1. Application Development
2. Basic API Concepts

Basic API Concepts

Flink programs are regular programs that implement transformations on distributed collections (e.g., filtering, mapping, updating state, joining, grouping, defining windows, aggregating). Collections are initially created from sources (e.g., by reading from files, kafka topics, or from local, in-memory collections). Results are returned via sinks, which may for example write the data to (distributed) files, or to standard output (for example, the command line terminal). Flink programs run in a variety of contexts, standalone, or embedded in other programs. The execution can happen in a local JVM, or on clusters of many machines.

Depending on the type of data sources, i.e. bounded or unbounded sources, you would either write a batch program or a streaming program where the DataSet API is used for batch and the DataStream API is used for streaming. This guide will introduce the basic concepts that are common to both APIs but please see our Streaming Guide and Batch Guide for concrete information about writing programs with each API.

NOTE: When showing actual examples of how the APIs can be used we will use StreamingExecutionEnvironment and the DataStream API. The concepts are exactly the same in the DataSet API, just replace by ExecutionEnvironment and DataSet.

• DataSet and DataStream
• Anatomy of a Flink Program
• Lazy Evaluation
• Specifying Keys
• Specifying Transformation Functions
• Supported Data Types
• Accumulators & Counters

DataSet and DataStream

Flink has the special classes DataSet and DataStream to represent data in a program. You can think of them as immutable collections of data that can contain duplicates. In the case of DataSet the data is finite while for a DataStream the number of elements can be unbounded.

These collections differ from regular Java collections in some key ways. First, they are immutable, meaning that once they are created you cannot add or remove elements. You can also not simply inspect the elements inside.

A collection is initially created by adding a source in a Flink program and new collections are derived from these by transforming them using API methods such as map, filter and so on.

Anatomy of a Flink Program

Flink program programs look like regular programs that transform collections of data. Each program consists of the same basic parts:

1. Obtain an execution environment,
2. Load/create the initial data,
3. Specify transformations on this data,
4. Specify where to put the results of your computations,
5. Trigger the program execution

getExecutionEnvironment()

createLocalEnvironment()

createRemoteEnvironment(String host, int port, String... jarFiles)

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

DataStream<String> text = env.readTextFile("file:///path/to/file");

DataStream<String> input = ...;

DataStream<Integer> parsed = input.map(new MapFunction<String, Integer>() {
    @Override
    public Integer map(String value) {
        return Integer.parseInt(value);
    }
});

writeAsText(String path)

print()

getExecutionEnvironment()

createLocalEnvironment()

createRemoteEnvironment(host: String, port: Int, jarFiles: String*)

val env = StreamExecutionEnvironment.getExecutionEnvironment()

val text: DataStream[String] = env.readTextFile("file:///path/to/file")

val input: DataSet[String] = ...

val mapped = input.map { x => x.toInt }

writeAsText(path: String)

print()

Once you specified the complete program you need to trigger the program execution by calling execute() on the StreamExecutionEnvironment. Depending on the type of the ExecutionEnvironment the execution will be triggered on your local machine or submit your program for execution on a cluster.

The execute() method is returning a JobExecutionResult, this contains execution times and accumulator results.

Please see the Streaming Guide for information about streaming data sources and sink and for more in-depths information about the supported transformations on DataStream.

Check out the Batch Guide for information about batch data sources and sink and for more in-depths information about the supported transformations on DataSet.

Back to top

Lazy Evaluation

All Flink programs are executed lazily: When the program’s main method is executed, the data loading and transformations do not happen directly. Rather, each operation is created and added to the program’s plan. The operations are actually executed when the execution is explicitly triggered by an execute() call on the execution environment. Whether the program is executed locally or on a cluster depends on the type of execution environment

The lazy evaluation lets you construct sophisticated programs that Flink executes as one holistically planned unit.

Back to top

Specifying Keys

Some transformations (join, coGroup, keyBy, groupBy) require that a key be defined on a collection of elements. Other transformations (Reduce, GroupReduce, Aggregate, Windows) allow data being grouped on a key before they are applied.

A DataSet is grouped as

DataSet<...> input = // [...]
DataSet<...> reduced = input
  .groupBy(/*define key here*/)
  .reduceGroup(/*do something*/);

while a key can be specified on a DataStream using

DataStream<...> input = // [...]
DataStream<...> windowed = input
  .keyBy(/*define key here*/)
  .window(/*window specification*/);

The data model of Flink is not based on key-value pairs. Therefore, you do not need to physically pack the data set types into keys and values. Keys are “virtual”: they are defined as functions over the actual data to guide the grouping operator.

NOTE: In the following discussion we will use the DataStream API and keyBy. For the DataSet API you just have to replace by DataSet and groupBy.

Define keys for Tuples

The simplest case is grouping Tuples on one or more fields of the Tuple:

DataStream<Tuple3<Integer,String,Long>> input = // [...]
KeyedStream<Tuple3<Integer,String,Long>,Tuple> keyed = input.keyBy(0)

val input: DataStream[(Int, String, Long)] = // [...]
val keyed = input.keyBy(0)

The tuples are grouped on the first field (the one of Integer type).

DataStream<Tuple3<Integer,String,Long>> input = // [...]
KeyedStream<Tuple3<Integer,String,Long>,Tuple> keyed = input.keyBy(0,1)

val input: DataSet[(Int, String, Long)] = // [...]
val grouped = input.groupBy(0,1)

Here, we group the tuples on a composite key consisting of the first and the second field.

A note on nested Tuples: If you have a DataStream with a nested tuple, such as:

DataStream<Tuple3<Tuple2<Integer, Float>,String,Long>> ds;

Specifying keyBy(0) will cause the system to use the full Tuple2 as a key (with the Integer and Float being the key). If you want to “navigate” into the nested Tuple2, you have to use field expression keys which are explained below.

Define keys using Field Expressions

You can use String-based field expressions to reference nested fields and define keys for grouping, sorting, joining, or coGrouping.

Field expressions make it very easy to select fields in (nested) composite types such as Tuple and POJO types.

// some ordinary POJO (Plain old Java Object)
public class WC {
  public String word;
  public int count;
}
DataStream<WC> words = // [...]
DataStream<WC> wordCounts = words.keyBy("word").window(/*window specification*/);

public static class WC {
  public ComplexNestedClass complex; //nested POJO
  private int count;
  // getter / setter for private field (count)
  public int getCount() {
    return count;
  }
  public void setCount(int c) {
    this.count = c;
  }
}
public static class ComplexNestedClass {
  public Integer someNumber;
  public float someFloat;
  public Tuple3<Long, Long, String> word;
  public IntWritable hadoopCitizen;
}

// some ordinary POJO (Plain old Java Object)
class WC(var word: String, var count: Int) {
  def this() { this("", 0L) }
}
val words: DataStream[WC] = // [...]
val wordCounts = words.keyBy("word").window(/*window specification*/)

// or, as a case class, which is less typing
case class WC(word: String, count: Int)
val words: DataStream[WC] = // [...]
val wordCounts = words.keyBy("word").window(/*window specification*/)

class WC(var complex: ComplexNestedClass, var count: Int) {
  def this() { this(null, 0) }
}

class ComplexNestedClass(
    var someNumber: Int,
    someFloat: Float,
    word: (Long, Long, String),
    hadoopCitizen: IntWritable) {
  def this() { this(0, 0, (0, 0, ""), new IntWritable(0)) }
}

Define keys using Key Selector Functions

An additional way to define keys are “key selector” functions. A key selector function takes a single element as input and returns the key for the element. The key can be of any type and be derived from arbitrary computations.

The following example shows a key selector function that simply returns the field of an object:

// some ordinary POJO
public class WC {public String word; public int count;}
DataStream<WC> words = // [...]
KeyedStream<WC> kyed = words
  .keyBy(new KeySelector<WC, String>() {
     public String getKey(WC wc) { return wc.word; }
   });

// some ordinary case class
case class WC(word: String, count: Int)
val words: DataStream[WC] = // [...]
val keyed = words.keyBy( _.word )

Back to top

Specifying Transformation Functions

Most transformations require user-defined functions. This section lists different ways of how they can be specified

class MyMapFunction implements MapFunction<String, Integer> {
  public Integer map(String value) { return Integer.parseInt(value); }
});
data.map(new MyMapFunction());

data.map(new MapFunction<String, Integer> () {
  public Integer map(String value) { return Integer.parseInt(value); }
});

data.filter(s -> s.startsWith("http://"));

data.reduce((i1,i2) -> i1 + i2);

class MyMapFunction implements MapFunction<String, Integer> {
  public Integer map(String value) { return Integer.parseInt(value); }
});

class MyMapFunction extends RichMapFunction<String, Integer> {
  public Integer map(String value) { return Integer.parseInt(value); }
});

data.map(new MyMapFunction());

data.map (new RichMapFunction<String, Integer>() {
  public Integer map(String value) { return Integer.parseInt(value); }
});

val data: DataSet[String] = // [...]
data.filter { _.startsWith("http://") }

val data: DataSet[Int] = // [...]
data.reduce { (i1,i2) => i1 + i2 }
// or
data.reduce { _ + _ }

data.map { x => x.toInt }

class MyMapFunction extends RichMapFunction[String, Int] {
  def map(in: String):Int = { in.toInt }
})

data.map(new MyMapFunction())

data.map (new RichMapFunction[String, Int] {
  def map(in: String):Int = { in.toInt }
})

Rich functions provide, in addition to the user-defined function (map, reduce, etc), four methods: open, close, getRuntimeContext, and setRuntimeContext. These are useful for parameterizing the function (see Passing Parameters to Functions), creating and finalizing local state, accessing broadcast variables (see Broadcast Variables, and for accessing runtime information such as accumulators and counters (see Accumulators and Counters, and information on iterations (see Iterations).

Back to top

Supported Data Types

Flink places some restrictions on the type of elements that can be in a DataSet or DataStream. The reason for this is that the system analyzes the types to determine efficient execution strategies.

There are six different categories of data types:

1. Java Tuples and Scala Case Classes
2. Java POJOs
3. Primitive Types
4. Regular Classes
5. Values
6. Hadoop Writables
7. Special Types

Tuples and Case Classes

DataStream<Tuple2<String, Integer>> wordCounts = env.fromElements(
    new Tuple2<String, Integer>("hello", 1),
    new Tuple2<String, Integer>("world", 2));

wordCounts.map(new MapFunction<Tuple2<String, Integer>, Integer>() {
    @Override
    public String map(Tuple2<String, Integer> value) throws Exception {
        return value.f1;
    }
});

wordCounts.keyBy(0); // also valid .keyBy("f0")

case class WordCount(word: String, count: Int)
val input = env.fromElements(
    WordCount("hello", 1),
    WordCount("world", 2)) // Case Class Data Set

input.keyBy("word")// key by field expression "word"

val input2 = env.fromElements(("hello", 1), ("world", 2)) // Tuple2 Data Set

input2.keyBy(0, 1) // key by field positions 0 and 1

POJOs

Java and Scala classes are treated by Flink as a special POJO data type if they fulfill the following requirements:

• The class must be public.

• It must have a public constructor without arguments (default constructor).

• All fields are either public or must be accessible through getter and setter functions. For a field called foo the getter and setter methods must be named getFoo() and setFoo().

• The type of a field must be supported by Flink. At the moment, Flink uses Avro to serialize arbitrary objects (such as Date).

Flink analyzes the structure of POJO types, i.e., it learns about the fields of a POJO. As a result POJO types are easier to use than general types. Moreover, Flink can process POJOs more efficiently than general types.

The following example shows a simple POJO with two public fields.

public class WordWithCount {

    public String word;
    public int count;

    public WordWithCount() {}

    public WordWithCount(String word, int count) {
        this.word = word;
        this.count = count;
    }
}

DataStream<WordWithCount> wordCounts = env.fromElements(
    new WordWithCount("hello", 1),
    new WordWithCount("world", 2));

wordCounts.keyBy("word"); // key by field expression "word"

class WordWithCount(var word: String, var count: Int) {
    def this() {
      this(null, -1)
    }
}

val input = env.fromElements(
    new WordWithCount("hello", 1),
    new WordWithCount("world", 2)) // Case Class Data Set

input.keyBy("word")// key by field expression "word"

Primitive Types

Flink supports all Java and Scala primitive types such as Integer, String, and Double.

General Class Types

Flink supports most Java and Scala classes (API and custom). Restrictions apply to classes containing fields that cannot be serialized, like file pointers, I/O streams, or other native resources. Classes that follow the Java Beans conventions work well in general.

All classes that are not identified as POJO types (see POJO requirements above) are handled by Flink as general class types. Flink treats these data types as black boxes and is not able to access their their content (i.e., for efficient sorting). General types are de/serialized using the serialization framework Kryo.

Values

Value types describe their serialization and deserialization manually. Instead of going through a general purpose serialization framework, they provide custom code for those operations by means of implementing the org.apache.flinktypes.Value interface with the methods read and write. Using a Value type is reasonable when general purpose serialization would be highly inefficient. An example would be a data type that implements a sparse vector of elements as an array. Knowing that the array is mostly zero, one can use a special encoding for the non-zero elements, while the general purpose serialization would simply write all array elements.

The org.apache.flinktypes.CopyableValue interface supports manual internal cloning logic in a similar way.

Flink comes with pre-defined Value types that correspond to basic data types. (ByteValue, ShortValue, IntValue, LongValue, FloatValue, DoubleValue, StringValue, CharValue, BooleanValue). These Value types act as mutable variants of the basic data types: Their value can be altered, allowing programmers to reuse objects and take pressure off the garbage collector.

Hadoop Writables

You can use types that implement the org.apache.hadoop.Writable interface. The serialization logic defined in the write()and readFields() methods will be used for serialization.

Special Types

You can use special types, including Scala’s Either, Option, and Try. The Java API has its own custom implementation of Either. Similarly to Scala’s Either, it represents a value of one two possible types, Left or Right. Either can be useful for error handling or operators that need to output two different types of records.

Type Erasure & Type Inference

Note: This Section is only relevant for Java.

The Java compiler throws away much of the generic type information after compilation. This is known as type erasure in Java. It means that at runtime, an instance of an object does not know its generic type any more. For example, instances of DataStream<String> and DataStream<Long> look the same to the JVM.

Flink requires type information at the time when it prepares the program for execution (when the main method of the program is called). The Flink Java API tries to reconstruct the type information that was thrown away in various ways and store it explicitly in the data sets and operators. You can retrieve the type via DataStream.getType(). The method returns an instance of TypeInformation, which is Flink’s internal way of representing types.

The type inference has its limits and needs the “cooperation” of the programmer in some cases. Examples for that are methods that create data sets from collections, such as ExecutionEnvironment.fromCollection(), where you can pass an argument that describes the type. But also generic functions like MapFunction<I, O> may need extra type information.

The ResultTypeQueryable interface can be implemented by input formats and functions to tell the API explicitly about their return type. The input types that the functions are invoked with can usually be inferred by the result types of the previous operations.

Back to top

Accumulators & Counters

Accumulators are simple constructs with an add operation and a final accumulated result, which is available after the job ended.

The most straightforward accumulator is a counter: You can increment it using the Accumulator.add(V value) method. At the end of the job Flink will sum up (merge) all partial results and send the result to the client. Accumulators are useful during debugging or if you quickly want to find out more about your data.

Flink currently has the following built-in accumulators. Each of them implements the Accumulator interface.

• IntCounter, LongCounter and DoubleCounter: See below for an example using a counter.
• Histogram: A histogram implementation for a discrete number of bins. Internally it is just a map from Integer to Integer. You can use this to compute distributions of values, e.g. the distribution of words-per-line for a word count program.

How to use accumulators:

First you have to create an accumulator object (here a counter) in the user-defined transformation function where you want to use it.

private IntCounter numLines = new IntCounter();

Second you have to register the accumulator object, typically in the open() method of the rich function. Here you also define the name.

getRuntimeContext().addAccumulator("num-lines", this.numLines);

You can now use the accumulator anywhere in the operator function, including in the open() and close() methods.

this.numLines.add(1);

The overall result will be stored in the JobExecutionResult object which is returned from the execute() method of the execution environment (currently this only works if the execution waits for the completion of the job).

myJobExecutionResult.getAccumulatorResult("num-lines")

All accumulators share a single namespace per job. Thus you can use the same accumulator in different operator functions of your job. Flink will internally merge all accumulators with the same name.

A note on accumulators and iterations: Currently the result of accumulators is only available after the overall job has ended. We plan to also make the result of the previous iteration available in the next iteration. You can use Aggregators to compute per-iteration statistics and base the termination of iterations on such statistics.

Custom accumulators:

To implement your own accumulator you simply have to write your implementation of the Accumulator interface. Feel free to create a pull request if you think your custom accumulator should be shipped with Flink.

You have the choice to implement either Accumulator or SimpleAccumulator.

Accumulator<V,R> is most flexible: It defines a type V for the value to add, and a result type R for the final result. E.g. for a histogram, V is a number and R is a histogram. SimpleAccumulator is for the cases where both types are the same, e.g. for counters.

Back to top