This document gives a deep-dive into the available transformations on DataSets. For a general introduction to the Flink Java API, please refer to the API guide
The Map transformation applies a user-defined MapFunction on each element of a DataSet. It implements a one-to-one mapping, that is, exactly one element must be returned by the function.
The following code transforms a DataSet of Integer pairs into a DataSet of Integers:
// MapFunction that adds two integer values public class IntAdder implements MapFunction<Tuple2<Integer, Integer>, Integer> { @Override public Integer map(Tuple2<Integer, Integer> in) { return in.f0 + in.f1; } } // [...] DataSet<Tuple2<Integer, Integer>> intPairs = // [...] DataSet<Integer> intSums = intPairs.map(new IntAdder());
The FlatMap transformation applies a user-defined FlatMapFunction on each element of a DataSet. This variant of a map function can return arbitrary many result elements (including none) for each input element.
The following code transforms a DataSet of text lines into a DataSet of words:
// FlatMapFunction that tokenizes a String by whitespace characters and emits all String tokens. public class Tokenizer implements FlatMapFunction<String, String> { @Override public void flatMap(String value, Collector<String> out) { for (String token : value.split("\\W")) { out.collect(token); } } } // [...] DataSet<String> textLines = // [...] DataSet<String> words = textLines.flatMap(new Tokenizer());
The Filter transformation applies a user-defined FilterFunction on each element of a DataSet and retains only those elements for which the function returns true.
The following code removes all Integers smaller than zero from a DataSet:
// FilterFunction that filters out all Integers smaller than zero. public class NaturalNumberFilter implements FilterFunction<Integer> { @Override public boolean filter(Integer number) { return number >= 0; } } // [...] DataSet<Integer> intNumbers = // [...] DataSet<Integer> naturalNumbers = intNumbers.filter(new NaturalNumberFilter());
The Project transformation removes or moves Tuple fields of a Tuple DataSet. The project(int...) method selects Tuple fields that should be retained by their index and defines their order in the output Tuple. The types(Class<?> ...)method must give the types of the output Tuple fields.
Projections do not require the definition of a user function.
The following code shows different ways to apply a Project transformation on a DataSet:
DataSet<Tuple3<Integer, Double, String>> in = // [...] // converts Tuple3<Integer, Double, String> into Tuple2<String, Integer> DataSet<Tuple2<String, Integer>> out = in.project(2,0).types(String.class, Integer.class);
The reduce operations can operate on grouped data sets. Specifying the key to be used for grouping can be done in two ways:
KeySelector function orTuple DataSet only).Please look at the reduce examples to see how the grouping keys are specified.
A Reduce transformation that is applied on a grouped DataSet reduces each group to a single element using a user-defined ReduceFunction. For each group of input elements, a ReduceFunction successively combines pairs of elements into one element until only a single element for each group remains.
A KeySelector function extracts a key value from each element of a DataSet. The extracted key value is used to group the DataSet. The following code shows how to group a POJO DataSet using a KeySelector function and to reduce it with a ReduceFunction.
// some ordinary POJO public class WC { public String word; public int count; // [...] } // ReduceFunction that sums Integer attributes of a POJO public class WordCounter implements ReduceFunction<WC> { @Override public WC reduce(WC in1, WC in2) { return new WC(in1.word, in1.count + in2.count); } } // [...] DataSet<WC> words = // [...] DataSet<WC> wordCounts = words // DataSet grouping with inline-defined KeySelector function .groupBy( new KeySelector<WC, String>() { public String getKey(WC wc) { return wc.word; } }) // apply ReduceFunction on grouped DataSet .reduce(new WordCounter());
Field position keys specify one or more fields of a Tuple DataSet that are used as grouping keys. The following code shows how to use field position keys and apply a ReduceFunction.
DataSet<Tuple3<String, Integer, Double>> tuples = // [...] DataSet<Tuple3<String, Integer, Double>> reducedTuples = tuples // group DataSet on first and second field of Tuple .groupBy(0,1) // apply ReduceFunction on grouped DataSet .reduce(new MyTupleReducer());
A GroupReduce transformation that is applied on a grouped DataSet calls a user-defined GroupReduceFunction for each group. The difference between this and Reduce is that the user defined function gets the whole group at once. The function is invoked with an Iterable over all elements of a group and can return an arbitrary number of result elements using the collector.
The following code shows how duplicate strings can be removed from a DataSet grouped by Integer.
public class DistinctReduce implements GroupReduceFunction<Tuple2<Integer, String>, Tuple2<Integer, String>> { @Override public void reduce(Iterable<Tuple2<Integer, String>> in, Collector<Tuple2<Integer, String>> out) { Set<String> uniqStrings = new HashSet<String>(); Integer key = null; // add all strings of the group to the set for (Tuple2<Integer, String> t : in) { key = t.f0; uniqStrings.add(t.f1); } // emit all unique strings. for (String s : uniqStrings) { out.collect(new Tuple2<Integer, String>(key, s)); } } } // [...] DataSet<Tuple2<Integer, String>> input = // [...] DataSet<Tuple2<Integer, String>> output = input .groupBy(0) // group DataSet by the first tuple field .reduceGroup(new DistinctReduce()); // apply GroupReduceFunction
Works analogous to KeySelector functions in Reduce transformations.
A GroupReduceFunction accesses the elements of a group using an Iterable. Optionally, the Iterable can hand out the elements of a group in a specified order. In many cases this can help to reduce the complexity of a user-defined GroupReduceFunction and improve its efficiency. Right now, this feature is only available for DataSets of Tuples.
The following code shows another example how to remove duplicate Strings in a DataSet grouped by an Integer and sorted by String.
// GroupReduceFunction that removes consecutive identical elements public class DistinctReduce implements GroupReduceFunction<Tuple2<Integer, String>, Tuple2<Integer, String>> { @Override public void reduce(Iterable<Tuple2<Integer, String>> in, Collector<Tuple2<Integer, String>> out) { Integer key = null; String comp = null; for (Tuple2<Integer, String> t : in) { key = t.f0; String next = t.f1; // check if strings are different if (com == null || !next.equals(comp)) { out.collect(new Tuple2<Integer, String>(key, next)); comp = next; } } } } // [...] DataSet<Tuple2<Integer, String>> input = // [...] DataSet<Double> output = input .groupBy(0) // group DataSet by first field .sortGroup(1, Order.ASCENDING) // sort groups on second tuple field .reduceGroup(new DistinctReduce());
Note: A GroupSort often comes for free if the grouping is established using a sort-based execution strategy of an operator before the reduce operation.
In contrast to a ReduceFunction, a GroupReduceFunction is not necessarily combinable. In order to make a GroupReduceFunction combinable, you need to use the RichGroupReduceFunction variant, implement (override) the combine() method, and annotate the GroupReduceFunction with the @Combinable annotation as shown here:
// Combinable GroupReduceFunction that computes two sums. // Note that we use the RichGroupReduceFunction because it defines the combine method @Combinable public class MyCombinableGroupReducer extends RichGroupReduceFunction<Tuple3<String, Integer, Double>, Tuple3<String, Integer, Double>> { @Override public void reduce(Iterable<Tuple3<String, Integer, Double>> in, Collector<Tuple3<String, Integer, Double>> out) { String key = null int intSum = 0; double doubleSum = 0.0; for (Tuple3<String, Integer, Double> curr : in) { key = curr.f0; intSum += curr.f1; doubleSum += curr.f2; } // emit a tuple with both sums out.collect(new Tuple3<String, Integer, Double>(key, intSum, doubleSum)); } @Override public void combine(Iterable<Tuple3<String, Integer, Double>> in, Collector<Tuple3<String, Integer, Double>> out)) { // in some cases combine() calls can simply be forwarded to reduce(). this.reduce(in, out); } }
There are some common aggregation operations that are frequently used. The Aggregate transformation provides the following build-in aggregation functions:
The Aggregate transformation can only be applied on a Tuple DataSet and supports only field positions keys for grouping.
The following code shows how to apply an Aggregation transformation on a DataSet grouped by field position keys:
DataSet<Tuple3<Integer, String, Double>> input = // [...] DataSet<Tuple3<Integer, String, Double>> output = input .groupBy(1) // group DataSet on second field .aggregate(SUM, 0) // compute sum of the first field .and(MIN, 2); // compute minimum of the third field
To apply multiple aggregations on a DataSet it is necessary to use the .and() function after the first aggregate, that means .aggregate(SUM, 0).and(MIN, 2) produces the sum of field 0 and the minimum of field 2 of the original DataSet. In contrast to that .aggregate(SUM, 0).aggregate(MIN, 2) will apply an aggregation on an aggregation. In the given example it would produce the minimum of field 2 after calculating the sum of field 0 grouped by field 1.
Note: The set of aggregation functions will be extended in the future.
The Reduce transformation applies a user-defined ReduceFunction to all elements of a DataSet. The ReduceFunction subsequently combines pairs of elements into one element until only a single element remains.
The following code shows how to sum all elements of an Integer DataSet:
// ReduceFunction that sums Integers public class IntSummer implements ReduceFunction<Integer> { @Override public Integer reduce(Integer num1, Integer num2) { return num1 + num2; } } // [...] DataSet<Integer> intNumbers = // [...] DataSet<Integer> sum = intNumbers.reduce(new IntSummer());
Reducing a full DataSet using the Reduce transformation implies that the final Reduce operation cannot be done in parallel. However, a ReduceFunction is automatically combinable such that a Reduce transformation does not limit scalability for most use cases.
The GroupReduce transformation applies a user-defined GroupReduceFunction on all elements of a DataSet. A GroupReduceFunction can iterate over all elements of DataSet and return an arbitrary number of result elements.
The following example shows how to apply a GroupReduce transformation on a full DataSet:
DataSet<Integer> input = // [...] // apply a (preferably combinable) GroupReduceFunction to a DataSet DataSet<Double> output = input.reduceGroup(new MyGroupReducer());
Note: A GroupReduce transformation on a full DataSet cannot be done in parallel if the GroupReduceFunction is not combinable. Therefore, this can be a very compute intensive operation. See the paragraph on “Combineable GroupReduceFunctions” above to learn how to implement a combinable GroupReduceFunction.
There are some common aggregation operations that are frequently used. The Aggregate transformation provides the following build-in aggregation functions:
The Aggregate transformation can only be applied on a Tuple DataSet.
The following code shows how to apply an Aggregation transformation on a full DataSet:
DataSet<Tuple2<Integer, Double>> input = // [...] DataSet<Tuple2<Integer, Double>> output = input .aggregate(SUM, 0) // compute sum of the first field .and(MIN, 1); // compute minimum of the second field
Note: Extending the set of supported aggregation functions is on our roadmap.
The Join transformation joins two DataSets into one DataSet. The elements of both DataSets are joined on one or more keys which can be specified using
KeySelector function orTuple DataSet only).There are a few different ways to perform a Join transformation which are shown in the following.
The default Join transformation produces a new Tuple``DataSet with two fields. Each tuple holds a joined element of the first input DataSet in the first tuple field and a matching element of the second input DataSet in the second field.
The following code shows a default Join transformation using field position keys:
DataSet<Tuple2<Integer, String>> input1 = // [...] DataSet<Tuple2<Double, Integer>> input2 = // [...] // result dataset is typed as Tuple2 DataSet<Tuple2<Tuple2<Integer, String>, Tuple2<Double, Integer>>> result = input1.join(input2) .where(0) // key of the first input .equalTo(1); // key of the second input
A Join transformation can also call a user-defined JoinFunction to process joining tuples. A JoinFunction receives one element of the first input DataSet and one element of the second input DataSet and returns exactly one element.
The following code performs a join of DataSet with custom java objects and a Tuple DataSet using KeySelector functions and shows how to call a user-defined JoinFunction:
// some POJO public class Rating { public String name; public String category; public int points; } // Join function that joins a custom POJO with a Tuple public class PointWeighter implements JoinFunction<Rating, Tuple2<String, Double>, Tuple2<String, Double>> { @Override public Tuple2<String, Double> join(Rating rating, Tuple2<String, Double> weight) { // multiply the points and rating and construct a new output tuple return new Tuple2<String, Double>(rating.name, rating.points * weight.f1); } } DataSet<Rating> ratings = // [...] DataSet<Tuple2<String, Double>> weights = // [...] DataSet<Tuple2<String, Double>> weightedRatings = ratings.join(weights) // key of the first input .where(new KeySelection<Rating, String>() { public String getKey(Rating r) { return r.category; } }) // key of the second input .equalTo(new KeySelection<Tuple2<String, Double>, String>() { public String getKey(Tuple2<String, Double> t) { return t.f0; } }) // applying the JoinFunction on joining pairs .with(new PointWeighter());
Analogous to Map and FlatMap, a FlatJoin function behaves in the same way as a JoinFunction, but instead of returning one element, it can return (collect), zero, one, or more elements. {% highlight java %} public class PointWeighter implements FlatJoinFunction<Rating, Tuple2<String, Double>, Tuple2<String, Double>> { @Override public void join(Rating rating, Tuple2<String, Double> weight, Collector<Tuple2<String, Double>> out) { if (weight.f1 > 0.1) { out.collect(new Tuple2<String, Double>(rating.name, rating.points * weight.f1)); } } }
DataSet<Tuple2<String, Double>> weightedRatings = ratings.join(weights) // [...] {% endhighlight %}
A Join transformation can construct result tuples using a projection as shown here:
DataSet<Tuple3<Integer, Byte, String>> input1 = // [...] DataSet<Tuple2<Integer, Double>> input2 = // [...] DataSet<Tuple4<Integer, String, Double, Byte> result = input1.join(input2) // key definition on first DataSet using a field position key .where(0) // key definition of second DataSet using a field position key .equalTo(0) // select and reorder fields of matching tuples .projectFirst(0,2).projectSecond(1).projectFirst(1) .types(Integer.class, String.class, Double.class, Byte.class);
projectFirst(int...) and projectSecond(int...) select the fields of the first and second joined input that should be assembled into an output Tuple. The order of indexes defines the order of fields in the output tuple. The join projection works also for non-Tuple DataSets. In this case, projectFirst() or projectSecond() must be called without arguments to add a joined element to the output Tuple.
In order to guide the optimizer to pick the right execution strategy, you can hint the size of a DataSet to join as shown here:
DataSet<Tuple2<Integer, String>> input1 = // [...] DataSet<Tuple2<Integer, String>> input2 = // [...] DataSet<Tuple2<Tuple2<Integer, String>, Tuple2<Integer, String>>> result1 = // hint that the second DataSet is very small input1.joinWithTiny(input2) .where(0) .equalTo(0); DataSet<Tuple2<Tuple2<Integer, String>, Tuple2<Integer, String>>> result2 = // hint that the second DataSet is very large input1.joinWithHuge(input2) .where(0) .equalTo(0);
The Cross transformation combines two DataSets into one DataSet. It builds all pairwise combinations of the elements of both input DataSets, i.e., it builds a Cartesian product. The Cross transformation either calls a user-defined CrossFunction on each pair of elements or applies a projection. Both modes are shown in the following.
Note: Cross is potentially a very compute-intensive operation which can challenge even large compute clusters!
A Cross transformation can call a user-defined CrossFunction. A CrossFunction receives one element of the first input and one element of the second input and returns exactly one result element.
The following code shows how to apply a Cross transformation on two DataSets using a CrossFunction:
public class Coord { public int id; public int x; public int y; } // CrossFunction computes the Euclidean distance between two Coord objects. public class EuclideanDistComputer implements CrossFunction<Coord, Coord, Tuple3<Integer, Integer, Double>> { @Override public Tuple3<Integer, Integer, Double> cross(Coord c1, Coord c2) { // compute Euclidean distance of coordinates double dist = sqrt(pow(c1.x - c2.x, 2) + pow(c1.y - c2.y, 2)); return new Tuple3<Integer, Integer, Double>(c1.id, c2.id, dist); } } DataSet<Coord> coords1 = // [...] DataSet<Coord> coords2 = // [...] DataSet<Tuple3<Integer, Integer, Double>> distances = coords1.cross(coords2) // apply CrossFunction .with(new EuclideanDistComputer());
A Cross transformation can also construct result tuples using a projection as shown here:
DataSet<Tuple3<Integer, Byte, String>> input1 = // [...] DataSet<Tuple2<Integer, Double>> input2 = // [...] DataSet<Tuple4<Integer, Byte, Integer, Double> result = input1.cross(input2) // select and reorder fields of matching tuples .projectSecond(0).projectFirst(1,0).projectSecond(1) .types(Integer.class, Byte.class, Integer.class, Double.class);
The field selection in a Cross projection works the same way as in the projection of Join results.
In order to guide the optimizer to pick the right execution strategy, you can hint the size of a DataSet to cross as shown here:
DataSet<Tuple2<Integer, String>> input1 = // [...] DataSet<Tuple2<Integer, String>> input2 = // [...] DataSet<Tuple4<Integer, String, Integer, String>> udfResult = // hint that the second DataSet is very small input1.crossWithTiny(input2) // apply any Cross function (or projection) .with(new MyCrosser()); DataSet<Tuple3<Integer, Integer, String>> projectResult = // hint that the second DataSet is very large input1.crossWithHuge(input2) // apply a projection (or any Cross function) .projectFirst(0,1).projectSecond(1).types(Integer.class, String.class, String.class)
The CoGroup transformation jointly processes groups of two DataSets. Both DataSets are grouped on a defined key and groups of both DataSets that share the same key are handed together to a user-defined CoGroupFunction. If for a specific key only one DataSet has a group, the CoGroupFunction is called with this group and an empty group. A CoGroupFunction can separately iterate over the elements of both groups and return an arbitrary number of result elements.
Similar to Reduce, GroupReduce, and Join, keys can be defined using
KeySelector function orTuple DataSet only).// Some CoGroupFunction definition class MyCoGrouper implements CoGroupFunction<Tuple2<String, Integer>, Tuple2<String, Double>, Double> { @Override public void coGroup(Iterable<Tuple2<String, Integer>> iVals, Iterable<Tuple2<String, Double>> dVals, Collector<Double> out) { Set<Integer> ints = new HashSet<Integer>(); // add all Integer values in group to set for (Tuple2<String, Integer>> val : iVale) { ints.add(val.f1); } // multiply each Double value with each unique Integer values of group for (Tuple2<String, Double> val : dVals) { for (Integer i : ints) { out.collect(val.f1 * i); } } } } // [...] DataSet<Tuple2<String, Integer>> iVals = // [...] DataSet<Tuple2<String, Double>> dVals = // [...] DataSet<Double> output = iVals.coGroup(dVals) // group first DataSet on first tuple field .where(0) // group second DataSet on first tuple field .equalTo(0) // apply CoGroup function on each pair of groups .with(new MyCoGrouper());
Works analogous to key selector functions in Join transformations.
Produces the union of two DataSets, which have to be of the same type. A union of more than two DataSets can be implemented with multiple union calls, as shown here:
DataSet<Tuple2<String, Integer>> vals1 = // [...] DataSet<Tuple2<String, Integer>> vals2 = // [...] DataSet<Tuple2<String, Integer>> vals3 = // [...] DataSet<Tuple2<String, Integer>> unioned = vals1.union(vals2) .union(vals3);