Flink DataSet API 编程指南

DataSet programs in Flink are regular programs that implement transformations on data sets(e.g., filtering, mapping, joining, grouping). The data sets are initially created from certainsources (e.g., by reading files, or from local collections). Results are returned via sinks, which may forexample write the data to (distributed) files, or to standard output (for example the command lineterminal). 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.

Please see basic concepts for an introductionto the basic concepts of the Flink API.

In order to create your own Flink DataSet program, we encourage you to start with theanatomy of a Flink Programand gradually add your owntransformations. The remaining sections act as references for additionaloperations and advanced features.

• Example Program
• DataSet Transformations
• Data Sources
  • Read Compressed Files
• Data Sinks
• Iteration Operators
• Operating on data objects in functions
  • Object-Reuse Disabled (DEFAULT)
  • Object-Reuse Enabled
• Debugging
  • Local Execution Environment
  • Collection Data Sources and Sinks
• Semantic Annotations
• Broadcast Variables
• Distributed Cache
• Passing Parameters to Functions

Example Program

The following program is a complete, working example of WordCount. You can copy & paste the codeto run it locally. You only have to include the correct Flink’s library into your project(see Section Linking with Flink) and specify the imports. Then you are readyto go!

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()
  }
}

DataSet Transformations

Data transformations transform one or more DataSets into a new DataSet. Programs can combinemultiple transformations into sophisticated assemblies.

This section gives a brief overview of the available transformations. The transformationsdocumentation has a full description of all transformations withexamples.

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

data.flatMap(new FlatMapFunction<String, String>() {  public void flatMap(String value, Collector<String> out) {    for (String s : value.split(" ")) {      out.collect(s);    }  }});

data.mapPartition(new MapPartitionFunction<String, Long>() {  public void mapPartition(Iterable<String> values, Collector<Long> out) {    long c = 0;    for (String s : values) {      c++;    }    out.collect(c);  }});

data.filter(new FilterFunction<Integer>() {  public boolean filter(Integer value) { return value > 1000; }});

data.reduce(new ReduceFunction<Integer> {  public Integer reduce(Integer a, Integer b) { return a + b; }});

data.reduceGroup(new GroupReduceFunction<Integer, Integer> {  public void reduce(Iterable<Integer> values, Collector<Integer> out) {    int prefixSum = 0;    for (Integer i : values) {      prefixSum += i;      out.collect(prefixSum);    }  }});

Dataset<Tuple3<Integer, String, Double>> input = // […]DataSet<Tuple3<Integer, String, Double>> output = input.aggregate(SUM, 0).and(MIN, 2);

    Dataset<Tuple3<Integer, String, Double>> input = // […]DataSet<Tuple3<Integer, String, Double>> output = input.sum(0).andMin(2);    

data.distinct();

result = input1.join(input2)               .where(0)       // key of the first input (tuple field 0)               .equalTo(1);    // key of the second input (tuple field 1)

// This executes a join by broadcasting the first data set// using a hash table for the broadcast dataresult = input1.join(input2, JoinHint.BROADCASTHASH_FIRST)               .where(0).equalTo(1);

input1.leftOuterJoin(input2) // rightOuterJoin or fullOuterJoin for right or full outer joins      .where(0)              // key of the first input (tuple field 0)      .equalTo(1)            // key of the second input (tuple field 1)      .with(new JoinFunction<String, String, String>() {          public String join(String v1, String v2) {             // NOTE:             // - v2 might be null for leftOuterJoin             // - v1 might be null for rightOuterJoin             // - v1 OR v2 might be null for fullOuterJoin          }      });

data1.coGroup(data2)     .where(0)     .equalTo(1)     .with(new CoGroupFunction<String, String, String>() {         public void coGroup(Iterable<String> in1, Iterable<String> in2, Collector<String> out) {           out.collect(…);         }      });

DataSet<Integer> data1 = // […]DataSet<String> data2 = // […]DataSet<Tuple2<Integer, String>> result = data1.cross(data2);

DataSet<String> data1 = // […]DataSet<String> data2 = // […]DataSet<String> result = data1.union(data2);

DataSet<String> in = // […]DataSet<String> result = in.rebalance()                           .map(new Mapper());

DataSet<Tuple2<String,Integer>> in = // […]DataSet<Integer> result = in.partitionByHash(0)                            .mapPartition(new PartitionMapper());

DataSet<Tuple2<String,Integer>> in = // […]DataSet<Integer> result = in.partitionByRange(0)                            .mapPartition(new PartitionMapper());

DataSet<Tuple2<String,Integer>> in = // […]DataSet<Integer> result = in.partitionCustom(partitioner, key)                            .mapPartition(new PartitionMapper());

DataSet<Tuple2<String,Integer>> in = // […]DataSet<Integer> result = in.sortPartition(1, Order.ASCENDING)                            .mapPartition(new PartitionMapper());

DataSet<Tuple2<String,Integer>> in = // […]// regular data setDataSet<Tuple2<String,Integer>> result1 = in.first(3);// grouped data setDataSet<Tuple2<String,Integer>> result2 = in.groupBy(0)                                            .first(3);// grouped-sorted data setDataSet<Tuple2<String,Integer>> result3 = in.groupBy(0)                                            .sortGroup(1, Order.ASCENDING)                                            .first(3);

The following transformations are available on data sets of Tuples:

DataSet<Tuple3<Integer, Double, String>> in = // […]DataSet<Tuple2<String, Integer>> out = in.project(2,0);

DataSet<Tuple3<Integer, Double, String>> in = // […]// a DataSet with a single tuple with minimum values for the Integer and String fields.DataSet<Tuple3<Integer, Double, String>> out = in.minBy(0, 2);// a DataSet with one tuple for each group with the minimum value for the Double field.DataSet<Tuple3<Integer, Double, String>> out2 = in.groupBy(2)                                                  .minBy(1);

data.map { x => x.toInt }

data.flatMap { str => str.split(" ") }

data.mapPartition { in => in map { (, 1) } }

data.filter {  > 1000 }

data.reduce {  +  }

data.reduceGroup { elements => elements.sum }

val input: DataSet[(Int, String, Double)] = // […]val output: DataSet[(Int, String, Double)] = input.aggregate(SUM, 0).aggregate(MIN, 2)

val input: DataSet[(Int, String, Double)] = // […]val output: DataSet[(Int, String, Double)] = input.sum(0).min(2)

         data.distinct()      

// In this case tuple fields are used as keys. "0" is the join field on the first tuple// "1" is the join field on the second tuple.val result = input1.join(input2).where(0).equalTo(1)

// This executes a join by broadcasting the first data set// using a hash table for the broadcast dataval result = input1.join(input2, JoinHint.BROADCASTHASH_FIRST)                   .where(0).equalTo(1)

val joined = left.leftOuterJoin(right).where(0).equalTo(1) {   (left, right) =>     val a = if (left == null) "none" else left._1     (a, right)  }

data1.coGroup(data2).where(0).equalTo(1)

val data1: DataSet[Int] = // […]val data2: DataSet[String] = // […]val result: DataSet[(Int, String)] = data1.cross(data2)

data.union(data2)

val data1: DataSet[Int] = // […]val result: DataSet[(Int, String)] = data1.rebalance().map(…)

val in: DataSet[(Int, String)] = // […]val result = in.partitionByHash(0).mapPartition { … }

val in: DataSet[(Int, String)] = // […]val result = in.partitionByRange(0).mapPartition { … }

val in: DataSet[(Int, String)] = // […]val result = in  .partitionCustom(partitioner, key).mapPartition { … }

val in: DataSet[(Int, String)] = // […]val result = in.sortPartition(1, Order.ASCENDING).mapPartition { … }

val in: DataSet[(Int, String)] = // […]// regular data setval result1 = in.first(3)// grouped data setval result2 = in.groupBy(0).first(3)// grouped-sorted data setval result3 = in.groupBy(0).sortGroup(1, Order.ASCENDING).first(3)

The following transformations are available on data sets of Tuples:

val in: DataSet[(Int, Double, String)] = // […]// a data set with a single tuple with minimum values for the Int and String fields.val out: DataSet[(Int, Double, String)] = in.minBy(0, 2)// a data set with one tuple for each group with the minimum value for the Double field.val out2: DataSet[(Int, Double, String)] = in.groupBy(2)                                             .minBy(1)

Extraction from tuples, case classes and collections via anonymous pattern matching, like the following:

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

is not supported by the API out-of-the-box. To use this feature, you should use a Scala API extension.

The parallelism of a transformation can be defined by setParallelism(int) whilename(String) assigns a custom name to a transformation which is helpful for debugging. The same ispossible for Data Sources and Data Sinks.

withParameters(Configuration) passes Configuration objects, which can be accessed from the open() method inside the user function.

Data Sources

Data sources create the initial data sets, such as from files or from Java collections. The generalmechanism of creating data sets is abstracted behind anInputFormat.Flink comeswith several built-in formats to create data sets from common file formats. Many of them haveshortcut methods on the ExecutionEnvironment.

File-based:

• readTextFile(path) / TextInputFormat - Reads files line wise and returns them as Strings.

• readTextFileWithValue(path) / TextValueInputFormat - Reads files line wise and returns them asStringValues. StringValues are mutable strings.

• readCsvFile(path) / CsvInputFormat - Parses files of comma (or another char) delimited fields.Returns a DataSet of tuples or POJOs. Supports the basic java types and their Value counterparts as fieldtypes.

• readFileOfPrimitives(path, Class) / PrimitiveInputFormat - Parses files of new-line (or another char sequence)delimited primitive data types such as String or Integer.

• readFileOfPrimitives(path, delimiter, Class) / PrimitiveInputFormat - Parses files of new-line (or another char sequence) delimited primitive data types such as String or Integer using the given delimiter.

Collection-based:

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

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

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

• fromParallelCollection(SplittableIterator, Class) - Creates a data set from an iterator, inparallel. 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, inparallel.

Generic:

• readFile(inputFormat, path) / FileInputFormat - Accepts a file input format.

• createInput(inputFormat) / InputFormat - Accepts a generic input format.

Examples

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 SequenceFileInputFormat
DataSet<Tuple2<IntWritable, Text>> tuples =
 env.createInput(HadoopInputs.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(
      JDBCInputFormat.buildJDBCInputFormat()
                     .setDrivername("org.apache.derby.jdbc.EmbeddedDriver")
                     .setDBUrl("jdbc:derby:memory:persons")
                     .setQuery("select name, age from persons")
                     .setRowTypeInfo(new RowTypeInfo(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.INT_TYPE_INFO))
                     .finish()
    );
// 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.

Configuring CSV Parsing

Flink offers a number of configuration options for CSV parsing:

• types(Class … types) specifies the types of the fields to parse. It is mandatory to configure the types of the parsed fields.In case of the type class Boolean.class, “True” (case-insensitive), “False” (case-insensitive), “1” and “0” are treated as booleans.

• lineDelimiter(String del) specifies the delimiter of individual records. The default line delimiter is the new-line character '\n'.

• fieldDelimiter(String del) specifies the delimiter that separates fields of a record. The default field delimiter is the comma character ','.

• includeFields(boolean … flag), includeFields(String mask), or includeFields(long bitMask) defines which fields to read from the input file (and which to ignore). By default the first n fields (as defined by the number of types in the types() call) are parsed.

• parseQuotedStrings(char quoteChar) enables quoted string parsing. Strings are parsed as quoted strings if the first character of the string field is the quote character (leading or tailing whitespaces are not trimmed). Field delimiters within quoted strings are ignored. Quoted string parsing fails if the last character of a quoted string field is not the quote character or if the quote character appears at some point which is not the start or the end of the quoted string field (unless the quote character is escaped using ‘'). If quoted string parsing is enabled and the first character of the field is not the quoting string, the string is parsed as unquoted string. By default, quoted string parsing is disabled.

• ignoreComments(String commentPrefix) specifies a comment prefix. All lines that start with the specified comment prefix are not parsed and ignored. By default, no lines are ignored.

• ignoreInvalidLines() enables lenient parsing, i.e., lines that cannot be correctly parsed are ignored. By default, lenient parsing is disabled and invalid lines raise an exception.

• ignoreFirstLine() configures the InputFormat to ignore the first line of the input file. By default no line is ignored.

Recursive Traversal of the Input Path Directory

For file-based inputs, when the input path is a directory, nested files are not enumerated by default. Instead, only the files inside the base directory are read, while nested files are ignored. Recursive enumeration of nested files can be enabled through the recursive.file.enumeration configuration parameter, like in the following example.

// 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);

Data sources create the initial data sets, such as from files or from Java collections. The generalmechanism of creating data sets is abstracted behind anInputFormat.Flink comeswith several built-in formats to create data sets from common file formats. Many of them haveshortcut methods on the ExecutionEnvironment.

File-based:

• readTextFile(path) / TextInputFormat - Reads files line wise and returns them as Strings.

• readTextFileWithValue(path) / TextValueInputFormat - Reads files line wise and returns them asStringValues. StringValues are mutable strings.

• readCsvFile(path) / CsvInputFormat - Parses files of comma (or another char) delimited fields.Returns a DataSet of tuples, case class objects, or POJOs. Supports the basic java types and their Value counterparts as fieldtypes.

• readFileOfPrimitives(path, delimiter) / PrimitiveInputFormat - Parses files of new-line (or another char sequence)delimited primitive data types such as String or Integer using the given delimiter.

• readSequenceFile(Key, Value, path) / SequenceFileInputFormat - Creates a JobConf and reads file from the specified path with type SequenceFileInputFormat, Key class and Value class and returns them as Tuple2.

Collection-based:

• fromCollection(Iterable) - Creates a data set from an Iterable. All elementsreturned by the Iterable must be of the same type.

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

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

• fromParallelCollection(SplittableIterator) - Creates a data set from an iterator, inparallel. 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, inparallel.

Generic:

• readFile(inputFormat, path) / FileInputFormat - Accepts a file input format.

• createInput(inputFormat) / InputFormat - Accepts a generic input format.

Examples

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 SequenceFileInputFormat
val tuples = env.createInput(HadoopInputs.readSequenceFile(classOf[IntWritable], classOf[Text],
 "hdfs://nnHost:nnPort/path/to/file"))

Configuring CSV Parsing

Flink offers a number of configuration options for CSV parsing:

• lineDelimiter: String specifies the delimiter of individual records. The default line delimiter is the new-line character '\n'.

• fieldDelimiter: String specifies the delimiter that separates fields of a record. The default field delimiter is the comma character ','.

• includeFields: Array[Int] defines which fields to read from the input file (and which to ignore). By default the first n fields (as defined by the number of types in the types() call) are parsed.

• pojoFields: Array[String] specifies the fields of a POJO that are mapped to CSV fields. Parsers for CSV fields are automatically initialized based on the type and order of the POJO fields.

• parseQuotedStrings: Character enables quoted string parsing. Strings are parsed as quoted strings if the first character of the string field is the quote character (leading or tailing whitespaces are not trimmed). Field delimiters within quoted strings are ignored. Quoted string parsing fails if the last character of a quoted string field is not the quote character. If quoted string parsing is enabled and the first character of the field is not the quoting string, the string is parsed as unquoted string. By default, quoted string parsing is disabled.

• ignoreComments: String specifies a comment prefix. All lines that start with the specified comment prefix are not parsed and ignored. By default, no lines are ignored.

• lenient: Boolean enables lenient parsing, i.e., lines that cannot be correctly parsed are ignored. By default, lenient parsing is disabled and invalid lines raise an exception.

• ignoreFirstLine: Boolean configures the InputFormat to ignore the first line of the input file. By default no line is ignored.

Recursive Traversal of the Input Path Directory

For file-based inputs, when the input path is a directory, nested files are not enumerated by default. Instead, only the files inside the base directory are read, while nested files are ignored. Recursive enumeration of nested files can be enabled through the recursive.file.enumeration configuration parameter, like in the following example.

// 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)

Read Compressed Files

Flink currently supports transparent decompression of input files if these are marked with an appropriate file extension. In particular, this means that no further configuration of the input formats is necessary and any FileInputFormat support the compression, including custom input formats. Please notice that compressed files might not be read in parallel, thus impacting job scalability.

The following table lists the currently supported compression methods.

Data Sinks

Data sinks consume DataSets and are used to store or return them. Data sink operations are describedusing anOutputFormat.Flink comes with a variety of built-in output formats that are encapsulated behind operations on theDataSet:

• writeAsText() / TextOutputFormat - Writes elements line-wise as Strings. The Strings areobtained by calling the toString() method of each element.
• writeAsFormattedText() / TextOutputFormat - Write elements line-wise as Strings. The Stringsare obtained by calling a user-defined format() method for each element.
• writeAsCsv(…) / CsvOutputFormat - Writes tuples as comma-separated value files. Row and fielddelimiters are configurable. The value for each field comes from the toString() method of the objects.
• print() / printToErr() / print(String msg) / printToErr(String msg) - Prints the toString() valueof each element on the standard out / standard error stream. Optionally, a prefix (msg) can be provided which isprepended to the output. This can help to distinguish between different calls to print. If the parallelism isgreater than 1, the output will also be prepended with the identifier of the task which produced the output.
• write() / FileOutputFormat - Method and base class for custom file outputs. Supportscustom object-to-bytes conversion.
• output()/ OutputFormat - Most generic output method, for data sinks that are not file based(such as storing the result in a database).

A DataSet can be input to multiple operations. Programs can write or print a data set and at thesame time run additional transformations on them.

Examples

Standard data sink methods:

// 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;
        }
    });

Using a custom output format:

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()
    );

Locally Sorted Output

The output of a data sink can be locally sorted on specified fields in specified orders using tuple field positions or field expressions. This works for every output format.

The following examples show how to use this feature:

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(...);

Globally sorted output is not supported yet.

Data sinks consume DataSets and are used to store or return them. Data sink operations are describedusing anOutputFormat.Flink comes with a variety of built-in output formats that are encapsulated behind operations on theDataSet:

• writeAsText() / TextOutputFormat - Writes elements line-wise as Strings. The Strings areobtained by calling the toString() method of each element.
• writeAsCsv(…) / CsvOutputFormat - Writes tuples as comma-separated value files. Row and fielddelimiters 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 thestandard out / standard error stream.
• write() / FileOutputFormat - Method and base class for custom file outputs. Supportscustom object-to-bytes conversion.
• output()/ OutputFormat - Most generic output method, for data sinks that are not file based(such as storing the result in a database).

A DataSet can be input to multiple operations. Programs can write or print a data set and at thesame time run additional transformations on them.

Examples

Standard data sink methods:

// 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")

Locally Sorted Output

The output of a data sink can be locally sorted on specified fields in specified orders using tuple field positions or field expressions. This works for every output format.

The following examples show how to use this feature:

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(...)

Globally sorted output is not supported yet.

Iteration Operators

Iterations implement loops in Flink programs. The iteration operators encapsulate a part of theprogram and execute it repeatedly, feeding back the result of one iteration (the partial solution)into the next iteration. There are two types of iterations in Flink: BulkIteration andDeltaIteration.

This section provides quick examples on how to use both operators. Check out the Introduction toIterations page for a more detailed introduction.

Bulk Iterations

To create a BulkIteration call the iterate(int) method of the DataSet the iteration should startat. This will return an IterativeDataSet, which can be transformed with the regular operators. Thesingle argument to the iterate call specifies the maximum number of iterations.

To specify the end of an iteration call the closeWith(DataSet) method on the IterativeDataSet tospecify which transformation should be fed back to the next iteration. You can optionally specify atermination criterion with closeWith(DataSet, DataSet), which evaluates the second DataSet andterminates the iteration, if this DataSet is empty. If no termination criterion is specified, theiteration terminates after the given maximum number iterations.

The following example iteratively estimates the number Pi. The goal is to count the number of randompoints, which fall into the unit circle. In each iteration, a random point is picked. If this pointlies inside the unit circle, we increment the count. Pi is then estimated as the resulting countdivided by the number of iterations multiplied by 4.

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");

You can also check out theK-Means example,which uses a BulkIteration to cluster a set of unlabeled points.

Delta Iterations

Delta iterations exploit the fact that certain algorithms do not change every data point of thesolution in each iteration.

In addition to the partial solution that is fed back (called workset) in every iteration, deltaiterations maintain state across iterations (called solution set), which can be updated throughdeltas. The result of the iterative computation is the state after the last iteration. Please referto the Introduction to Iterations for an overview of the basic principle of deltaiterations.

Defining a DeltaIteration is similar to defining a BulkIteration. For delta iterations, two datasets form the input to each iteration (workset and solution set), and two data sets are produced asthe result (new workset, solution set delta) in each iteration.

To create a DeltaIteration call the iterateDelta(DataSet, int, int) (or iterateDelta(DataSet,int, int[]) respectively). This method is called on the initial solution set. The arguments are theinitial delta set, the maximum number of iterations and the key positions. The returnedDeltaIteration object gives you access to the DataSets representing the workset and solution setvia the methods iteration.getWorkset() and iteration.getSolutionSet().

Below is an example for the syntax of a delta iteration

// 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);

Bulk Iterations

To create a BulkIteration call the iterate(int) method of the DataSet the iteration should startat and also specify a step function. The step function gets the input DataSet for the currentiteration and must return a new DataSet. The parameter of the iterate call is the maximum numberof iterations after which to stop.

There is also the iterateWithTermination(int) function that accepts a step function thatreturns two DataSets: The result of the iteration step and a termination criterion. The iterationsare stopped once the termination criterion DataSet is empty.

The following example iteratively estimates the number Pi. The goal is to count the number of randompoints, which fall into the unit circle. In each iteration, a random point is picked. If this pointlies inside the unit circle, we increment the count. Pi is then estimated as the resulting countdivided by the number of iterations multiplied by 4.

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")

You can also check out theK-Means example,which uses a BulkIteration to cluster a set of unlabeled points.

Delta Iterations

Delta iterations exploit the fact that certain algorithms do not change every data point of thesolution in each iteration.

In addition to the partial solution that is fed back (called workset) in every iteration, deltaiterations maintain state across iterations (called solution set), which can be updated throughdeltas. The result of the iterative computation is the state after the last iteration. Please referto the Introduction to Iterations for an overview of the basic principle of deltaiterations.

Defining a DeltaIteration is similar to defining a BulkIteration. For delta iterations, two datasets form the input to each iteration (workset and solution set), and two data sets are produced asthe result (new workset, solution set delta) in each iteration.

To create a DeltaIteration call the iterateDelta(initialWorkset, maxIterations, key) on theinitial solution set. The step function takes two parameters: (solutionSet, workset), and mustreturn two values: (solutionSetDelta, newWorkset).

Below is an example for the syntax of a delta iteration

// 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()

Operating on data objects in functions

Flink’s runtime exchanges data with user functions in form of Java objects. Functions receive input objects from the runtime as method parameters and return output objects as result. Because these objects are accessed by user functions and runtime code, it is very important to understand and follow the rules about how the user code may access, i.e., read and modify, these objects.

User functions receive objects from Flink’s runtime either as regular method parameters (like a MapFunction) or through an Iterable parameter (like a GroupReduceFunction). We refer to objects that the runtime passes to a user function as input objects. User functions can emit objects to the Flink runtime either as a method return value (like a MapFunction) or through a Collector (like a FlatMapFunction). We refer to objects which have been emitted by the user function to the runtime as output objects.

Flink’s DataSet API features two modes that differ in how Flink’s runtime creates or reuses input objects. This behavior affects the guarantees and constraints for how user functions may interact with input and output objects. The following sections define these rules and give coding guidelines to write safe user function code.

Object-Reuse Disabled (DEFAULT)

By default, Flink operates in object-reuse disabled mode. This mode ensures that functions always receive new input objects within a function call. The object-reuse disabled mode gives better guarantees and is safer to use. However, it comes with a certain processing overhead and might cause higher Java garbage collection activity. The following table explains how user functions may access input and output objects in object-reuse disabled mode.

Coding guidelines for the object-reuse disabled (default) mode:

• Do not remember and read input objects across method calls.
• Do not read objects after you emitted them.

Object-Reuse Enabled

In object-reuse enabled mode, Flink’s runtime minimizes the number of object instantiations. This can improve the performance and can reduce the Java garbage collection pressure. The object-reuse enabled mode is activated by calling ExecutionConfig.enableObjectReuse(). The following table explains how user functions may access input and output objects in object-reuse enabled mode.

Coding guidelines for object-reuse enabled:

• Do not remember input objects received from an Iterable.
• Do not remember and read input objects across method calls.
• Do not modify or emit input objects, except for input objects of MapFunction, FlatMapFunction, MapPartitionFunction, GroupReduceFunction, GroupCombineFunction, CoGroupFunction, and InputFormat.next(reuse).
• To reduce object instantiations, you can always emit a dedicated output object which is repeatedly modified but never read.

Debugging

Before running a data analysis program on a large data set in a distributed cluster, it is a goodidea to make sure that the implemented algorithm works as desired. Hence, implementing data analysisprograms is usually an incremental process of checking results, debugging, and improving.

Flink provides a few nice features to significantly ease the development process of data analysisprograms by supporting local debugging from within an IDE, injection of test data, and collection ofresult data. This section give some hints how to ease the development of Flink programs.

Local Execution Environment

A LocalEnvironment starts a Flink system within the same JVM process it was created in. If youstart the LocalEnvironment from an IDE, you can set breakpoints in your code and easily debug yourprogram.

A LocalEnvironment is created and used as follows:

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()

Collection Data Sources and Sinks

Providing input for an analysis program and checking its output is cumbersome when done by creatinginput files and reading output files. Flink features special data sources and sinks which are backedby Java collections to ease testing. Once a program has been tested, the sources and sinks can beeasily replaced by sources and sinks that read from / write to external data stores such as HDFS.

Collection data sources can be used as follows:

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);

A collection data sink is specified as follows:

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

Note: Currently, the collection data sink is restricted to local execution, as a debugging tool.

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)

Note: Currently, the collection data source requires that data types and iterators implementSerializable. Furthermore, collection data sources can not be executed in parallel (parallelism = 1).

Semantic Annotations

Semantic annotations can be used to give Flink hints about the behavior of a function.They tell the system which fields of a function’s input the function reads and evaluates andwhich fields it unmodified forwards from its input to its output.Semantic annotations are a powerful means to speed up execution, because theyallow the system to reason about reusing sort orders or partitions across multiple operations. Usingsemantic annotations may eventually save the program from unnecessary data shuffling or unnecessarysorts and significantly improve the performance of a program.

Note: The use of semantic annotations is optional. However, it is absolutely crucial tobe conservative when providing semantic annotations!Incorrect semantic annotations will cause Flink to make incorrect assumptions about your program andmight eventually lead to incorrect results.If the behavior of an operator is not clearly predictable, no annotation should be provided.Please read the documentation carefully.

The following semantic annotations are currently supported.

Forwarded Fields Annotation

Forwarded fields information declares input fields which are unmodified forwarded by a function to the same position or to another position in the output.This information is used by the optimizer to infer whether a data property such as sorting orpartitioning is preserved by a function.For functions that operate on groups of input elements such as GroupReduce, GroupCombine, CoGroup, and MapPartition, all fields that are defined as forwarded fields must always be jointly forwarded from the same input element. The forwarded fields of each element that is emitted by a group-wise function may originate from a different element of the function’s input group.

Field forward information is specified using field expressions.Fields that are forwarded to the same position in the output can be specified by their position.The specified position must be valid for the input and output data type and have the same type.For example the String "f2" declares that the third field of a Java input tuple is always equal to the third field in the output tuple.

Fields which are unmodified forwarded to another position in the output are declared by specifying thesource field in the input and the target field in the output as field expressions.The String "f0->f2" denotes that the first field of the Java input tuple isunchanged copied to the third field of the Java output tuple. The wildcard expression can be used to refer to a whole input or output type, i.e., "f0->" denotes that the output of a function is always equal to the first field of its Java input tuple.

Multiple forwarded fields can be declared in a single String by separating them with semicolons as "f0; f2->f1; f3->f2" or in separate Strings "f0", "f2->f1", "f3->f2". When specifying forwarded fields it is not required that all forwarded fields are declared, but all declarations must be correct.

Forwarded field information can be declared by attaching Java annotations on function class definitions orby passing them as operator arguments after invoking a function on a DataSet as shown below.

Function Class Annotations

• @ForwardedFields for single input functions such as Map and Reduce.
• @ForwardedFieldsFirst for the first input of a functions with two inputs such as Join and CoGroup.
• @ForwardedFieldsSecond for the second input of a functions with two inputs such as Join and CoGroup.

Operator Arguments

• data.map(myMapFnc).withForwardedFields() for single input function such as Map and Reduce.
• data1.join(data2).where().equalTo().with(myJoinFnc).withForwardFieldsFirst() for the first input of a function with two inputs such as Join and CoGroup.
• data1.join(data2).where().equalTo().with(myJoinFnc).withForwardFieldsSecond() for the second input of a function with two inputs such as Join and CoGroup.

Please note that it is not possible to overwrite field forward information which was specified as a class annotation by operator arguments.

Example

The following example shows how to declare forwarded field information using a function class annotation:

@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)
  }
}

Non-Forwarded Fields

Non-forwarded fields information declares all fields which are not preserved on the same position in a function’s output.The values of all other fields are considered to be preserved at the same position in the output.Hence, non-forwarded fields information is inverse to forwarded fields information.Non-forwarded field information for group-wise operators such as GroupReduce, GroupCombine, CoGroup, and MapPartition must fulfill the same requirements as for forwarded field information.

IMPORTANT: The specification of non-forwarded fields information is optional. However if used,ALL! non-forwarded fields must be specified, because all other fields are considered to be forwarded in place. It is safe to declare a forwarded field as non-forwarded.

Non-forwarded fields are specified as a list of field expressions. The list can be either given as a single String with field expressions separated by semicolons or as multiple Strings.For example both "f1; f3" and "f1", "f3" declare that the second and fourth field of a Java tupleare not preserved in place and all other fields are preserved in place.Non-forwarded field information can only be specified for functions which have identical input and output types.

Non-forwarded field information is specified as function class annotations using the following annotations:

• @NonForwardedFields for single input functions such as Map and Reduce.
• @NonForwardedFieldsFirst for the first input of a function with two inputs such as Join and CoGroup.
• @NonForwardedFieldsSecond for the second input of a function with two inputs such as Join and CoGroup.

Example

The following example shows how to declare non-forwarded field information:

@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)
  }
}

Read Fields

Read fields information declares all fields that are accessed and evaluated by a function, i.e.,all fields that are used by the function to compute its result.For example, fields which are evaluated in conditional statements or used for computations must be marked as read when specifying read fields information.Fields which are only unmodified forwarded to the output without evaluating their values or fields which are not accessed at all are not considered to be read.

IMPORTANT: The specification of read fields information is optional. However if used,ALL! read fields must be specified. It is safe to declare a non-read field as read.

Read fields are specified as a list of field expressions. The list can be either given as a single String with field expressions separated by semicolons or as multiple Strings.For example both "f1; f3" and "f1", "f3" declare that the second and fourth field of a Java tuple are read and evaluated by the function.

Read field information is specified as function class annotations using the following annotations:

• @ReadFields for single input functions such as Map and Reduce.
• @ReadFieldsFirst for the first input of a function with two inputs such as Join and CoGroup.
• @ReadFieldsSecond for the second input of a function with two inputs such as Join and CoGroup.

Example

The following example shows how to declare read field information:

@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)
    }
  }
}

Broadcast Variables

Broadcast variables allow you to make a data set available to all parallel instances of anoperation, in addition to the regular input of the operation. This is useful for auxiliary datasets, or data-dependent parameterization. The data set will then be accessible at the operator as aCollection.

• Broadcast: broadcast sets are registered by name via withBroadcastSet(DataSet, String), and
• Access: accessible via getRuntimeContext().getBroadcastVariable(String) at the target operator.

// 1. The DataSet to be broadcast
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 broadcast 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

Make sure that the names (broadcastSetName in the previous example) match when registering andaccessing broadcast data sets. For a complete example program, have a look atK-Means Algorithm.

// 1. The DataSet to be broadcast
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 broadcast DataSet as a Collection
      broadcastSet = getRuntimeContext().getBroadcastVariable[String]("broadcastSetName").asScala
    }
    def map(in: String): String = {
        ...
    }
}).withBroadcastSet(toBroadcast, "broadcastSetName") // 2. Broadcast the DataSet

Make sure that the names (broadcastSetName in the previous example) match when registering andaccessing broadcast data sets. For a complete example program, have a look atKMeans Algorithm.

Note: As the content of broadcast variables is kept in-memory on each node, it should not becometoo large. For simpler things like scalar values you can simply make parameters part of the closureof a function, or use the withParameters(…) method to pass in a configuration.

Distributed Cache

Flink offers a distributed cache, similar to Apache Hadoop, to make files locally accessible to parallel instances of user functions. This functionality can be used to share files that contain static external data such as dictionaries or machine-learned regression models.

The cache works as follows. A program registers a file or directory of a local or remote filesystem such as HDFS or S3 under a specific name in its ExecutionEnvironment as a cached file. When the program is executed, Flink automatically copies the file or directory to the local filesystem of all workers. A user function can look up the file or directory under the specified name and access it from the worker’s local filesystem.

The distributed cache is used as follows:

Register the file or directory in the ExecutionEnvironment.

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();

Access the cached file or directory in a user function (here a MapFunction). The function must extend a RichFunction class because it needs access to the RuntimeContext.

// 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
      ...
    }
}

Register the file or directory in the ExecutionEnvironment.

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()

Access the cached file in a user function (here a MapFunction). The function must extend a RichFunction class because it needs access to the RuntimeContext.

// 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
    ...
  }
}

Passing Parameters to Functions

Parameters can be passed to functions using either the constructor or the withParameters(Configuration) method. The parameters are serialized as part of the function object and shipped to all parallel task instances.

Check also the best practices guide on how to pass command line arguments to functions.

Via Constructor

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
  }
}

Via withParameters(Configuration)

This method takes a Configuration object as an argument, which will be passed to the rich function’s open()method. The Configuration object is a Map from String keys to different value types.

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)

Globally via the ExecutionConfig

Flink also allows to pass custom configuration values to the ExecutionConfig interface of the environment. Since the execution config is accessible in all (rich) user functions, the custom configuration will be available globally in all functions.

Setting a custom global configuration

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)

Please note that you can also pass a custom class extending the ExecutionConfig.GlobalJobParameters class as the global job parameters to the execution config. The interface allows to implement the Map<String, String> toMap() method which will in turn show the values from the configuration in the web frontend.

Accessing values from the global configuration

Objects in the global job parameters are accessible in many places in the system. All user functions implementing a RichFunction interface have access through the runtime context.

public static final class Tokenizer extends RichFlatMapFunction<String, Tuple2<String, Integer>> {
    private String mykey;
    @Override
    public void open(Configuration parameters) throws Exception {
      super.open(parameters);
      ExecutionConfig.GlobalJobParameters globalParams = getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
      Configuration globConf = (Configuration) globalParams;
      mykey = globConf.getString("mykey", null);
    }
    // ... more here ...