http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming/libs/cep.md ---------------------------------------------------------------------- diff --git a/docs/apis/streaming/libs/cep.md b/docs/apis/streaming/libs/cep.md deleted file mode 100644 index ef35d32..0000000 --- a/docs/apis/streaming/libs/cep.md +++ /dev/null @@ -1,659 +0,0 @@ ---- -title: "FlinkCEP - Complex event processing for Flink" -# Top navigation -top-nav-group: libs -top-nav-pos: 2 -top-nav-title: CEP -# Sub navigation -sub-nav-group: streaming -sub-nav-id: cep -sub-nav-pos: 1 -sub-nav-parent: libs -sub-nav-title: Event Processing (CEP) ---- -<!-- -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. ---> - -FlinkCEP is the complex event processing library for Flink. -It allows you to easily detect complex event patterns in a stream of endless data. -Complex events can then be constructed from matching sequences. -This gives you the opportunity to quickly get hold of what's really important in your data. - -<span class="label label-danger">Attention</span> The events in the `DataStream` to which -you want to apply pattern matching have to implement proper `equals()` and `hashCode()` methods -because these are used for comparing and matching events. - -* This will be replaced by the TOC -{:toc} - -## Getting Started - -If you want to jump right in, you have to [set up a Flink program]({{ site.baseurl }}/apis/common/index.html#linking-with-flink). -Next, you have to add the FlinkCEP dependency to the `pom.xml` of your project. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight xml %} -<dependency> - <groupId>org.apache.flink</groupId> - <artifactId>flink-cep{{ site.scala_version_suffix }}</artifactId> - <version>{{site.version }}</version> -</dependency> -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight xml %} -<dependency> - <groupId>org.apache.flink</groupId> - <artifactId>flink-cep-scala{{ site.scala_version_suffix }}</artifactId> - <version>{{site.version }}</version> -</dependency> -{% endhighlight %} -</div> -</div> - -Note that FlinkCEP is currently not part of the binary distribution. -See linking with it for cluster execution [here]({{site.baseurl}}/apis/cluster_execution.html#linking-with-modules-not-contained-in-the-binary-distribution). - -Now you can start writing your first CEP program using the pattern API. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -DataStream<Event> input = ... - -Pattern<Event, ?> pattern = Pattern.begin("start").where(evt -> evt.getId() == 42) - .next("middle").subtype(SubEvent.class).where(subEvt -> subEvt.getVolume() >= 10.0) - .followedBy("end").where(evt -> evt.getName().equals("end")); - -PatternStream<Event> patternStream = CEP.pattern(input, pattern); - -DataStream<Alert> result = patternStream.select(pattern -> { - return createAlertFrom(pattern); -}); -{% endhighlight %} -</div> -<div data-lang="scala" markdown="1"> -{% highlight scala %} -val input: DataStream[Event] = ... - -val pattern = Pattern.begin("start").where(_.getId == 42) - .next("middle").subtype(classOf[SubEvent]).where(_.getVolume >= 10.0) - .followedBy("end").where(_.getName == "end") - -val patternStream = CEP.pattern(input, pattern) - -val result: DataStream[Alert] = patternStream.select(createAlert(_)) -{% endhighlight %} -</div> -</div> - -Note that we use use Java 8 lambdas in our Java code examples to make them more succinct. - -## The Pattern API - -The pattern API allows you to quickly define complex event patterns. - -Each pattern consists of multiple stages or what we call states. -In order to go from one state to the next, the user can specify conditions. -These conditions can be the contiguity of events or a filter condition on an event. - -Each pattern has to start with an initial state: - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -Pattern<Event, ?> start = Pattern.<Event>begin("start"); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -val start : Pattern[Event, _] = Pattern.begin("start") -{% endhighlight %} -</div> -</div> - -Each state must have an unique name to identify the matched events later on. -Additionally, we can specify a filter condition for the event to be accepted as the start event via the `where` method. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -start.where(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) { - return ... // some condition - } -}); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -start.where(event => ... /* some condition */) -{% endhighlight %} -</div> -</div> - -We can also restrict the type of the accepted event to some subtype of the initial event type (here `Event`) via the `subtype` method. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -start.subtype(SubEvent.class).where(new FilterFunction<SubEvent>() { - @Override - public boolean filter(SubEvent value) { - return ... // some condition - } -}); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -start.subtype(classOf[SubEvent]).where(subEvent => ... /* some condition */) -{% endhighlight %} -</div> -</div> - -As it can be seen here, the subtype condition can also be combined with an additional filter condition on the subtype. -In fact you can always provide multiple conditions by calling `where` and `subtype` multiple times. -These conditions will then be combined using the logical AND operator. - -In order to construct or conditions, one has to call the `or` method with a respective filter function. -Any existing filter function is then ORed with the given one. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -pattern.where(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) { - return ... // some condition - } -}).or(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) { - return ... // or condition - } -}); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -pattern.where(event => ... /* some condition */).or(event => ... /* or condition */) -{% endhighlight %} -</div> -</div> - -Next, we can append further states to detect complex patterns. -We can control the contiguity of two succeeding events to be accepted by the pattern. - -Strict contiguity means that two matching events have to succeed directly. -This means that no other events can occur in between. -A strict contiguity pattern state can be created via the `next` method. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -Pattern<Event, ?> strictNext = start.next("middle"); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -val strictNext: Pattern[Event, _] = start.next("middle") -{% endhighlight %} -</div> -</div> - -Non-strict contiguity means that other events are allowed to occur in-between two matching events. -A non-strict contiguity pattern state can be created via the `followedBy` method. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -Pattern<Event, ?> nonStrictNext = start.followedBy("middle"); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -val nonStrictNext : Pattern[Event, _] = start.followedBy("middle") -{% endhighlight %} -</div> -</div> -It is also possible to define a temporal constraint for the pattern to be valid. -For example, one can define that a pattern should occur within 10 seconds via the `within` method. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -next.within(Time.seconds(10)); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -next.within(Time.seconds(10)) -{% endhighlight %} -</div> -</div> - -<br /> - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -<table class="table table-bordered"> - <thead> - <tr> - <th class="text-left" style="width: 25%">Pattern Operation</th> - <th class="text-center">Description</th> - </tr> - </thead> - <tbody> - <tr> - <td><strong>Begin</strong></td> - <td> - <p>Defines a starting pattern state:</p> -{% highlight java %} -Pattern<Event, ?> start = Pattern.<Event>begin("start"); -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Next</strong></td> - <td> - <p>Appends a new pattern state. A matching event has to directly succeed the previous matching event:</p> -{% highlight java %} -Pattern<Event, ?> next = start.next("next"); -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>FollowedBy</strong></td> - <td> - <p>Appends a new pattern state. Other events can occur between a matching event and the previous matching event:</p> -{% highlight java %} -Pattern<Event, ?> followedBy = start.followedBy("next"); -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Where</strong></td> - <td> - <p>Defines a filter condition for the current pattern state. Only if an event passes the filter, it can match the state:</p> -{% highlight java %} -patternState.where(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) throws Exception { - return ... // some condition - } -}); -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Or</strong></td> - <td> - <p>Adds a new filter condition which is ORed with an existing filter condition. Only if an event passes the filter condition, it can match the state:</p> -{% highlight java %} -patternState.where(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) throws Exception { - return ... // some condition - } -}).or(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) throws Exception { - return ... // alternative condition - } -}); -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Subtype</strong></td> - <td> - <p>Defines a subtype condition for the current pattern state. Only if an event is of this subtype, it can match the state:</p> -{% highlight java %} -patternState.subtype(SubEvent.class); -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Within</strong></td> - <td> - <p>Defines the maximum time interval for an event sequence to match the pattern. If a non-completed event sequence exceeds this time, it is discarded:</p> -{% highlight java %} -patternState.within(Time.seconds(10)); -{% endhighlight %} - </td> - </tr> - </tbody> -</table> -</div> - -<div data-lang="scala" markdown="1"> -<table class="table table-bordered"> - <thead> - <tr> - <th class="text-left" style="width: 25%">Pattern Operation</th> - <th class="text-center">Description</th> - </tr> - </thead> - <tbody> - <tr> - <td><strong>Begin</strong></td> - <td> - <p>Defines a starting pattern state:</p> -{% highlight scala %} -val start = Pattern.begin[Event]("start") -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Next</strong></td> - <td> - <p>Appends a new pattern state. A matching event has to directly succeed the previous matching event:</p> -{% highlight scala %} -val next = start.next("middle") -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>FollowedBy</strong></td> - <td> - <p>Appends a new pattern state. Other events can occur between a matching event and the previous matching event:</p> -{% highlight scala %} -val followedBy = start.followedBy("middle") -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Where</strong></td> - <td> - <p>Defines a filter condition for the current pattern state. Only if an event passes the filter, it can match the state:</p> -{% highlight scala %} -patternState.where(event => ... /* some condition */) -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Or</strong></td> - <td> - <p>Adds a new filter condition which is ORed with an existing filter condition. Only if an event passes the filter condition, it can match the state:</p> -{% highlight scala %} -patternState.where(event => ... /* some condition */) - .or(event => ... /* alternative condition */) -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Subtype</strong></td> - <td> - <p>Defines a subtype condition for the current pattern state. Only if an event is of this subtype, it can match the state:</p> -{% highlight scala %} -patternState.subtype(classOf[SubEvent]) -{% endhighlight %} - </td> - </tr> - <tr> - <td><strong>Within</strong></td> - <td> - <p>Defines the maximum time interval for an event sequence to match the pattern. If a non-completed event sequence exceeds this time, it is discarded:</p> -{% highlight scala %} -patternState.within(Time.seconds(10)) -{% endhighlight %} - </td> - </tr> - </tbody> -</table> -</div> - -</div> - -### Detecting Patterns - -In order to run a stream of events against your pattern, you have to create a `PatternStream`. -Given an input stream `input` and a pattern `pattern`, you create the `PatternStream` by calling - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -DataStream<Event> input = ... -Pattern<Event, ?> pattern = ... - -PatternStream<Event> patternStream = CEP.pattern(input, pattern); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -val input : DataStream[Event] = ... -val pattern : Pattern[Event, _] = ... - -val patternStream: PatternStream[Event] = CEP.pattern(input, pattern) -{% endhighlight %} -</div> -</div> - -### Selecting from Patterns -Once you have obtained a `PatternStream` you can select from detected event sequences via the `select` or `flatSelect` methods. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -The `select` method requires a `PatternSelectFunction` implementation. -A `PatternSelectFunction` has a `select` method which is called for each matching event sequence. -It receives a map of string/event pairs of the matched events. -The string is defined by the name of the state to which the event has been matched. -The `select` method can return exactly one result. - -{% highlight java %} -class MyPatternSelectFunction<IN, OUT> implements PatternSelectFunction<IN, OUT> { - @Override - public OUT select(Map<String, IN> pattern) { - IN startEvent = pattern.get("start"); - IN endEvent = pattern.get("end"); - return new OUT(startEvent, endEvent); - } -} -{% endhighlight %} - -A `PatternFlatSelectFunction` is similar to the `PatternSelectFunction`, with the only distinction that it can return an arbitrary number of results. -In order to do this, the `select` method has an additional `Collector` parameter which is used for the element output. - -{% highlight java %} -class MyPatternFlatSelectFunction<IN, OUT> implements PatternFlatSelectFunction<IN, OUT> { - @Override - public void select(Map<String, IN> pattern, Collector<OUT> collector) { - IN startEvent = pattern.get("start"); - IN endEvent = pattern.get("end"); - - for (int i = 0; i < startEvent.getValue(); i++ ) { - collector.collect(new OUT(startEvent, endEvent)); - } - } -} -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -The `select` method takes a selection function as argument, which is called for each matching event sequence. -It receives a map of string/event pairs of the matched events. -The string is defined by the name of the state to which the event has been matched. -The selection function returns exactly one result per call. - -{% highlight scala %} -def selectFn(pattern : mutable.Map[String, IN]): OUT = { - val startEvent = pattern.get("start").get - val endEvent = pattern.get("end").get - OUT(startEvent, endEvent) -} -{% endhighlight %} - -The `flatSelect` method is similar to the `select` method. Their only difference is that the function passed to the `flatSelect` method can return an arbitrary number of results per call. -In order to do this, the function for `flatSelect` has an additional `Collector` parameter which is used for the element output. - -{% highlight scala %} -def flatSelectFn(pattern : mutable.Map[String, IN], collector : Collector[OUT]) = { - val startEvent = pattern.get("start").get - val endEvent = pattern.get("end").get - for (i <- 0 to startEvent.getValue) { - collector.collect(OUT(startEvent, endEvent)) - } -} -{% endhighlight %} -</div> -</div> - -### Handling Timed Out Partial Patterns - -Whenever a pattern has a window length associated via the `within` key word, it is possible that partial event patterns will be discarded because they exceed the window length. -In order to react to these timeout events the `select` and `flatSelect` API calls allow to specify a timeout handler. -This timeout handler is called for each partial event pattern which has timed out. -The timeout handler receives all so far matched events of the partial pattern and the timestamp when the timeout was detected. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -In order to treat partial patterns, the `select` and `flatSelect` API calls offer an overloaded version which takes as the first parameter a `PatternTimeoutFunction`/`PatternFlatTimeoutFunction` and as second parameter the known `PatternSelectFunction`/`PatternFlatSelectFunction`. -The return type of the timeout function can be different from the select function. -The timeout event and the select event are wrapped in `Either.Left` and `Either.Right` respectively so that the resulting data stream is of type `org.apache.flink.types.Either`. - -{% highlight java %} -PatternStream<Event> patternStream = CEP.pattern(input, pattern); - -DataStream<Either<TimeoutEvent, ComplexEvent>> result = patternStream.select( - new PatternTimeoutFunction<Event, TimeoutEvent>() {...}, - new PatternSelectFunction<Event, ComplexEvent>() {...} -); - -DataStream<Either<TimeoutEvent, ComplexEvent>> flatResult = patternStream.flatSelect( - new PatternFlatTimeoutFunction<Event, TimeoutEvent>() {...}, - new PatternFlatSelectFunction<Event, ComplexEvent>() {...} -); -{% endhighlight %} - -</div> - -<div data-lang="scala" markdown="1"> -In order to treat partial patterns, the `select` API call offers an overloaded version which takes as the first parameter a timeout function and as second parameter a selection function. -The timeout function is called with a map of string-event pairs of the partial match which has timed out and a long indicating when the timeout occurred. -The string is defined by the name of the state to which the event has been matched. -The timeout function returns exactly one result per call. -The return type of the timeout function can be different from the select function. -The timeout event and the select event are wrapped in `Left` and `Right` respectively so that the resulting data stream is of type `Either`. - -{% highlight scala %} -val patternStream: PatternStream[Event] = CEP.pattern(input, pattern) - -DataStream[Either[TimeoutEvent, ComplexEvent]] result = patternStream.select{ - (pattern: mutable.Map[String, Event], timestamp: Long) => TimeoutEvent() -} { - pattern: mutable.Map[String, Event] => ComplexEvent() -} -{% endhighlight %} - -The `flatSelect` API call offers the same overloaded version which takes as the first parameter a timeout function and as second parameter a selection function. -In contrast to the `select` functions, the `flatSelect` functions are called with an `Collector`. -The collector can be used to emit an arbitrary number of events. - -{% highlight scala %} -val patternStream: PatternStream[Event] = CEP.pattern(input, pattern) - -DataStream[Either[TimeoutEvent, ComplexEvent]] result = patternStream.flatSelect{ - (pattern: mutable.Map[String, Event], timestamp: Long, out: Collector[TimeoutEvent]) => - out.collect(TimeoutEvent()) -} { - (pattern: mutable.Map[String, Event], out: Collector[ComplexEvent]) => - out.collect(ComplexEvent()) -} -{% endhighlight %} - -</div> -</div> - -## Examples - -The following example detects the pattern `start, middle(name = "error") -> end(name = "critical")` on a keyed data stream of `Events`. -The events are keyed by their ids and a valid pattern has to occur within 10 seconds. -The whole processing is done with event time. - -<div class="codetabs" markdown="1"> -<div data-lang="java" markdown="1"> -{% highlight java %} -StreamExecutionEnvironment env = ... -env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); - -DataStream<Event> input = ... - -DataStream<Event> partitionedInput = input.keyBy(new KeySelector<Event, Integer>() { - @Override - public Integer getKey(Event value) throws Exception { - return value.getId(); - } -}); - -Pattern<Event, ?> pattern = Pattern.<Event>begin("start") - .next("middle").where(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) throws Exception { - return value.getName().equals("error"); - } - }).followedBy("end").where(new FilterFunction<Event>() { - @Override - public boolean filter(Event value) throws Exception { - return value.getName().equals("critical"); - } - }).within(Time.seconds(10)); - -PatternStream<Event> patternStream = CEP.pattern(partitionedInput, pattern); - -DataStream<Alert> alerts = patternStream.select(new PatternSelectFunction<Event, Alert>() { - @Override - public Alert select(Map<String, Event> pattern) throws Exception { - return createAlert(pattern); - } -}); -{% endhighlight %} -</div> - -<div data-lang="scala" markdown="1"> -{% highlight scala %} -val env : StreamExecutionEnvironment = ... -env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) - -val input : DataStream[Event] = ... - -val partitionedInput = input.keyBy(event => event.getId) - -val pattern = Pattern.begin("start") - .next("middle").where(_.getName == "error") - .followedBy("end").where(_.getName == "critical") - .within(Time.seconds(10)) - -val patternStream = CEP.pattern(partitionedInput, pattern) - -val alerts = patternStream.select(createAlert(_))) -{% endhighlight %} -</div> -</div>
http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming/libs/index.md ---------------------------------------------------------------------- diff --git a/docs/apis/streaming/libs/index.md b/docs/apis/streaming/libs/index.md deleted file mode 100644 index 4ba7e94..0000000 --- a/docs/apis/streaming/libs/index.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -title: "Streaming Libraries" -sub-nav-group: streaming -sub-nav-id: libs -sub-nav-pos: 7 -sub-nav-title: Libraries ---- -<!-- -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. 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d="m291.49606 396.49606l0 0c0 -5.2445374 4.251526 -9.496063 9.496063 -9.496063l0 0c2.5185242 0 4.9338684 1.0004883 6.7147217 2.7813416c1.7808533 1.7808533 2.7813416 4.1961975 2.78134 16 6.7147217l0 0c0 5.2445374 -4.251526 9.496063 -9.496063 9.496063l0 0c-5.2445374 0 -9.496063 -4.251526 -9.496063 -9.496063z" fill-rule="nonzero"></path><path fill="#9900ff" d="m315.0 396.49606l0 0c0 -5.2445374 4.251526 -9.496063 9.496063 -9.496063l0 0c2.5185242 0 4.9338684 1.0004883 6.7147217 2.7813416c1.7808533 1.7808533 2.7813416 4.1961975 2.7813416 6.7147217l0 0c0 5.2445374 -4.251526 9.496063 -9.496063 9.496063l0 0c-5.2445374 0 -9.496063 -4.251526 -9.496063 -9.496063z" fill-rule="nonzero"></path><path fill="#9900ff" d="m558.01575 396.49606l0 0c0 -5.2445374 4.251526 -9.496063 9.496094 -9.496063l0 0c2.5184937 0 4.933838 1.0004883 6.7147217 2.7813416c1.7808228 1.7808533 2.781311 4.1961975 2.781311 6.7147217l0 0c0 5.2445374 -4.251526 9.496063 -9.496033 9.496063l0 0c-5.244568 0 -9.496094 -4.251526 -9.496094 -9.496063z" fill-rule="nonzero"></path></g></svg> - http://git-wip-us.apache.org/repos/asf/flink/blob/844c874b/docs/apis/streaming/savepoints.md ---------------------------------------------------------------------- diff --git a/docs/apis/streaming/savepoints.md b/docs/apis/streaming/savepoints.md deleted file mode 100644 index 53a1ee8..0000000 --- a/docs/apis/streaming/savepoints.md +++ /dev/null @@ -1,110 +0,0 @@ ---- -title: "Savepoints" -is_beta: false -sub-nav-group: streaming -sub-nav-pos: 6 ---- -<!-- -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. ---> - -Programs written in the [Data Stream API](index.html) can resume execution from a **savepoint**. Savepoints allow both updating your programs and your Flink cluster without losing any state. This page covers all steps to trigger, restore, and dispose savepoints. For more details on how Flink handles state and failures, check out the [State in Streaming Programs](state_backends.html) and [Fault Tolerance](fault_tolerance.html) pages. - -* toc -{:toc} - -## Overview - -Savepoints are **manually triggered checkpoints**, which take a snapshot of the program and write it out to a state backend. They rely on the regular checkpointing mechanism for this. During execution programs are periodically snapshotted on the worker nodes and produce checkpoints. For recovery only the last completed checkpoint is needed and older checkpoints can be safely discarded as soon as a new one is completed. - -Savepoints are similar to these periodic checkpoints except that they are **triggered by the user** and **don't automatically expire** when newer checkpoints are completed. - -<img src="fig/savepoints-overview.png" class="center" /> - -In the above example the workers produce checkpoints **c<sub>1</sub>**, **c<sub>2</sub>**, **c<sub>3</sub>**, and **c<sub>4</sub>** for job *0xA312Bc*. Periodic checkpoints **c<sub>1</sub>** and **c<sub>3</sub>** have already been *discarded* and **c<sub>4</sub>** is the *latest checkpoint*. **c<sub>2</sub> is special**. It is the state associated with the savepoint **s<sub>1</sub>** and has been triggered by the user and it doesn't expire automatically (as c<sub>1</sub> and c<sub>3</sub> did after the completion of newer checkpoints). - -Note that **s<sub>1</sub>** is only a **pointer to the actual checkpoint data c<sub>2</sub>**. This means that the actual state is *not copied* for the savepoint and periodic checkpoint data is kept around. - -## Configuration - -Savepoints point to regular checkpoints and store their state in a configured [state backend](state_backends.html). Currently, the supported state backends are **jobmanager** and **filesystem**. The state backend configuration for the regular periodic checkpoints is **independent** of the savepoint state backend configuration. Checkpoint data is **not copied** for savepoints, but points to the configured checkpoint state backend. - -### JobManager - -This is the **default backend** for savepoints. - -Savepoints are stored on the heap of the job manager. They are *lost* after the job manager is shut down. This mode is only useful if you want to *stop* and *resume* your program while the **same cluster** keeps running. It is *not recommended* for production use. Savepoints are *not* part of the [job manager's highly available]({{ site.baseurl }}/setup/jobmanager_high_availability.html) state. - -<pre> -savepoints.state.backend: jobmanager -</pre> - -**Note**: If you don't configure a specific state backend for the savepoints, the jobmanager backend will be used. - -### File system - -Savepoints are stored in the configured **file system directory**. They are available between cluster instances and allow to move your program to another cluster. - -<pre> -savepoints.state.backend: filesystem -savepoints.state.backend.fs.dir: hdfs:///flink/savepoints -</pre> - -**Note**: If you don't configure a specific directory, the job manager backend will be used. - -**Important**: A savepoint is a pointer to a completed checkpoint. That means that the state of a savepoint is not only found in the savepoint file itself, but also needs the actual checkpoint data (e.g. in a set of further files). Therefore, using the *filesystem* backend for savepoints and the *jobmanager* backend for checkpoints does not work, because the required checkpoint data won't be available after a job manager restart. - -## Changes to your program - -Savepoints **work out of the box**, but it is **highly recommended** that you slightly adjust your programs in order to be able to work with savepoints in future versions of your program. - -<img src="fig/savepoints-program_ids.png" class="center" /> - -For savepoints **only stateful tasks matter**. In the above example, the source and map tasks are stateful whereas the sink is not stateful. Therefore, only the state of the source and map tasks are part of the savepoint. - -Each task is identified by its **generated task IDs** and **subtask index**. In the above example the state of the source (**s<sub>1</sub>**, **s<sub>2</sub>**) and map tasks (**m<sub>1</sub>**, **m<sub>2</sub>**) is identified by their respective task ID (*0xC322EC* for the source tasks and *0x27B3EF* for the map tasks) and subtask index. There is no state for the sinks (**t<sub>1</sub>**, **t<sub>2</sub>**). Their IDs therefore do not matter. - -<span class="label label-danger">Important</span> The IDs are generated **deterministically** from your program structure. This means that as long as your program does not change, the IDs do not change. **The only allowed changes are within the user function, e.g. you can change the implemented `MapFunction` without changing the topology**. In this case, it is straight forward to restore the state from a savepoint by mapping it back to the same task IDs and subtask indexes. This allows you to work with savepoints out of the box, but gets problematic as soon as you make changes to the topology, because they result in changed IDs and the savepoint state cannot be mapped to your program any more. - -<span class="label label-info">Recommended</span> In order to be able to change your program and **have fixed IDs**, the *DataStream* API provides a method to manually specify the task IDs. Each operator provides a **`uid(String)`** method to override the generated ID. The ID is a String, which will be deterministically hashed to a 16-byte hash value. It is **important** that the specified IDs are **unique per transformation and job**. If this is not the case, job submission will fail. - -{% highlight scala %} -DataStream<String> stream = env. - // Stateful source (e.g. Kafka) with ID - .addSource(new StatefulSource()) - .uid("source-id") - .shuffle() - // The stateful mapper with ID - .map(new StatefulMapper()) - .uid("mapper-id") - -// Stateless sink (no specific ID required) -stream.print() -{% endhighlight %} - -## Command-line client - -You control the savepoints via the [command line client]({{site.baseurl}}/apis/cli.html#savepoints). - -## Current limitations - -- **Parallelism**: When restoring a savepoint, the parallelism of the program has to match the parallelism of the original program from which the savepoint was drawn. There is no mechanism to re-partition the savepoint's state yet. - -- **Chaining**: Chained operators are identified by the ID of the first task. It's not possible to manually assign an ID to an intermediate chained task, e.g. in the chain `[ a -> b -> c ]` only **a** can have its ID assigned manually, but not **b** or **c**. To work around this, you can [manually define the task chains](index.html#task-chaining-and-resource-groups). If you rely on the automatic ID assignment, a change in the chaining behaviour will also change the IDs. - -- **Disposing custom state handles**: Disposing an old savepoint does not work with custom state handles (if you are using a custom state backend), because the user code class loader is not available during disposal.
