Start YARN session with 4 Task Managers (each with 4 GB of Heapspace):
wget http://artfiles.org/apache.org/flink/flink-0.8.1/flink-0.8.1-bin-hadoop2-yarn.tgz tar xvzf flink-0.8.1-bin-hadoop2-yarn.tgz cd flink-yarn-0.8.1/ ./bin/yarn-session.sh -n 4 -jm 1024 -tm 4096
-s flag for the number of processing slots per Task Manager. We recommend to set the number of slots to the number of processors per machine.
Apache Hadoop YARN is a cluster resource management framework. It allows to run various distributed applications on top of a cluster. Flink runs on YARN next to other applications. Users do not have to setup or install anything if there is already a YARN setup.
If you have troubles using the Flink YARN client, have a look in the FAQ section.
Follow these instructions to learn how to launch a Flink Session within your YARN cluster.
A session will start all required Flink services (JobManager and TaskManagers) so that you can submit programs to the cluster. Note that you can run multiple programs per session.
Download the YARN tgz package on the download page. It contains the required files.
Extract the package using:
tar xvzf flink-0.8.1-bin-hadoop2-yarn.tgz cd flink-yarn-0.8.1/
If you want to build the YARN .tgz file from sources, follow the build instructions. You can find the result of the build in
flink-dist/target/flink-0.8.1-bin/flink-yarn-0.8.1/ (Note: The version might be different for you ).
Use the following command to start a session
This command will show you the following overview:
Usage: Required -n,--container <arg> Number of Yarn container to allocate (=Number of Task Managers) Optional -D <arg> Dynamic Properties -jm,--jobManagerMemory <arg> Memory for JobManager Container [in MB] -q,--query Display available YARN resources (memory, cores) -qu,--queue <arg> Specify YARN queue. -s,--slots <arg> Number of slots per TaskManager -tm,--taskManagerMemory <arg> Memory per TaskManager Container [in MB] -tmc,--taskManagerCores <arg> Virtual CPU cores per TaskManager -v,--verbose Verbose debug mode
Please note that the Client requires the
HADOOP_CONF_DIR) environment variable to be set to read the YARN and HDFS configuration.
Example: Issue the following command to allocate 10 Task Managers, with 8 GB of memory and 32 processing slots each:
./bin/yarn-session.sh -n 10 -tm 8192 -s 32
The system will use the configuration in
conf/flink-config.yaml. Please follow our configuration guide if you want to change something.
Flink on YARN will overwrite the following configuration parameters
jobmanager.rpc.address (because the JobManager is always allocated at different machines),
taskmanager.tmp.dirs (we are using the tmp directories given by YARN) and
parallelization.degree.default if the number of slots has been specified.
If you don’t want to change the configuration file to pass configuration parameters, there is the option to pass dynamic properties via the
-D flag. So you can pass parameters this way:
The example invocation starts 11 containers, since there is one additional container for the ApplicationMaster and Job Manager.
Once Flink is deployed in your YARN cluster, it will show you the connection details of the Job Manager.
The client has to remain open to keep the deployment running. We suggest to use
screen, which will start a detachable shell:
CTRL+a, then press
dto detach the screen session,
screen -rto resume again.
Use the following command to submit a Flink program to the YARN cluster:
Please refer to the documentation of the commandline client.
The command will show you a help menu like this:
[...] Action "run" compiles and runs a program. Syntax: run [OPTIONS] <jar-file> <arguments> "run" action arguments: -c,--class <classname> Class with the program entry point ("main" method or "getPlan()" method. Only needed if the JAR file does not specify the class in its manifest. -m,--jobmanager <host:port> Address of the JobManager (master) to which to connect. Use this flag to connect to a different JobManager than the one specified in the configuration. -p,--parallelism <parallelism> The parallelism with which to run the program. Optional flag to override the default value specified in the configuration
Use the run action to submit a job to YARN. The client is able to determine the address of the JobManager. In the rare event of a problem, you can also pass the JobManager address using the
-m argument. The JobManager address is visible in the YARN console.
wget -O apache-license-v2.txt http://www.apache.org/licenses/LICENSE-2.0.txt ./bin/flink run -j ./examples/flink-java-examples-0.8.1-WordCount.jar \ -a 1 file://`pwd`/apache-license-v2.txt file://`pwd`/wordcount-result.txt
If there is the following error, make sure that all TaskManagers started:
Exception in thread "main" org.apache.flink.compiler.CompilerException: Available instances could not be determined from job manager: Connection timed out.
You can check the number of TaskManagers in the JobManager web interface. The address of this interface is printed in the YARN session console.
If the TaskManagers do not show up after a minute, you should investigate the issue using the log files.
There are many reasons why a Flink YARN session deployment can fail. A misconfigured Hadoop setup (HDFS permissions, YARN configuration), version incompatibilities (running Flink with vanilla Hadoop dependencies on Cloudera Hadoop) or other errors.
In cases where the Flink YARN session fails during the deployment itself, users have to rely on the logging capabilities of Hadoop YARN. The most useful feature for that is the YARN log aggregation.
To enable it, users have to set the
yarn.log-aggregation-enable property to
true in the
Once that is enabled, users can use the following command to retrieve all log files of a (failed) YARN session.
yarn logs -applicationId <application ID>
Note that it takes a few seconds after the session has finished until the logs show up.
The Flink YARN client also prints error messages in the terminal if errors occur during runtime (for example if a TaskManager stops working after some time).
In addition to that, there is the YARN Resource Manager webinterface (by default on port 8088). The port of the Resource Manager web interface is determined by the
yarn.resourcemanager.webapp.address configuration value.
It allows to access log files for running YARN applications and shows diagnostics for failed apps.
Users using Hadoop distributions from companies like Hortonworks, Cloudera or MapR might have to build Flink against their specific versions of Hadoop (HDFS) and YARN. Please read the build instructions for more details.
This section briefly describes how Flink and YARN interact.
The YARN client needs to access the Hadoop configuration to connect to the YARN resource manager and to HDFS. It determines the Hadoop configuration using the following strategy:
HADOOP_CONF_PATHare set (in that order). If one of these variables are set, they are used to read the configuration.
HADOOP_HOMEenvironment variable. If it is set, the client tries to access
$HADOOP_HOME/etc/hadoop(Hadoop 2) and
When starting a new Flink YARN session, the client first checks if the requested resources (containers and memory) are available. After that, it uploads a jar that contains Flink and the configuration to HDFS (step 1).
The next step of the client is to request (step 2) a YARN container to start the ApplicationMaster (step 3). Since the client registered the configuration and jar-file as a resource for the container, the NodeManager of YARN running on that particular machine will take care of preparing the container (e.g. downloading the files). Once that has finished, the ApplicationMaster (AM) is started.
The JobManager and AM are running in the same container. Once they successfully started, the AM knows the address of the JobManager (its own host). It is generating a new Flink configuration file for the TaskManagers (so that they can connect to the JobManager). The file is also uploaded to HDFS. Additionally, the AM container is also serving Flink’s web interface. The ports Flink is using for its services are the standard ports configured by the user + the application id as an offset. This allows users to execute multiple Flink YARN sessions in parallel.
After that, the AM starts allocating the containers for Flink’s TaskManagers, which will download the jar file and the modified configuration from the HDFS. Once these steps are completed, Flink is set up and ready to accept Jobs.