First steps

    Flink is designed to process continuous streams of data at a lightning fast pace. This short guide will show you how to download the latest stable version of Flink, install, and run it. You will also run an example Flink job and view it in the web UI.

    Flink runs on all UNIX-like environments, i.e. Linux, Mac OS X, and Cygwin (for Windows). You need to have Java 8 or 11 installed. To check the Java version installed, type in your terminal:

    Next, [download the latest binary release](http://nightlies.apache.org/flink/flink-docs-release-1.14/docs/try-flink/local_installation/

    ) of Flink, then extract the archive:

    1. $ tar -xzf flink-*.tgz

    Navigate to the extracted directory and list the contents by issuing:

    You should see something like:

    For now, you may want to note that:

    • bin/ directory contains the binary as well as several bash scripts that manage various jobs and tasks
    • conf/ directory contains configuration files, including flink-conf.yaml

    You should see an output like this:

    output

    Flink is now running as a background process. You can check its status with the following command:

    1. $ ps aux | grep flink

    You should be able to navigate to the web UI at to view the Flink dashboard and see that the cluster is up and running.

    To quickly stop the cluster and all running components, you can use the provided script:

    Flink provides a CLI tool, bin/flink, that can run programs packaged as Java ARchives (JAR) and control their execution. Submitting a job means uploading the job’s JAR file and related dependencies to the running Flink cluster and executing it.

    Flink releases come with example jobs, which you can find in the examples/ folder.

    To deploy the example word count job to the running cluster, issue the following command:

    You can verify the output by viewing the logs:

    1. $ tail log/flink-*-taskexecutor-*.out

    Additionally, you can check Flink’s to monitor the status of the cluster and running job.

    You can view the data flow plan for the execution:

    Here for the job execution, Flink has two operators. The first is the source operator which reads data from the collection source. The second operator is the transformation operator which aggregates counts of words. Learn more about DataStream operators.

    You can also look at the timeline of the job execution:

    data flow timeline

    You have successfully ran a ! Feel free to select any other JAR archive from the examples/ folder or deploy your own job!

    Summary

    In this guide, you downloaded Flink, explored the project directory, started and stopped a local cluster, and submitted a sample Flink job!

    To learn more about Flink fundamentals, check out the section. If you want to try something more hands-on, try one of the tutorials.