knaufk commented on a change in pull request #15064: URL: https://github.com/apache/flink/pull/15064#discussion_r595809035
########## File path: docs/content/docs/deployment/elastic_scaling.md ########## @@ -0,0 +1,112 @@ +--- +title: Elastic Scaling +weight: 5 +type: docs + +--- +<!-- +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. +--> + +# Elastic Scaling + +Apache Flink allows you to rescale your jobs. You can either do this manually by stopping the job and restarting from the savepoint created during shutdown with a different parallelism. + +This page describes options where Flink automatically adjusts the parallelism. + +## Reactive Mode + +{{< hint danger >}} +Reactive mode is a MVP ("minimum viable product") feature. The Flink community is actively looking for feedback by users through our mailing lists. Please check the limitations listed on this page. +{{< /hint >}} + +Reactive Mode configures a job so that it always uses all resources available in the cluster. Adding a TaskManager will scale up your job, removing resources will scale it down. Flink will manage the parallelism of the job, always setting them to the highest possible values. + +Reactive Mode restarts a job on a rescaling event, restoring it from the latest completed checkpoint. This means that there is no overhead of manually creating a savepoint (which is needed for manually rescaling a job). Also, the amount of data that is reprocessed after rescaling depends on the checkpointing interval, and the restore time depends on the state size. + +The Reactive Mode allows Flink users to implement a powerful autoscaling mechanism, by having an external service monitor certain metrics, such as consumer lag, aggregate CPU utilization, throughput or latency. As soon as these metrics are above or below a certain threshold, additional TaskManagers can be added or removed from the Flink cluster. This could be implemented through changing the [replica factor](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/#replicas) of a Kubernetes deployment, or an [autoscaling](https://docs.aws.amazon.com/autoscaling/ec2/userguide/AutoScalingGroup.html) group. This external service only needs to handle the resource allocation and deallocation. Flink will take care of keeping the job running with the resources available. + +### Getting started + +If you just want to try out Reactive Mode, follow these instructions. They assume that you are deploying Flink on one machine. + +```bash + +# these instructions assume you are in the root directory of a Flink distribution. + +# Put Job into lib/ directory +cp ./examples/streaming/TopSpeedWindowing.jar lib/ +# Submit Job in Reactive Mode +./bin/standalone-job.sh start -Drestart-strategy=fixeddelay -Drestart-strategy.fixed-delay.attempts=100000 -Dscheduler-mode=reactive -j org.apache.flink.streaming.examples.windowing.TopSpeedWindowing +# Start first TaskManager +./bin/taskmanager.sh start +``` + +Let's quickly examine the used submission command: +- `./bin/standalone-job.sh start` deploys Flink in [Application Mode]({{< ref "docs/deployment/overview" >}}#application-mode) +- `-Drestart-strategy=fixeddelay` and `-Drestart-strategy.fixed-delay.attempts=100000` configure the job to restart on failure. This is needed for supporting scale-down. +- `-Dscheduler-mode=reactive` enables Reactive Mode. +- the last argument is passing the Job's main class name. + +You have now started a Flink job in Reactive Mode. The [web interface](http://localhost:8081) shows that the job is running on one TaskManager. If you want to scale up the job, simply add another TaskManager to the cluster: +```bash +# Start additional TaskManager +./bin/taskmanager.sh start +``` + +To scale down, remove a TaskManager instance. +```bash +# Remove a TaskManager +./bin/taskmanager.sh stop +``` + +### Usage + +#### Configuration + +To enable Reactive Mode, you need to configure `scheduler-mode` to `reactive`. + +The **parallelism of individual operators in a job will be determined by the scheduler**. It is not configurable. + +The only way of influencing the parallelism is by setting a max parallelism for a operator (which will be respected by the scheduler). The maxParallelism is bounded by 2^15 (32768), which is the value that Reactive Mode uses if nothing else is configured. If there is no maxParallelism defined for an operator, `32768` will be used as a default. +If you manually set a parallelism in your job for individual operators or the entire job, this setting will be ignored. + +Note that such a high maxParallelism might affect performance of the job, since more internal structures are needed to maintain [some internal structures](https://flink.apache.org/features/2017/07/04/flink-rescalable-state.html) of Flink. + +#### Recommendations + +- **Configure periodic checkpointing for stateful jobs**: Reactive mode restores from the latest completed checkpoint on a rescale event. If no periodic checkpointing is enabled, your program will loose its state. +- **Configure a restarting strategy**: Reactive mode will respect the configured restarting strategy: If no restarting strategy is configured, reactive mode will fail your job, instead of scaling it. Review comment: ```suggestion - **Configure a restarting strategy**: Reactive mode will respect the configured restarting strategy: If no restarting strategy is configured, reactive mode will fail your job, instead of scaling it. ``` Isn't the default "infinite restarts" if checkpointing is configured? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
