Flink DataStream API Programming Guide #

We will now give an overview of each of those steps, please refer to the respective sections for more details. Note that all core classes of the Java DataStream API can be found in org.apache.flink.streaming.api .

The StreamExecutionEnvironment is the basis for all Flink programs. You can obtain one using these static methods on StreamExecutionEnvironment:

getExecutionEnvironment();

createLocalEnvironment();

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

Typically, you only need to use getExecutionEnvironment(), since this will do the right thing depending on the context: if you are executing your program inside an IDE or as a regular Java program it will create a local environment that will execute your program on your local machine. If you created a JAR file from your program, and invoke it through the command line, the Flink cluster manager will execute your main method and getExecutionEnvironment() will return an execution environment for executing your program on a cluster.

For specifying data sources the execution environment has several methods to read from files using various methods: you can just read them line by line, as CSV files, or using any of the other provided sources. To just read a text file as a sequence of lines, you can use:

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

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

This will give you a DataStream on which you can then apply transformations to create new derived DataStreams.

You apply transformations by calling methods on DataStream with a transformation functions. For example, a map transformation looks like this:

DataStream<String> input = ...;

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

This will create a new DataStream by converting every String in the original collection to an Integer.

Once you have a DataStream containing your final results, you can write it to an outside system by creating a sink. These are just some example methods for creating a sink:

writeAsText(String path);

print();

We will now give an overview of each of those steps, please refer to the respective sections for more details. Note that all core classes of the Scala DataStream API can be found in org.apache.flink.streaming.api.scala .

The StreamExecutionEnvironment is the basis for all Flink programs. You can obtain one using these static methods on StreamExecutionEnvironment:

getExecutionEnvironment()

createLocalEnvironment()

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

Typically, you only need to use getExecutionEnvironment(), since this will do the right thing depending on the context: if you are executing your program inside an IDE or as a regular Java program it will create a local environment that will execute your program on your local machine. If you created a JAR file from your program, and invoke it through the command line, the Flink cluster manager will execute your main method and getExecutionEnvironment() will return an execution environment for executing your program on a cluster.

For specifying data sources the execution environment has several methods to read from files using various methods: you can just read them line by line, as CSV files, or using any of the other provided sources. To just read a text file as a sequence of lines, you can use:

val env = StreamExecutionEnvironment.getExecutionEnvironment()

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

This will give you a DataStream on which you can then apply transformations to create new derived DataStreams.

You apply transformations by calling methods on DataStream with a transformation functions. For example, a map transformation looks like this:

val input: DataSet[String] = ...

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

This will create a new DataStream by converting every String in the original collection to an Integer.

Once you have a DataStream containing your final results, you can write it to an outside system by creating a sink. These are just some example methods for creating a sink:

writeAsText(path: String)

print()

Sources are where your program reads its input from. You can attach a source to your program by using StreamExecutionEnvironment.addSource(sourceFunction). Flink comes with a number of pre-implemented source functions, but you can always write your own custom sources by implementing the SourceFunction for non-parallel sources, or by implementing the ParallelSourceFunction interface or extending the RichParallelSourceFunction for parallel sources.

There are several predefined stream sources accessible from the StreamExecutionEnvironment:

File-based:

• readTextFile(path) - Reads text files, i.e. files that respect the TextInputFormat specification, line-by-line and returns them as Strings.

• readFile(fileInputFormat, path) - Reads (once) files as dictated by the specified file input format.

• readFile(fileInputFormat, path, watchType, interval, pathFilter, typeInfo) - This is the method called internally by the two previous ones. It reads files in the path based on the given fileInputFormat. Depending on the provided watchType, this source may periodically monitor (every interval ms) the path for new data (FileProcessingMode.PROCESS_CONTINUOUSLY), or process once the data currently in the path and exit (FileProcessingMode.PROCESS_ONCE). Using the pathFilter, the user can further exclude files from being processed.

  IMPLEMENTATION:

  Under the hood, Flink splits the file reading process into two sub-tasks, namely directory monitoring and data reading. Each of these sub-tasks is implemented by a separate entity. Monitoring is implemented by a single, non-parallel (parallelism = 1) task, while reading is performed by multiple tasks running in parallel. The parallelism of the latter is equal to the job parallelism. The role of the single monitoring task is to scan the directory (periodically or only once depending on the watchType), find the files to be processed, divide them in splits, and assign these splits to the downstream readers. The readers are the ones who will read the actual data. Each split is read by only one reader, while a reader can read multiple splits, one-by-one.

  IMPORTANT NOTES:

  1. If the watchType is set to FileProcessingMode.PROCESS_CONTINUOUSLY, when a file is modified, its contents are re-processed entirely. This can break the “exactly-once” semantics, as appending data at the end of a file will lead to all its contents being re-processed.

  2. If the watchType is set to FileProcessingMode.PROCESS_ONCE, the source scans the path once and exits, without waiting for the readers to finish reading the file contents. Of course the readers will continue reading until all file contents are read. Closing the source leads to no more checkpoints after that point. This may lead to slower recovery after a node failure, as the job will resume reading from the last checkpoint.

Socket-based:

• socketTextStream - Reads from a socket. Elements can be separated by a delimiter.

Collection-based:

• fromCollection(Collection) - Creates a data stream from the Java Java.util.Collection. All elements in the collection must be of the same type.

• fromCollection(Iterator, Class) - Creates a data stream from an iterator. The class specifies the data type of the elements returned by the iterator.

• fromElements(T ...) - Creates a data stream from the given sequence of objects. All objects must be of the same type.

• fromParallelCollection(SplittableIterator, Class) - Creates a data stream from an iterator, in parallel. The class specifies the data type of the elements returned by the iterator.

• generateSequence(from, to) - Generates the sequence of numbers in the given interval, in parallel.

Custom:

• addSource - Attach a new source function. For example, to read from Apache Kafka you can use addSource(new FlinkKafkaConsumer<>(...)). See connectors for more details.

Sources are where your program reads its input from. You can attach a source to your program by using StreamExecutionEnvironment.addSource(sourceFunction). Flink comes with a number of pre-implemented source functions, but you can always write your own custom sources by implementing the SourceFunction for non-parallel sources, or by implementing the ParallelSourceFunction interface or extending the RichParallelSourceFunction for parallel sources.

There are several predefined stream sources accessible from the StreamExecutionEnvironment:

File-based:

• readTextFile(path) - Reads text files, i.e. files that respect the TextInputFormat specification, line-by-line and returns them as Strings.

• readFile(fileInputFormat, path) - Reads (once) files as dictated by the specified file input format.

• readFile(fileInputFormat, path, watchType, interval, pathFilter) - This is the method called internally by the two previous ones. It reads files in the path based on the given fileInputFormat. Depending on the provided watchType, this source may periodically monitor (every interval ms) the path for new data (FileProcessingMode.PROCESS_CONTINUOUSLY), or process once the data currently in the path and exit (FileProcessingMode.PROCESS_ONCE). Using the pathFilter, the user can further exclude files from being processed.

  IMPLEMENTATION:

  Under the hood, Flink splits the file reading process into two sub-tasks, namely directory monitoring and data reading. Each of these sub-tasks is implemented by a separate entity. Monitoring is implemented by a single, non-parallel (parallelism = 1) task, while reading is performed by multiple tasks running in parallel. The parallelism of the latter is equal to the job parallelism. The role of the single monitoring task is to scan the directory (periodically or only once depending on the watchType), find the files to be processed, divide them in splits, and assign these splits to the downstream readers. The readers are the ones who will read the actual data. Each split is read by only one reader, while a reader can read multiple splits, one-by-one.

  IMPORTANT NOTES:

  1. If the watchType is set to FileProcessingMode.PROCESS_CONTINUOUSLY, when a file is modified, its contents are re-processed entirely. This can break the “exactly-once” semantics, as appending data at the end of a file will lead to all its contents being re-processed.

  2. If the watchType is set to FileProcessingMode.PROCESS_ONCE, the source scans the path once and exits, without waiting for the readers to finish reading the file contents. Of course the readers will continue reading until all file contents are read. Closing the source leads to no more checkpoints after that point. This may lead to slower recovery after a node failure, as the job will resume reading from the last checkpoint.

Socket-based:

• socketTextStream - Reads from a socket. Elements can be separated by a delimiter.

Collection-based:

• fromCollection(Seq) - Creates a data stream from the Java Java.util.Collection. All elements in the collection must be of the same type.

• fromCollection(Iterator) - Creates a data stream from an iterator. The class specifies the data type of the elements returned by the iterator.

• fromElements(elements: _*) - Creates a data stream from the given sequence of objects. All objects must be of the same type.

• fromParallelCollection(SplittableIterator) - Creates a data stream from an iterator, in parallel. The class specifies the data type of the elements returned by the iterator.

• generateSequence(from, to) - Generates the sequence of numbers in the given interval, in parallel.

Custom:

• addSource - Attach a new source function. For example, to read from Apache Kafka you can use addSource(new FlinkKafkaConsumer<>(...)). See connectors for more details.

Data sinks consume DataStreams and forward them to files, sockets, external systems, or print them. Flink comes with a variety of built-in output formats that are encapsulated behind operations on the DataStreams:

• writeAsText() / TextOutputFormat - Writes elements line-wise as Strings. The Strings are obtained by calling the toString() method of each element.

• writeAsCsv(...) / CsvOutputFormat - Writes tuples as comma-separated value files. Row and field delimiters are configurable. The value for each field comes from the toString() method of the objects.

• print() / printToErr() - Prints the toString() value of each element on the standard out / standard error stream. Optionally, a prefix (msg) can be provided which is prepended to the output. This can help to distinguish between different calls to print. If the parallelism is greater than 1, the output will also be prepended with the identifier of the task which produced the output.

• writeUsingOutputFormat() / FileOutputFormat - Method and base class for custom file outputs. Supports custom object-to-bytes conversion.

• writeToSocket - Writes elements to a socket according to a SerializationSchema

• addSink - Invokes a custom sink function. Flink comes bundled with connectors to other systems (such as Apache Kafka) that are implemented as sink functions.

Data sinks consume DataStreams and forward them to files, sockets, external systems, or print them. Flink comes with a variety of built-in output formats that are encapsulated behind operations on the DataStreams:

• writeAsText() / TextOutputFormat - Writes elements line-wise as Strings. The Strings are obtained by calling the toString() method of each element.

• writeAsCsv(...) / CsvOutputFormat - Writes tuples as comma-separated value files. Row and field delimiters are configurable. The value for each field comes from the toString() method of the objects.

• print() / printToErr() - Prints the toString() value of each element on the standard out / standard error stream. Optionally, a prefix (msg) can be provided which is prepended to the output. This can help to distinguish between different calls to print. If the parallelism is greater than 1, the output will also be prepended with the identifier of the task which produced the output.

• writeUsingOutputFormat() / FileOutputFormat - Method and base class for custom file outputs. Supports custom object-to-bytes conversion.

• writeToSocket - Writes elements to a socket according to a SerializationSchema

• addSink - Invokes a custom sink function. Flink comes bundled with connectors to other systems (such as Apache Kafka) that are implemented as sink functions.

Iterative streaming programs implement a step function and embed it into an IterativeStream. As a DataStream program may never finish, there is no maximum number of iterations. Instead, you need to specify which part of the stream is fed back to the iteration and which part is forwarded downstream using a side output or a filter. Here, we show an example using filters. First, we define an IterativeStream

IterativeStream<Integer> iteration = input.iterate();

Then, we specify the logic that will be executed inside the loop using a series of transformations (here a simple map transformation)

DataStream<Integer> iterationBody = iteration.map(/* this is executed many times */);

To close an iteration and define the iteration tail, call the closeWith(feedbackStream) method of the IterativeStream. The DataStream given to the closeWith function will be fed back to the iteration head. A common pattern is to use a filter to separate the part of the stream that is fed back, and the part of the stream which is propagated forward. These filters can, e.g., define the “termination” logic, where an element is allowed to propagate downstream rather than being fed back.

iteration.closeWith(iterationBody.filter(/* one part of the stream */));
DataStream<Integer> output = iterationBody.filter(/* some other part of the stream */);

For example, here is program that continuously subtracts 1 from a series of integers until they reach zero:

DataStream<Long> someIntegers = env.generateSequence(0, 1000);

IterativeStream<Long> iteration = someIntegers.iterate();

DataStream<Long> minusOne = iteration.map(new MapFunction<Long, Long>() {
  @Override
  public Long map(Long value) throws Exception {
    return value - 1 ;
  }
});

DataStream<Long> stillGreaterThanZero = minusOne.filter(new FilterFunction<Long>() {
  @Override
  public boolean filter(Long value) throws Exception {
    return (value > 0);
  }
});

iteration.closeWith(stillGreaterThanZero);

DataStream<Long> lessThanZero = minusOne.filter(new FilterFunction<Long>() {
  @Override
  public boolean filter(Long value) throws Exception {
    return (value <= 0);
  }
});

Iterative streaming programs implement a step function and embed it into an IterativeStream. As a DataStream program may never finish, there is no maximum number of iterations. Instead, you need to specify which part of the stream is fed back to the iteration and which part is forwarded downstream using a side output or a filter. Here, we show an example iteration where the body (the part of the computation that is repeated) is a simple map transformation, and the elements that are fed back are distinguished by the elements that are forwarded downstream using filters.

val iteratedStream = someDataStream.iterate(
  iteration => {
    val iterationBody = iteration.map(/* this is executed many times */)
    (iterationBody.filter(/* one part of the stream */), iterationBody.filter(/* some other part of the stream */))
})

For example, here is program that continuously subtracts 1 from a series of integers until they reach zero:

val someIntegers: DataStream[Long] = env.generateSequence(0, 1000)

val iteratedStream = someIntegers.iterate(
  iteration => {
    val minusOne = iteration.map( v => v - 1)
    val stillGreaterThanZero = minusOne.filter (_ > 0)
    val lessThanZero = minusOne.filter(_ <= 0)
    (stillGreaterThanZero, lessThanZero)
  }
)