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Bundled Examples

The following example programs showcase different applications of Flink from simple word counting to graph algorithms. The code samples illustrate the use of Flink’s API.

The full source code of the following and more examples can be found in the flink-examples-batch or flink-examples-streaming module of the Flink source repository.

Running an example

In order to run a Flink example, we assume you have a running Flink instance available. The “Setup” tab in the navigation describes various ways of starting Flink.

The easiest way is running the ./bin/ script, which will start a JobManager locally.

Each binary release of Flink contains an examples directory with jar files for each of the examples on this page.

To run the WordCount example, issue the following command:

./bin/flink run ./examples/batch/WordCount.jar

The other examples can be started in a similar way.

Note that many examples run without passing any arguments for them, by using build-in data. To run WordCount with real data, you have to pass the path to the data:

./bin/flink run ./examples/batch/WordCount.jar --input /path/to/some/text/data --output /path/to/result

Note that non-local file systems require a schema prefix, such as hdfs://.

Word Count

WordCount is the “Hello World” of Big Data processing systems. It computes the frequency of words in a text collection. The algorithm works in two steps: First, the texts are splits the text to individual words. Second, the words are grouped and counted.

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

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

DataSet<Tuple2<String, Integer>> counts =
        // split up the lines in pairs (2-tuples) containing: (word,1)
        text.flatMap(new Tokenizer())
        // group by the tuple field "0" and sum up tuple field "1"

counts.writeAsCsv(outputPath, "\n", " ");

// User-defined functions
public static class Tokenizer implements FlatMapFunction<String, Tuple2<String, Integer>> {

    public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
        // normalize and split the line
        String[] tokens = value.toLowerCase().split("\\W+");

        // emit the pairs
        for (String token : tokens) {
            if (token.length() > 0) {
                out.collect(new Tuple2<String, Integer>(token, 1));

The WordCount example implements the above described algorithm with input parameters: --input <path> --output <path>. As test data, any text file will do.

val env = ExecutionEnvironment.getExecutionEnvironment

// get input data
val text = env.readTextFile("/path/to/file")

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

counts.writeAsCsv(outputPath, "\n", " ")

The WordCount example implements the above described algorithm with input parameters: --input <path> --output <path>. As test data, any text file will do.

Page Rank

The PageRank algorithm computes the “importance” of pages in a graph defined by links, which point from one pages to another page. It is an iterative graph algorithm, which means that it repeatedly applies the same computation. In each iteration, each page distributes its current rank over all its neighbors, and compute its new rank as a taxed sum of the ranks it received from its neighbors. The PageRank algorithm was popularized by the Google search engine which uses the importance of webpages to rank the results of search queries.

In this simple example, PageRank is implemented with a bulk iteration and a fixed number of iterations.

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// read the pages and initial ranks by parsing a CSV file
DataSet<Tuple2<Long, Double>> pagesWithRanks = env.readCsvFile(pagesInputPath)
						   .types(Long.class, Double.class)

// the links are encoded as an adjacency list: (page-id, Array(neighbor-ids))
DataSet<Tuple2<Long, Long[]>> pageLinkLists = getLinksDataSet(env);

// set iterative data set
IterativeDataSet<Tuple2<Long, Double>> iteration = pagesWithRanks.iterate(maxIterations);

DataSet<Tuple2<Long, Double>> newRanks = iteration
        // join pages with outgoing edges and distribute rank
        .join(pageLinkLists).where(0).equalTo(0).flatMap(new JoinVertexWithEdgesMatch())
        // collect and sum ranks
        // apply dampening factor
        .map(new Dampener(DAMPENING_FACTOR, numPages));

DataSet<Tuple2<Long, Double>> finalPageRanks = iteration.closeWith(
        // termination condition
        .filter(new EpsilonFilter()));

finalPageRanks.writeAsCsv(outputPath, "\n", " ");

// User-defined functions

public static final class JoinVertexWithEdgesMatch
                    implements FlatJoinFunction<Tuple2<Long, Double>, Tuple2<Long, Long[]>,
                                            Tuple2<Long, Double>> {

    public void join(<Tuple2<Long, Double> page, Tuple2<Long, Long[]> adj,
                        Collector<Tuple2<Long, Double>> out) {
        Long[] neigbors = adj.f1;
        double rank = page.f1;
        double rankToDistribute = rank / ((double) neigbors.length);

        for (int i = 0; i < neigbors.length; i++) {
            out.collect(new Tuple2<Long, Double>(neighbors[i], rankToDistribute));

public static final class Dampener implements MapFunction<Tuple2<Long,Double>, Tuple2<Long,Double>> {
    private final double dampening, randomJump;

    public Dampener(double dampening, double numVertices) {
        this.dampening = dampening;
        this.randomJump = (1 - dampening) / numVertices;

    public Tuple2<Long, Double> map(Tuple2<Long, Double> value) {
        value.f1 = (value.f1 * dampening) + randomJump;
        return value;

public static final class EpsilonFilter
                implements FilterFunction<Tuple2<Tuple2<Long, Double>, Tuple2<Long, Double>>> {

    public boolean filter(Tuple2<Tuple2<Long, Double>, Tuple2<Long, Double>> value) {
        return Math.abs(value.f0.f1 - value.f1.f1) > EPSILON;

The PageRank program implements the above example. It requires the following parameters to run: --pages <path> --links <path> --output <path> --numPages <n> --iterations <n>.

// User-defined types
case class Link(sourceId: Long, targetId: Long)
case class Page(pageId: Long, rank: Double)
case class AdjacencyList(sourceId: Long, targetIds: Array[Long])

// set up execution environment
val env = ExecutionEnvironment.getExecutionEnvironment

// read the pages and initial ranks by parsing a CSV file
val pages = env.readCsvFile[Page](pagesInputPath)

// the links are encoded as an adjacency list: (page-id, Array(neighbor-ids))
val links = env.readCsvFile[Link](linksInputPath)

// assign initial ranks to pages
val pagesWithRanks = => Page(p, 1.0 / numPages))

// build adjacency list from link input
val adjacencyLists = links
  // initialize lists
  .map(e => AdjacencyList(e.sourceId, Array(e.targetId)))
  // concatenate lists
  .groupBy("sourceId").reduce {
  (l1, l2) => AdjacencyList(l1.sourceId, l1.targetIds ++ l2.targetIds)

// start iteration
val finalRanks = pagesWithRanks.iterateWithTermination(maxIterations) {
  currentRanks =>
    val newRanks = currentRanks
      // distribute ranks to target pages
      .join(adjacencyLists).where("pageId").equalTo("sourceId") {
        (page, adjacent, out: Collector[Page]) =>
        for (targetId <- adjacent.targetIds) {
          out.collect(Page(targetId, page.rank / adjacent.targetIds.length))
      // collect ranks and sum them up
      .groupBy("pageId").aggregate(SUM, "rank")
      // apply dampening factor
      .map { p =>
        Page(p.pageId, (p.rank * DAMPENING_FACTOR) + ((1 - DAMPENING_FACTOR) / numPages))

    // terminate if no rank update was significant
    val termination = currentRanks.join(newRanks).where("pageId").equalTo("pageId") {
      (current, next, out: Collector[Int]) =>
        // check for significant update
        if (math.abs(current.rank - next.rank) > EPSILON) out.collect(1)

    (newRanks, termination)

val result = finalRanks

// emit result
result.writeAsCsv(outputPath, "\n", " ")

he PageRank program implements the above example. It requires the following parameters to run: --pages <path> --links <path> --output <path> --numPages <n> --iterations <n>.

Input files are plain text files and must be formatted as follows: - Pages represented as an (long) ID separated by new-line characters. * For example "1\n2\n12\n42\n63\n" gives five pages with IDs 1, 2, 12, 42, and 63. - Links are represented as pairs of page IDs which are separated by space characters. Links are separated by new-line characters: * For example "1 2\n2 12\n1 12\n42 63\n" gives four (directed) links (1)->(2), (2)->(12), (1)->(12), and (42)->(63).

For this simple implementation it is required that each page has at least one incoming and one outgoing link (a page can point to itself).

Connected Components

The Connected Components algorithm identifies parts of a larger graph which are connected by assigning all vertices in the same connected part the same component ID. Similar to PageRank, Connected Components is an iterative algorithm. In each step, each vertex propagates its current component ID to all its neighbors. A vertex accepts the component ID from a neighbor, if it is smaller than its own component ID.

This implementation uses a delta iteration: Vertices that have not changed their component ID do not participate in the next step. This yields much better performance, because the later iterations typically deal only with a few outlier vertices.

// read vertex and edge data
DataSet<Long> vertices = getVertexDataSet(env);
DataSet<Tuple2<Long, Long>> edges = getEdgeDataSet(env).flatMap(new UndirectEdge());

// assign the initial component IDs (equal to the vertex ID)
DataSet<Tuple2<Long, Long>> verticesWithInitialId = DuplicateValue<Long>());

// open a delta iteration
DeltaIteration<Tuple2<Long, Long>, Tuple2<Long, Long>> iteration =
        verticesWithInitialId.iterateDelta(verticesWithInitialId, maxIterations, 0);

// apply the step logic:
DataSet<Tuple2<Long, Long>> changes = iteration.getWorkset()
        // join with the edges
        .join(edges).where(0).equalTo(0).with(new NeighborWithComponentIDJoin())
        // select the minimum neighbor component ID
        .groupBy(0).aggregate(Aggregations.MIN, 1)
        // update if the component ID of the candidate is smaller
        .flatMap(new ComponentIdFilter());

// close the delta iteration (delta and new workset are identical)
DataSet<Tuple2<Long, Long>> result = iteration.closeWith(changes, changes);

// emit result
result.writeAsCsv(outputPath, "\n", " ");

// User-defined functions

public static final class DuplicateValue<T> implements MapFunction<T, Tuple2<T, T>> {

    public Tuple2<T, T> map(T vertex) {
        return new Tuple2<T, T>(vertex, vertex);

public static final class UndirectEdge
                    implements FlatMapFunction<Tuple2<Long, Long>, Tuple2<Long, Long>> {
    Tuple2<Long, Long> invertedEdge = new Tuple2<Long, Long>();

    public void flatMap(Tuple2<Long, Long> edge, Collector<Tuple2<Long, Long>> out) {
        invertedEdge.f0 = edge.f1;
        invertedEdge.f1 = edge.f0;

public static final class NeighborWithComponentIDJoin
                implements JoinFunction<Tuple2<Long, Long>, Tuple2<Long, Long>, Tuple2<Long, Long>> {

    public Tuple2<Long, Long> join(Tuple2<Long, Long> vertexWithComponent, Tuple2<Long, Long> edge) {
        return new Tuple2<Long, Long>(edge.f1, vertexWithComponent.f1);

public static final class ComponentIdFilter
                    implements FlatMapFunction<Tuple2<Tuple2<Long, Long>, Tuple2<Long, Long>>,
                                            Tuple2<Long, Long>> {

    public void flatMap(Tuple2<Tuple2<Long, Long>, Tuple2<Long, Long>> value,
                        Collector<Tuple2<Long, Long>> out) {
        if (value.f0.f1 < value.f1.f1) {

The ConnectedComponents program implements the above example. It requires the following parameters to run: --vertices <path> --edges <path> --output <path> --iterations <n>.

// set up execution environment
val env = ExecutionEnvironment.getExecutionEnvironment

// read vertex and edge data
// assign the initial components (equal to the vertex id)
val vertices = getVerticesDataSet(env).map { id => (id, id) }

// undirected edges by emitting for each input edge the input edges itself and an inverted
// version
val edges = getEdgesDataSet(env).flatMap { edge => Seq(edge, (edge._2, edge._1)) }

// open a delta iteration
val verticesWithComponents = vertices.iterateDelta(vertices, maxIterations, Array(0)) {
  (s, ws) =>

    // apply the step logic: join with the edges
    val allNeighbors = ws.join(edges).where(0).equalTo(0) { (vertex, edge) =>
      (edge._2, vertex._2)

    // select the minimum neighbor
    val minNeighbors = allNeighbors.groupBy(0).min(1)

    // update if the component of the candidate is smaller
    val updatedComponents = minNeighbors.join(s).where(0).equalTo(0) {
      (newVertex, oldVertex, out: Collector[(Long, Long)]) =>
        if (newVertex._2 < oldVertex._2) out.collect(newVertex)

    // delta and new workset are identical
    (updatedComponents, updatedComponents)

verticesWithComponents.writeAsCsv(outputPath, "\n", " ")

The ConnectedComponents program implements the above example. It requires the following parameters to run: --vertices <path> --edges <path> --output <path> --iterations <n>.

Input files are plain text files and must be formatted as follows: - Vertices represented as IDs and separated by new-line characters. * For example "1\n2\n12\n42\n63\n" gives five vertices with (1), (2), (12), (42), and (63). - Edges are represented as pairs for vertex IDs which are separated by space characters. Edges are separated by new-line characters: * For example "1 2\n2 12\n1 12\n42 63\n" gives four (undirected) links (1)-(2), (2)-(12), (1)-(12), and (42)-(63).

Relational Query

The Relational Query example assumes two tables, one with orders and the other with lineitems as specified by the TPC-H decision support benchmark. TPC-H is a standard benchmark in the database industry. See below for instructions how to generate the input data.

The example implements the following SQL query.

SELECT l_orderkey, o_shippriority, sum(l_extendedprice) as revenue
    FROM orders, lineitem
WHERE l_orderkey = o_orderkey
    AND o_orderstatus = "F"
    AND YEAR(o_orderdate) > 1993
    AND o_orderpriority LIKE "5%"
GROUP BY l_orderkey, o_shippriority;

The Flink program, which implements the above query looks as follows.

// get orders data set: (orderkey, orderstatus, orderdate, orderpriority, shippriority)
DataSet<Tuple5<Integer, String, String, String, Integer>> orders = getOrdersDataSet(env);
// get lineitem data set: (orderkey, extendedprice)
DataSet<Tuple2<Integer, Double>> lineitems = getLineitemDataSet(env);

// orders filtered by year: (orderkey, custkey)
DataSet<Tuple2<Integer, Integer>> ordersFilteredByYear =
        // filter orders
            new FilterFunction<Tuple5<Integer, String, String, String, Integer>>() {
                public boolean filter(Tuple5<Integer, String, String, String, Integer> t) {
                    // status filter
                    if(!t.f1.equals(STATUS_FILTER)) {
                        return false;
                    // year filter
                    } else if(Integer.parseInt(t.f2.substring(0, 4)) <= YEAR_FILTER) {
                        return false;
                    // order priority filter
                    } else if(!t.f3.startsWith(OPRIO_FILTER)) {
                        return false;
                    return true;
        // project fields out that are no longer required
        .project(0,4).types(Integer.class, Integer.class);

// join orders with lineitems: (orderkey, shippriority, extendedprice)
DataSet<Tuple3<Integer, Integer, Double>> lineitemsOfOrders =
                            .types(Integer.class, Integer.class, Double.class);

// extendedprice sums: (orderkey, shippriority, sum(extendedprice))
DataSet<Tuple3<Integer, Integer, Double>> priceSums =
        // group by order and sum extendedprice
        lineitemsOfOrders.groupBy(0,1).aggregate(Aggregations.SUM, 2);

// emit result

The Relational Query program implements the above query. It requires the following parameters to run: --orders <path> --lineitem <path> --output <path>.

Coming soon…

The Relational Query program implements the above query. It requires the following parameters to run: --orders <path> --lineitem <path> --output <path>.

The orders and lineitem files can be generated using the TPC-H benchmark suite’s data generator tool (DBGEN). Take the following steps to generate arbitrary large input files for the provided Flink programs:

  1. Download and unpack DBGEN
  2. Make a copy of makefile.suite called Makefile and perform the following changes:
CC       = gcc
  1. Build DBGEN using make
  2. Generate lineitem and orders relations using dbgen. A scale factor (-s) of 1 results in a generated data set with about 1 GB size.
./dbgen -T o -s 1