When running Flink applications, the JVM will load various classes over time. These classes can be divided into two domains:
The Flink Framework domain: This includes all code in the
/lib directory in the Flink directory.
By default these are the classes of Apache Flink and its core dependencies.
The User Code domain: These are all classes that are included in the JAR file submitted via the CLI or web interface. That includes the job’s classes, and all libraries and connectors that are put into the uber JAR.
The class loading behaves slightly different for various Flink setups:
When starting a Flink cluster, the JobManagers and TaskManagers are started with the Flink framework classes in the classpath. The classes from all jobs that are submitted against the cluster are loaded dynamically.
YARN classloading differs between single job deployments and sessions:
When submitting a Flink job directly to YARN (via
bin/flink run -m yarn-cluster ...), dedicated TaskManagers and
JobManagers are started for that job. Those JVMs have both Flink framework classes and user code classes in their classpath.
That means that there is no dynamic classloading involved in that case.
When starting a YARN session, the JobManagers and TaskManagers are started with the Flink framework classes in the classpath. The classes from all jobs that are submitted against the session are loaded dynamically.
Mesos setups following this documentation currently behave very much like the a YARN session: The TaskManager and JobManager processes are started with the Flink framework classes in classpath, job classes are loaded dynamically when the jobs are submitted.
All components (JobManger, TaskManager, Client, ApplicationMaster, …) log their classpath setting on startup. They can be found as part of the environment information at the beginning of the log.
When running a setup where the Flink JobManager and TaskManagers are exclusive to one particular job, one can put JAR files
directly into the
/lib folder to make sure they are part of the classpath and not loaded dynamically.
It usually works to put the job’s JAR file into the
/lib directory. The JAR will be part of both the classpath
(the AppClassLoader) and the dynamic class loader (FlinkUserCodeClassLoader).
Because the AppClassLoader is the parent of the FlinkUserCodeClassLoader (and Java loads parent-first), this should
result in classes being loaded only once.
For setups where the job’s JAR file cannot be put to the
/lib folder (for example because the setup is a session that is
used by multiple jobs), it may still be possible to put common libraries to the
/lib folder, and avoid dynamic class loading
In some cases, a transformation function, source, or sink needs to manually load classes (dynamically via reflection). To do that, it needs the classloader that has access to the job’s classes.
In that case, the functions (or sources or sinks) can be made a
RichFunction (for example
and access the user code class loader via
When you see an exception in the style
com.foo.X cannot be cast to com.foo.X, it means that multiple versions of the class
com.foo.X have been loaded by different class loaders, and types of that class are attempted to be assigned to each other.
The reason is in most cases that an object of the
com.foo.X class loaded from a previous execution attempt is still cached somewhere,
and picked up by a restarted task/operator that reloaded the code. Note that this is again only possible in deployments that use
dynamic class loading.
Common causes of cached object instances:
When using Apache Avro: The SpecificDatumReader caches instances of records. Avoid using
SpecificData.INSTANCE. See also
Using certain serialization frameworks for cloning objects (such as Apache Avro)
Interning objects (for example via Guava’s Interners)
All scenarios that involve dynamic class loading (i.e., standalone, sessions, mesos, …) rely on classes being unloaded again. Class unloading means that the Garbage Collector finds that no objects from a class exist and more, and thus removes the class (the code, static variable, metadata, etc).
Whenever a TaskManager starts (or restarts) a task, it will load that specific task’s code. Unless classes can be unloaded, this will become a memory leak, as new versions of classes are loaded and the total number of loaded classes accumulates over time. This typically manifests itself though a OutOfMemoryError: PermGen.
Common causes for class leaks and suggested fixes:
Lingering Threads: Make sure the application functions/sources/sinks shuts down all threads. Lingering threads cost resources themselves and additionally typically hold references to (user code) objects, preventing garbage collection and unloading of the classes.
Interners: Avoid caching objects in special structures that live beyond the lifetime of the functions/sources/sinks. Examples are Guava’s interners, or Avro’s class/object caches in the serializers.
Apache Flink loads many classes by default into its classpath. If a user uses a different version of a library that Flink is using, often
NoSuchMethodError are the result.
Through Hadoop, Flink for example depends on the
aws-sdk library or on
protobuf-java. If your user code is using these libraries and you run into issues we recommend relocating the dependency in your user code jar.
Apache Maven offers the maven-shade-plugin, which allows one to change the package of a class after compiling it (so the code you are writing is not affected by the shading). For example if you have the
com.amazonaws packages from the aws sdk in your user code jar, the shade plugin would relocate them into the
org.myorg.shaded.com.amazonaws package, so that your code is calling your aws sdk version.
This documentation page explains relocating classes using the shade plugin.
Note that some of Flink’s dependencies, such as
guava are shaded away by the maintainers of Flink, so users usually don’t have to worry about it.