs.rollCount	10	Number of events written to file before it rolled (0 = never roll based on number of events)
hdfs.idleTimeout	0	Timeout after which inactive files get closed (0 = disable automatic closing of idle files)
hdfs.batchSize	100	number of events written to file before it is flushed to HDFS
hdfs.codeC	–	Compression codec. one of following : gzip, bzip2, lzo, lzop, snappy
hdfs.fileType	SequenceFile	File format: currently SequenceFile, DataStream or CompressedStream (1)DataStream will not compress output file and please don’t set codeC (2)CompressedStream requires set hdfs.codeC with an available codeC
hdfs.maxOpenFiles	5000	Allow only this number of open files. If this number is exceeded, the oldest file is closed.
hdfs.minBlockReplicas	–	Specify minimum number of replicas per HDFS block. If not specified, it comes from the default Hadoop config in the classpath.
hdfs.writeFormat	Writable	Format for sequence file records. One of Text or Writable. Set to Text before creating data files with Flume, otherwise those files cannot be read by either Apache Impala (incubating) or Apache Hive.
hdfs.threadsPoolSize	10	Number of threads per HDFS sink for HDFS IO ops (open, write, etc.)
hdfs.rollTimerPoolSize	1	Number of threads per HDFS sink for scheduling timed file rolling
hdfs.kerberosPrincipal	–	Kerberos user principal for accessing secure HDFS
hdfs.kerberosKeytab	–	Kerberos keytab for accessing secure HDFS
hdfs.proxyUser
hdfs.round	false	Should the timestamp be rounded down (if true, affects all time based escape sequences except %t)
hdfs.roundValue	1	Rounded down to the highest multiple of this (in the unit configured using hdfs.roundUnit), less than current time.
hdfs.roundUnit	second	The unit of the round down value - second, minute or hour.
hdfs.timeZone	Local Time	Name of the timezone that should be used for resolving the directory path, e.g. America/Los_Angeles.
hdfs.useLocalTimeStamp	false	Use the local time (instead of the timestamp from the event header) while replacing the escape sequences.
hdfs.closeTries	0	Number of times the sink must try renaming a file, after initiating a close attempt. If set to 1, this sink will not re-try a failed rename (due to, for example, NameNode or DataNode failure), and may leave the file in an open state with a .tmp extension. If set to 0, the sink will try to rename the file until the file is eventually renamed (there is no limit on the number of times it would try). The file may still remain open if the close call fails but the data will be intact and in this case, the file will be closed only after a Flume restart.
hdfs.retryInterval	180	Time in seconds between consecutive attempts to close a file. Each close call costs multiple RPC round-trips to the Namenode, so setting this too low can cause a lot of load on the name node. If set to 0 or less, the sink will not attempt to close the file if the first attempt fails, and may leave the file open or with a ”.tmp” extension.
serializer	TEXT	Other possible options include avro_event or the fully-qualified class name of an implementation of the EventSerializer.Builder interface.
serializer.*

Deprecated Properties

Name	Default	Description
hdfs.callTimeout	30000	Number of milliseconds allowed for HDFS operations, such as open, write, flush, close. This number should be increased if many HDFS timeout operations are occurring.

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = hdfs
a1.sinks.k1.channel = c1
a1.sinks.k1.hdfs.path = /flume/events/%Y-%m-%d/%H%M/%S
a1.sinks.k1.hdfs.filePrefix = events-
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.hdfs.roundUnit = minute

The above configuration will round down the timestamp to the last 10th minute. For example, an event with timestamp 11:54:34 AM, June 12, 2012 will cause the hdfs path to become /flume/events/2012-06-12/1150/00.

Hive Sink

This sink streams events containing delimited text or JSON data directly into a Hive table or partition. Events are written using Hive transactions. As soon as a set of events are committed to Hive, they become immediately visible to Hive queries. Partitions to which flume will stream to can either be pre-created or, optionally, Flume can create them if they are missing. Fields from incoming event data are mapped to corresponding columns in the Hive table.

Name	Default	Description
channel	–
type	–	The component type name, needs to be hive
hive.metastore	–	Hive metastore URI (eg thrift://a.b.com:9083 )
hive.database	–	Hive database name
hive.table	–	Hive table name
hive.partition	–	Comma separate list of partition values identifying the partition to write to. May contain escape sequences. E.g: If the table is partitioned by (continent: string, country :string, time : string) then ‘Asia,India,2014-02-26-01-21’ will indicate continent=Asia,country=India,time=2014-02-26-01-21
hive.txnsPerBatchAsk	100	Hive grants a batch of transactions instead of single transactions to streaming clients like Flume. This setting configures the number of desired transactions per Transaction Batch. Data from all transactions in a single batch end up in a single file. Flume will write a maximum of batchSize events in each transaction in the batch. This setting in conjunction with batchSize provides control over the size of each file. Note that eventually Hive will transparently compact these files into larger files.
heartBeatInterval	240	(In seconds) Interval between consecutive heartbeats sent to Hive to keep unused transactions from expiring. Set this value to 0 to disable heartbeats.
autoCreatePartitions	true	Flume will automatically create the necessary Hive partitions to stream to
batchSize	15000	Max number of events written to Hive in a single Hive transaction
maxOpenConnections	500	Allow only this number of open connections. If this number is exceeded, the least recently used connection is closed.
callTimeout	10000	(In milliseconds) Timeout for Hive & HDFS I/O operations, such as openTxn, write, commit, abort.
serializer		Serializer is responsible for parsing out field from the event and mapping them to columns in the hive table. Choice of serializer depends upon the format of the data in the event. Supported serializers: DELIMITED and JSON
roundUnit	minute	The unit of the round down value - second, minute or hour.
roundValue	1	Rounded down to the highest multiple of this (in the unit configured using hive.roundUnit), less than current time
timeZone	Local Time	Name of the timezone that should be used for resolving the escape sequences in partition, e.g. America/Los_Angeles.
useLocalTimeStamp	false	Use the local time (instead of the timestamp from the event header) while replacing the escape sequences.

Following serializers are provided for Hive sink:

JSON: Handles UTF8 encoded Json (strict syntax) events and requires no configration. Object names in the JSON are mapped directly to columns with the same name in the Hive table. Internally uses org.apache.hive.hcatalog.data.JsonSerDe but is independent of the Serde of the Hive table. This serializer requires HCatalog to be installed.

DELIMITED: Handles simple delimited textual events. Internally uses LazySimpleSerde but is independent of the Serde of the Hive table.

Name	Default	Description
serializer.delimiter	,	(Type: string) The field delimiter in the incoming data. To use special characters, surround them with double quotes like “\t”
serializer.fieldnames	–	The mapping from input fields to columns in hive table. Specified as a comma separated list (no spaces) of hive table columns names, identifying the input fields in order of their occurrence. To skip fields leave the column name unspecified. Eg. ‘time,,ip,message’ indicates the 1st, 3rd and 4th fields in input map to time, ip and message columns in the hive table.
serializer.serdeSeparator	Ctrl-A	(Type: character) Customizes the separator used by underlying serde. There can be a gain in efficiency if the fields in serializer.fieldnames are in same order as table columns, the serializer.delimiter is same as the serializer.serdeSeparator and number of fields in serializer.fieldnames is less than or equal to number of table columns, as the fields in incoming event body do not need to be reordered to match order of table columns. Use single quotes for special characters like ‘\t’. Ensure input fields do not contain this character. NOTE: If serializer.delimiter is a single character, preferably set this to the same character

The following are the escape sequences supported:

Alias	Description
%{host}	Substitute value of event header named “host”. Arbitrary header names are supported.
%t	Unix time in milliseconds
%a	locale’s short weekday name (Mon, Tue, ...)
%A	locale’s full weekday name (Monday, Tuesday, ...)
%b	locale’s short month name (Jan, Feb, ...)
%B	locale’s long month name (January, February, ...)
%c	locale’s date and time (Thu Mar 3 23:05:25 2005)
%d	day of month (01)
%D	date; same as %m/%d/%y
%H	hour (00..23)
%I	hour (01..12)
%j	day of year (001..366)
%k	hour ( 0..23)
%m	month (01..12)
%M	minute (00..59)
%p	locale’s equivalent of am or pm
%s	seconds since 1970-01-01 00:00:00 UTC
%S	second (00..59)
%y	last two digits of year (00..99)
%Y	year (2010)
%z	+hhmm numeric timezone (for example, -0400)

Note

For all of the time related escape sequences, a header with the key “timestamp” must exist among the headers of the event (unless useLocalTimeStamp is set to true). One way to add this automatically is to use the TimestampInterceptor.

Example Hive table :

create table weblogs ( id int , msg string )
    partitioned by (continent string, country string, time string)
    clustered by (id) into 5 buckets
    stored as orc;

Example for agent named a1:

a1.channels = c1
a1.channels.c1.type = memory
a1.sinks = k1
a1.sinks.k1.type = hive
a1.sinks.k1.channel = c1
a1.sinks.k1.hive.metastore = thrift://127.0.0.1:9083
a1.sinks.k1.hive.database = logsdb
a1.sinks.k1.hive.table = weblogs
a1.sinks.k1.hive.partition = asia,%{country},%Y-%m-%d-%H-%M
a1.sinks.k1.useLocalTimeStamp = false
a1.sinks.k1.round = true
a1.sinks.k1.roundValue = 10
a1.sinks.k1.roundUnit = minute
a1.sinks.k1.serializer = DELIMITED
a1.sinks.k1.serializer.delimiter = "\t"
a1.sinks.k1.serializer.serdeSeparator = '\t'
a1.sinks.k1.serializer.fieldnames =id,,msg

The above configuration will round down the timestamp to the last 10th minute. For example, an event with timestamp header set to 11:54:34 AM, June 12, 2012 and ‘country’ header set to ‘india’ will evaluate to the partition (continent=’asia’,country=’india’,time=‘2012-06-12-11-50’. The serializer is configured to accept tab separated input containing three fields and to skip the second field.

Logger Sink

Logs event at INFO level. Typically useful for testing/debugging purpose. Required properties are in bold. This sink is the only exception which doesn’t require the extra configuration explained in the Logging raw data section.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be logger
maxBytesToLog	16	Maximum number of bytes of the Event body to log

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = logger
a1.sinks.k1.channel = c1

Avro Sink

This sink forms one half of Flume’s tiered collection support. Flume events sent to this sink are turned into Avro events and sent to the configured hostname / port pair. The events are taken from the configured Channel in batches of the configured batch size. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be avro.
hostname	–	The hostname or IP address to bind to.
port	–	The port # to listen on.
batch-size	100	number of event to batch together for send.
connect-timeout	20000	Amount of time (ms) to allow for the first (handshake) request.
request-timeout	20000	Amount of time (ms) to allow for requests after the first.
reset-connection-interval	none	Amount of time (s) before the connection to the next hop is reset. This will force the Avro Sink to reconnect to the next hop. This will allow the sink to connect to hosts behind a hardware load-balancer when news hosts are added without having to restart the agent.
compression-type	none	This can be “none” or “deflate”. The compression-type must match the compression-type of matching AvroSource
compression-level	6	The level of compression to compress event. 0 = no compression and 1-9 is compression. The higher the number the more compression
ssl	false	Set to true to enable SSL for this AvroSink. When configuring SSL, you can optionally set a “truststore”, “truststore-password”, “truststore-type”, and specify whether to “trust-all-certs”.
trust-all-certs	false	If this is set to true, SSL server certificates for remote servers (Avro Sources) will not be checked. This should NOT be used in production because it makes it easier for an attacker to execute a man-in-the-middle attack and “listen in” on the encrypted connection.
truststore	–	The path to a custom Java truststore file. Flume uses the certificate authority information in this file to determine whether the remote Avro Source’s SSL authentication credentials should be trusted. If not specified, then the global keystore will be used. If the global keystore not specified either, then the default Java JSSE certificate authority files (typically “jssecacerts” or “cacerts” in the Oracle JRE) will be used.
truststore-password	–	The password for the truststore. If not specified, then the global keystore password will be used (if defined).
truststore-type	JKS	The type of the Java truststore. This can be “JKS” or other supported Java truststore type. If not specified, then the global keystore type will be used (if defined, otherwise the defautl is JKS).
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude. SSLv3 will always be excluded in addition to the protocols specified.
maxIoWorkers	2 * the number of available processors in the machine	The maximum number of I/O worker threads. This is configured on the NettyAvroRpcClient NioClientSocketChannelFactory.

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = avro
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = 10.10.10.10
a1.sinks.k1.port = 4545

Thrift Sink

This sink forms one half of Flume’s tiered collection support. Flume events sent to this sink are turned into Thrift events and sent to the configured hostname / port pair. The events are taken from the configured Channel in batches of the configured batch size.

Thrift sink can be configured to start in secure mode by enabling kerberos authentication. To communicate with a Thrift source started in secure mode, the Thrift sink should also operate in secure mode. client-principal and client-keytab are the properties used by the Thrift sink to authenticate to the kerberos KDC. The server-principal represents the principal of the Thrift source this sink is configured to connect to in secure mode. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be thrift.
hostname	–	The hostname or IP address to bind to.
port	–	The port # to listen on.
batch-size	100	number of event to batch together for send.
connect-timeout	20000	Amount of time (ms) to allow for the first (handshake) request.
request-timeout	20000	Amount of time (ms) to allow for requests after the first.
connection-reset-interval	none	Amount of time (s) before the connection to the next hop is reset. This will force the Thrift Sink to reconnect to the next hop. This will allow the sink to connect to hosts behind a hardware load-balancer when news hosts are added without having to restart the agent.
ssl	false	Set to true to enable SSL for this ThriftSink. When configuring SSL, you can optionally set a “truststore”, “truststore-password” and “truststore-type”
truststore	–	The path to a custom Java truststore file. Flume uses the certificate authority information in this file to determine whether the remote Thrift Source’s SSL authentication credentials should be trusted. If not specified, then the global keystore will be used. If the global keystore not specified either, then the default Java JSSE certificate authority files (typically “jssecacerts” or “cacerts” in the Oracle JRE) will be used.
truststore-password	–	The password for the truststore. If not specified, then the global keystore password will be used (if defined).
truststore-type	JKS	The type of the Java truststore. This can be “JKS” or other supported Java truststore type. If not specified, then the global keystore type will be used (if defined, otherwise the defautl is JKS).
exclude-protocols	SSLv3	Space-separated list of SSL/TLS protocols to exclude
kerberos	false	Set to true to enable kerberos authentication. In kerberos mode, client-principal, client-keytab and server-principal are required for successful authentication and communication to a kerberos enabled Thrift Source.
client-principal	—-	The kerberos principal used by the Thrift Sink to authenticate to the kerberos KDC.
client-keytab	—-	The keytab location used by the Thrift Sink in combination with the client-principal to authenticate to the kerberos KDC.
server-principal	–	The kerberos principal of the Thrift Source to which the Thrift Sink is configured to connect to.

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = thrift
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = 10.10.10.10
a1.sinks.k1.port = 4545

IRC Sink

The IRC sink takes messages from attached channel and relays those to configured IRC destinations. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be irc
hostname	–	The hostname or IP address to connect to
port	6667	The port number of remote host to connect
nick	–	Nick name
user	–	User name
password	–	User password
chan	–	channel
name
splitlines	–	(boolean)
splitchars	n	line separator (if you were to enter the default value into the config file, then you would need to escape the backslash, like this: “\n”)

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = irc
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = irc.yourdomain.com
a1.sinks.k1.nick = flume
a1.sinks.k1.chan = #flume

File Roll Sink

Stores events on the local filesystem. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be file_roll.
sink.directory	–	The directory where files will be stored
sink.pathManager	DEFAULT	The PathManager implementation to use.
sink.pathManager.extension	–	The file extension if the default PathManager is used.
sink.pathManager.prefix	–	A character string to add to the beginning of the file name if the default PathManager is used
sink.rollInterval	30	Roll the file every 30 seconds. Specifying 0 will disable rolling and cause all events to be written to a single file.
sink.serializer	TEXT	Other possible options include avro_event or the FQCN of an implementation of EventSerializer.Builder interface.
sink.batchSize	100

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = file_roll
a1.sinks.k1.channel = c1
a1.sinks.k1.sink.directory = /var/log/flume

Null Sink

Discards all events it receives from the channel. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be null.
batchSize	100

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = null
a1.sinks.k1.channel = c1

HBaseSinks

HBaseSink

This sink writes data to HBase. The Hbase configuration is picked up from the first hbase-site.xml encountered in the classpath. A class implementing HbaseEventSerializer which is specified by the configuration is used to convert the events into HBase puts and/or increments. These puts and increments are then written to HBase. This sink provides the same consistency guarantees as HBase, which is currently row-wise atomicity. In the event of Hbase failing to write certain events, the sink will replay all events in that transaction.

The HBaseSink supports writing data to secure HBase. To write to secure HBase, the user the agent is running as must have write permissions to the table the sink is configured to write to. The principal and keytab to use to authenticate against the KDC can be specified in the configuration. The hbase-site.xml in the Flume agent’s classpath must have authentication set to kerberos (For details on how to do this, please refer to HBase documentation).

For convenience, two serializers are provided with Flume. The SimpleHbaseEventSerializer (org.apache.flume.sink.hbase.SimpleHbaseEventSerializer) writes the event body as-is to HBase, and optionally increments a column in Hbase. This is primarily an example implementation. The RegexHbaseEventSerializer (org.apache.flume.sink.hbase.RegexHbaseEventSerializer) breaks the event body based on the given regex and writes each part into different columns.

The type is the FQCN: org.apache.flume.sink.hbase.HBaseSink.

Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be hbase
table	–	The name of the table in Hbase to write to.
columnFamily	–	The column family in Hbase to write to.
zookeeperQuorum	–	The quorum spec. This is the value for the property hbase.zookeeper.quorum in hbase-site.xml
znodeParent	/hbase	The base path for the znode for the -ROOT- region. Value of zookeeper.znode.parent in hbase-site.xml
batchSize	100	Number of events to be written per txn.
coalesceIncrements	false	Should the sink coalesce multiple increments to a cell per batch. This might give better performance if there are multiple increments to a limited number of cells.
serializer	org.apache.flume.sink.hbase.SimpleHbaseEventSerializer	Default increment column = “iCol”, payload column = “pCol”.
serializer.*	–	Properties to be passed to the serializer.
kerberosPrincipal	–	Kerberos user principal for accessing secure HBase
kerberosKeytab	–	Kerberos keytab for accessing secure HBase

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = hbase
a1.sinks.k1.table = foo_table
a1.sinks.k1.columnFamily = bar_cf
a1.sinks.k1.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer
a1.sinks.k1.channel = c1

HBase2Sink

HBase2Sink is the equivalent of HBaseSink for HBase version 2. The provided functionality and the configuration parameters are the same as in case of HBaseSink (except the hbase2 tag in the sink type and the package/class names).

The type is the FQCN: org.apache.flume.sink.hbase2.HBase2Sink.

Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be hbase2
table	–	The name of the table in HBase to write to.
columnFamily	–	The column family in HBase to write to.
zookeeperQuorum	–	The quorum spec. This is the value for the property hbase.zookeeper.quorum in hbase-site.xml
znodeParent	/hbase	The base path for the znode for the -ROOT- region. Value of zookeeper.znode.parent in hbase-site.xml
batchSize	100	Number of events to be written per txn.
coalesceIncrements	false	Should the sink coalesce multiple increments to a cell per batch. This might give better performance if there are multiple increments to a limited number of cells.
serializer	org.apache.flume.sink.hbase2.SimpleHBase2EventSerializer	Default increment column = “iCol”, payload column = “pCol”.
serializer.*	–	Properties to be passed to the serializer.
kerberosPrincipal	–	Kerberos user principal for accessing secure HBase
kerberosKeytab	–	Kerberos keytab for accessing secure HBase

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = hbase2
a1.sinks.k1.table = foo_table
a1.sinks.k1.columnFamily = bar_cf
a1.sinks.k1.serializer = org.apache.flume.sink.hbase2.RegexHBase2EventSerializer
a1.sinks.k1.channel = c1

AsyncHBaseSink

This sink writes data to HBase using an asynchronous model. A class implementing AsyncHbaseEventSerializer which is specified by the configuration is used to convert the events into HBase puts and/or increments. These puts and increments are then written to HBase. This sink uses the Asynchbase API to write to HBase. This sink provides the same consistency guarantees as HBase, which is currently row-wise atomicity. In the event of Hbase failing to write certain events, the sink will replay all events in that transaction. AsyncHBaseSink can only be used with HBase 1.x. The async client library used by AsyncHBaseSink is not available for HBase 2. The type is the FQCN: org.apache.flume.sink.hbase.AsyncHBaseSink. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be asynchbase
table	–	The name of the table in Hbase to write to.
zookeeperQuorum	–	The quorum spec. This is the value for the property hbase.zookeeper.quorum in hbase-site.xml
znodeParent	/hbase	The base path for the znode for the -ROOT- region. Value of zookeeper.znode.parent in hbase-site.xml
columnFamily	–	The column family in Hbase to write to.
batchSize	100	Number of events to be written per txn.
coalesceIncrements	false	Should the sink coalesce multiple increments to a cell per batch. This might give better performance if there are multiple increments to a limited number of cells.
timeout	60000	The length of time (in milliseconds) the sink waits for acks from hbase for all events in a transaction.
serializer	org.apache.flume.sink.hbase.SimpleAsyncHbaseEventSerializer
serializer.*	–	Properties to be passed to the serializer.
async.*	–	Properties to be passed to asyncHbase library. These properties have precedence over the old zookeeperQuorum and znodeParent values. You can find the list of the available properties at the documentation page of AsyncHBase.

Note that this sink takes the Zookeeper Quorum and parent znode information in the configuration. Zookeeper Quorum and parent node configuration may be specified in the flume configuration file. Alternatively, these configuration values are taken from the first hbase-site.xml file in the classpath.

If these are not provided in the configuration, then the sink will read this information from the first hbase-site.xml file in the classpath.

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = asynchbase
a1.sinks.k1.table = foo_table
a1.sinks.k1.columnFamily = bar_cf
a1.sinks.k1.serializer = org.apache.flume.sink.hbase.SimpleAsyncHbaseEventSerializer
a1.sinks.k1.channel = c1

MorphlineSolrSink

This sink extracts data from Flume events, transforms it, and loads it in near-real-time into Apache Solr servers, which in turn serve queries to end users or search applications.

This sink is well suited for use cases that stream raw data into HDFS (via the HdfsSink) and simultaneously extract, transform and load the same data into Solr (via MorphlineSolrSink). In particular, this sink can process arbitrary heterogeneous raw data from disparate data sources and turn it into a data model that is useful to Search applications.

The ETL functionality is customizable using a morphline configuration file that defines a chain of transformation commands that pipe event records from one command to another.

Morphlines can be seen as an evolution of Unix pipelines where the data model is generalized to work with streams of generic records, including arbitrary binary payloads. A morphline command is a bit like a Flume Interceptor. Morphlines can be embedded into Hadoop components such as Flume.

Commands to parse and transform a set of standard data formats such as log files, Avro, CSV, Text, HTML, XML, PDF, Word, Excel, etc. are provided out of the box, and additional custom commands and parsers for additional data formats can be added as morphline plugins. Any kind of data format can be indexed and any Solr documents for any kind of Solr schema can be generated, and any custom ETL logic can be registered and executed.

Morphlines manipulate continuous streams of records. The data model can be described as follows: A record is a set of named fields where each field has an ordered list of one or more values. A value can be any Java Object. That is, a record is essentially a hash table where each hash table entry contains a String key and a list of Java Objects as values. (The implementation uses Guava’s ArrayListMultimap, which is a ListMultimap). Note that a field can have multiple values and any two records need not use common field names.

This sink fills the body of the Flume event into the _attachment_body field of the morphline record, as well as copies the headers of the Flume event into record fields of the same name. The commands can then act on this data.

Routing to a SolrCloud cluster is supported to improve scalability. Indexing load can be spread across a large number of MorphlineSolrSinks for improved scalability. Indexing load can be replicated across multiple MorphlineSolrSinks for high availability, for example using Flume features such as Load balancing Sink Processor. MorphlineInterceptor can also help to implement dynamic routing to multiple Solr collections (e.g. for multi-tenancy).

The morphline and solr jars required for your environment must be placed in the lib directory of the Apache Flume installation.

The type is the FQCN: org.apache.flume.sink.solr.morphline.MorphlineSolrSink

Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be org.apache.flume.sink.solr.morphline.MorphlineSolrSink
morphlineFile	–	The relative or absolute path on the local file system to the morphline configuration file. Example: /etc/flume-ng/conf/morphline.conf
morphlineId	null	Optional name used to identify a morphline if there are multiple morphlines in a morphline config file
batchSize	1000	The maximum number of events to take per flume transaction.
batchDurationMillis	1000	The maximum duration per flume transaction (ms). The transaction commits after this duration or when batchSize is exceeded, whichever comes first.
handlerClass	org.apache.flume.sink.solr.morphline.MorphlineHandlerImpl	The FQCN of a class implementing org.apache.flume.sink.solr.morphline.MorphlineHandler
isProductionMode	false	This flag should be enabled for mission critical, large-scale online production systems that need to make progress without downtime when unrecoverable exceptions occur. Corrupt or malformed parser input data, parser bugs, and errors related to unknown Solr schema fields produce unrecoverable exceptions.
recoverableExceptionClasses	org.apache.solr.client.solrj.SolrServerException	Comma separated list of recoverable exceptions that tend to be transient, in which case the corresponding task can be retried. Examples include network connection errors, timeouts, etc. When the production mode flag is set to true, the recoverable exceptions configured using this parameter will not be ignored and hence will lead to retries.
isIgnoringRecoverableExceptions	false	This flag should be enabled, if an unrecoverable exception is accidentally misclassified as recoverable. This enables the sink to make progress and avoid retrying an event forever.

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = org.apache.flume.sink.solr.morphline.MorphlineSolrSink
a1.sinks.k1.channel = c1
a1.sinks.k1.morphlineFile = /etc/flume-ng/conf/morphline.conf
# a1.sinks.k1.morphlineId = morphline1
# a1.sinks.k1.batchSize = 1000
# a1.sinks.k1.batchDurationMillis = 1000

Kite Dataset Sink

Experimental sink that writes events to a Kite Dataset. This sink will deserialize the body of each incoming event and store the resulting record in a Kite Dataset. It determines target Dataset by loading a dataset by URI.

The only supported serialization is avro, and the record schema must be passed in the event headers, using either flume.avro.schema.literal with the JSON schema representation or flume.avro.schema.url with a URL where the schema may be found (hdfs:/... URIs are supported). This is compatible with the Log4jAppender flume client and the spooling directory source’s Avro deserializer using deserializer.schemaType = LITERAL.

Note 1: The flume.avro.schema.hash header is not supported. Note 2: In some cases, file rolling may occur slightly after the roll interval has been exceeded. However, this delay will not exceed 5 seconds. In most cases, the delay is neglegible.

Property Name	Default	Description
channel	–
type	–	Must be org.apache.flume.sink.kite.DatasetSink
kite.dataset.uri	–	URI of the dataset to open
kite.repo.uri	–	URI of the repository to open (deprecated; use kite.dataset.uri instead)
kite.dataset.namespace	–	Namespace of the Dataset where records will be written (deprecated; use kite.dataset.uri instead)
kite.dataset.name	–	Name of the Dataset where records will be written (deprecated; use kite.dataset.uri instead)
kite.batchSize	100	Number of records to process in each batch
kite.rollInterval	30	Maximum wait time (seconds) before data files are released
kite.flushable.commitOnBatch	true	If true, the Flume transaction will be commited and the writer will be flushed on each batch of kite.batchSize records. This setting only applies to flushable datasets. When true, it’s possible for temp files with commited data to be left in the dataset directory. These files need to be recovered by hand for the data to be visible to DatasetReaders.
kite.syncable.syncOnBatch	true	Controls whether the sink will also sync data when committing the transaction. This setting only applies to syncable datasets. Syncing gaurentees that data will be written on stable storage on the remote system while flushing only gaurentees that data has left Flume’s client buffers. When the kite.flushable.commitOnBatch property is set to false, this property must also be set to false.
kite.entityParser	avro	Parser that turns Flume Events into Kite entities. Valid values are avro and the fully-qualified class name of an implementation of the EntityParser.Builder interface.
kite.failurePolicy	retry	Policy that handles non-recoverable errors such as a missing Schema in the Event header. The default value, retry, will fail the current batch and try again which matches the old behavior. Other valid values are save, which will write the raw Event to the kite.error.dataset.uri dataset, and the fully-qualified class name of an implementation of the FailurePolicy.Builder interface.
kite.error.dataset.uri	–	URI of the dataset where failed events are saved when kite.failurePolicy is set to save. Required when the kite.failurePolicy is set to save.
auth.kerberosPrincipal	–	Kerberos user principal for secure authentication to HDFS
auth.kerberosKeytab	–	Kerberos keytab location (local FS) for the principal
auth.proxyUser	–	The effective user for HDFS actions, if different from the kerberos principal

Kafka Sink

This is a Flume Sink implementation that can publish data to a Kafka topic. One of the objective is to integrate Flume with Kafka so that pull based processing systems can process the data coming through various Flume sources.

This currently supports Kafka server releases 0.10.1.0 or higher. Testing was done up to 2.0.1 that was the highest avilable version at the time of the release.

Required properties are marked in bold font.

Property Name	Default	Description
type	–	Must be set to org.apache.flume.sink.kafka.KafkaSink
kafka.bootstrap.servers	–	List of brokers Kafka-Sink will connect to, to get the list of topic partitions This can be a partial list of brokers, but we recommend at least two for HA. The format is comma separated list of hostname:port
kafka.topic	default-flume-topic	The topic in Kafka to which the messages will be published. If this parameter is configured, messages will be published to this topic. If the event header contains a “topic” field, the event will be published to that topic overriding the topic configured here. Arbitrary header substitution is supported, eg. %{header} is replaced with value of event header named “header”. (If using the substitution, it is recommended to set “auto.create.topics.enable” property of Kafka broker to true.)
flumeBatchSize	100	How many messages to process in one batch. Larger batches improve throughput while adding latency.
kafka.producer.acks	1	How many replicas must acknowledge a message before its considered successfully written. Accepted values are 0 (Never wait for acknowledgement), 1 (wait for leader only), -1 (wait for all replicas) Set this to -1 to avoid data loss in some cases of leader failure.
useFlumeEventFormat	false	By default events are put as bytes onto the Kafka topic directly from the event body. Set to true to store events as the Flume Avro binary format. Used in conjunction with the same property on the KafkaSource or with the parseAsFlumeEvent property on the Kafka Channel this will preserve any Flume headers for the producing side.
defaultPartitionId	–	Specifies a Kafka partition ID (integer) for all events in this channel to be sent to, unless overriden by partitionIdHeader. By default, if this property is not set, events will be distributed by the Kafka Producer’s partitioner - including by key if specified (or by a partitioner specified by kafka.partitioner.class).
partitionIdHeader	–	When set, the sink will take the value of the field named using the value of this property from the event header and send the message to the specified partition of the topic. If the value represents an invalid partition, an EventDeliveryException will be thrown. If the header value is present then this setting overrides defaultPartitionId.
allowTopicOverride	true	When set, the sink will allow a message to be produced into a topic specified by the topicHeader property (if provided).
topicHeader	topic	When set in conjunction with allowTopicOverride will produce a message into the value of the header named using the value of this property. Care should be taken when using in conjunction with the Kafka Source topicHeader property to avoid creating a loopback.
timestampHeader	–	The header containing the caller provided timestamp value to use. If not provided the current time will be used.
header.NAME	–	Used to identify which headers from the Flume Event should be passed to Kafka. The value of NAME should match the Flume header name and the value should be the name of the header to be used as the Kafka header name.
kafka.producer.security.protocol	PLAINTEXT	Set to SASL_PLAINTEXT, SASL_SSL or SSL if writing to Kafka using some level of security. See below for additional info on secure setup.
more producer security props		If using SASL_PLAINTEXT, SASL_SSL or SSL refer to Kafka security for additional properties that need to be set on producer.
Other Kafka Producer Properties	–	These properties are used to configure the Kafka Producer. Any producer property supported by Kafka can be used. The only requirement is to prepend the property name with the prefix kafka.producer. For example: kafka.producer.linger.ms

Note

Kafka Sink uses the topic and key properties from the FlumeEvent headers to send events to Kafka. If topic exists in the headers, the event will be sent to that specific topic, overriding the topic configured for the Sink. If key exists in the headers, the key will used by Kafka to partition the data between the topic partitions. Events with same key will be sent to the same partition. If the key is null, events will be sent to random partitions.

The Kafka sink also provides defaults for the key.serializer(org.apache.kafka.common.serialization.StringSerializer) and value.serializer(org.apache.kafka.common.serialization.ByteArraySerializer). Modification of these parameters is not recommended.

Deprecated Properties

Property Name	Default	Description
brokerList	–	Use kafka.bootstrap.servers
topic	default-flume-topic	Use kafka.topic
batchSize	100	Use kafka.flumeBatchSize
requiredAcks	1	Use kafka.producer.acks

An example configuration of a Kafka sink is given below. Properties starting with the prefix kafka.producer the Kafka producer. The properties that are passed when creating the Kafka producer are not limited to the properties given in this example. Also it is possible to include your custom properties here and access them inside the preprocessor through the Flume Context object passed in as a method argument.

a1.sinks.k1.channel = c1
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.topic = mytopic
a1.sinks.k1.kafka.bootstrap.servers = localhost:9092
a1.sinks.k1.kafka.flumeBatchSize = 20
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.kafka.producer.linger.ms = 1
a1.sinks.k1.kafka.producer.compression.type = snappy

Security and Kafka Sink:

Secure authentication as well as data encryption is supported on the communication channel between Flume and Kafka. For secure authentication SASL/GSSAPI (Kerberos V5) or SSL (even though the parameter is named SSL, the actual protocol is a TLS implementation) can be used from Kafka version 0.9.0.

As of now data encryption is solely provided by SSL/TLS.

Setting kafka.producer.security.protocol to any of the following value means:

• SASL_PLAINTEXT - Kerberos or plaintext authentication with no data encryption
• SASL_SSL - Kerberos or plaintext authentication with data encryption
• SSL - TLS based encryption with optional authentication.

Warning

There is a performance degradation when SSL is enabled, the magnitude of which depends on the CPU type and the JVM implementation. Reference: Kafka security overview and the jira for tracking this issue: KAFKA-2561

TLS and Kafka Sink:

Please read the steps described in Configuring Kafka Clients SSL to learn about additional configuration settings for fine tuning for example any of the following: security provider, cipher suites, enabled protocols, truststore or keystore types.

Example configuration with server side authentication and data encryption.

a1.sinks.sink1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.sink1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.sinks.sink1.kafka.topic = mytopic
a1.sinks.sink1.kafka.producer.security.protocol = SSL
# optional, the global truststore can be used alternatively
a1.sinks.sink1.kafka.producer.ssl.truststore.location = /path/to/truststore.jks
a1.sinks.sink1.kafka.producer.ssl.truststore.password = <password to access the truststore>

Specyfing the truststore is optional here, the global truststore can be used instead. For more details about the global SSL setup, see the SSL/TLS support section.

Note: By default the property ssl.endpoint.identification.algorithm is not defined, so hostname verification is not performed. In order to enable hostname verification, set the following properties

a1.sinks.sink1.kafka.producer.ssl.endpoint.identification.algorithm = HTTPS

Once enabled, clients will verify the server’s fully qualified domain name (FQDN) against one of the following two fields:

1. Common Name (CN) https://tools.ietf.org/html/rfc6125#section-2.3
2. Subject Alternative Name (SAN) https://tools.ietf.org/html/rfc5280#section-4.2.1.6

If client side authentication is also required then additionally the following needs to be added to Flume agent configuration or the global SSL setup can be used (see SSL/TLS support section). Each Flume agent has to have its client certificate which has to be trusted by Kafka brokers either individually or by their signature chain. Common example is to sign each client certificate by a single Root CA which in turn is trusted by Kafka brokers.

# optional, the global keystore can be used alternatively
a1.sinks.sink1.kafka.producer.ssl.keystore.location = /path/to/client.keystore.jks
a1.sinks.sink1.kafka.producer.ssl.keystore.password = <password to access the keystore>

If keystore and key use different password protection then ssl.key.password property will provide the required additional secret for producer keystore:

a1.sinks.sink1.kafka.producer.ssl.key.password = <password to access the key>

Kerberos and Kafka Sink:

To use Kafka sink with a Kafka cluster secured with Kerberos, set the producer.security.protocol property noted above for producer. The Kerberos keytab and principal to be used with Kafka brokers is specified in a JAAS file’s “KafkaClient” section. “Client” section describes the Zookeeper connection if needed. See Kafka doc for information on the JAAS file contents. The location of this JAAS file and optionally the system wide kerberos configuration can be specified via JAVA_OPTS in flume-env.sh:

JAVA_OPTS="$JAVA_OPTS -Djava.security.krb5.conf=/path/to/krb5.conf"
JAVA_OPTS="$JAVA_OPTS -Djava.security.auth.login.config=/path/to/flume_jaas.conf"

Example secure configuration using SASL_PLAINTEXT:

a1.sinks.sink1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.sink1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.sinks.sink1.kafka.topic = mytopic
a1.sinks.sink1.kafka.producer.security.protocol = SASL_PLAINTEXT
a1.sinks.sink1.kafka.producer.sasl.mechanism = GSSAPI
a1.sinks.sink1.kafka.producer.sasl.kerberos.service.name = kafka

Example secure configuration using SASL_SSL:

a1.sinks.sink1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.sink1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.sinks.sink1.kafka.topic = mytopic
a1.sinks.sink1.kafka.producer.security.protocol = SASL_SSL
a1.sinks.sink1.kafka.producer.sasl.mechanism = GSSAPI
a1.sinks.sink1.kafka.producer.sasl.kerberos.service.name = kafka
# optional, the global truststore can be used alternatively
a1.sinks.sink1.kafka.producer.ssl.truststore.location = /path/to/truststore.jks
a1.sinks.sink1.kafka.producer.ssl.truststore.password = <password to access the truststore>

Sample JAAS file. For reference of its content please see client config sections of the desired authentication mechanism (GSSAPI/PLAIN) in Kafka documentation of SASL configuration. Unlike the Kafka Source or Kafka Channel a “Client” section is not required, unless it is needed by other connecting components. Also please make sure that the operating system user of the Flume processes has read privileges on the jaas and keytab files.

KafkaClient {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  storeKey=true
  keyTab="/path/to/keytabs/flume.keytab"
  principal="flume/flumehost1.example.com@YOURKERBEROSREALM";
};

HTTP Sink

Behaviour of this sink is that it will take events from the channel, and send those events to a remote service using an HTTP POST request. The event content is sent as the POST body.

Error handling behaviour of this sink depends on the HTTP response returned by the target server. The sink backoff/ready status is configurable, as is the transaction commit/rollback result and whether the event contributes to the successful event drain count.

Any malformed HTTP response returned by the server where the status code is not readable will result in a backoff signal and the event is not consumed from the channel.

Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be http.
endpoint	–	The fully qualified URL endpoint to POST to
connectTimeout	5000	The socket connection timeout in milliseconds
requestTimeout	5000	The maximum request processing time in milliseconds
contentTypeHeader	text/plain	The HTTP Content-Type header
acceptHeader	text/plain	The HTTP Accept header value
defaultBackoff	true	Whether to backoff by default on receiving all HTTP status codes
defaultRollback	true	Whether to rollback by default on receiving all HTTP status codes
defaultIncrementMetrics	false	Whether to increment metrics by default on receiving all HTTP status codes
backoff.CODE	–	Configures a specific backoff for an individual (i.e. 200) code or a group (i.e. 2XX) code
rollback.CODE	–	Configures a specific rollback for an individual (i.e. 200) code or a group (i.e. 2XX) code
incrementMetrics.CODE	–	Configures a specific metrics increment for an individual (i.e. 200) code or a group (i.e. 2XX) code

Note that the most specific HTTP status code match is used for the backoff, rollback and incrementMetrics configuration options. If there are configuration values for both 2XX and 200 status codes, then 200 HTTP codes will use the 200 value, and all other HTTP codes in the 201-299 range will use the 2XX value.

Any empty or null events are consumed without any request being made to the HTTP endpoint.

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = http
a1.sinks.k1.channel = c1
a1.sinks.k1.endpoint = http://localhost:8080/someuri
a1.sinks.k1.connectTimeout = 2000
a1.sinks.k1.requestTimeout = 2000
a1.sinks.k1.acceptHeader = application/json
a1.sinks.k1.contentTypeHeader = application/json
a1.sinks.k1.defaultBackoff = true
a1.sinks.k1.defaultRollback = true
a1.sinks.k1.defaultIncrementMetrics = false
a1.sinks.k1.backoff.4XX = false
a1.sinks.k1.rollback.4XX = false
a1.sinks.k1.incrementMetrics.4XX = true
a1.sinks.k1.backoff.200 = false
a1.sinks.k1.rollback.200 = false
a1.sinks.k1.incrementMetrics.200 = true

Custom Sink

A custom sink is your own implementation of the Sink interface. A custom sink’s class and its dependencies must be included in the agent’s classpath when starting the Flume agent. The type of the custom sink is its FQCN. Required properties are in bold.

Property Name	Default	Description
channel	–
type	–	The component type name, needs to be your FQCN

Example for agent named a1:

a1.channels = c1
a1.sinks = k1
a1.sinks.k1.type = org.example.MySink
a1.sinks.k1.channel = c1

Flume Channels

Channels are the repositories where the events are staged on a agent. Source adds the events and Sink removes it.

Memory Channel

The events are stored in an in-memory queue with configurable max size. It’s ideal for flows that need higher throughput and are prepared to lose the staged data in the event of a agent failures. Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be memory
capacity	100	The maximum number of events stored in the channel
transactionCapacity	100	The maximum number of events the channel will take from a source or give to a sink per transaction
keep-alive	3	Timeout in seconds for adding or removing an event
byteCapacityBufferPercentage	20	Defines the percent of buffer between byteCapacity and the estimated total size of all events in the channel, to account for data in headers. See below.
byteCapacity	see description	Maximum total bytes of memory allowed as a sum of all events in this channel. The implementation only counts the Event body, which is the reason for providing the byteCapacityBufferPercentage configuration parameter as well. Defaults to a computed value equal to 80% of the maximum memory available to the JVM (i.e. 80% of the -Xmx value passed on the command line). Note that if you have multiple memory channels on a single JVM, and they happen to hold the same physical events (i.e. if you are using a replicating channel selector from a single source) then those event sizes may be double-counted for channel byteCapacity purposes. Setting this value to 0 will cause this value to fall back to a hard internal limit of about 200 GB.

Example for agent named a1:

a1.channels = c1
a1.channels.c1.type = memory
a1.channels.c1.capacity = 10000
a1.channels.c1.transactionCapacity = 10000
a1.channels.c1.byteCapacityBufferPercentage = 20
a1.channels.c1.byteCapacity = 800000

JDBC Channel

The events are stored in a persistent storage that’s backed by a database. The JDBC channel currently supports embedded Derby. This is a durable channel that’s ideal for flows where recoverability is important. Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be jdbc
db.type	DERBY	Database vendor, needs to be DERBY.
driver.class	org.apache.derby.jdbc.EmbeddedDriver	Class for vendor’s JDBC driver
driver.url	(constructed from other properties)	JDBC connection URL
db.username	“sa”	User id for db connection
db.password	–	password for db connection
connection.properties.file	–	JDBC Connection property file path
create.schema	true	If true, then creates db schema if not there
create.index	true	Create indexes to speed up lookups
create.foreignkey	true
transaction.isolation	“READ_COMMITTED”	Isolation level for db session READ_UNCOMMITTED, READ_COMMITTED, SERIALIZABLE, REPEATABLE_READ
maximum.connections	10	Max connections allowed to db
maximum.capacity	0 (unlimited)	Max number of events in the channel
sysprop.*		DB Vendor specific properties
sysprop.user.home		Home path to store embedded Derby database

Example for agent named a1:

a1.channels = c1
a1.channels.c1.type = jdbc

Kafka Channel

The events are stored in a Kafka cluster (must be installed separately). Kafka provides high availability and replication, so in case an agent or a kafka broker crashes, the events are immediately available to other sinks

The Kafka channel can be used for multiple scenarios:

1. With Flume source and sink - it provides a reliable and highly available channel for events
2. With Flume source and interceptor but no sink - it allows writing Flume events into a Kafka topic, for use by other apps
3. With Flume sink, but no source - it is a low-latency, fault tolerant way to send events from Kafka to Flume sinks such as HDFS, HBase or Solr

This currently supports Kafka server releases 0.10.1.0 or higher. Testing was done up to 2.0.1 that was the highest avilable version at the time of the release.

The configuration parameters are organized as such:

1. Configuration values related to the channel generically are applied at the channel config level, eg: a1.channel.k1.type =
2. Configuration values related to Kafka or how the Channel operates are prefixed with “kafka.”, (this are analgous to CommonClient Configs) eg: a1.channels.k1.kafka.topic and a1.channels.k1.kafka.bootstrap.servers. This is not dissimilar to how the hdfs sink operates
3. Properties specific to the producer/consumer are prefixed by kafka.producer or kafka.consumer
4. Where possible, the Kafka paramter names are used, eg: bootstrap.servers and acks

This version of flume is backwards-compatible with previous versions, however deprecated properties are indicated in the table below and a warning message is logged on startup when they are present in the configuration file.

Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be org.apache.flume.channel.kafka.KafkaChannel
kafka.bootstrap.servers	–	List of brokers in the Kafka cluster used by the channel This can be a partial list of brokers, but we recommend at least two for HA. The format is comma separated list of hostname:port
kafka.topic	flume-channel	Kafka topic which the channel will use
kafka.consumer.group.id	flume	Consumer group ID the channel uses to register with Kafka. Multiple channels must use the same topic and group to ensure that when one agent fails another can get the data Note that having non-channel consumers with the same ID can lead to data loss.
parseAsFlumeEvent	true	Expecting Avro datums with FlumeEvent schema in the channel. This should be true if Flume source is writing to the channel and false if other producers are writing into the topic that the channel is using. Flume source messages to Kafka can be parsed outside of Flume by using org.apache.flume.source.avro.AvroFlumeEvent provided by the flume-ng-sdk artifact
pollTimeout	500	The amount of time(in milliseconds) to wait in the “poll()” call of the consumer. https://kafka.apache.org/090/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll(long)
defaultPartitionId	–	Specifies a Kafka partition ID (integer) for all events in this channel to be sent to, unless overriden by partitionIdHeader. By default, if this property is not set, events will be distributed by the Kafka Producer’s partitioner - including by key if specified (or by a partitioner specified by kafka.partitioner.class).
partitionIdHeader	–	When set, the producer will take the value of the field named using the value of this property from the event header and send the message to the specified partition of the topic. If the value represents an invalid partition the event will not be accepted into the channel. If the header value is present then this setting overrides defaultPartitionId.
kafka.consumer.auto.offset.reset	latest	What to do when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g. because that data has been deleted): earliest: automatically reset the offset to the earliest offset latest: automatically reset the offset to the latest offset none: throw exception to the consumer if no previous offset is found for the consumer’s group anything else: throw exception to the consumer.
kafka.producer.security.protocol	PLAINTEXT	Set to SASL_PLAINTEXT, SASL_SSL or SSL if writing to Kafka using some level of security. See below for additional info on secure setup.
kafka.consumer.security.protocol	PLAINTEXT	Same as kafka.producer.security.protocol but for reading/consuming from Kafka.
more producer/consumer security props		If using SASL_PLAINTEXT, SASL_SSL or SSL refer to Kafka security for additional properties that need to be set on producer/consumer.

Deprecated Properties

Property Name	Default	Description
brokerList	–	List of brokers in the Kafka cluster used by the channel This can be a partial list of brokers, but we recommend at least two for HA. The format is comma separated list of hostname:port
topic	flume-channel	Use kafka.topic
groupId	flume	Use kafka.consumer.group.id
readSmallestOffset	false	Use kafka.consumer.auto.offset.reset
migrateZookeeperOffsets	true	When no Kafka stored offset is found, look up the offsets in Zookeeper and commit them to Kafka. This should be true to support seamless Kafka client migration from older versions of Flume. Once migrated this can be set to false, though that should generally not be required. If no Zookeeper offset is found the kafka.consumer.auto.offset.reset configuration defines how offsets are handled.

Note

Due to the way the channel is load balanced, there may be duplicate events when the agent first starts up

Example for agent named a1:

a1.channels.channel1.type = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.channel1.kafka.bootstrap.servers = kafka-1:9092,kafka-2:9092,kafka-3:9092
a1.channels.channel1.kafka.topic = channel1
a1.channels.channel1.kafka.consumer.group.id = flume-consumer

Security and Kafka Channel:

Secure authentication as well as data encryption is supported on the communication channel between Flume and Kafka. For secure authentication SASL/GSSAPI (Kerberos V5) or SSL (even though the parameter is named SSL, the actual protocol is a TLS implementation) can be used from Kafka version 0.9.0.

As of now data encryption is solely provided by SSL/TLS.

Setting kafka.producer|consumer.security.protocol to any of the following value means:

• SASL_PLAINTEXT - Kerberos or plaintext authentication with no data encryption
• SASL_SSL - Kerberos or plaintext authentication with data encryption
• SSL - TLS based encryption with optional authentication.

Warning

There is a performance degradation when SSL is enabled, the magnitude of which depends on the CPU type and the JVM implementation. Reference: Kafka security overview and the jira for tracking this issue: KAFKA-2561

TLS and Kafka Channel:

Please read the steps described in Configuring Kafka Clients SSL to learn about additional configuration settings for fine tuning for example any of the following: security provider, cipher suites, enabled protocols, truststore or keystore types.

Example configuration with server side authentication and data encryption.

a1.channels.channel1.type = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.channel1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.channels.channel1.kafka.topic = channel1
a1.channels.channel1.kafka.consumer.group.id = flume-consumer
a1.channels.channel1.kafka.producer.security.protocol = SSL
# optional, the global truststore can be used alternatively
a1.channels.channel1.kafka.producer.ssl.truststore.location = /path/to/truststore.jks
a1.channels.channel1.kafka.producer.ssl.truststore.password = <password to access the truststore>
a1.channels.channel1.kafka.consumer.security.protocol = SSL
# optional, the global truststore can be used alternatively
a1.channels.channel1.kafka.consumer.ssl.truststore.location = /path/to/truststore.jks
a1.channels.channel1.kafka.consumer.ssl.truststore.password = <password to access the truststore>

Specyfing the truststore is optional here, the global truststore can be used instead. For more details about the global SSL setup, see the SSL/TLS support section.

Note: By default the property ssl.endpoint.identification.algorithm is not defined, so hostname verification is not performed. In order to enable hostname verification, set the following properties

a1.channels.channel1.kafka.producer.ssl.endpoint.identification.algorithm = HTTPS
a1.channels.channel1.kafka.consumer.ssl.endpoint.identification.algorithm = HTTPS

Once enabled, clients will verify the server’s fully qualified domain name (FQDN) against one of the following two fields:

1. Common Name (CN) https://tools.ietf.org/html/rfc6125#section-2.3
2. Subject Alternative Name (SAN) https://tools.ietf.org/html/rfc5280#section-4.2.1.6

If client side authentication is also required then additionally the following needs to be added to Flume agent configuration or the global SSL setup can be used (see SSL/TLS support section). Each Flume agent has to have its client certificate which has to be trusted by Kafka brokers either individually or by their signature chain. Common example is to sign each client certificate by a single Root CA which in turn is trusted by Kafka brokers.

# optional, the global keystore can be used alternatively
a1.channels.channel1.kafka.producer.ssl.keystore.location = /path/to/client.keystore.jks
a1.channels.channel1.kafka.producer.ssl.keystore.password = <password to access the keystore>
# optional, the global keystore can be used alternatively
a1.channels.channel1.kafka.consumer.ssl.keystore.location = /path/to/client.keystore.jks
a1.channels.channel1.kafka.consumer.ssl.keystore.password = <password to access the keystore>

If keystore and key use different password protection then ssl.key.password property will provide the required additional secret for both consumer and producer keystores:

a1.channels.channel1.kafka.producer.ssl.key.password = <password to access the key>
a1.channels.channel1.kafka.consumer.ssl.key.password = <password to access the key>

Kerberos and Kafka Channel:

To use Kafka channel with a Kafka cluster secured with Kerberos, set the producer/consumer.security.protocol properties noted above for producer and/or consumer. The Kerberos keytab and principal to be used with Kafka brokers is specified in a JAAS file’s “KafkaClient” section. “Client” section describes the Zookeeper connection if needed. See Kafka doc for information on the JAAS file contents. The location of this JAAS file and optionally the system wide kerberos configuration can be specified via JAVA_OPTS in flume-env.sh:

JAVA_OPTS="$JAVA_OPTS -Djava.security.krb5.conf=/path/to/krb5.conf"
JAVA_OPTS="$JAVA_OPTS -Djava.security.auth.login.config=/path/to/flume_jaas.conf"

Example secure configuration using SASL_PLAINTEXT:

a1.channels.channel1.type = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.channel1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.channels.channel1.kafka.topic = channel1
a1.channels.channel1.kafka.consumer.group.id = flume-consumer
a1.channels.channel1.kafka.producer.security.protocol = SASL_PLAINTEXT
a1.channels.channel1.kafka.producer.sasl.mechanism = GSSAPI
a1.channels.channel1.kafka.producer.sasl.kerberos.service.name = kafka
a1.channels.channel1.kafka.consumer.security.protocol = SASL_PLAINTEXT
a1.channels.channel1.kafka.consumer.sasl.mechanism = GSSAPI
a1.channels.channel1.kafka.consumer.sasl.kerberos.service.name = kafka

Example secure configuration using SASL_SSL:

a1.channels.channel1.type = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.channel1.kafka.bootstrap.servers = kafka-1:9093,kafka-2:9093,kafka-3:9093
a1.channels.channel1.kafka.topic = channel1
a1.channels.channel1.kafka.consumer.group.id = flume-consumer
a1.channels.channel1.kafka.producer.security.protocol = SASL_SSL
a1.channels.channel1.kafka.producer.sasl.mechanism = GSSAPI
a1.channels.channel1.kafka.producer.sasl.kerberos.service.name = kafka
# optional, the global truststore can be used alternatively
a1.channels.channel1.kafka.producer.ssl.truststore.location = /path/to/truststore.jks
a1.channels.channel1.kafka.producer.ssl.truststore.password = <password to access the truststore>
a1.channels.channel1.kafka.consumer.security.protocol = SASL_SSL
a1.channels.channel1.kafka.consumer.sasl.mechanism = GSSAPI
a1.channels.channel1.kafka.consumer.sasl.kerberos.service.name = kafka
# optional, the global truststore can be used alternatively
a1.channels.channel1.kafka.consumer.ssl.truststore.location = /path/to/truststore.jks
a1.channels.channel1.kafka.consumer.ssl.truststore.password = <password to access the truststore>

Sample JAAS file. For reference of its content please see client config sections of the desired authentication mechanism (GSSAPI/PLAIN) in Kafka documentation of SASL configuration. Since the Kafka Source may also connect to Zookeeper for offset migration, the “Client” section was also added to this example. This won’t be needed unless you require offset migration, or you require this section for other secure components. Also please make sure that the operating system user of the Flume processes has read privileges on the jaas and keytab files.

Client {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  storeKey=true
  keyTab="/path/to/keytabs/flume.keytab"
  principal="flume/flumehost1.example.com@YOURKERBEROSREALM";
};

KafkaClient {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  storeKey=true
  keyTab="/path/to/keytabs/flume.keytab"
  principal="flume/flumehost1.example.com@YOURKERBEROSREALM";
};

File Channel

Required properties are in bold.

Property Name Default	Description
type	–	The component type name, needs to be file.
checkpointDir	~/.flume/file-channel/checkpoint	The directory where checkpoint file will be stored
useDualCheckpoints	false	Backup the checkpoint. If this is set to true, backupCheckpointDir must be set
backupCheckpointDir	–	The directory where the checkpoint is backed up to. This directory must not be the same as the data directories or the checkpoint directory
dataDirs	~/.flume/file-channel/data	Comma separated list of directories for storing log files. Using multiple directories on separate disks can improve file channel peformance
transactionCapacity	10000	The maximum size of transaction supported by the channel
checkpointInterval	30000	Amount of time (in millis) between checkpoints
maxFileSize	2146435071	Max size (in bytes) of a single log file
minimumRequiredSpace	524288000	Minimum Required free space (in bytes). To avoid data corruption, File Channel stops accepting take/put requests when free space drops below this value
capacity	1000000	Maximum capacity of the channel
keep-alive	3	Amount of time (in sec) to wait for a put operation
use-log-replay-v1	false	Expert: Use old replay logic
use-fast-replay	false	Expert: Replay without using queue
checkpointOnClose	true	Controls if a checkpoint is created when the channel is closed. Creating a checkpoint on close speeds up subsequent startup of the file channel by avoiding replay.
encryption.activeKey	–	Key name used to encrypt new data
encryption.cipherProvider	–	Cipher provider type, supported types: AESCTRNOPADDING
encryption.keyProvider	–	Key provider type, supported types: JCEKSFILE
encryption.keyProvider.keyStoreFile	–	Path to the keystore file
encrpytion.keyProvider.keyStorePasswordFile	–	Path to the keystore password file
encryption.keyProvider.keys	–	List of all keys (e.g. history of the activeKey setting)
encyption.keyProvider.keys.*.passwordFile	–	Path to the optional key password file

Note

By default the File Channel uses paths for checkpoint and data directories that are within the user home as specified above. As a result if you have more than one File Channel instances active within the agent, only one will be able to lock the directories and cause the other channel initialization to fail. It is therefore necessary that you provide explicit paths to all the configured channels, preferably on different disks. Furthermore, as file channel will sync to disk after every commit, coupling it with a sink/source that batches events together may be necessary to provide good performance where multiple disks are not available for checkpoint and data directories.

Example for agent named a1:

a1.channels = c1
a1.channels.c1.type = file
a1.channels.c1.checkpointDir = /mnt/flume/checkpoint
a1.channels.c1.dataDirs = /mnt/flume/data

Encryption

Below is a few sample configurations:

Generating a key with a password seperate from the key store password:

keytool -genseckey -alias key-0 -keypass keyPassword -keyalg AES \
  -keysize 128 -validity 9000 -keystore test.keystore \
  -storetype jceks -storepass keyStorePassword

Generating a key with the password the same as the key store password:

keytool -genseckey -alias key-1 -keyalg AES -keysize 128 -validity 9000 \
  -keystore src/test/resources/test.keystore -storetype jceks \
  -storepass keyStorePassword

a1.channels.c1.encryption.activeKey = key-0
a1.channels.c1.encryption.cipherProvider = AESCTRNOPADDING
a1.channels.c1.encryption.keyProvider = key-provider-0
a1.channels.c1.encryption.keyProvider = JCEKSFILE
a1.channels.c1.encryption.keyProvider.keyStoreFile = /path/to/my.keystore
a1.channels.c1.encryption.keyProvider.keyStorePasswordFile = /path/to/my.keystore.password
a1.channels.c1.encryption.keyProvider.keys = key-0

Let’s say you have aged key-0 out and new files should be encrypted with key-1:

a1.channels.c1.encryption.activeKey = key-1
a1.channels.c1.encryption.cipherProvider = AESCTRNOPADDING
a1.channels.c1.encryption.keyProvider = JCEKSFILE
a1.channels.c1.encryption.keyProvider.keyStoreFile = /path/to/my.keystore
a1.channels.c1.encryption.keyProvider.keyStorePasswordFile = /path/to/my.keystore.password
a1.channels.c1.encryption.keyProvider.keys = key-0 key-1

The same scenerio as above, however key-0 has its own password:

a1.channels.c1.encryption.activeKey = key-1
a1.channels.c1.encryption.cipherProvider = AESCTRNOPADDING
a1.channels.c1.encryption.keyProvider = JCEKSFILE
a1.channels.c1.encryption.keyProvider.keyStoreFile = /path/to/my.keystore
a1.channels.c1.encryption.keyProvider.keyStorePasswordFile = /path/to/my.keystore.password
a1.channels.c1.encryption.keyProvider.keys = key-0 key-1
a1.channels.c1.encryption.keyProvider.keys.key-0.passwordFile = /path/to/key-0.password

Spillable Memory Channel

The events are stored in an in-memory queue and on disk. The in-memory queue serves as the primary store and the disk as overflow. The disk store is managed using an embedded File channel. When the in-memory queue is full, additional incoming events are stored in the file channel. This channel is ideal for flows that need high throughput of memory channel during normal operation, but at the same time need the larger capacity of the file channel for better tolerance of intermittent sink side outages or drop in drain rates. The throughput will reduce approximately to file channel speeds during such abnormal situations. In case of an agent crash or restart, only the events stored on disk are recovered when the agent comes online. This channel is currently experimental and not recommended for use in production.

Required properties are in bold. Please refer to file channel for additional required properties.

Property Name	Default	Description
type	–	The component type name, needs to be SPILLABLEMEMORY
memoryCapacity	10000	Maximum number of events stored in memory queue. To disable use of in-memory queue, set this to zero.
overflowCapacity	100000000	Maximum number of events stored in overflow disk (i.e File channel). To disable use of overflow, set this to zero.
overflowTimeout	3	The number of seconds to wait before enabling disk overflow when memory fills up.
byteCapacityBufferPercentage	20	Defines the percent of buffer between byteCapacity and the estimated total size of all events in the channel, to account for data in headers. See below.
byteCapacity	see description	Maximum bytes of memory allowed as a sum of all events in the memory queue. The implementation only counts the Event body, which is the reason for providing the byteCapacityBufferPercentage configuration parameter as well. Defaults to a computed value equal to 80% of the maximum memory available to the JVM (i.e. 80% of the -Xmx value passed on the command line). Note that if you have multiple memory channels on a single JVM, and they happen to hold the same physical events (i.e. if you are using a replicating channel selector from a single source) then those event sizes may be double-counted for channel byteCapacity purposes. Setting this value to 0 will cause this value to fall back to a hard internal limit of about 200 GB.
avgEventSize	500	Estimated average size of events, in bytes, going into the channel
<file channel properties>	see file channel	Any file channel property with the exception of ‘keep-alive’ and ‘capacity’ can be used. The keep-alive of file channel is managed by Spillable Memory Channel. Use ‘overflowCapacity’ to set the File channel’s capacity.

In-memory queue is considered full if either memoryCapacity or byteCapacity limit is reached.

Example for agent named a1:

a1.channels = c1
a1.channels.c1.type = SPILLABLEMEMORY
a1.channels.c1.memoryCapacity = 10000
a1.channels.c1.overflowCapacity = 1000000
a1.channels.c1.byteCapacity = 800000
a1.channels.c1.checkpointDir = /mnt/flume/checkpoint
a1.channels.c1.dataDirs = /mnt/flume/data

To disable the use of the in-memory queue and function like a file channel:

a1.channels = c1
a1.channels.c1.type = SPILLABLEMEMORY
a1.channels.c1.memoryCapacity = 0
a1.channels.c1.overflowCapacity = 1000000
a1.channels.c1.checkpointDir = /mnt/flume/checkpoint
a1.channels.c1.dataDirs = /mnt/flume/data

To disable the use of overflow disk and function purely as a in-memory channel:

a1.channels = c1
a1.channels.c1.type = SPILLABLEMEMORY
a1.channels.c1.memoryCapacity = 100000
a1.channels.c1.overflowCapacity = 0

Pseudo Transaction Channel

Warning

The Pseudo Transaction Channel is only for unit testing purposes and is NOT meant for production use.

Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be org.apache.flume.channel.PseudoTxnMemoryChannel
capacity	50	The max number of events stored in the channel
keep-alive	3	Timeout in seconds for adding or removing an event

Custom Channel

A custom channel is your own implementation of the Channel interface. A custom channel’s class and its dependencies must be included in the agent’s classpath when starting the Flume agent. The type of the custom channel is its FQCN. Required properties are in bold.

Property Name	Default	Description
type	–	The component type name, needs to be a FQCN

Example for agent named a1:

a1.channels = c1
a1.channels.c1.type = org.example.MyChannel

Flume Channel Selectors

If the type is not specified, then defaults to “replicating”.

Replicating Channel Selector (default)

Required properties are in bold.

Property Name	Default	Description
selector.type	replicating	The component type name, needs to be replicating
selector.optional	–	Set of channels to be marked as optional

Example for agent named a1 and it’s source called r1:

a1.sources = r1
a1.channels = c1 c2 c3
a1.sources.r1.selector.type = replicating
a1.sources.r1.channels = c1 c2 c3
a1.sources.r1.selector.optional = c3

In the above configuration, c3 is an optional channel. Failure to write to c3 is simply ignored. Since c1 and c2 are not marked optional, failure to write to those channels will cause the transaction to fail.

Load Balancing Channel Selector

Load balancing channel selector provides the ability to load-balance flow over multiple channels. This effectively allows the incoming data to be processed on multiple threads. It maintains an indexed list of active channels on which the load must be distributed. Implementation supports distributing load using either via round_robin or random selection mechanisms. The choice of selection mechanism defaults to round_robin type, but can be overridden via configuration.

Required properties are in bold.

Property Name	Default	Description
selector.type	replicating	The component type name, needs to be load_balancing
selector.policy	round_robin	Selection mechanism. Must be either round_robin or random.

Example for agent named a1 and it’s source called r1:

a1.sources = r1
a1.channels = c1 c2 c3 c4
a1.sources.r1.channels = c1 c2 c3 c4
a1.sources.r1.selector.type = load_balancing
a1.sources.r1.selector.policy = round_robin

Multiplexing Channel Selector

Required properties are in bold.

Property Name	Default	Description
selector.type	replicating	The component type name, needs to be multiplexing
selector.header	flume.selector.header
selector.default	–
selector.mapping.*	–

Example for agent named a1 and it’s source called r1:

a1.sources = r1
a1.channels = c1 c2 c3 c4
a1.sources.r1.selector.type = multiplexing
a1.sources.r1.selector.header = state
a1.sources.r1.selector.mapping.CZ = c1
a1.sources.r1.selector.mapping.US = c2 c3
a1.sources.r1.selector.default = c4

Custom Channel Selector

A custom channel selector is your own implementation of the ChannelSelector interface. A custom channel selector’s class and its dependencies must be included in the agent’s classpath when starting the Flume agent. The type of the custom channel selector is its FQCN.

Property Name	Default	Description
selector.type	–	The component type name, needs to be your FQCN

Example for agent named a1 and its source called r1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.selector.type = org.example.MyChannelSelector

Flume Sink Processors

Sink groups allow users to group multiple sinks into one entity. Sink processors can be used to provide load balancing capabilities over all sinks inside the group or to achieve fail over from one sink to another in case of temporal failure.

Required properties are in bold.

Property Name	Default	Description
sinks	–	Space-separated list of sinks that are participating in the group
processor.type	default	The component type name, needs to be default, failover or load_balance

Example for agent named a1:

a1.sinkgroups = g1
a1.sinkgroups.g1.sinks = k1 k2
a1.sinkgroups.g1.processor.type = load_balance

Default Sink Processor

Default sink processor accepts only a single sink. User is not forced to create processor (sink group) for single sinks. Instead user can follow the source - channel - sink pattern that was explained above in this user guide.

Failover Sink Processor

Failover Sink Processor maintains a prioritized list of sinks, guaranteeing that so long as one is available events will be processed (delivered).

The failover mechanism works by relegating failed sinks to a pool where they are assigned a cool down period, increasing with sequential failures before they are retried. Once a sink successfully sends an event, it is restored to the live pool. The Sinks have a priority associated with them, larger the number, higher the priority. If a Sink fails while sending a Event the next Sink with highest priority shall be tried next for sending Events. For example, a sink with priority 100 is activated before the Sink with priority 80. If no priority is specified, thr priority is determined based on the order in which the Sinks are specified in configuration.

To configure, set a sink groups processor to failover and set priorities for all individual sinks. All specified priorities must be unique. Furthermore, upper limit to failover time can be set (in milliseconds) using maxpenalty property.

Required properties are in bold.

Property Name	Default	Description
sinks	–	Space-separated list of sinks that are participating in the group
processor.type	default	The component type name, needs to be failover
processor.priority.<sinkName>	–	Priority value. <sinkName> must be one of the sink instances associated with the current sink group A higher priority value Sink gets activated earlier. A larger absolute value indicates higher priority
processor.maxpenalty	30000	The maximum backoff period for the failed Sink (in millis)

Example for agent named a1:

a1.sinkgroups = g1
a1.sinkgroups.g1.sinks = k1 k2
a1.sinkgroups.g1.processor.type = failover
a1.sinkgroups.g1.processor.priority.k1 = 5
a1.sinkgroups.g1.processor.priority.k2 = 10
a1.sinkgroups.g1.processor.maxpenalty = 10000

Load balancing Sink Processor

Load balancing sink processor provides the ability to load-balance flow over multiple sinks. It maintains an indexed list of active sinks on which the load must be distributed. Implementation supports distributing load using either via round_robin or random selection mechanisms. The choice of selection mechanism defaults to round_robin type, but can be overridden via configuration. Custom selection mechanisms are supported via custom classes that inherits from AbstractSinkSelector.

When invoked, this selector picks the next sink using its configured selection mechanism and invokes it. For round_robin and random In case the selected sink fails to deliver the event, the processor picks the next available sink via its configured selection mechanism. This implementation does not blacklist the failing sink and instead continues to optimistically attempt every available sink. If all sinks invocations result in failure, the selector propagates the failure to the sink runner.

If backoff is enabled, the sink processor will blacklist sinks that fail, removing them for selection for a given timeout. When the timeout ends, if the sink is still unresponsive timeout is increased exponentially to avoid potentially getting stuck in long waits on unresponsive sinks. With this disabled, in round-robin all the failed sinks load will be passed to the next sink in line and thus not evenly balanced

Required properties are in bold.

Property Name	Default	Description
processor.sinks	–	Space-separated list of sinks that are participating in the group
processor.type	default	The component type name, needs to be load_balance
processor.backoff	false	Should failed sinks be backed off exponentially.
processor.selector	round_robin	Selection mechanism. Must be either round_robin, random or FQCN of custom class that inherits from AbstractSinkSelector
processor.selector.maxTimeOut	30000	Used by backoff selectors to limit exponential backoff (in milliseconds)

Example for agent named a1:

a1.sinkgroups = g1
a1.sinkgroups.g1.sinks = k1 k2
a1.sinkgroups.g1.processor.type = load_balance
a1.sinkgroups.g1.processor.backoff = true
a1.sinkgroups.g1.processor.selector = random

Custom Sink Processor

Custom sink processors are not supported at the moment.

Event Serializers

The file_roll sink and the hdfs sink both support the EventSerializer interface. Details of the EventSerializers that ship with Flume are provided below.

Body Text Serializer

Alias: text. This interceptor writes the body of the event to an output stream without any transformation or modification. The event headers are ignored. Configuration options are as follows:

Property Name	Default	Description
appendNewline	true	Whether a newline will be appended to each event at write time. The default of true assumes that events do not contain newlines, for legacy reasons.

Example for agent named a1:

a1.sinks = k1
a1.sinks.k1.type = file_roll
a1.sinks.k1.channel = c1
a1.sinks.k1.sink.directory = /var/log/flume
a1.sinks.k1.sink.serializer = text
a1.sinks.k1.sink.serializer.appendNewline = false

“Flume Event” Avro Event Serializer

Alias: avro_event.

This interceptor serializes Flume events into an Avro container file. The schema used is the same schema used for Flume events in the Avro RPC mechanism.

This serializer inherits from the AbstractAvroEventSerializer class.

Configuration options are as follows:

Property Name	Default	Description
syncIntervalBytes	2048000	Avro sync interval, in approximate bytes.
compressionCodec	null	Avro compression codec. For supported codecs, see Avro’s CodecFactory docs.

Example for agent named a1:

a1.sinks.k1.type = hdfs
a1.sinks.k1.channel = c1
a1.sinks.k1.hdfs.path = /flume/events/%Y-%m-%d/%H%M/%S
a1.sinks.k1.serializer = avro_event
a1.sinks.k1.serializer.compressionCodec = snappy

Avro Event Serializer

Alias: This serializer does not have an alias, and must be specified using the fully-qualified class name class name.

This serializes Flume events into an Avro container file like the “Flume Event” Avro Event Serializer, however the record schema is configurable. The record schema may be specified either as a Flume configuration property or passed in an event header.

To pass the record schema as part of the Flume configuration, use the property schemaURL as listed below.

To pass the record schema in an event header, specify either the event header flume.avro.schema.literal containing a JSON-format representation of the schema or flume.avro.schema.url with a URL where the schema may be found (hdfs:/... URIs are supported).

This serializer inherits from the AbstractAvroEventSerializer class.

Configuration options are as follows:

Property Name	Default	Description
syncIntervalBytes	2048000	Avro sync interval, in approximate bytes.
compressionCodec	null	Avro compression codec. For supported codecs, see Avro’s CodecFactory docs.
schemaURL	null	Avro schema URL. Schemas specified in the header ovverride this option.

Example for agent named a1:

a1.sinks.k1.type = hdfs
a1.sinks.k1.channel = c1
a1.sinks.k1.hdfs.path = /flume/events/%y-%m-%d/%H%M/%S
a1.sinks.k1.serializer = org.apache.flume.sink.hdfs.AvroEventSerializer$Builder
a1.sinks.k1.serializer.compressionCodec = snappy
a1.sinks.k1.serializer.schemaURL = hdfs://namenode/path/to/schema.avsc

Flume Interceptors

Flume has the capability to modify/drop events in-flight. This is done with the help of interceptors. Interceptors are classes that implement org.apache.flume.interceptor.Interceptor interface. An interceptor can modify or even drop events based on any criteria chosen by the developer of the interceptor. Flume supports chaining of interceptors. This is made possible through by specifying the list of interceptor builder class names in the configuration. Interceptors are specified as a whitespace separated list in the source configuration. The order in which the interceptors are specified is the order in which they are invoked. The list of events returned by one interceptor is passed to the next interceptor in the chain. Interceptors can modify or drop events. If an interceptor needs to drop events, it just does not return that event in the list that it returns. If it is to drop all events, then it simply returns an empty list. Interceptors are named components, here is an example of how they are created through configuration:

a1.sources = r1
a1.sinks = k1
a1.channels = c1
a1.sources.r1.interceptors = i1 i2
a1.sources.r1.interceptors.i1.type = org.apache.flume.interceptor.HostInterceptor$Builder
a1.sources.r1.interceptors.i1.preserveExisting = false
a1.sources.r1.interceptors.i1.hostHeader = hostname
a1.sources.r1.interceptors.i2.type = org.apache.flume.interceptor.TimestampInterceptor$Builder
a1.sinks.k1.filePrefix = FlumeData.%{CollectorHost}.%Y-%m-%d
a1.sinks.k1.channel = c1

Note that the interceptor builders are passed to the type config parameter. The interceptors are themselves configurable and can be passed configuration values just like they are passed to any other configurable component. In the above example, events are passed to the HostInterceptor first and the events returned by the HostInterceptor are then passed along to the TimestampInterceptor. You can specify either the fully qualified class name (FQCN) or the alias timestamp. If you have multiple collectors writing to the same HDFS path, then you could also use the HostInterceptor.

Timestamp Interceptor

This interceptor inserts into the event headers, the time in millis at which it processes the event. This interceptor inserts a header with key timestamp (or as specified by the header property) whose value is the relevant timestamp. This interceptor can preserve an existing timestamp if it is already present in the configuration.

Property Name	Default	Description
type	–	The component type name, has to be timestamp or the FQCN
headerName	timestamp	The name of the header in which to place the generated timestamp.
preserveExisting	false	If the timestamp already exists, should it be preserved - true or false

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.channels =  c1
a1.sources.r1.type = seq
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = timestamp

Host Interceptor

This interceptor inserts the hostname or IP address of the host that this agent is running on. It inserts a header with key host or a configured key whose value is the hostname or IP address of the host, based on configuration.

Property Name	Default	Description
type	–	The component type name, has to be host
preserveExisting	false	If the host header already exists, should it be preserved - true or false
useIP	true	Use the IP Address if true, else use hostname.
hostHeader	host	The header key to be used.

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = host

Static Interceptor

Static interceptor allows user to append a static header with static value to all events.

The current implementation does not allow specifying multiple headers at one time. Instead user might chain multiple static interceptors each defining one static header.

Property Name	Default	Description
type	–	The component type name, has to be static
preserveExisting	true	If configured header already exists, should it be preserved - true or false
key	key	Name of header that should be created
value	value	Static value that should be created

Example for agent named a1:

a1.sources = r1
a1.channels = c1
a1.sources.r1.channels =  c1
a1.sources.r1.type = seq
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = static
a1.sources.r1.interceptors.i1.key = datacenter
a1.sources.r1.interceptors.i1.value = NEW_YORK

Remove Header Interceptor

This interceptor manipulates Flume event headers, by removing one or many headers. It can remove a statically defined header, headers based on a regular expression or headers in a list. If none of these is defined, or if no header matches the criteria, the Flume events are not modified.

Note that if only one header needs to be removed, specifying it by name provides performance benefits over the other 2 methods.

Property Name	Default	Description
type	–	The component type name has to be remove_header
withName	–	Name of the header to remove
fromList	–	List of headers to remove, separated with the separator specified by fromListSeparator
fromListSeparator	\s*,\s*	Regular expression used to separate multiple header names in the list specified by fromList. Default is a comma surrounded by any number of whitespace characters
matching	–	All the headers which names match this regular expression are removed

UUID Interceptor

This interceptor sets a universally unique identifier on all events that are intercepted. An example UUID is b5755073-77a9-43c1-8fad-b7a586fc1b97, which represents a 128-bit value.

Consider using UUIDInterceptor to automatically assign a UUID to an event if no application level unique key for the event is available. It can be important to assign UUIDs to events as soon as they enter the Flume network; that is, in the first Flume Source of the flow. This enables subsequent deduplication of events in the face of replication and redelivery in a Flume network that is designed for high availability and high performance. If an application level key is available, this is preferable over an auto-generated UUID because it enables subsequent updates and deletes of event in data stores using said well known application level key.

Property Name	Default	Description
type	–	The component type name has to be org.apache.flume.sink.solr.morphline.UUIDInterceptor$Builder
headerName	id	The name of the Flume header to modify
preserveExisting	true	If the UUID header already exists, should it be preserved - true or false
prefix	“”	The prefix string constant to prepend to each generated UUID

Morphline Interceptor

This interceptor filters the events through a morphline configuration file that defines a chain of transformation commands that pipe records from one command to another. For example the morphline can ignore certain events or alter or insert certain event headers via regular expression based pattern matching, or it can auto-detect and set a MIME type via Apache Tika on events that are intercepted. For example, this kind of packet sniffing can be used for content based dynamic routing in a Flume topology. MorphlineInterceptor can also help to implement dynamic routing to multiple Apache Solr collections (e.g. for multi-tenancy).

Currently, there is a restriction in that the morphline of an interceptor must not generate more than one output record for each input event. This interceptor is not intended for heavy duty ETL processing - if you need this consider moving ETL processing from the Flume Source to a Flume Sink, e.g. to a MorphlineSolrSink.

Required properties are in bold.

Property Name	Default	Description
type	–	The component type name has to be org.apache.flume.sink.solr.morphline.MorphlineInterceptor$Builder
morphlineFile	–	The relative or absolute path on the local file system to the morphline configuration file. Example: /etc/flume-ng/conf/morphline.conf
morphlineId	null	Optional name used to identify a morphline if there are multiple morphlines in a morphline config file

Sample flume.conf file:

a1.sources.avroSrc.interceptors = morphlineinterceptor
a1.sources.avroSrc.interceptors.morphlineinterceptor.type = org.apache.flume.sink.solr.morphline.MorphlineInterceptor$Builder
a1.sources.avroSrc.interceptors.morphlineinterceptor.morphlineFile = /etc/flume-ng/conf/morphline.conf
a1.sources.avroSrc.interceptors.morphlineinterceptor.morphlineId = morphline1

Search and Replace Interceptor

This interceptor provides simple string-based search-and-replace functionality based on Java regular expressions. Backtracking / group capture is also available. This interceptor uses the same rules as in the Java Matcher.replaceAll() method.

Property Name	Default	Description
type	–	The component type name has to be search_replace
searchPattern	–	The pattern to search for and replace.
replaceString	–	The replacement string.
charset	UTF-8	The charset of the event body. Assumed by default to be UTF-8.

Example configuration:

a1.sources.avroSrc.interceptors = search-replace
a1.sources.avroSrc.interceptors.search-replace.type = search_replace

# Remove leading alphanumeric characters in an event body.
a1.sources.avroSrc.interceptors.search-replace.searchPattern = ^[A-Za-z0-9_]+
a1.sources.avroSrc.interceptors.search-replace.replaceString =

Another example:

a1.sources.avroSrc.interceptors = search-replace
a1.sources.avroSrc.interceptors.search-replace.type = search_replace

# Use grouping operators to reorder and munge words on a line.
a1.sources.avroSrc.interceptors.search-replace.searchPattern = The quick brown ([a-z]+) jumped over the lazy ([a-z]+)
a1.sources.avroSrc.interceptors.search-replace.replaceString = The hungry $2 ate the careless $1

Regex Filtering Interceptor

This interceptor filters events selectively by interpreting the event body as text and matching the text against a configured regular expression. The supplied regular expression can be used to include events or exclude events.

Property Name	Default	Description
type	–	The component type name has to be regex_filter
regex	”.*”	Regular expression for matching against events
excludeEvents	false	If true, regex determines events to exclude, otherwise regex determines events to include.

Regex Extractor Interceptor

This interceptor extracts regex match groups using a specified regular expression and appends the match groups as headers on the event. It also supports pluggable serializers for formatting the match groups before adding them as event headers.

Property Name	Default	Description
type	–	The component type name has to be regex_extractor
regex	–	Regular expression for matching against events
serializers	–	Space-separated list of serializers for mapping matches to header names and serializing their values. (See example below) Flume provides built-in support for the following serializers: org.apache.flume.interceptor.RegexExtractorInterceptorPassThroughSerializer org.apache.flume.interceptor.RegexExtractorInterceptorMillisSerializer
serializers.<s1>.type	default	Must be default (org.apache.flume.interceptor.RegexExtractorInterceptorPassThroughSerializer), org.apache.flume.interceptor.RegexExtractorInterceptorMillisSerializer, or the FQCN of a custom class that implements org.apache.flume.interceptor.RegexExtractorInterceptorSerializer
serializers.<s1>.name	–
serializers.*	–	Serializer-specific properties

The serializers are used to map the matches to a header name and a formatted header value; by default, you only need to specify the header name and the default org.apache.flume.interceptor.RegexExtractorInterceptorPassThroughSerializer will be used. This serializer simply maps the matches to the specified header name and passes the value through as it was extracted by the regex. You can plug custom serializer implementations into the extractor using the fully qualified class name (FQCN) to format the matches in anyway you like.

Example 1:

If the Flume event body contained 1:2:3.4foobar5 and the following configuration was used

a1.sources.r1.interceptors.i1.regex = (\\d):(\\d):(\\d)
a1.sources.r1.interceptors.i1.serializers = s1 s2 s3
a1.sources.r1.interceptors.i1.serializers.s1.name = one
a1.sources.r1.interceptors.i1.serializers.s2.name = two
a1.sources.r1.interceptors.i1.serializers.s3.name = three

The extracted event will contain the same body but the following headers will have been added one=>1, two=>2, three=>3

Example 2:

If the Flume event body contained 2012-10-18 18:47:57,614 some log line and the following configuration was used

a1.sources.r1.interceptors.i1.regex = ^(?:\\n)?(\\d\\d\\d\\d-\\d\\d-\\d\\d\\s\\d\\d:\\d\\d)
a1.sources.r1.interceptors.i1.serializers = s1
a1.sources.r1.interceptors.i1.serializers.s1.type = org.apache.flume.interceptor.RegexExtractorInterceptorMillisSerializer
a1.sources.r1.interceptors.i1.serializers.s1.name = timestamp
a1.sources.r1.interceptors.i1.serializers.s1.pattern = yyyy-MM-dd HH:mm

the extracted event will contain the same body but the following headers will have been added timestamp=>1350611220000

Flume Properties

Property Name	Default	Description
flume.called.from.service	–	If this property is specified then the Flume agent will continue polling for the config file even if the config file is not found at the expected location. Otherwise, the Flume agent will terminate if the config doesn’t exist at the expected location. No property value is needed when setting this property (eg, just specifying -Dflume.called.from.service is enough)

Property: flume.called.from.service

Flume periodically polls, every 30 seconds, for changes to the specified config file. A Flume agent loads a new configuration from the config file if either an existing file is polled for the first time, or if an existing file’s modification date has changed since the last time it was polled. Renaming or moving a file does not change its modification time. When a Flume agent polls a non-existent file then one of two things happens: 1. When the agent polls a non-existent config file for the first time, then the agent behaves according to the flume.called.from.service property. If the property is set, then the agent will continue polling (always at the same period – every 30 seconds). If the property is not set, then the agent immediately terminates. ...OR... 2. When the agent polls a non-existent config file and this is not the first time the file is polled, then the agent makes no config changes for this polling period. The agent continues polling rather than terminating.

Configuration Filters

Flume provides a tool for injecting sensitive or generated data into the configuration in the form of configuration filters. A configuration key can be set as the value of configuration properties and it will be replaced by the configuration filter with the value it represents.

Common usage of config filters

The format is similar to the Java Expression Language, however it is currently not a fully working EL expression parser, just a format that looks like it.

<agent_name>.configfilters = <filter_name>
<agent_name>.configfilters.<filter_name>.type = <filter_type>

<agent_name>.sources.<source_name>.parameter = ${<filter_name>['<key_for_sensitive_or_generated_data>']}
<agent_name>.sinks.<sink_name>.parameter = ${<filter_name>['<key_for_sensitive_or_generated_data>']}
<agent_name>.<component_type>.<component_name>.parameter = ${<filter_name>['<key_for_sensitive_or_generated_data>']}
#or
<agent_name>.<component_type>.<component_name>.parameter = ${<filter_name>["<key_for_sensitive_or_generated_data>"]}
#or
<agent_name>.<component_type>.<component_name>.parameter = ${<filter_name>[<key_for_sensitive_or_generated_data>]}
#or
<agent_name>.<component_type>.<component_name>.parameter = some_constant_data${<filter_name>[<key_for_sensitive_or_generated_data>]}

Environment Variable Config Filter

Property Name	Default	Description
type	–	The component type name has to be env

Example

To hide a password in the configuration set its value as in the following example.

a1.sources = r1
a1.channels = c1
a1.configfilters = f1

a1.configfilters.f1.type = env

a1.sources.r1.channels =  c1
a1.sources.r1.type = http
a1.sources.r1.keystorePassword = ${f1['my_keystore_password']} #will get the value Secret123

Here the a1.sources.r1.keystorePassword configuration property will get the value of the my_keystore_password environment variable. One way to set the environment variable is to run flume agent like this:

$ my_keystore_password=Secret123 bin/flume-ng agent --conf conf --conf-file example.conf ...

External Process Config Filter

Property Name	Default	Description
type	–	The component type name has to be external
command	–	The command that will be executed to get the value for the given key. The command will be called like: <command> <key> And expected to return a single line value with exit code 0.
charset	UTF-8	The characterset of the returned string.

Example

To hide a password in the configuration set its value as in the following example.

a1.sources = r1
a1.channels = c1
a1.configfilters = f1

a1.configfilters.f1.type = external
a1.configfilters.f1.command = /usr/bin/passwordResolver.sh
a1.configfilters.f1.charset = UTF-8

a1.sources.r1.channels =  c1
a1.sources.r1.type = http
a1.sources.r1.keystorePassword = ${f1['my_keystore_password']} #will get the value Secret123

In this example flume will run the following command to get the value

$ /usr/bin/passwordResolver.sh my_keystore_password

The passwordResolver.sh will return Secret123 with an exit code 0.

Example 2

To generate a part of the directory for rolling file sink set its value as in the following example.

a1.sources = r1
a1.channels = c1
a1.configfilters = f1

a1.configfilters.f1.type = external
a1.configfilters.f1.command = /usr/bin/generateUniqId.sh
a1.configfilters.f1.charset = UTF-8

a1.sinks = k1
a1.sinks.k1.type = file_roll
a1.sinks.k1.channel = c1
a1.sinks.k1.sink.directory = /var/log/flume/agent_${f1['agent_name']} # will be /var/log/flume/agent_1234

In this example flume will run the following command to get the value

$ /usr/bin/generateUniqId.sh agent_name

The generateUniqId.sh will return 1234 with an exit code 0.

Hadoop Credential Store Config Filter

A hadoop-common library needed on the classpath for this feature (2.6+ version). If hadoop is installed the agent adds it to the classpath automatically

Property Name	Default	Description
type	–	The component type name has to be hadoop
credential.provider.path	–	The provider path. See hadoop documentation _here: https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/CredentialProviderAPI.html#Configuring_the_Provider_Path
credstore.java-keystore-provider.password-file	–	The name of the password file if a file is used to store the password. The file must e on the classpath. Provider password can be set with the HADOOP_CREDSTORE_PASSWORD environment variable or left empty.

Example

To hide a password in the configuration set its value as in the following example.

a1.sources = r1
a1.channels = c1
a1.configfilters = f1

a1.configfilters.f1.type = hadoop
a1.configfilters.f1.credential.provider.path = jceks://file/<path_to_jceks file>

a1.sources.r1.channels =  c1
a1.sources.r1.type = http
a1.sources.r1.keystorePassword = ${f1['my_keystore_password']} #will get the value from the credential store

Log4J Appender

Appends Log4j events to a flume agent’s avro source. A client using this appender must have the flume-ng-sdk in the classpath (eg, flume-ng-sdk-1.10.1.jar). Required properties are in bold.

Property Name	Default	Description
Hostname	–	The hostname on which a remote Flume agent is running with an avro source.
Port	–	The port at which the remote Flume agent’s avro source is listening.
UnsafeMode	false	If true, the appender will not throw exceptions on failure to send the events.
AvroReflectionEnabled	false	Use Avro Reflection to serialize Log4j events. (Do not use when users log strings)
AvroSchemaUrl	–	A URL from which the Avro schema can be retrieved.

Sample log4j.properties file:

#...
log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
log4j.appender.flume.Hostname = example.com
log4j.appender.flume.Port = 41414
log4j.appender.flume.UnsafeMode = true

# configure a class's logger to output to the flume appender
log4j.logger.org.example.MyClass = DEBUG,flume
#...

By default each event is converted to a string by calling toString(), or by using the Log4j layout, if specified.

If the event is an instance of org.apache.avro.generic.GenericRecord, org.apache.avro.specific.SpecificRecord, or if the property AvroReflectionEnabled is set to true then the event will be serialized using Avro serialization.

Serializing every event with its Avro schema is inefficient, so it is good practice to provide a schema URL from which the schema can be retrieved by the downstream sink, typically the HDFS sink. If AvroSchemaUrl is not specified, then the schema will be included as a Flume header.

Sample log4j.properties file configured to use Avro serialization:

#...
log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
log4j.appender.flume.Hostname = example.com
log4j.appender.flume.Port = 41414
log4j.appender.flume.AvroReflectionEnabled = true
log4j.appender.flume.AvroSchemaUrl = hdfs://namenode/path/to/schema.avsc

# configure a class's logger to output to the flume appender
log4j.logger.org.example.MyClass = DEBUG,flume
#...

Load Balancing Log4J Appender

Appends Log4j events to a list of flume agent’s avro source. A client using this appender must have the flume-ng-sdk in the classpath (eg, flume-ng-sdk-1.10.1.jar). This appender supports a round-robin and random scheme for performing the load balancing. It also supports a configurable backoff timeout so that down agents are removed temporarily from the set of hosts Required properties are in bold.

Property Name	Default	Description
Hosts	–	A space-separated list of host:port at which Flume (through an AvroSource) is listening for events
Selector	ROUND_ROBIN	Selection mechanism. Must be either ROUND_ROBIN, RANDOM or custom FQDN to class that inherits from LoadBalancingSelector.
MaxBackoff	–	A long value representing the maximum amount of time in milliseconds the Load balancing client will backoff from a node that has failed to consume an event. Defaults to no backoff
UnsafeMode	false	If true, the appender will not throw exceptions on failure to send the events.
AvroReflectionEnabled	false	Use Avro Reflection to serialize Log4j events.
AvroSchemaUrl	–	A URL from which the Avro schema can be retrieved.

Sample log4j.properties file configured using defaults:

#...
log4j.appender.out2 = org.apache.flume.clients.log4jappender.LoadBalancingLog4jAppender
log4j.appender.out2.Hosts = localhost:25430 localhost:25431

# configure a class's logger to output to the flume appender
log4j.logger.org.example.MyClass = DEBUG,flume
#...

Sample log4j.properties file configured using RANDOM load balancing:

#...
log4j.appender.out2 = org.apache.flume.clients.log4jappender.LoadBalancingLog4jAppender
log4j.appender.out2.Hosts = localhost:25430 localhost:25431
log4j.appender.out2.Selector = RANDOM

# configure a class's logger to output to the flume appender
log4j.logger.org.example.MyClass = DEBUG,flume
#...

Sample log4j.properties file configured using backoff:

#...
log4j.appender.out2 = org.apache.flume.clients.log4jappender.LoadBalancingLog4jAppender
log4j.appender.out2.Hosts = localhost:25430 localhost:25431 localhost:25432
log4j.appender.out2.Selector = ROUND_ROBIN
log4j.appender.out2.MaxBackoff = 30000

# configure a class's logger to output to the flume appender
log4j.logger.org.example.MyClass = DEBUG,flume
#...

Security

The HDFS sink, HBase sink, Thrift source, Thrift sink and Kite Dataset sink all support Kerberos authentication. Please refer to the corresponding sections for configuring the Kerberos-related options.

Flume agent will authenticate to the kerberos KDC as a single principal, which will be used by different components that require kerberos authentication. The principal and keytab configured for Thrift source, Thrift sink, HDFS sink, HBase sink and DataSet sink should be the same, otherwise the component will fail to start.

Monitoring

Monitoring in Flume is still a work in progress. Changes can happen very often. Several Flume components report metrics to the JMX platform MBean server. These metrics can be queried using Jconsole.

Available Component Metrics

The following tables show what metrics are available for components. Each component only maintains a set of metrics, indicated by an ‘x’, the unmaintained ones show default values, that is 0. These tables tell you where you can expect meaningful data. The name of the metrics should be descriptive enough, for more information you have to dig into the source code of the components.

Sources 1

	Avro	Exec	HTTP	JMS	Kafka	MultiportSyslogTCP	Scribe
AppendAcceptedCount	x
AppendBatchAcceptedCount	x		x	x
AppendBatchReceivedCount	x		x	x
AppendReceivedCount	x
ChannelWriteFail	x		x	x	x	x	x
EventAcceptedCount	x	x	x	x	x	x	x
EventReadFail			x	x	x	x	x
EventReceivedCount	x	x	x	x	x	x	x
GenericProcessingFail			x			x
KafkaCommitTimer					x
KafkaEmptyCount					x
KafkaEventGetTimer					x
OpenConnectionCount	x

Sources 2

	SequenceGenerator	SpoolDirectory	SyslogTcp	SyslogUDP	Taildir	Thrift
AppendAcceptedCount						x
AppendBatchAcceptedCount	x	x			x	x
AppendBatchReceivedCount		x			x	x
AppendReceivedCount						x
ChannelWriteFail	x	x	x	x	x	x
EventAcceptedCount	x	x	x	x	x	x
EventReadFail		x	x	x	x
EventReceivedCount		x	x	x	x	x
GenericProcessingFail		x			x
KafkaCommitTimer
KafkaEmptyCount
KafkaEventGetTimer
OpenConnectionCount

Sinks 1

	Avro/Thrift	AsyncHBase	HBase	HBase2
BatchCompleteCount	x	x	x	x
BatchEmptyCount	x	x	x	x
BatchUnderflowCount	x	x	x	x
ChannelReadFail	x			x
ConnectionClosedCount	x	x	x	x
ConnectionCreatedCount	x	x	x	x
ConnectionFailedCount	x	x	x	x
EventDrainAttemptCount	x	x	x	x
EventDrainSuccessCount	x	x	x	x
EventWriteFail	x			x
KafkaEventSendTimer
RollbackCount

Sinks 2

	HDFSEvent	Hive	Http	Kafka	Morphline	RollingFile
BatchCompleteCount	x	x			x
BatchEmptyCount	x	x		x	x
BatchUnderflowCount	x	x		x	x
ChannelReadFail	x	x	x	x	x	x
ConnectionClosedCount	x	x				x
ConnectionCreatedCount	x	x				x
ConnectionFailedCount	x	x				x
EventDrainAttemptCount	x	x	x		x	x
EventDrainSuccessCount	x	x	x	x	x	x
EventWriteFail	x	x	x	x	x	x
KafkaEventSendTimer				x
RollbackCount				x

Channels

	File	Kafka	Memory	PseudoTxnMemory	SpillableMemory
ChannelCapacity	x		x		x
ChannelSize	x		x	x	x
CheckpointBackupWriteErrorCount	x
CheckpointWriteErrorCount	x
EventPutAttemptCount	x	x	x	x	x
EventPutErrorCount	x
EventPutSuccessCount	x	x	x	x	x
EventTakeAttemptCount	x	x	x	x	x
EventTakeErrorCount	x
EventTakeSuccessCount	x	x	x	x	x
KafkaCommitTimer		x
KafkaEventGetTimer		x
KafkaEventSendTimer		x
Open	x
RollbackCounter		x
Unhealthy	x

JMX Reporting

JMX Reporting can be enabled by specifying JMX parameters in the JAVA_OPTS environment variable using flume-env.sh, like

export JAVA_OPTS=”-Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.port=5445 -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false”

NOTE: The sample above disables the security. To enable Security, please refer https://docs.oracle.com/javase/6/docs/technotes/guides/management/agent.html

Ganglia Reporting

Flume can also report these metrics to Ganglia 3 or Ganglia 3.1 metanodes. To report metrics to Ganglia, a flume agent must be started with this support. The Flume agent has to be started by passing in the following parameters as system properties prefixed by flume.monitoring., and can be specified in the flume-env.sh:

Property Name	Default	Description
type	–	The component type name, has to be ganglia
hosts	–	Comma-separated list of hostname:port of Ganglia servers
pollFrequency	60	Time, in seconds, between consecutive reporting to Ganglia server
isGanglia3	false	Ganglia server version is 3. By default, Flume sends in Ganglia 3.1 format

We can start Flume with Ganglia support as follows:

$ bin/flume-ng agent --conf-file example.conf --name a1 -Dflume.monitoring.type=ganglia -Dflume.monitoring.hosts=com.example:1234,com.example2:5455

JSON Reporting

Flume can also report metrics in a JSON format. To enable reporting in JSON format, Flume hosts a Web server on a configurable port. Flume reports metrics in the following JSON format:

{
"typeName1.componentName1" : {"metric1" : "metricValue1", "metric2" : "metricValue2"},
"typeName2.componentName2" : {"metric3" : "metricValue3", "metric4" : "metricValue4"}
}

Here is an example:

{
"CHANNEL.fileChannel":{"EventPutSuccessCount":"468085",
                      "Type":"CHANNEL",
                      "StopTime":"0",
                      "EventPutAttemptCount":"468086",
                      "ChannelSize":"233428",
                      "StartTime":"1344882233070",
                      "EventTakeSuccessCount":"458200",
                      "ChannelCapacity":"600000",
                      "EventTakeAttemptCount":"458288"},
"CHANNEL.memChannel":{"EventPutSuccessCount":"22948908",
                   "Type":"CHANNEL",
                   "StopTime":"0",
                   "EventPutAttemptCount":"22948908",
                   "ChannelSize":"5",
                   "StartTime":"1344882209413",
                   "EventTakeSuccessCount":"22948900",
                   "ChannelCapacity":"100",
                   "EventTakeAttemptCount":"22948908"}
}

Property Name	Default	Description
type	–	The component type name, has to be http
port	41414	The port to start the server on.

We can start Flume with JSON Reporting support as follows:

$ bin/flume-ng agent --conf-file example.conf --name a1 -Dflume.monitoring.type=http -Dflume.monitoring.port=34545

Metrics will then be available at http://<hostname>:<port>/metrics webpage. Custom components can report metrics as mentioned in the Ganglia section above.

Custom Reporting

It is possible to report metrics to other systems by writing servers that do the reporting. Any reporting class has to implement the interface, org.apache.flume.instrumentation.MonitorService. Such a class can be used the same way the GangliaServer is used for reporting. They can poll the platform mbean server to poll the mbeans for metrics. For example, if an HTTP monitoring service called HTTPReporting can be used as follows:

$ bin/flume-ng agent --conf-file example.conf --name a1 -Dflume.monitoring.type=com.example.reporting.HTTPReporting -Dflume.monitoring.node=com.example:332

Property Name	Default	Description
type	–	The component type name, has to be FQCN

Reporting metrics from custom components

Any custom flume components should inherit from the org.apache.flume.instrumentation.MonitoredCounterGroup class. The class should then provide getter methods for each of the metrics it exposes. See the code below. The MonitoredCounterGroup expects a list of attributes whose metrics are exposed by this class. As of now, this class only supports exposing metrics as long values.

public class SinkCounter extends MonitoredCounterGroup implements
    SinkCounterMBean {

  private static final String COUNTER_CONNECTION_CREATED =
    "sink.connection.creation.count";

  private static final String COUNTER_CONNECTION_CLOSED =
    "sink.connection.closed.count";

  private static final String COUNTER_CONNECTION_FAILED =
    "sink.connection.failed.count";

  private static final String COUNTER_BATCH_EMPTY =
    "sink.batch.empty";

  private static final String COUNTER_BATCH_UNDERFLOW =
      "sink.batch.underflow";

  private static final String COUNTER_BATCH_COMPLETE =
    "sink.batch.complete";

  private static final String COUNTER_EVENT_DRAIN_ATTEMPT =
    "sink.event.drain.attempt";

  private static final String COUNTER_EVENT_DRAIN_SUCCESS =
    "sink.event.drain.sucess";

  private static final String[] ATTRIBUTES = {
    COUNTER_CONNECTION_CREATED, COUNTER_CONNECTION_CLOSED,
    COUNTER_CONNECTION_FAILED, COUNTER_BATCH_EMPTY,
    COUNTER_BATCH_UNDERFLOW, COUNTER_BATCH_COMPLETE,
    COUNTER_EVENT_DRAIN_ATTEMPT, COUNTER_EVENT_DRAIN_SUCCESS
  };


  public SinkCounter(String name) {
    super(MonitoredCounterGroup.Type.SINK, name, ATTRIBUTES);
  }

  @Override
  public long getConnectionCreatedCount() {
    return get(COUNTER_CONNECTION_CREATED);
  }

  public long incrementConnectionCreatedCount() {
    return increment(COUNTER_CONNECTION_CREATED);
  }

}

Tools

File Channel Integrity Tool

File Channel Integrity tool verifies the integrity of individual Events in the File channel and removes corrupted Events.

The tools can be run as follows:

$bin/flume-ng tool --conf ./conf FCINTEGRITYTOOL -l ./datadir

where datadir is the comma separated list of data directory to be verified.

Following are the options available

Option Name	Description
h/help	Displays help
l/dataDirs	Comma-separated list of data directories which the tool must verify

Event Validator Tool

Event validator tool can be used to validate the File Channel Event’s in application specific way. The tool applies the user provider validation login on each event and drop the event which do not confirm to the logic.

The tools can be run as follows:

$bin/flume-ng tool --conf ./conf FCINTEGRITYTOOL -l ./datadir -e org.apache.flume.MyEventValidator -DmaxSize 2000

where datadir is the comma separated list of data directory to be verified.

Following are the options available

Option Name	Description
h/help	Displays help
l/dataDirs	Comma-separated list of data directories which the tool must verify
e/eventValidator	Fully Qualified Name of Event Validator Implementation. The jar must be on Flume classpath

The Event validator implementation must implement EventValidator interface. It’s recommended not to throw any exception from the implementation as they are treated as invalid events. Additional parameters can be passed to EventValitor implementation via -D options.

Let’s see an example of simple size based Event Validator, which shall reject event’s larger than maximum size specified.

public static class MyEventValidator implements EventValidator {

  private int value = 0;

  private MyEventValidator(int val) {
    value = val;
  }

  @Override
  public boolean validateEvent(Event event) {
    return event.getBody() <= value;
  }

  public static class Builder implements EventValidator.Builder {

    private int sizeValidator = 0;

    @Override
    public EventValidator build() {
      return new DummyEventVerifier(sizeValidator);
    }

    @Override
    public void configure(Context context) {
      binaryValidator = context.getInteger("maxSize");
    }
  }
}

Topology Design Considerations

Flume is very flexible and allows a large range of possible deployment scenarios. If you plan to use Flume in a large, production deployment, it is prudent to spend some time thinking about how to express your problem in terms of a Flume topology. This section covers a few considerations.

Is Flume a good fit for your problem?

If you need to ingest textual log data into Hadoop/HDFS then Flume is the right fit for your problem, full stop. For other use cases, here are some guidelines:

Flume is designed to transport and ingest regularly-generated event data over relatively stable, potentially complex topologies. The notion of “event data” is very broadly defined. To Flume, an event is just a generic blob of bytes. There are some limitations on how large an event can be - for instance, it cannot be larger than what you can store in memory or on disk on a single machine - but in practice, flume events can be everything from textual log entries to image files. The key property of an event is that they are generated in a continuous, streaming fashion. If your data is not regularly generated (i.e. you are trying to do a single bulk load of data into a Hadoop cluster) then Flume will still work, but it is probably overkill for your situation. Flume likes relatively stable topologies. Your topologies do not need to be immutable, because Flume can deal with changes in topology without losing data and can also tolerate periodic reconfiguration due to fail-over or provisioning. It probably won’t work well if you plant to change topologies every day, because reconfiguration takes some thought and overhead.

Flow reliability in Flume

The reliability of a Flume flow depends on several factors. By adjusting these factors, you can achieve a wide array of reliability options with Flume.

What type of channel you use. Flume has both durable channels (those which will persist data to disk) and non durable channels (those which will lose data if a machine fails). Durable channels use disk-based storage, and data stored in such channels will persist across machine restarts or non disk-related failures.

Whether your channels are sufficiently provisioned for the workload. Channels in Flume act as buffers at various hops. These buffers have a fixed capacity, and once that capacity is full you will create back pressure on earlier points in the flow. If this pressure propagates to the source of the flow, Flume will become unavailable and may lose data.

Whether you use redundant topologies. Flume let’s you replicate flows across redundant topologies. This can provide a very easy source of fault tolerance and one which is overcomes both disk or machine failures. 

The best way to think about reliability in a Flume topology is to consider various failure scenarios and their outcomes. What happens if a disk fails? What happens if a machine fails? What happens if your terminal sink (e.g. HDFS) goes down for some time and you have back pressure? The space of possible designs is huge, but the underlying questions you need to ask are just a handful.

Flume topology design

The first step in designing a Flume topology is to enumerate all sources and destinations (terminal sinks) for your data. These will define the edge points of your topology. The next consideration is whether to introduce intermediate aggregation tiers or event routing. If you are collecting data form a large number of sources, it can be helpful to aggregate the data in order to simplify ingestion at the terminal sink. An aggregation tier can also smooth out burstiness from sources or unavailability at sinks, by acting as a buffer. If you are routing data between different locations, you may also want to split flows at various points: this creates sub-topologies which may themselves include aggregation points.

Sizing a Flume deployment

Once you have an idea of what your topology will look like, the next question is how much hardware and networking capacity is needed. This starts by quantifying how much data you generate. That is not always a simple task! Most data streams are bursty (for instance, due to diurnal patterns) and potentially unpredictable. A good starting point is to think about the maximum throughput you’ll have in each tier of the topology, both in terms of events per second and bytes per second. Once you know the required throughput of a given tier, you can calulate a lower bound on how many nodes you require for that tier. To determine attainable throughput, it’s best to experiment with Flume on your hardware, using synthetic or sampled event data. In general, disk-based channels should get 10’s of MB/s and memory based channels should get 100’s of MB/s or more. Performance will vary widely, however depending on hardware and operating environment.

Sizing aggregate throughput gives you a lower bound on the number of nodes you will need to each tier. There are several reasons to have additional nodes, such as increased redundancy and better ability to absorb bursts in load.

Troubleshooting

Handling agent failures

If the Flume agent goes down, then the all the flows hosted on that agent are aborted. Once the agent is restarted, then flow will resume. The flow using file channel or other stable channel will resume processing events where it left off. If the agent can’t be restarted on the same hardware, then there is an option to migrate the database to another hardware and setup a new Flume agent that can resume processing the events saved in the db. The database HA futures can be leveraged to move the Flume agent to another host.

Compatibility

HDFS

Currently Flume supports HDFS 0.20.2 and 0.23.

AVRO

TBD

Additional version requirements

TBD

Tracing

TBD

More Sample Configs

TBD

Component Summary

Component Interface	Type Alias	Implementation Class
org.apache.flume.Channel	memory	org.apache.flume.channel.MemoryChannel
org.apache.flume.Channel	jdbc	org.apache.flume.channel.jdbc.JdbcChannel
org.apache.flume.Channel	file	org.apache.flume.channel.file.FileChannel
org.apache.flume.Channel	–	org.apache.flume.channel.PseudoTxnMemoryChannel
org.apache.flume.Channel	–	org.example.MyChannel
org.apache.flume.Source	avro	org.apache.flume.source.AvroSource
org.apache.flume.Source	netcat	org.apache.flume.source.NetcatSource
org.apache.flume.Source	seq	org.apache.flume.source.SequenceGeneratorSource
org.apache.flume.Source	exec	org.apache.flume.source.ExecSource
org.apache.flume.Source	syslogtcp	org.apache.flume.source.SyslogTcpSource
org.apache.flume.Source	multiport_syslogtcp	org.apache.flume.source.MultiportSyslogTCPSource
org.apache.flume.Source	syslogudp	org.apache.flume.source.SyslogUDPSource
org.apache.flume.Source	spooldir	org.apache.flume.source.SpoolDirectorySource
org.apache.flume.Source	http	org.apache.flume.source.http.HTTPSource
org.apache.flume.Source	thrift	org.apache.flume.source.ThriftSource
org.apache.flume.Source	jms	org.apache.flume.source.jms.JMSSource
org.apache.flume.Source	–	org.apache.flume.source.avroLegacy.AvroLegacySource
org.apache.flume.Source	–	org.apache.flume.source.thriftLegacy.ThriftLegacySource
org.apache.flume.Source	–	org.example.MySource
org.apache.flume.Sink	null	org.apache.flume.sink.NullSink
org.apache.flume.Sink	logger	org.apache.flume.sink.LoggerSink
org.apache.flume.Sink	avro	org.apache.flume.sink.AvroSink
org.apache.flume.Sink	hdfs	org.apache.flume.sink.hdfs.HDFSEventSink
org.apache.flume.Sink	hbase	org.apache.flume.sink.hbase.HBaseSink
org.apache.flume.Sink	hbase2	org.apache.flume.sink.hbase2.HBase2Sink
org.apache.flume.Sink	asynchbase	org.apache.flume.sink.hbase.AsyncHBaseSink
org.apache.flume.Sink	file_roll	org.apache.flume.sink.RollingFileSink
org.apache.flume.Sink	irc	org.apache.flume.sink.irc.IRCSink
org.apache.flume.Sink	thrift	org.apache.flume.sink.ThriftSink
org.apache.flume.Sink	–	org.example.MySink
org.apache.flume.ChannelSelector	replicating	org.apache.flume.channel.ReplicatingChannelSelector
org.apache.flume.ChannelSelector	multiplexing	org.apache.flume.channel.MultiplexingChannelSelector
org.apache.flume.ChannelSelector	–	org.example.MyChannelSelector
org.apache.flume.SinkProcessor	default	org.apache.flume.sink.DefaultSinkProcessor
org.apache.flume.SinkProcessor	failover	org.apache.flume.sink.FailoverSinkProcessor
org.apache.flume.SinkProcessor	load_balance	org.apache.flume.sink.LoadBalancingSinkProcessor
org.apache.flume.SinkProcessor	–
org.apache.flume.interceptor.Interceptor	timestamp	org.apache.flume.interceptor.TimestampInterceptor$Builder
org.apache.flume.interceptor.Interceptor	host	org.apache.flume.interceptor.HostInterceptor$Builder
org.apache.flume.interceptor.Interceptor	static	org.apache.flume.interceptor.StaticInterceptor$Builder
org.apache.flume.interceptor.Interceptor	regex_filter	org.apache.flume.interceptor.RegexFilteringInterceptor$Builder
org.apache.flume.interceptor.Interceptor	regex_extractor	org.apache.flume.interceptor.RegexFilteringInterceptor$Builder
org.apache.flume.channel.file.encryption.KeyProvider$Builder	jceksfile	org.apache.flume.channel.file.encryption.JCEFileKeyProvider
org.apache.flume.channel.file.encryption.KeyProvider$Builder	–	org.example.MyKeyProvider
org.apache.flume.channel.file.encryption.CipherProvider	aesctrnopadding	org.apache.flume.channel.file.encryption.AESCTRNoPaddingProvider
org.apache.flume.channel.file.encryption.CipherProvider	–	org.example.MyCipherProvider
org.apache.flume.serialization.EventSerializer$Builder	text	org.apache.flume.serialization.BodyTextEventSerializer$Builder
org.apache.flume.serialization.EventSerializer$Builder	avro_event	org.apache.flume.serialization.FlumeEventAvroEventSerializer$Builder
org.apache.flume.serialization.EventSerializer$Builder	–	org.example.MyEventSerializer$Builder

Alias Conventions

These conventions for alias names are used in the component-specific examples above, to keep the names short and consistent across all examples.

Alias Name	Alias Type
a	agent
c	channel
r	source
k	sink
g	sink group
i	interceptor
y	key
h	host
s	serializer