Project: Flink (App Definition and Development / Streaming & Messaging)
File: etl.md

title: Data Pipelines & ETL weight: 4 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. --> One very common use case for Apache Flink is to implement ETL (extract, transform, load) pipelines that take data from one or more sources, perform some transformations and/or enrichments, and then store the results somewhere. In this section we are going to look at how to use Flink's DataStream API to implement this kind of application. Note that Flink's are well suited for many ETL use cases. But regardless of whether you ultimately use the DataStream API directly, or not, having a solid understanding the basics presented here will prove valuable. This section covers `map()` and `flatmap()`, the basic operations used to implement stateless transformations. The examples in this section assume you are familiar with the Taxi Ride data used in the hands-on exercises in the {{< training_repo >}}. In the first exercise you filtered a stream of taxi ride events. In that same code base there's a `GeoUtils` class that provides a static method `GeoUtils.mapToGridCell(float lon, float lat)` which maps a location (longitude, latitude) to a grid cell that refers to an area that is approximately 100x100 meters in size. Now let's enrich our stream of taxi ride objects by adding `startCell` and `endCell` fields to each event. You can create an `EnrichedRide` object that extends `TaxiRide`, adding these fields: ```java public static class EnrichedRide extends TaxiRide { public int startCell; public int endCell; public EnrichedRide() {} public EnrichedRide(TaxiRide ride) { this.rideId = ride.rideId; this.isStart = ride.isStart; ... this.startCell = GeoUtils.mapToGridCell(ride.startLon, ride.startLat); this.endCell = GeoUtils.mapToGridCell(ride.endLon, ride.endLat); } public String toString() { return super.toString() + "," + Integer.toString(this.startCell) + "," + Integer.toString(this.endCell); } } ``` You can then create an application that transforms the stream ```java DataStream<TaxiRide> rides = env.addSource(new TaxiRideSource(...)); DataStream<EnrichedRide> enrichedNYCRides = rides .filter(new RideCleansingSolution.NYCFilter()) .map(new Enrichment()); enrichedNYCRides.print(); ``` with this `MapFunction`: ```java public static class Enrichment implements MapFunction<TaxiRide, EnrichedRide> { @Override public EnrichedRide map(TaxiRide taxiRide) throws Exception { return new EnrichedRide(taxiRide); } } ``` A `MapFunction` is suitable only when performing a one-to-one transformation: for each and every stream element coming in, `map()` will emit one transformed element. Otherwise, you will want to use `flatmap()` ```java DataStream<TaxiRide> rides = env.addSource(new TaxiRideSource(...)); DataStream<EnrichedRide> enrichedNYCRides = rides .flatMap(new NYCEnrichment()); enrichedNYCRides.print(); ``` together with a `FlatMapFunction`: ```java public static class NYCEnrichment implements FlatMapFunction<TaxiRide, EnrichedRide> { @Override public void flatMap(TaxiRide taxiRide, Collector<EnrichedRide> out) throws Exception { FilterFunction<TaxiRide> valid = new RideCleansing.NYCFilter(); if (valid.filter(taxiRide)) {

EnrichedRide(taxiRide)); } } } ``` With the `Collector` provided in this interface, the `flatmap()` method can emit as many stream elements as you like, including none at all. {{< top >}} It is often very useful to be able to partition a stream around one of its attributes, so that all events with the same value of that attribute are grouped together. For example, suppose you wanted to find the longest taxi rides starting in each of the grid cells. Thinking in terms of a SQL query, this would mean doing some sort of GROUP BY with the `startCell`, while in Flink this is done with `keyBy(KeySelector)` ```java rides .flatMap(new NYCEnrichment()) .keyBy(enrichedRide -> enrichedRide.startCell); ``` Every `keyBy` causes a network shuffle that repartitions the stream. In general this is pretty expensive, since it involves network communication along with serialization and deserialization. {{< img src="/fig/keyBy.png" alt="keyBy and network shuffle" class="offset" width="45%" >}} KeySelectors aren't limited to extracting a key from your events. They can, instead, compute the key in whatever way you want, so long as the resulting key is deterministic, and has valid implementations of `hashCode()` and `equals()`. This restriction rules out KeySelectors that generate random numbers, or that return Arrays or Enums, but you can have composite keys using Tuples or POJOs, for example, so long as their elements follow these same rules. The keys must be produced in a deterministic way, because they are recomputed whenever they are needed, rather than being attached to the stream records. For example, rather than creating a new `EnrichedRide` class with a `startCell` field that we then use as a key via ```java keyBy(enrichedRide -> enrichedRide.startCell); ``` we could do this, instead: ```java keyBy(ride -> GeoUtils.mapToGridCell(ride.startLon, ride.startLat)); ``` This bit of code creates a new stream of tuples containing the `startCell` and duration (in minutes) for each end-of-ride event: ```java import org.joda.time.Interval; DataStream<Tuple2<Integer, Minutes>> minutesByStartCell = enrichedNYCRides .flatMap(new FlatMapFunction<EnrichedRide, Tuple2<Integer, Minutes>>() { @Override public void flatMap(EnrichedRide ride, Collector<Tuple2<Integer, Minutes>> out) throws Exception { if (!ride.isStart) { Interval rideInterval = new Interval(ride.startTime, ride.endTime); Minutes duration = rideInterval.toDuration().toStandardMinutes(); out.collect(new Tuple2<>(ride.startCell, duration)); } } }); ``` Now it is possible to produce a stream that contains only those rides that are the longest rides ever seen (to that point) for each `startCell`. There are a variety of ways that the field to use as the key can be expressed. Earlier you saw an example with an `EnrichedRide` POJO, where the field to use as the key was specified with its name. This case involves `Tuple2` objects, and the index within the tuple (starting from 0) is used to specify the key. ```java minutesByStartCell .keyBy(value -> value.f0) // .keyBy(value -> value.startCell) .maxBy(1) // duration .print(); ``` The output stream now contains a record for each key every time the duration reaches a new maximum -- as shown here with cell 50797: ... 4> (64549,5M) 4> (46298,18M) 1> (51549,14M) 1> (53043,13M) 1> (56031,22M) 1> (50797,6M) ... 1> (50797,8M) ... 1> (50797,11M) ... 1> (50797,12M) This is the first example in this training that involves stateful streaming. Though the state is being handled transparently, Flink has to keep track of the maximum duration for each distinct

Whenever state gets involved in your application, you should think about how large the state might become. Whenever the key space is unbounded, then so is the amount of state Flink will need. When working with streams, it generally makes more sense to think in terms of aggregations over finite windows, rather than over the entire stream. `maxBy()`, used above, is just one example of a number of aggregator functions available on Flink's `KeyedStream`s. There is also a more general purpose `reduce()` function that you can use to implement your own custom aggregations. {{< top >}} Your applications are certainly capable of using state without getting Flink involved in managing it -- but Flink offers some compelling features for the state it manages: local*: Flink state is kept local to the machine that processes it, and can be accessed at memory speed durable*: Flink state is fault-tolerant, i.e., it is automatically checkpointed at regular intervals, and is restored upon failure vertically scalable*: Flink state can be kept in embedded RocksDB instances that scale by adding more local disk horizontally scalable*: Flink state is redistributed as your cluster grows and shrinks In this section you will learn how to work with Flink's APIs that manage keyed state. At this point you have already seen several of Flink's function interfaces, including `FilterFunction`, `MapFunction`, and `FlatMapFunction`. These are all examples of the Single Abstract Method pattern. For each of these interfaces, Flink also provides a so-called "rich" variant, e.g., `RichFlatMapFunction`, which has some additional methods, including: `open(Configuration c)` `close()` `getRuntimeContext()` `open()` is called once, during operator initialization. This is an opportunity to load some static data, or to open a connection to an external service, for example. `getRuntimeContext()` provides access to a whole suite of potentially interesting things, but most notably it is how you can create and access state managed by Flink. In this example, imagine you have a stream of events that you want to de-duplicate, so that you only keep the first event with each key. Here's an application that does that, using a `RichFlatMapFunction` called `Deduplicator`: ```java private static class Event { public final String key; public final long timestamp; ... } public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.addSource(new EventSource()) .keyBy(e -> e.key) .flatMap(new Deduplicator()) .print(); env.execute(); } ``` To accomplish this, `Deduplicator` will need to somehow remember, for each key, whether or not there has already been an event for that key. It will do so using Flink's keyed state interface. When you are working with a keyed stream like this one, Flink will maintain a key/value store for each item of state being managed. Flink supports several different types of keyed state, and this example uses the simplest one, namely `ValueState`. This means that for each key, Flink will store a single object -- in this case, an object of type `Boolean`. Our `Deduplicator` class has two methods: `open()` and `flatMap()`. The open method establishes the use of managed state by defining a `ValueStateDescriptor<Boolean>`. The arguments to the constructor specify a name for this item of keyed state ("keyHasBeenSeen"), and provide information that can be used to serialize these objects (in this case,

```java public static class Deduplicator extends RichFlatMapFunction<Event, Event> { ValueState<Boolean> keyHasBeenSeen; @Override public void open(Configuration conf) { ValueStateDescriptor<Boolean> desc = new ValueStateDescriptor<>("keyHasBeenSeen", Types.BOOLEAN); keyHasBeenSeen = getRuntimeContext().getState(desc); } @Override public void flatMap(Event event, Collector<Event> out) throws Exception { if (keyHasBeenSeen.value() == null) { out.collect(event); keyHasBeenSeen.update(true); } } } ``` When the flatMap method calls `keyHasBeenSeen.value()`, Flink's runtime looks up the value of this piece of state for the key in context, and only if it is `null` does it go ahead and collect the event to the output. It also updates `keyHasBeenSeen` to `true` in this case. This mechanism for accessing and updating key-partitioned state may seem rather magical, since the key is not explicitly visible in the implementation of our `Deduplicator`. When Flink's runtime calls the `open` method of our `RichFlatMapFunction`, there is no event, and thus no key in context at that moment. But when it calls the `flatMap` method, the key for the event being processed is available to the runtime, and is used behind the scenes to determine which entry in Flink's state backend is being operated on. When deployed to a distributed cluster, there will be many instances of this `Deduplicator`, each of which will responsible for a disjoint subset of the entire keyspace. Thus, when you see a single item of `ValueState`, such as ```java ValueState<Boolean> keyHasBeenSeen; ``` understand that this represents not just a single Boolean, but rather a distributed, sharded, key/value store. There's a potential problem with the example above: What will happen if the key space is unbounded? Flink is storing somewhere an instance of `Boolean` for every distinct key that is used. If there's a bounded set of keys then this will be fine, but in applications where the set of keys is growing in an unbounded way, it's necessary to clear the state for keys that are no longer needed. This is done by calling `clear()` on the state object, as in: ```java keyHasBeenSeen.clear(); ``` You might want to do this, for example, after a period of inactivity for a given key. You'll see how to use Timers to do this when you learn about `ProcessFunction`s in the section on event-driven applications. There's also a option that you can configure with the state descriptor that specifies when you want the state for stale keys to be automatically cleared. It is also possible to work with managed state in non-keyed contexts. This is sometimes called . The interfaces involved are somewhat different, and since it is unusual for user-defined functions to need non-keyed state, it is not covered here. This feature is most often used in the implementation of sources and sinks. {{< top >}} Sometimes instead of applying a pre-defined transformation like this: {{< img src="/fig/transformation.svg" alt="simple transformation" width="45%" >}} you want to be able to dynamically alter some aspects of the transformation -- by streaming in thresholds, or rules, or other parameters. The pattern in Flink that supports this is something called connected streams, wherein a single operator has two input streams, like this: {{< img src="/fig/connected-streams.svg" alt="connected streams" width="45%" >}} Connected streams can also be used to implement streaming

In this example, a control stream is used to specify words which must be filtered out of the `streamOfWords`. A `RichCoFlatMapFunction` called `ControlFunction` is applied to the connected streams to get this done. ```java public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStream<String> control = env .fromElements("DROP", "IGNORE") .keyBy(x -> x); DataStream<String> streamOfWords = env .fromElements("Apache", "DROP", "Flink", "IGNORE") .keyBy(x -> x); control .connect(streamOfWords) .flatMap(new ControlFunction()) .print(); env.execute(); } ``` Note that the two streams being connected must be keyed in compatible ways. The role of a `keyBy` is to partition a stream's data, and when keyed streams are connected, they must be partitioned in the same way. This ensures that all of the events from both streams with the same key are sent to the same instance. This makes it possible, then, to join the two streams on that key, for example. In this case the streams are both of type `DataStream<String>`, and both streams are keyed by the string. As you will see below, this `RichCoFlatMapFunction` is storing a Boolean value in keyed state, and this Boolean is shared by the two streams. ```java public static class ControlFunction extends RichCoFlatMapFunction<String, String, String> { private ValueState<Boolean> blocked; @Override public void open(Configuration config) { blocked = getRuntimeContext() .getState(new ValueStateDescriptor<>("blocked", Boolean.class)); } @Override public void flatMap1(String control_value, Collector<String> out) throws Exception { blocked.update(Boolean.TRUE); } @Override public void flatMap2(String data_value, Collector<String> out) throws Exception { if (blocked.value() == null) { out.collect(data_value); } } } ``` A `RichCoFlatMapFunction` is a kind of `FlatMapFunction` that can be applied to a pair of connected streams, and it has access to the rich function interface. This means that it can be made stateful. The `blocked` Boolean is being used to remember the keys (words, in this case) that have been mentioned on the `control` stream, and those words are being filtered out of the `streamOfWords` stream. This is keyed state, and it is shared between the two streams, which is why the two streams have to share the same keyspace. `flatMap1` and `flatMap2` are called by the Flink runtime with elements from each of the two connected streams -- in our case, elements from the `control` stream are passed into `flatMap1`, and elements from `streamOfWords` are passed into `flatMap2`. This was determined by the order in which the two streams are connected with `control.connect(streamOfWords)`. It is important to recognize that you have no control over the order in which the `flatMap1` and `flatMap2` callbacks are called. These two input streams are racing against each other, and the Flink runtime will do what it wants to regarding consuming events from one stream or the other. In cases where timing and/or ordering matter, you may find it necessary to buffer events in managed Flink state until your application is ready to process them. (Note: if you are truly desperate, it is possible to exert some limited control over the order in which a two-input operator consumes its inputs by using a custom Operator that implements the {{< javadoc name="InputSelectable" file="org/apache/flink/streaming/api/operators/InputSelectable.html" >}} {{< top >}} The hands-on exercise that goes with this section is the {{< training_link file="/rides-and-fares" name="Rides and Fares" >}}. {{< top >}} {{< top >}}