Flink DataSet API Programming Guide

public class WordCountExample {
    public static void main(String[] args) throws Exception {
        final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        DataSet<String> text = env.fromElements(
            "Who's there?",
            "I think I hear them. Stand, ho! Who's there?");

        DataSet<Tuple2<String, Integer>> wordCounts = text
            .flatMap(new LineSplitter())
            .groupBy(0)
            .sum(1);

        wordCounts.print();
    }

    public static class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {
        @Override
        public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
            for (String word : line.split(" ")) {
                out.collect(new Tuple2<String, Integer>(word, 1));
            }
        }
    }
}

import org.apache.flink.api.scala._

object WordCount {
  def main(args: Array[String]) {

    val env = ExecutionEnvironment.getExecutionEnvironment
    val text = env.fromElements(
      "Who's there?",
      "I think I hear them. Stand, ho! Who's there?")

    val counts = text.flatMap { _.toLowerCase.split("\\W+") filter { _.nonEmpty } }
      .map { (_, 1) }
      .groupBy(0)
      .sum(1)

    counts.print()
  }
}

val data: DataSet[(Int, String, Double)] = // [...]
data.map {
  case (id, name, temperature) => // [...]
}

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// read text file from local files system
DataSet<String> localLines = env.readTextFile("file:///path/to/my/textfile");

// read text file from a HDFS running at nnHost:nnPort
DataSet<String> hdfsLines = env.readTextFile("hdfs://nnHost:nnPort/path/to/my/textfile");

// read a CSV file with three fields
DataSet<Tuple3<Integer, String, Double>> csvInput = env.readCsvFile("hdfs:///the/CSV/file")
	                       .types(Integer.class, String.class, Double.class);

// read a CSV file with five fields, taking only two of them
DataSet<Tuple2<String, Double>> csvInput = env.readCsvFile("hdfs:///the/CSV/file")
                               .includeFields("10010")  // take the first and the fourth field
	                       .types(String.class, Double.class);

// read a CSV file with three fields into a POJO (Person.class) with corresponding fields
DataSet<Person>> csvInput = env.readCsvFile("hdfs:///the/CSV/file")
                         .pojoType(Person.class, "name", "age", "zipcode");


// read a file from the specified path of type TextInputFormat
DataSet<Tuple2<LongWritable, Text>> tuples =
 env.readHadoopFile(new TextInputFormat(), LongWritable.class, Text.class, "hdfs://nnHost:nnPort/path/to/file");

// read a file from the specified path of type SequenceFileInputFormat
DataSet<Tuple2<IntWritable, Text>> tuples =
 env.readSequenceFile(IntWritable.class, Text.class, "hdfs://nnHost:nnPort/path/to/file");

// creates a set from some given elements
DataSet<String> value = env.fromElements("Foo", "bar", "foobar", "fubar");

// generate a number sequence
DataSet<Long> numbers = env.generateSequence(1, 10000000);

// Read data from a relational database using the JDBC input format
DataSet<Tuple2<String, Integer> dbData =
    env.createInput(
      // create and configure input format
      JDBCInputFormat.buildJDBCInputFormat()
                     .setDrivername("org.apache.derby.jdbc.EmbeddedDriver")
                     .setDBUrl("jdbc:derby:memory:persons")
                     .setQuery("select name, age from persons")
                     .finish(),
      // specify type information for DataSet
      new TupleTypeInfo(Tuple2.class, STRING_TYPE_INFO, INT_TYPE_INFO)
    );

// Note: Flink's program compiler needs to infer the data types of the data items which are returned
// by an InputFormat. If this information cannot be automatically inferred, it is necessary to
// manually provide the type information as shown in the examples above.

// enable recursive enumeration of nested input files
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// create a configuration object
Configuration parameters = new Configuration();

// set the recursive enumeration parameter
parameters.setBoolean("recursive.file.enumeration", true);

// pass the configuration to the data source
DataSet<String> logs = env.readTextFile("file:///path/with.nested/files")
			  .withParameters(parameters);

val env  = ExecutionEnvironment.getExecutionEnvironment

// read text file from local files system
val localLines = env.readTextFile("file:///path/to/my/textfile")

// read text file from a HDFS running at nnHost:nnPort
val hdfsLines = env.readTextFile("hdfs://nnHost:nnPort/path/to/my/textfile")

// read a CSV file with three fields
val csvInput = env.readCsvFile[(Int, String, Double)]("hdfs:///the/CSV/file")

// read a CSV file with five fields, taking only two of them
val csvInput = env.readCsvFile[(String, Double)](
  "hdfs:///the/CSV/file",
  includedFields = Array(0, 3)) // take the first and the fourth field

// CSV input can also be used with Case Classes
case class MyCaseClass(str: String, dbl: Double)
val csvInput = env.readCsvFile[MyCaseClass](
  "hdfs:///the/CSV/file",
  includedFields = Array(0, 3)) // take the first and the fourth field

// read a CSV file with three fields into a POJO (Person) with corresponding fields
val csvInput = env.readCsvFile[Person](
  "hdfs:///the/CSV/file",
  pojoFields = Array("name", "age", "zipcode"))

// create a set from some given elements
val values = env.fromElements("Foo", "bar", "foobar", "fubar")

// generate a number sequence
val numbers = env.generateSequence(1, 10000000);

// read a file from the specified path of type TextInputFormat
val tuples = env.readHadoopFile(new TextInputFormat, classOf[LongWritable],
 classOf[Text], "hdfs://nnHost:nnPort/path/to/file")

// read a file from the specified path of type SequenceFileInputFormat
val tuples = env.readSequenceFile(classOf[IntWritable], classOf[Text],
 "hdfs://nnHost:nnPort/path/to/file")

// enable recursive enumeration of nested input files
val env  = ExecutionEnvironment.getExecutionEnvironment

// create a configuration object
val parameters = new Configuration

// set the recursive enumeration parameter
parameters.setBoolean("recursive.file.enumeration", true)

// pass the configuration to the data source
env.readTextFile("file:///path/with.nested/files").withParameters(parameters)

// text data
DataSet<String> textData = // [...]

// write DataSet to a file on the local file system
textData.writeAsText("file:///my/result/on/localFS");

// write DataSet to a file on a HDFS with a namenode running at nnHost:nnPort
textData.writeAsText("hdfs://nnHost:nnPort/my/result/on/localFS");

// write DataSet to a file and overwrite the file if it exists
textData.writeAsText("file:///my/result/on/localFS", WriteMode.OVERWRITE);

// tuples as lines with pipe as the separator "a|b|c"
DataSet<Tuple3<String, Integer, Double>> values = // [...]
values.writeAsCsv("file:///path/to/the/result/file", "\n", "|");

// this writes tuples in the text formatting "(a, b, c)", rather than as CSV lines
values.writeAsText("file:///path/to/the/result/file");

// this writes values as strings using a user-defined TextFormatter object
values.writeAsFormattedText("file:///path/to/the/result/file",
    new TextFormatter<Tuple2<Integer, Integer>>() {
        public String format (Tuple2<Integer, Integer> value) {
            return value.f1 + " - " + value.f0;
        }
    });

DataSet<Tuple3<String, Integer, Double>> myResult = [...]

// write Tuple DataSet to a relational database
myResult.output(
    // build and configure OutputFormat
    JDBCOutputFormat.buildJDBCOutputFormat()
                    .setDrivername("org.apache.derby.jdbc.EmbeddedDriver")
                    .setDBUrl("jdbc:derby:memory:persons")
                    .setQuery("insert into persons (name, age, height) values (?,?,?)")
                    .finish()
    );

DataSet<Tuple3<Integer, String, Double>> tData = // [...]
DataSet<Tuple2<BookPojo, Double>> pData = // [...]
DataSet<String> sData = // [...]

// sort output on String field in ascending order
tData.sortPartition(1, Order.ASCENDING).print();

// sort output on Double field in descending and Integer field in ascending order
tData.sortPartition(2, Order.DESCENDING).sortPartition(0, Order.ASCENDING).print();

// sort output on the "author" field of nested BookPojo in descending order
pData.sortPartition("f0.author", Order.DESCENDING).writeAsText(...);

// sort output on the full tuple in ascending order
tData.sortPartition("*", Order.ASCENDING).writeAsCsv(...);

// sort atomic type (String) output in descending order
sData.sortPartition("*", Order.DESCENDING).writeAsText(...);

// text data
val textData: DataSet[String] = // [...]

// write DataSet to a file on the local file system
textData.writeAsText("file:///my/result/on/localFS")

// write DataSet to a file on a HDFS with a namenode running at nnHost:nnPort
textData.writeAsText("hdfs://nnHost:nnPort/my/result/on/localFS")

// write DataSet to a file and overwrite the file if it exists
textData.writeAsText("file:///my/result/on/localFS", WriteMode.OVERWRITE)

// tuples as lines with pipe as the separator "a|b|c"
val values: DataSet[(String, Int, Double)] = // [...]
values.writeAsCsv("file:///path/to/the/result/file", "\n", "|")

// this writes tuples in the text formatting "(a, b, c)", rather than as CSV lines
values.writeAsText("file:///path/to/the/result/file");

// this writes values as strings using a user-defined formatting
values map { tuple => tuple._1 + " - " + tuple._2 }
  .writeAsText("file:///path/to/the/result/file")

val tData: DataSet[(Int, String, Double)] = // [...]
val pData: DataSet[(BookPojo, Double)] = // [...]
val sData: DataSet[String] = // [...]

// sort output on String field in ascending order
tData.sortPartition(1, Order.ASCENDING).print;

// sort output on Double field in descending and Int field in ascending order
tData.sortPartition(2, Order.DESCENDING).sortPartition(0, Order.ASCENDING).print;

// sort output on the "author" field of nested BookPojo in descending order
pData.sortPartition("_1.author", Order.DESCENDING).writeAsText(...);

// sort output on the full tuple in ascending order
tData.sortPartition("_", Order.ASCENDING).writeAsCsv(...);

// sort atomic type (String) output in descending order
sData.sortPartition("_", Order.DESCENDING).writeAsText(...);

final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// Create initial IterativeDataSet
IterativeDataSet<Integer> initial = env.fromElements(0).iterate(10000);

DataSet<Integer> iteration = initial.map(new MapFunction<Integer, Integer>() {
    @Override
    public Integer map(Integer i) throws Exception {
        double x = Math.random();
        double y = Math.random();

        return i + ((x * x + y * y < 1) ? 1 : 0);
    }
});

// Iteratively transform the IterativeDataSet
DataSet<Integer> count = initial.closeWith(iteration);

count.map(new MapFunction<Integer, Double>() {
    @Override
    public Double map(Integer count) throws Exception {
        return count / (double) 10000 * 4;
    }
}).print();

env.execute("Iterative Pi Example");

// read the initial data sets
DataSet<Tuple2<Long, Double>> initialSolutionSet = // [...]

DataSet<Tuple2<Long, Double>> initialDeltaSet = // [...]

int maxIterations = 100;
int keyPosition = 0;

DeltaIteration<Tuple2<Long, Double>, Tuple2<Long, Double>> iteration = initialSolutionSet
    .iterateDelta(initialDeltaSet, maxIterations, keyPosition);

DataSet<Tuple2<Long, Double>> candidateUpdates = iteration.getWorkset()
    .groupBy(1)
    .reduceGroup(new ComputeCandidateChanges());

DataSet<Tuple2<Long, Double>> deltas = candidateUpdates
    .join(iteration.getSolutionSet())
    .where(0)
    .equalTo(0)
    .with(new CompareChangesToCurrent());

DataSet<Tuple2<Long, Double>> nextWorkset = deltas
    .filter(new FilterByThreshold());

iteration.closeWith(deltas, nextWorkset)
	.writeAsCsv(outputPath);

val env = ExecutionEnvironment.getExecutionEnvironment()

// Create initial DataSet
val initial = env.fromElements(0)

val count = initial.iterate(10000) { iterationInput: DataSet[Int] =>
  val result = iterationInput.map { i =>
    val x = Math.random()
    val y = Math.random()
    i + (if (x * x + y * y < 1) 1 else 0)
  }
  result
}

val result = count map { c => c / 10000.0 * 4 }

result.print()

env.execute("Iterative Pi Example");

// read the initial data sets
val initialSolutionSet: DataSet[(Long, Double)] = // [...]

val initialWorkset: DataSet[(Long, Double)] = // [...]

val maxIterations = 100
val keyPosition = 0

val result = initialSolutionSet.iterateDelta(initialWorkset, maxIterations, Array(keyPosition)) {
  (solution, workset) =>
    val candidateUpdates = workset.groupBy(1).reduceGroup(new ComputeCandidateChanges())
    val deltas = candidateUpdates.join(solution).where(0).equalTo(0)(new CompareChangesToCurrent())

    val nextWorkset = deltas.filter(new FilterByThreshold())

    (deltas, nextWorkset)
}

result.writeAsCsv(outputPath)

env.execute()

final ExecutionEnvironment env = ExecutionEnvironment.createLocalEnvironment();

DataSet<String> lines = env.readTextFile(pathToTextFile);
// build your program

env.execute();

val env = ExecutionEnvironment.createLocalEnvironment()

val lines = env.readTextFile(pathToTextFile)
// build your program

env.execute();

final ExecutionEnvironment env = ExecutionEnvironment.createLocalEnvironment();

// Create a DataSet from a list of elements
DataSet<Integer> myInts = env.fromElements(1, 2, 3, 4, 5);

// Create a DataSet from any Java collection
List<Tuple2<String, Integer>> data = ...
DataSet<Tuple2<String, Integer>> myTuples = env.fromCollection(data);

// Create a DataSet from an Iterator
Iterator<Long> longIt = ...
DataSet<Long> myLongs = env.fromCollection(longIt, Long.class);

DataSet<Tuple2<String, Integer>> myResult = ...

List<Tuple2<String, Integer>> outData = new ArrayList<Tuple2<String, Integer>>();
myResult.output(new LocalCollectionOutputFormat(outData));

val env = ExecutionEnvironment.createLocalEnvironment()

// Create a DataSet from a list of elements
val myInts = env.fromElements(1, 2, 3, 4, 5)

// Create a DataSet from any Collection
val data: Seq[(String, Int)] = ...
val myTuples = env.fromCollection(data)

// Create a DataSet from an Iterator
val longIt: Iterator[Long] = ...
val myLongs = env.fromCollection(longIt)

@ForwardedFields("f0->f2")
public class MyMap implements
              MapFunction<Tuple2<Integer, Integer>, Tuple3<String, Integer, Integer>> {
  @Override
  public Tuple3<String, Integer, Integer> map(Tuple2<Integer, Integer> val) {
    return new Tuple3<String, Integer, Integer>("foo", val.f1 / 2, val.f0);
  }
}

@ForwardedFields("_1->_3")
class MyMap extends MapFunction[(Int, Int), (String, Int, Int)]{
   def map(value: (Int, Int)): (String, Int, Int) = {
    return ("foo", value._2 / 2, value._1)
  }
}

@NonForwardedFields("f1") // second field is not forwarded
public class MyMap implements
              MapFunction<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>> {
  @Override
  public Tuple2<Integer, Integer> map(Tuple2<Integer, Integer> val) {
    return new Tuple2<Integer, Integer>(val.f0, val.f1 / 2);
  }
}

@NonForwardedFields("_2") // second field is not forwarded
class MyMap extends MapFunction[(Int, Int), (Int, Int)]{
  def map(value: (Int, Int)): (Int, Int) = {
    return (value._1, value._2 / 2)
  }
}

@ReadFields("f0; f3") // f0 and f3 are read and evaluated by the function.
public class MyMap implements
              MapFunction<Tuple4<Integer, Integer, Integer, Integer>,
                          Tuple2<Integer, Integer>> {
  @Override
  public Tuple2<Integer, Integer> map(Tuple4<Integer, Integer, Integer, Integer> val) {
    if(val.f0 == 42) {
      return new Tuple2<Integer, Integer>(val.f0, val.f1);
    } else {
      return new Tuple2<Integer, Integer>(val.f3+10, val.f1);
    }
  }
}

@ReadFields("_1; _4") // _1 and _4 are read and evaluated by the function.
class MyMap extends MapFunction[(Int, Int, Int, Int), (Int, Int)]{
   def map(value: (Int, Int, Int, Int)): (Int, Int) = {
    if (value._1 == 42) {
      return (value._1, value._2)
    } else {
      return (value._4 + 10, value._2)
    }
  }
}

// 1. The DataSet to be broadcasted
DataSet<Integer> toBroadcast = env.fromElements(1, 2, 3);

DataSet<String> data = env.fromElements("a", "b");

data.map(new RichMapFunction<String, String>() {
    @Override
    public void open(Configuration parameters) throws Exception {
      // 3. Access the broadcasted DataSet as a Collection
      Collection<Integer> broadcastSet = getRuntimeContext().getBroadcastVariable("broadcastSetName");
    }


    @Override
    public String map(String value) throws Exception {
        ...
    }
}).withBroadcastSet(toBroadcast, "broadcastSetName"); // 2. Broadcast the DataSet

// 1. The DataSet to be broadcasted
val toBroadcast = env.fromElements(1, 2, 3)

val data = env.fromElements("a", "b")

data.map(new RichMapFunction[String, String]() {
    var broadcastSet: Traversable[String] = null

    override def open(config: Configuration): Unit = {
      // 3. Access the broadcasted DataSet as a Collection
      broadcastSet = getRuntimeContext().getBroadcastVariable[String]("broadcastSetName").asScala
    }

    def map(in: String): String = {
        ...
    }
}).withBroadcastSet(toBroadcast, "broadcastSetName") // 2. Broadcast the DataSet

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// register a file from HDFS
env.registerCachedFile("hdfs:///path/to/your/file", "hdfsFile")

// register a local executable file (script, executable, ...)
env.registerCachedFile("file:///path/to/exec/file", "localExecFile", true)

// define your program and execute
...
DataSet<String> input = ...
DataSet<Integer> result = input.map(new MyMapper());
...
env.execute();

// extend a RichFunction to have access to the RuntimeContext
public final class MyMapper extends RichMapFunction<String, Integer> {

    @Override
    public void open(Configuration config) {

      // access cached file via RuntimeContext and DistributedCache
      File myFile = getRuntimeContext().getDistributedCache().getFile("hdfsFile");
      // read the file (or navigate the directory)
      ...
    }

    @Override
    public Integer map(String value) throws Exception {
      // use content of cached file
      ...
    }
}

val env = ExecutionEnvironment.getExecutionEnvironment

// register a file from HDFS
env.registerCachedFile("hdfs:///path/to/your/file", "hdfsFile")

// register a local executable file (script, executable, ...)
env.registerCachedFile("file:///path/to/exec/file", "localExecFile", true)

// define your program and execute
...
val input: DataSet[String] = ...
val result: DataSet[Integer] = input.map(new MyMapper())
...
env.execute()

// extend a RichFunction to have access to the RuntimeContext
class MyMapper extends RichMapFunction[String, Int] {

  override def open(config: Configuration): Unit = {

    // access cached file via RuntimeContext and DistributedCache
    val myFile: File = getRuntimeContext.getDistributedCache.getFile("hdfsFile")
    // read the file (or navigate the directory)
    ...
  }

  override def map(value: String): Int = {
    // use content of cached file
    ...
  }
}

DataSet<Integer> toFilter = env.fromElements(1, 2, 3);

toFilter.filter(new MyFilter(2));

private static class MyFilter implements FilterFunction<Integer> {

  private final int limit;

  public MyFilter(int limit) {
    this.limit = limit;
  }

  @Override
  public boolean filter(Integer value) throws Exception {
    return value > limit;
  }
}

val toFilter = env.fromElements(1, 2, 3)

toFilter.filter(new MyFilter(2))

class MyFilter(limit: Int) extends FilterFunction[Int] {
  override def filter(value: Int): Boolean = {
    value > limit
  }
}

DataSet<Integer> toFilter = env.fromElements(1, 2, 3);

Configuration config = new Configuration();
config.setInteger("limit", 2);

toFilter.filter(new RichFilterFunction<Integer>() {
    private int limit;

    @Override
    public void open(Configuration parameters) throws Exception {
      limit = parameters.getInteger("limit", 0);
    }

    @Override
    public boolean filter(Integer value) throws Exception {
      return value > limit;
    }
}).withParameters(config);

val toFilter = env.fromElements(1, 2, 3)

val c = new Configuration()
c.setInteger("limit", 2)

toFilter.filter(new RichFilterFunction[Int]() {
    var limit = 0

    override def open(config: Configuration): Unit = {
      limit = config.getInteger("limit", 0)
    }

    def filter(in: Int): Boolean = {
        in > limit
    }
}).withParameters(c)

Configuration conf = new Configuration();
conf.setString("mykey","myvalue");
final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
env.getConfig().setGlobalJobParameters(conf);

val env = ExecutionEnvironment.getExecutionEnvironment
val conf = new Configuration()
conf.setString("mykey", "myvalue")
env.getConfig.setGlobalJobParameters(conf)