This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.

Clusters & Deployment

When deciding how and where to run Flink, there’s a wide range of options available.

Deployment Modes

Flink can execute applications in one of three ways:

  • in Session Mode,
  • in a Per-Job Mode, or
  • in Application Mode.

The above modes differ in:

  • the cluster lifecycle and resource isolation guarantees
  • whether the application’s main() method is executed on the client or on the cluster.

Session Mode

Session mode assumes an already running cluster and uses the resources of that cluster to execute any submitted application. Applications executed in the same (session) cluster use, and consequently compete for, the same resources. This has the advantage that you do not pay the resource overhead of spinning up a full cluster for every submitted job. But, if one of the jobs misbehaves or brings down a Task Manager, then all jobs running on that Task Manager will be affected by the failure. This, apart from a negative impact on the job that caused the failure, implies a potential massive recovery process with all the restarting jobs accessing the filesystem concurrently and making it unavailable to other services. Additionally, having a single cluster running multiple jobs implies more load for the JobManager, who is responsible for the book-keeping of all the jobs in the cluster.

Per-Job Mode

Aiming at providing better resource isolation guarantees, the Per-Job mode uses the available cluster manager framework (e.g. YARN, Kubernetes) to spin up a cluster for each submitted job. This cluster is available to that job only. When the job finishes, the cluster is torn down and any lingering resources (files, etc) are cleared up. This provides better resource isolation, as a misbehaving job can only bring down its own Task Managers. In addition, it spreads the load of book-keeping across multiple JobManagers, as there is one per job. For these reasons, the Per-Job resource allocation model is the preferred mode by many production reasons.

Application Mode

In all the above modes, the application’s main() method is executed on the client side. This process includes downloading the application’s dependencies locally, executing the main() to extract a representation of the application that Flink’s runtime can understand (i.e. the JobGraph) and ship the dependencies and the JobGraph(s) to the cluster. This makes the Client a heavy resource consumer as it may need substantial network bandwidth to download dependencies and ship binaries to the cluster, and CPU cycles to execute the main(). This problem can be more pronounced when the Client is shared across users.

Building on this observation, the Application Mode creates a cluster per submitted application, but this time, the main() method of the application is executed on the JobManager. Creating a cluster per application can be seen as creating a session cluster shared only among the jobs of a particular application, and torn down when the application finishes. With this architecture, the Application Mode provides the same resource isolation and load balancing guarantees as the Per-Job mode, but at the granularity of a whole application. Executing the main() on the JobManager allows for saving the CPU cycles required, but also save the bandwidth required for downloading the dependencies locally. Furthermore, it allows for more even spread of the network load of downloading the dependencies of the applications in the cluster, as there is one JobManager per application.

Note: In the Application Mode, the main() is executed on the cluster and not on the client, as in the other modes. This may have implications for your code as, for example, any paths you register in your environment using the registerCachedFile() must be accessible by the JobManager of your application.

Compared to the Per-Job mode, the Application Mode allows the submission of applications consisting of multiple jobs. The order of job execution is not affected by the deployment mode but by the call used to launch the job. Using execute(), which is blocking, establishes an order and it will lead to the execution of the “next” job being postponed until “this” job finishes. Using executeAsync(), which is non-blocking, will lead to the “next” job starting before “this” job finishes.

Attention: The Application Mode allows for multi-execute() applications but High-Availability is not supported in these cases. High-Availability in Application Mode is only supported for single-execute() applications.


In Session Mode, the cluster lifecycle is independent of that of any job running on the cluster and the resources are shared across all jobs. The Per-Job mode pays the price of spinning up a cluster for every submitted job, but this comes with better isolation guarantees as the resources are not shared across jobs. In this case, the lifecycle of the cluster is bound to that of the job. Finally, the Application Mode creates a session cluster per application and executes the application’s main() method on the cluster.

Deployment Targets

Apache Flink ships with first class support for a number of common deployment targets.

Run Flink locally for basic testing and experimentation
Learn more
A simple solution for running Flink on bare metal or VM's
Learn more
Deploy Flink on-top of Apache Hadoop's resource manager
Learn more
A generic resource manager for running distriubted systems
Learn more
A popular solution for running Flink within a containerized environment
Learn more
An automated system for deploying containerized applications
Learn more

Vendor Solutions

A number of vendors offer managed or fully hosted Flink solutions. None of these vendors are officially supported or endorsed by the Apache Flink PMC. Please refer to vendor maintained documentation on how to use these products.

AliCloud Realtime Compute


Supported Environments: AliCloud

Amazon EMR


Supported Environments: AWS

Amazon Kinesis Data Analytics For Java


Supported Environments: AWS



Supported Environment: AWS Azure Google Cloud On-Premise



Supported Environment: AWS

Huawei Cloud Stream Service


Supported Environment: Huawei Cloud

Ververica Platform


Supported Environments: AliCloud AWS Azure Google Cloud On-Premise

Deployment Best Practices

How to provide dependencies in the classpath

Flink provides several approaches for providing dependencies (such as *.jar files or static data) to Flink or user-provided applications. These approaches differ based on the deployment mode and target, but also have commonalities, which are described here.

To provide a dependency, there are the following options:

  • files in the lib/ folder are added to the classpath used to start Flink. It is suitable for libraries such as Hadoop or file systems not available as plugins. Beware that classes added here can potentially interfere with Flink, for example if you are adding a different version of a library already provided by Flink.

  • plugins/<name>/ are loaded at runtime by Flink through separate classloaders to avoid conflicts with classes loaded and used by Flink. Only jar files which are prepared as plugins can be added here.

Download Maven dependencies locally

If you need to extend the Flink with a Maven dependency (and its transitive dependencies), you can use an Apache Maven pom.xml file to download all required files into a local folder:


<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="" xmlns:xsi=""

        <!-- Put your dependency here, for example a Hadoop GCS connector -->


Running mvn package in the same directory will create a jars/ folder containing all the jar files, which you can add to the desired folder, Docker image etc.