aljoscha commented on a change in pull request #14114: URL: https://github.com/apache/flink/pull/14114#discussion_r529620170
########## File path: docs/dev/datastream_execution_mode.md ########## @@ -0,0 +1,377 @@ +--- +title: "Execution Mode (Batch/Streaming)" +nav-id: datastream_execution_mode +nav-parent_id: streaming +nav-pos: 1 +--- +<!-- +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. +--> + +The DataStream API supports different runtime execution modes from which you +can choose depending on the requirements of your use case and the +characteristics of your job. + +There is the "classic" execution behavior of the DataStream API, which we call +`STREAMING` execution mode. This should be used for unbounded jobs that require +continuous incremental processing and are expected to stay online indefinitely. + +Additionally, there is a batch-style execution mode that we call `BATCH` +execution mode. This executes jobs in a way that is more reminiscent of batch +processing frameworks such as MapReduce. This should be used for bounded jobs +for which you have a known fixed input and which do not run continuously. + +Apache Flink's unified approach to stream and batch processing means that a +DataStream application executed over bounded input will produce the same +results regardless of the configured execution mode. By enabling `BATCH` +execution, we allow Flink to apply additional optimizations that we can only do +work when we know that our input is bounded. For example, different +join/aggregation strategies can be used, in addition to a different shuffle +implementation that allows more efficient failure recovery behavior. We will go +into some of the details of the execution behavior below. + +* This will be replaced by the TOC +{:toc} + +## When can/should I use BATCH execution mode? + +The `BATCH` execution mode can only be used for Jobs/Flink Programs that are +_bounded_. Boundedness is a property of a data source that tells us whether all +the input coming from that source is known before execution or whether new data +will show up, potentially indefinitely. A job, in turn, is bounded if all its +sources are bounded, and unbounded otherwise. + +`STREAMING` execution mode, on the other hand, can be used for both bounded and +unbounded jobs. + +As a rule of thumb, you should be using `BATCH` execution mode when your program +is bounded because this will be more efficient. You have to use `STREAMING` +execution mode when your program is unbounded because only this mode is general +enough to be able to deal with continuous data streams. Review comment: Yes, this is unfortunate. Though the fact that we cannot do checkpoints as soon as at least one task has finished, which in turn means that we can't get a "final" checkpoint has been a feature/bug of DataStream execution since the beginning. I wouldn't document it here but we can think about adding this to a general "caveats" section. I'm sure there would be other corner cases that are worth documenting 😅 ---------------------------------------------------------------- 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
