aljoscha commented on a change in pull request #14114: URL: https://github.com/apache/flink/pull/14114#discussion_r529620798
########## File path: docs/dev/datastream_execution_mode.md ########## @@ -0,0 +1,242 @@ +--- +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. + +We have these different execution modes because `BATCH` execution allows some +additional optimizations that we can only do when we now that our input is +bounded. For example, different join/aggregation strategies can be used, in +additional 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 for forever. 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. + +Another term that is typically used for bounded sources is _batch source_. Or, +we can say that we are working with a batch data set or a batch of data. Some +typical other terms for an unbounded source are _continuous source_, _streaming +source_, _stream_, or _infinite stream_. + +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. + +TODO: Should we even go into this? + +One obvious outlier case is when you want to use a bounded job to bootstrap +some job state that you then want to use in an unbounded job. For example, by +running a bounded job using STREAMING mode, taking a savepoint and then +restoring that savepoint on an unbounded job. This is a very specific use case +and one that might soon become obsolete when we allow producing a savepoint as +additional output of a BATCH execution job. + +## Configuring BATCH execution mode + +The execution mode can be configured via the `execution.runtime-mode` setting. +There are three possible values: + + - `STREAMING`: The classic DataStream execution mode + - `BATCH`: Batch-style execution on the DataStream API + - `AUTOMATIC`: Let the system decide based on the boundedness of the sources + + This can be configured either in the `flink-conf.yaml`, via command line + parameters of `bin/flink run ...`, or programmatically when + creating/configuring the `StreamExecutionEnvironment`. + + Here's how you can configure the execution mode via the command line: + + ```bash + $ bin/flink run -Dexecution.runtime-mode=BATCH examples/streaming/WordCount.jar + ``` + + This example shows how you can configure the execution mode in code: + + ```java +Configuration config = new Configuration(); +config.set(ExecutionOptions.RUNTIME_MODE, RuntimeExecutionMode.BATCH); +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(config); + ``` + +## Execution Behavior + +This section provides an overview of the execution behavior of BATCH execution +mode and contrasts it with STREAMING execution mode. For more details, please +refer to the FLIPs that introduced this feature: +[FLIP-134](https://cwiki.apache.org/confluence/x/4i94CQ) and +[FLIP-140](https://cwiki.apache.org/confluence/x/kDh4CQ). As well as the +documentation about [task scheduling (TODO)](<TODO>). + +### Task Scheduling And Network Shuffle + +Flink jobs consist of different operations that are connected together in a +dataflow graph. The system decides how to schedule the execution of these +operations on different processes/machines (TaskManager) and how data is +shuffled (sent) betwixt them. + +Multiple operations/operators can be chained together using a feature called +[chaining]({% link dev/stream/operators/index.md +%}#task-chaining-and-resource-groups). A group of one or multiple (chained) +operators that Flink considers as a unit of scheduling is called a _task_. +Often the term _subtask_ is used to refer to the individual instances of tasks +that are running in parallel on multiple TaskManagers but we will only use the +term _task_ here. + +Task scheduling and the network shuffle work different for BATCH execution mode +and STREAMING execution mode. Mostly due to the fact that we know our input +data is bounded in BATCH execution mode, which allows Flink to use more +efficient data structures and algorithms. + +We will use this example to explain the differences in task scheduling and +network transfer: + +```java +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); + +DataStreamSource<String> source = env.fromElements(...); + +source.name("source") + .map(...).name("map1") + .map(...).name("map2") + .rebalance() + .map(...).name("map3") + .map(...).name("map4") + .keyBy((value) -> value) + .map(...).name("map5") + .map(...).name("map6") + .sinkTo(...).name("sink"); Review comment: I added a figure. ---------------------------------------------------------------- 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. 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