This page contains a collection of best practices for Flink programmers on how to solve frequently encountered problems.
Almost all Flink applications, both batch and streaming, rely on external configuration parameters. They are used to specify input and output sources (like paths or addresses), system parameters (parallelism, runtime configuration), and application specific parameters (typically used within user functions).
Flink provides a simple utility called
ParameterTool to provide some basic tooling for solving these problems.
Please note that you don’t have to use the
ParameterTool described here. Other frameworks such as Commons CLI and
argparse4j also work well with Flink.
ParameterTool provides a set of predefined static methods for reading the configuration. The tool is internally expecting a
Map<String, String>, so it’s very easy to integrate it with your own configuration style.
The following method will read a Properties file and provide the key/value pairs:
This allows getting arguments like
--input hdfs:///mydata --elements 42 from the command line.
When starting a JVM, you can pass system properties to it:
-Dinput=hdfs:///mydata. You can also initialize the
ParameterTool from these system properties:
Now that we’ve got the parameters from somewhere (see above) we can use them in various ways.
Directly from the
ParameterTool itself has methods for accessing the values.
You can use the return values of these methods directly in the
main() method of the client submitting the application.
For example, you could set the parallelism of a operator like this:
ParameterTool is serializable, you can pass it to the functions itself:
and then use it inside the function for getting values from the command line.
Parameters registered as global job parameters in the
ExecutionConfig can be accessed as configuration values from the JobManager web interface and in all functions defined by the user.
Register the parameters globally:
Access them in any rich user function:
It is recommended to use POJOs (Plain old Java objects) instead of
TupleX for data types with many fields.
Also, POJOs can be used to give large
Tuple-types a name.
Instead of using:
It is much easier to create a custom type extending from the large Tuple type.
Note: This tutorial is applicable starting from Flink 0.10
Apache Flink is using slf4j as the logging abstraction in the code. Users are advised to use sfl4j as well in their user functions.
Flink is depending on Log4j by default. This page describes how to use Flink with Logback. Users reported that they were also able to set up centralized logging with Graylog using this tutorial.
To get a logger instance in the code, use the following code:
In all cases where classes are executed with a classpath created by a dependency manager such as Maven, Flink will pull log4j into the classpath.
Therefore, you will need to exclude log4j from Flink’s dependencies. The following description will assume a Maven project created from a Flink quickstart.
Change your projects
pom.xml file like this:
The following changes were done in the
log4j dependencies from all Flink dependencies: this causes Maven to ignore Flink’s transitive dependencies to log4j.
slf4j-log4j12 artifact from Flink’s dependencies: since we are going to use the slf4j to logback binding, we have to remove the slf4j to log4j binding.
log4j-over-slf4j is a tool which allows legacy applications which are directly using the Log4j APIs to use the Slf4j interface. Flink depends on Hadoop which is directly using Log4j for logging. Therefore, we need to redirect all logger calls from Log4j to Slf4j which is in turn logging to Logback.
Please note that you need to manually add the exclusions to all new Flink dependencies you are adding to the pom file.
You may also need to check if other (non-Flink) dependencies are pulling in log4j bindings. You can analyze the dependencies of your project with
This tutorial is applicable when running Flink on YARN or as a standalone cluster.
In order to use Logback instead of Log4j with Flink, you need to remove
sfl4j-log4j12-xxx.jar from the
Next, you need to put the following jar files into the
log4j-over-slf4j.jar: This bridge needs to be present in the classpath for redirecting logging calls from Hadoop (which is using Log4j) to Slf4j.
Note that you need to explicitly set the
lib/ directory when using a per-job YARN cluster.
The command to submit Flink on YARN with a custom logger is:
./bin/flink run -yt $FLINK_HOME/lib <... remaining arguments ...>