Project: Flink (App Definition and Development / Streaming & Messaging)
File: flink-operations-playground.md

title: 'Flink Operations Playground' nav-title: 'Flink Operations Playground' weight: 6 type: docs aliases: /try-flink/flink-operations-playground.html /getting-started/playground/flink-operations-playground.html <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> There are many ways to deploy and operate Apache Flink in various environments. Regardless of this variety, the fundamental building blocks of a Flink Cluster remain the same, and similar operational principles apply. In this playground, you will learn how to manage and run Flink Jobs. You will see how to deploy and monitor an application, experience how Flink recovers from Job failure, and perform everyday operational tasks like upgrades and rescaling. {{< unstable >}} {{< hint warning >}} Attention: The Apache Flink Docker images used for this playground are only available for released versions of Apache Flink. Since you are currently looking at the latest SNAPSHOT version of the documentation, all version references below will not work. Please switch the documentation to the latest released version via the release picker which you find on the left side below the menu. {{< /hint >}} {{< /unstable >}} This playground consists of a long living and a Kafka Cluster. A Flink Cluster always consists of a and one or more . The JobManager is responsible for handling submissions, the supervision of Jobs as well as resource management. The Flink TaskManagers are the worker processes and are responsible for the execution of the actual which make up a Flink Job. In this playground you will start with a single TaskManager, but scale out to more TaskManagers later. Additionally, this playground comes with a dedicated client container, which we use to submit the Flink Job initially and to perform various operational tasks later on. The client container is not needed by the Flink Cluster itself but only included for ease of use. The Kafka Cluster consists of a Zookeeper server and a Kafka Broker. {{< img src="/fig/flink-docker-playground.svg" alt="Flink Docker Playground" width="80%" >}} When the playground is started a Flink Job called Flink Event Count will be submitted to the JobManager. Additionally, two Kafka Topics input and output are created. {{< img src="/fig/click-event-count-example.svg" alt="Click Event Count Example" width="80%" >}} The Job consumes `ClickEvent`s from the input topic, each with a `timestamp` and a `page`. The events are then keyed by `page` and counted in 15 second . The results are written to the output

There are six different pages and we generate 1000 click events per page and 15 seconds. Hence, the output of the Flink job should show 1000 views per page and window. {{< top >}} The playground environment is set up in just a few steps. We will walk you through the necessary commands and show how to validate that everything is running correctly. We assume that you have (1.12+) and (2.1+) installed on your machine. The required configuration files are available in the repository. First checkout the code and build the docker image: ```bash git clone https://github.com/apache/flink-playgrounds.git cd flink-playgrounds/operations-playground docker-compose build ``` Then start the playground: ```bash docker-compose up -d ``` Afterwards, you can inspect the running Docker containers with the following command: ```bash docker-compose ps Name Command State Ports -- operations-playgroundclickevent-generator1 /docker-entrypoint.sh java ... Up 6123/tcp, 8081/tcp operations-playgroundclient1 /docker-entrypoint.sh flin ... Exit 0 operations-playgroundjobmanager1 /docker-entrypoint.sh jobm ... Up 6123/tcp, 0.0.0.0:8081->8081/tcp operations-playgroundkafka1 start-kafka.sh Up 0.0.0.0:9094->9094/tcp operations-playgroundtaskmanager1 /docker-entrypoint.sh task ... Up 6123/tcp, 8081/tcp operations-playgroundzookeeper1 /bin/sh -c /usr/sbin/sshd ... Up 2181/tcp, 22/tcp, 2888/tcp, 3888/tcp ``` This indicates that the client container has successfully submitted the Flink Job (`Exit 0`) and all cluster components as well as the data generator are running (`Up`). You can stop the playground environment by calling: ```bash docker-compose down -v ``` There are many things you can try and check out in this playground. In the following two sections we will show you how to interact with the Flink Cluster and demonstrate some of Flink's key features. The most natural starting point to observe your Flink Cluster is the WebUI exposed under . If everything went well, you'll see that the cluster initially consists of one TaskManager and executes a Job called Click Event Count. {{< img src="/fig/playground-webui.png" alt="Playground Flink WebUI" >}} The Flink WebUI contains a lot of useful and interesting information about your Flink Cluster and its Jobs (JobGraph, Metrics, Checkpointing Statistics, TaskManager Status,...). JobManager The JobManager logs can be tailed via `docker-compose`. ```bash docker-compose logs -f jobmanager ``` After the initial startup you should mainly see log messages for every checkpoint completion. TaskManager The TaskManager log can be tailed in the same way. ```bash docker-compose logs -f taskmanager ``` After the initial startup you should mainly see log messages for every checkpoint completion. The can be used from within the client container. For example, to print the `help` message of the Flink CLI you can run ```bash docker-compose run --no-deps client flink --help ``` The is exposed via `localhost:8081` on the host or via `jobmanager:8081` from the client container, e.g. to list all currently running jobs, you can run: ```bash curl localhost:8081/jobs ``` {{< unstable >}} {{< hint warning >}} Note: If the curl command is not available on your machine, you can run it from the client container (similar to the Flink CLI): ```bash docker-compose run --no-deps client curl jobmanager:8081/jobs ``` {{< /hint >}} {{< /unstable >}} You can look at the records that are written to the Kafka Topics by running ```bash //input topic (1000 records/s) docker-compose exec kafka

\ --bootstrap-server localhost:9092 --topic input //output topic (24 records/min) docker-compose exec kafka kafka-console-consumer.sh \ --bootstrap-server localhost:9092 --topic output ``` {{< top >}} Now that you learned how to interact with Flink and the Docker containers, let's have a look at some common operational tasks that you can try out on our playground. All of these tasks are independent of each other, i.e. you can perform them in any order. Most tasks can be executed via the and the . {{< tabs "70aebe04-2b76-473d-b293-7ac6c535abc8" >}} {{< tab "CLI" >}} Command ```bash docker-compose run --no-deps client flink list ``` Expected Output ```plain Waiting for response... Running/Restarting Jobs - 16.07.2019 16:37:55 : <job-id> : Click Event Count (RUNNING) -- No scheduled jobs. ``` {{< /tab >}} {{< tab "REST API" >}} Request ```bash curl localhost:8081/jobs ``` Expected Response (pretty-printed) ```bash { "jobs": [ { "id": "<job-id>", "status": "RUNNING" } ] } ``` {{< /tab >}} {{< /tabs >}} The JobID is assigned to a Job upon submission and is needed to perform actions on the Job via the CLI or REST API. Flink provides exactly-once processing guarantees under (partial) failure. In this playground you can observe and - to some extent - verify this behavior. As described , the events in this playground are generated such that each window contains exactly one thousand records. So, in order to verify that Flink successfully recovers from a TaskManager failure without data loss or duplication you can tail the output topic and check that - after recovery - all windows are present and the count is correct. For this, start reading from the output topic and leave this command running until after recovery (Step 3). ```bash docker-compose exec kafka kafka-console-consumer.sh \ --bootstrap-server localhost:9092 --topic output ``` In order to simulate a partial failure you can kill a TaskManager. In a production setup, this could correspond to a loss of the TaskManager process, the TaskManager machine or simply a transient exception being thrown from the framework or user code (e.g. due to the temporary unavailability of an external resource). ```bash docker-compose kill taskmanager ``` After a few seconds, the JobManager will notice the loss of the TaskManager, cancel the affected Job, and immediately resubmit it for recovery. When the Job gets restarted, its tasks remain in the `SCHEDULED` state, which is indicated by the purple colored squares (see screenshot below). {{< img src="/fig/playground-webui-failure.png" alt="Playground Flink WebUI" >}} {{< hint info >}} Note: Even though the tasks of the job are in SCHEDULED state and not RUNNING yet, the overall status of a Job is shown as RUNNING. {{< /hint >}} At this point, the tasks of the Job cannot move from the `SCHEDULED` state to `RUNNING` because there are no resources (TaskSlots provided by TaskManagers) to the run the tasks. Until a new TaskManager becomes available, the Job will go through a cycle of cancellations and resubmissions. In the meantime, the data generator keeps pushing `ClickEvent`s into the input

This is similar to a real production setup where data is produced while the Job to process it is down. Once you restart the TaskManager, it reconnects to the JobManager. ```bash docker-compose up -d taskmanager ``` When the JobManager is notified about the new TaskManager, it schedules the tasks of the recovering Job to the newly available TaskSlots. Upon restart, the tasks recover their state from the last successful that was taken before the failure and switch to the `RUNNING` state. The Job will quickly process the full backlog of input events (accumulated during the outage) from Kafka and produce output at a much higher rate (> 24 records/minute) until it reaches the head of the stream. In the output you will see that all keys (`page`s) are present for all time windows and that every count is exactly one thousand. Since we are using the in its "at-least-once" mode, there is a chance that you will see some duplicate output records. {{< hint info >}} Note: Most production setups rely on a resource manager (Kubernetes, Yarn) to automatically restart failed processes. {{< /hint >}} Upgrading a Flink Job always involves two steps: First, the Flink Job is gracefully stopped with a . A Savepoint is a consistent snapshot of the complete application state at a well-defined, globally consistent point in time (similar to a checkpoint). Second, the upgraded Flink Job is started from the Savepoint. In this context "upgrade" can mean different things including the following: An upgrade to the configuration (incl. the parallelism of the Job) An upgrade to the topology of the Job (added/removed Operators) An upgrade to the user-defined functions of the Job Before starting with the upgrade you might want to start tailing the output topic, in order to observe that no data is lost or corrupted in the course the upgrade. ```bash docker-compose exec kafka kafka-console-consumer.sh \ --bootstrap-server localhost:9092 --topic output ``` To gracefully stop the Job, you need to use the "stop" command of either the CLI or the REST API. For this you will need the JobID of the Job, which you can obtain by or from the WebUI. With the JobID you can proceed to stopping the Job: {{< tabs "cebdc094-9e9c-4ff9-bfc0-eeee846437e5" >}} {{< tab "CLI" >}} Command ```bash docker-compose run --no-deps client flink stop <job-id> ``` Expected Output ```bash Suspending job "<job-id>" with a savepoint. Savepoint completed. Path: file:<savepoint-path> ``` The Savepoint has been stored to the `state.savepoints.dir` configured in the , which is mounted under /tmp/flink-savepoints-directory/ on your local machine. You will need the path to this Savepoint in the next step. {{< /tab >}} {{< tab "REST API" >}} Request ```bash curl -X POST localhost:8081/jobs/<job-id>/stop -d '{"drain": false}' ``` Expected Response (pretty-printed) ```json { "request-id": "<trigger-id>" } ``` Request ```bash curl localhost:8081/jobs/<job-id>/savepoints/<trigger-id> ``` Expected Response (pretty-printed) ```json { "status": { "id": "COMPLETED" }, "operation": { "location": "<savepoint-path>" } } ``` {{< /tab >}} {{< /tabs >}} You can now restart the upgraded Job from this Savepoint. For simplicity, you can start by restarting it without any

{{< tabs "dcdc51ef-8a2a-45c4-beea-e87c2710db24" >}} {{< tab "CLI" >}} Command ```bash docker-compose run --no-deps client flink run -s <savepoint-path> \ -d /opt/ClickCountJob.jar \ --bootstrap.servers kafka:9092 --checkpointing --event-time ``` Expected Output ```bash Job has been submitted with JobID <job-id> ``` {{< /tab >}} {{< tab "REST API" >}} Request ```bash docker-compose run --no-deps client curl -X POST -H "Expect:" \ -F "jarfile=@/opt/ClickCountJob.jar" http://jobmanager:8081/jars/upload ``` Expected Response (pretty-printed) ```json { "filename": "/tmp/flink-web-<uuid>/flink-web-upload/<jar-id>", "status": "success" } ``` Request ```bash curl -X POST http://localhost:8081/jars/<jar-id>/run \ -d '{"programArgs": "--bootstrap.servers kafka:9092 --checkpointing --event-time", "savepointPath": "<savepoint-path>"}' ``` Expected Response (pretty-printed) ```json { "jobid": "<job-id>" } ``` {{< /tab >}} {{< /tabs >}} Once the Job is `RUNNING` again, you will see in the output Topic that records are produced at a higher rate while the Job is processing the backlog accumulated during the outage. Additionally, you will see that no data was lost during the upgrade: all windows are present with a count of exactly one thousand. Alternatively, you could also rescale the Job from this Savepoint by passing a different parallelism during resubmission. {{< tabs "d3615bcd-6a14-4184-b730-fd1d2b1ab76f" >}} {{< tab "CLI" >}} Command ```bash docker-compose run --no-deps client flink run -p 3 -s <savepoint-path> \ -d /opt/ClickCountJob.jar \ --bootstrap.servers kafka:9092 --checkpointing --event-time ``` Expected Output ```bash Starting execution of program Job has been submitted with JobID <job-id> ``` {{< /tab >}} {{< tab "REST API" >}} Request ```bash docker-compose run --no-deps client curl -X POST -H "Expect:" \ -F "jarfile=@/opt/ClickCountJob.jar" http://jobmanager:8081/jars/upload ``` Expected Response (pretty-printed) ```json { "filename": "/tmp/flink-web-<uuid>/flink-web-upload/<jar-id>", "status": "success" } ``` Request ```bash curl -X POST http://localhost:8081/jars/<jar-id>/run \ -d '{"parallelism": 3, "programArgs": "--bootstrap.servers kafka:9092 --checkpointing --event-time", "savepointPath": "<savepoint-path>"}' ``` Expected Response (pretty-printed ```json { "jobid": "<job-id>" } ``` {{< /tab >}} {{< /tabs >}} Now, the Job has been resubmitted, but it will not start as there are not enough TaskSlots to execute it with the increased parallelism (2 available, 3 needed). With ```bash docker-compose scale taskmanager=2 ``` you can add a second TaskManager with two TaskSlots to the Flink Cluster, which will automatically register with the JobManager. Shortly after adding the TaskManager the Job should start running again. Once the Job is "RUNNING" again, you will see in the output Topic that no data was lost during rescaling: all windows are present with a count of exactly one thousand. The JobManager exposes system and user via its REST API. The endpoint depends on the scope of these metrics. Metrics scoped to a Job can be listed via `jobs/<job-id>/metrics`. The actual value of a metric can be queried via the `get` query parameter. Request ```bash curl "localhost:8081/jobs/<jod-id>/metrics?get=lastCheckpointSize" ``` Expected Response (pretty-printed; no placeholders) ```json [ { "id": "lastCheckpointSize", "value": "9378" } ] ``` The REST API can not only be used to query metrics, but you can also retrieve detailed information about the status of a running

Request ```bash curl localhost:8081/jobs/<jod-id> ``` Expected Response (pretty-printed) ```json { "jid": "<job-id>", "name": "Click Event Count", "isStoppable": false, "state": "RUNNING", "start-time": 1564467066026, "end-time": -1, "duration": 374793, "now": 1564467440819, "timestamps": { "CREATED": 1564467066026, "FINISHED": 0, "SUSPENDED": 0, "FAILING": 0, "CANCELLING": 0, "CANCELED": 0, "RECONCILING": 0, "RUNNING": 1564467066126, "FAILED": 0, "RESTARTING": 0 }, "vertices": [ { "id": "<vertex-id>", "name": "ClickEvent Source", "parallelism": 2, "status": "RUNNING", "start-time": 1564467066423, "end-time": -1, "duration": 374396, "tasks": { "CREATED": 0, "FINISHED": 0, "DEPLOYING": 0, "RUNNING": 2, "CANCELING": 0, "FAILED": 0, "CANCELED": 0, "RECONCILING": 0, "SCHEDULED": 0 }, "metrics": { "read-bytes": 0, "read-bytes-complete": true, "write-bytes": 5033461, "write-bytes-complete": true, "read-records": 0, "read-records-complete": true, "write-records": 166351, "write-records-complete": true } }, { "id": "<vertex-id>", "name": "ClickEvent Counter", "parallelism": 2, "status": "RUNNING", "start-time": 1564467066469, "end-time": -1, "duration": 374350, "tasks": { "CREATED": 0, "FINISHED": 0, "DEPLOYING": 0, "RUNNING": 2, "CANCELING": 0, "FAILED": 0, "CANCELED": 0, "RECONCILING": 0, "SCHEDULED": 0 }, "metrics": { "read-bytes": 5085332, "read-bytes-complete": true, "write-bytes": 316, "write-bytes-complete": true, "read-records": 166305, "read-records-complete": true, "write-records": 6, "write-records-complete": true } }, { "id": "<vertex-id>", "name": "ClickEventStatistics Sink", "parallelism": 2, "status": "RUNNING", "start-time": 1564467066476, "end-time": -1, "duration": 374343, "tasks": { "CREATED": 0, "FINISHED": 0, "DEPLOYING": 0, "RUNNING": 2, "CANCELING": 0, "FAILED": 0, "CANCELED": 0, "RECONCILING": 0, "SCHEDULED": 0 }, "metrics": { "read-bytes": 20668, "read-bytes-complete": true, "write-bytes": 0, "write-bytes-complete": true, "read-records": 6, "read-records-complete": true, "write-records": 0, "write-records-complete": true } } ], "status-counts": { "CREATED": 0, "FINISHED": 0, "DEPLOYING": 0, "RUNNING": 4, "CANCELING": 0, "FAILED": 0, "CANCELED": 0, "RECONCILING": 0, "SCHEDULED": 0 }, "plan": { "jid": "<job-id>", "name": "Click Event Count", "type": "STREAMING", "nodes": [ { "id": "<vertex-id>", "parallelism": 2, "operator": "", "operator_strategy": "", "description": "ClickEventStatistics Sink", "inputs": [ { "num": 0, "id": "<vertex-id>", "ship_strategy": "FORWARD", "exchange": "pipelined_bounded" } ], "optimizer_properties": {} }, { "id": "<vertex-id>", "parallelism": 2, "operator": "", "operator_strategy": "", "description": "ClickEvent Counter", "inputs": [ { "num": 0, "id": "<vertex-id>", "ship_strategy": "HASH", "exchange": "pipelined_bounded" } ], "optimizer_properties": {} }, { "id": "<vertex-id>", "parallelism": 2, "operator": "", "operator_strategy": "", "description": "ClickEvent Source", "optimizer_properties": {} } ] } } ``` Please consult the for a complete list of possible queries including how to query metrics of different scopes (e.g. TaskManager metrics); {{< top >}} You might have noticed that the Click Event Count application was always started with `--checkpointing` and `--event-time` program arguments. By omitting these in the command of the client container in the `docker-compose.yaml`, you can change the behavior of the Job. `--checkpointing` enables , which is Flink's fault-tolerance mechanism. If you run without it and go through , you should will see that data is actually lost. `--event-time` enables for your Job. When disabled, the Job will assign events to windows based on the wall-clock time instead of the timestamp of the `ClickEvent`. Consequently, the number of events per window will not be exactly one thousand anymore. The Click Event Count application also has another option, turned off by default, that you can enable to explore the behavior of this job under backpressure. You can add this option in the command of the client container in `docker-compose.yaml`. `--backpressure` adds an additional operator into the middle of the job that causes severe backpressure during even-numbered minutes (e.g., during 10:12, but not during 10:13). This can be observed by inspecting various such as `outputQueueLength` and `outPoolUsage`, and/or by using the available in the WebUI.