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DataSet Transformations |
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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 Programming Guide
The Map transformation applies a user-defined map function 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());val intPairs: DataSet[(Int, Int)] = // [...]
val intSums = intPairs.map { pair => pair._1 + pair._2 }The FlatMap transformation applies a user-defined flat-map function 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());val textLines: DataSet[String] = // [...]
val words = textLines.flatMap { _.split(" ") }MapPartition transforms a parallel partition in a single function call. The map-partition function gets the partition as Iterable and can produce an arbitrary number of result values. The number of elements in each partition depends on the degree-of-parallelism and previous operations.
The following code transforms a DataSet of text lines into a DataSet of counts per partition:
public class PartitionCounter implements MapPartitionFunction<String, Long> {
public void mapPartition(Iterable<String> values, Collector<Long> out) {
long c = 0;
for (String s : values) {
c++;
}
out.collect(c);
}
}
// [...]
DataSet<String> textLines = // [...]
DataSet<Long> counts = textLines.mapPartition(new PartitionCounter());val textLines: DataSet[String] = // [...]
// Some is required because the return value must be a Collection.
// There is an implicit conversion from Option to a Collection.
val counts = texLines.mapPartition { in => Some(in.size) }The Filter transformation applies a user-defined filter function 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());val intNumbers: DataSet[Int] = // [...]
val naturalNumbers = intNumbers.filter { _ > 0 }IMPORTANT: The system assumes that the function does not modify the elements on which the predicate is applied. Violating this assumption can lead to incorrect results.
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.
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);The reduce operations can operate on grouped data sets. Specifying the key to be used for grouping can be done in many ways:
- key expressions
- a key-selector function
- one or more field position keys (Tuple DataSet only)
- Case Class fields (Case Classes 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 reduce function. For each group of input elements, a reduce function successively combines pairs of elements into one element until only a single element for each group remains.
A key-selector 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 key-selector function and to reduce it with a reduce function.
// 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 on field "word"
.groupBy("word")
// apply ReduceFunction on grouped DataSet
.reduce(new WordCounter());// some ordinary POJO
class WC(val word: String, val count: Int) {
def this() {
this(null, -1)
}
// [...]
}
val words: DataSet[WC] = // [...]
val wordCounts = words.groupBy { _.word } reduce {
(w1, w2) => new WC(w1.word, w1.count + w2.count)
}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 reduce function
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());val tuples = DataSet[(String, Int, Double)] = // [...]
// group on the first and second Tuple field
val reducedTuples = tuples.groupBy(0, 1).reduce { ... }When using Case Classes you can also specify the grouping key using the names of the fields:
case class MyClass(val a: String, b: Int, c: Double)
val tuples = DataSet[MyClass] = // [...]
// group on the first and second field
val reducedTuples = tuples.groupBy("a", "b").reduce { ... }A GroupReduce transformation that is applied on a grouped DataSet calls a user-defined group-reduce function 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.
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 GroupReduceFunctionWorks analogous to key-selector functions in Reduce transformations.
A group-reduce function 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 group-reduce function and improve its efficiency.
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());val input: DataSet[(Int, String)] = // [...]
val output = input.groupBy(0).sortGroup(1, Order.ASCENDING).reduceGroup {
(in, out: Collector[(Int, String)]) =>
var prev: (Int, String) = null
for (t <- in) {
if (prev == null || prev != t)
out.collect(t)
}
}
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 reduce function, a group-reduce function is not
necessarily combinable. In order to make a group-reduce function
combinable, you need to use the RichGroupReduceFunction variant,
implement (override) the combine() method, and annotate the
RichGroupReduceFunction 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);
}
}// Combinable GroupReduceFunction that computes two sums.
// Note that we use the RichGroupReduceFunction because it defines the combine method
@Combinable
class MyCombinableGroupReducer
extends RichGroupReduceFunction[(String, Int, Double), (String, Int, Double)] {}
def reduce(
in: java.lang.Iterable[(String, Int, Double)],
out: Collector[(String, Int, Double)]): Unit = {
val key: String = null
val intSum = 0
val doubleSum = 0.0
for (curr <- in) {
key = curr._1
intSum += curr._2
doubleSum += curr._3
}
// emit a tuple with both sums
out.collect(key, intSum, doubleSum);
}
def combine(
in: java.lang.Iterable[(String, Int, Double)],
out: Collector[(String, Int, Double)]): Unit = {
// in some cases combine() calls can simply be forwarded to reduce().
this.reduce(in, out)
}
}The GroupCombine transformation is the generalized form of the combine step in
the Combinable GroupReduceFunction. It is generalized in the sense that it
allows combining of input type I to an arbitrary output type O. In contrast,
the combine step in the GroupReduce only allows combining from input type I to
output type I. This is because the reduce step in the GroupReduceFunction
expects input type I.
In some applications, it is desirable to combine a DataSet into an intermediate format before performing additional transformations (e.g. to reduce data size). This can be achieved with a ComineGroup transformation with very little costs.
Note: The GroupCombine on a Grouped DataSet is performed in memory with a greedy strategy which may not process all data at once but in multiple steps. It is also performed on the individual partitions without a data exchange like in a GroupReduce transformation. This may lead to partial results.
The following example demonstrates the use of a CombineGroup transformation for an alternative WordCount implementation. In the implementation,
DataSet<String> input = [..] // The words received as input
DataSet<String> groupedInput = input.groupBy(0); // group identical words
DataSet<Tuple2<String, Integer>> combinedWords = groupedInput.combineGroup(new GroupCombineFunction<String, Tuple2<String, Integer>() {
public void combine(Iterable<String> words, Collector<Tuple2<String, Integer>>) { // combine
int count = 0;
for (String word : words) {
count++;
}
out.collect(new Tuple2(word, count));
}
});
DataSet<Tuple2<String, Integer>> groupedCombinedWords = combinedWords.groupBy(0); // group by words again
DataSet<Tuple2<String, Integer>> output = combinedWords.groupReduce(new GroupReduceFunction() { // group reduce with full data exchange
public void reduce(Iterable<Tuple2<String, Integer>>, Collector<Tuple2<String, Integer>>) {
int count = 0;
for (Tuple2<String, Integer> word : words) {
count++;
}
out.collect(new Tuple2(word, count));
}
});val input: DataSet[String] = [..] // The words received as input
val groupedInput: DataSet[String] = input.groupBy(0)
val combinedWords: DataSet[(String, Int)] = groupedInput.groupCombine {
(words, out: Collector[(String, Int)]) =>
var count = 0
for (word <- words) {
count++
}
out.collect(word, count)
}
val groupedCombinedWords: DataSet[(String, Int)] = combinedWords.groupBy(0)
val output: DataSet[(String, Int)] = groupedInput.groupCombine {
(words, out: Collector[(String, Int)]) =>
var count = 0
for ((word, Int) <- words) {
count++
}
out.collect(word, count)
}
The above alternative WordCount implementation demonstrates how the GroupCombine combines words before performing the GroupReduce transformation. The above example is just a proof of concept. Note, how the combine step changes the type of the DataSet which would normally required an additional Map transformation before executing the GroupReduce.
There are some common aggregation operations that are frequently used. The Aggregate transformation provides the following build-in aggregation functions:
- Sum,
- Min, and
- Max.
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 fieldval input: DataSet[(Int, String, Double)] = // [...]
val output = input.groupBy(1).aggregate(SUM, 0).and(MIN, 2)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 reduce function to all elements of a DataSet. The reduce function 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());val intNumbers = env.fromElements(1,2,3)
val sum = intNumbers.reduce (_ + _)Reducing a full DataSet using the Reduce transformation implies that the final Reduce operation cannot be done in parallel. However, a reduce function is automatically combinable such that a Reduce transformation does not limit scalability for most use cases.
The GroupReduce transformation applies a user-defined group-reduce function on all elements of a DataSet. A group-reduce 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());val input: DataSet[Int] = // [...]
val output = input.reduceGroup(new MyGroupReducer())Note: A GroupReduce transformation on a full DataSet cannot be done in parallel if the group-reduce function is not combinable. Therefore, this can be a very compute intensive operation. See the paragraph on "Combineable Group-Reduce Functions" above to learn how to implement a combinable group-reduce function.
The GroupCombine on a full DataSet works similar to the GroupCombine on a grouped DataSet. The data is partitioned on all nodes and then combined in a greedy fashion (i.e. only data fitting into memory is combined at once).
There are some common aggregation operations that are frequently used. The Aggregate transformation provides the following build-in aggregation functions:
- Sum,
- Min, and
- Max.
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 fieldval input: DataSet[(Int, String, Double)] = // [...]
val output = input.aggregate(SUM, 0).and(MIN, 2)
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
- a kex expression
- a key-selector function
- one or more field position keys (Tuple DataSet only).
- Case Class Fields
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:
public static class User { public String name; public int zip; }
public static class Store { public Manager mgr; public int zip; }
DataSet<User> input1 = // [...]
DataSet<Store> input2 = // [...]
// result dataset is typed as Tuple2
DataSet<Tuple2<User, Store>>
result = input1.join(input2)
.where("zip") // key of the first input (users)
.equalTo("zip"); // key of the second input (stores)val input1: DataSet[(Int, String)] = // [...]
val input2: DataSet[(Double, Int)] = // [...]
val result = input1.join(input2).where(0).equalTo(1)A Join transformation can also call a user-defined join function to process joining tuples. A join function 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 key-selector functions and shows how to use a user-defined join function:
// 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("category")
// key of the second input
.equalTo("f0")
// applying the JoinFunction on joining pairs
.with(new PointWeighter());case class Rating(name: String, category: String, points: Int)
val ratings: DataSet[Ratings] = // [...]
val weights: DataSet[(String, Double)] = // [...]
val weightedRatings = ratings.join(weights).where("category").equalTo(0) {
(rating, weight) => (rating.name, rating.points * weight._2)
}Analogous to Map and FlatMap, a FlatJoin behaves in the same way as a Join, but instead of returning one element, it can return (collect), zero, one, or more elements.
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) // [...]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);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.
case class Rating(name: String, category: String, points: Int)
val ratings: DataSet[Ratings] = // [...]
val weights: DataSet[(String, Double)] = // [...]
val weightedRatings = ratings.join(weights).where("category").equalTo(0) {
(rating, weight, out: Collector[(String, Double)] =>
if (weight._2 > 0.1) out.collect(left.name, left.points * right._2)
}
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);val input1: DataSet[(Int, String)] = // [...]
val input2: DataSet[(Int, String)] = // [...]
// hint that the second DataSet is very small
val result1 = input1.joinWithTiny(input2).where(0).equalTo(0)
// hint that the second DataSet is very large
val result1 = input1.joinWithHuge(input2).where(0).equalTo(0)
The Flink runtime can execute joins in various ways. Each possible way outperforms the others under different circumstances. The system tries to pick a reasonable way automatically, but allows you to manually pick a strategy, in case you want to enforce a specific way of executing the join.
DataSet<SomeType> input1 = // [...]
DataSet<AnotherType> input2 = // [...]
DataSet<Tuple2<SomeType, AnotherType> result =
input1.join(input2, BROADCAST_HASH_FIRST)
.where("id").equalTo("key");val input1: DataSet[SomeType] = // [...]
val input2: DataSet[AnotherType] = // [...]
// hint that the second DataSet is very small
val result1 = input1.join(input2, BROADCAST_HASH_FIRST).where("id").equalTo("key")
The following hints are available:
-
OPTIMIZER_CHOOSES: Equivalent to not giving a hint at all, leaves the choice to the system.
-
BROADCAST_HASH_FIRST: Broadcasts the first input and builds a hash table from it, which is probed by the second input. A good strategy if the first input is very small.
-
BROADCAST_HASH_SECOND: Broadcasts the second input and builds a hash table from it, which is probed by the first input. A good strategy if the second input is very small.
-
REPARTITION_HASH_FIRST: The system partitions (shuffles) each input (unless the input is already partitioned) and builds a hash table from the first input. This strategy is good if the first input is smaller than the second, but both inputs are still large. Note: This is the default fallback strategy that the system uses if no size estimates can be made and no pre-existing partitiongs and sort-orders can be re-used.
-
REPARTITION_HASH_SECOND: The system partitions (shuffles) each input (unless the input is already partitioned) and builds a hash table from the second input. This strategy is good if the second input is smaller than the first, but both inputs are still large.
-
REPARTITION_SORT_MERGE: The system partitions (shuffles) each input (unless the input is already partitioned) and sorts each input (unless it is already sorted). The inputs are joined by a streamed merge of the sorted inputs. This strategy is good if one or both of the inputs are already sorted.
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 cross function on each pair of elements or outputs a Tuple2. 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 cross function. A cross function 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 cross function:
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);The field selection in a Cross projection works the same way as in the projection of Join results.
case class Coord(id: Int, x: Int, y: Int)
val coords1: DataSet[Coord] = // [...]
val coords2: DataSet[Coord] = // [...]
val distances = coords1.cross(coords2) {
(c1, c2) =>
val dist = sqrt(pow(c1.x - c2.x, 2) + pow(c1.y - c2.y, 2))
(c1.id, c2.id, dist)
}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);val input1: DataSet[(Int, String)] = // [...]
val input2: DataSet[(Int, String)] = // [...]
// hint that the second DataSet is very small
val result1 = input1.crossWithTiny(input2)
// hint that the second DataSet is very large
val result1 = input1.crossWithHuge(input2)
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 co-group function. If for a specific key only one DataSet has a group, the co-group function is called with this group and an empty group. A co-group function 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 the different key-selection methods.
The example shows how to group by Field Position Keys (Tuple DataSets only). You can do the same with Pojo-types and key expressions.
// 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 : iVals) {
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());val iVals: DataSet[(String, Int)] = // [...]
val dVals: DataSet[(String, Double)] = // [...]
val output = iVals.coGroup(dVals).where(0).equalTo(0) {
(iVals, dVals, out: Collector[Double]) =>
val ints = iVals map { _._2 } toSet
for (dVal <- dVals) {
for (i <- ints) {
out.collect(dVal._2 * i)
}
}
}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);val vals1: DataSet[(String, Int)] = // [...]
val vals2: DataSet[(String, Int)] = // [...]
val vals3: DataSet[(String, Int)] = // [...]
val unioned = vals1.union(vals2).union(vals3)Evenly rebalances the parallel partitions of a DataSet to eliminate data skew.
DataSet<String> in = // [...]
// rebalance DataSet and apply a Map transformation.
DataSet<Tuple2<String, String>> out = in.rebalance()
.map(new Mapper());val in: DataSet[String] = // [...]
// rebalance DataSet and apply a Map transformation.
val out = in.rebalance().map { ... }Hash-partitions a DataSet on a given key. Keys can be specified as key expressions or field position keys (see Reduce examples for how to specify keys).
DataSet<Tuple2<String, Integer>> in = // [...]
// hash-partition DataSet by String value and apply a MapPartition transformation.
DataSet<Tuple2<String, String>> out = in.partitionByHash(0)
.mapPartition(new PartitionMapper());val in: DataSet[(String, Int)] = // [...]
// hash-partition DataSet by String value and apply a MapPartition transformation.
val out = in.partitionByHash(0).mapPartition { ... }Locally sorts all partitions of a DataSet on a specified field in a specified order.
Fields can be specified as field expressions or field positions (see Reduce examples for how to specify keys).
Partitions can be sorted on multiple fields by chaining sortPartition() calls.
DataSet<Tuple2<String, Integer>> in = // [...]
// Locally sort partitions in ascending order on the second String field and
// in descending order on the first String field.
// Apply a MapPartition transformation on the sorted partitions.
DataSet<Tuple2<String, String>> out = in.sortPartition(1, Order.ASCENDING)
.sortPartition(0, Order.DESCENDING)
.mapPartition(new PartitionMapper());val in: DataSet[(String, Int)] = // [...]
// Locally sort partitions in ascending order on the second String field and
// in descending order on the first String field.
// Apply a MapPartition transformation on the sorted partitions.
val out = in.sortPartition(1, Order.ASCENDING)
.sortPartition(0, Order.DESCENDING)
.mapPartition { ... }Returns the first n (arbitrary) elements of a DataSet. First-n can be applied on a regular DataSet, a grouped DataSet, or a grouped-sorted DataSet. Grouping keys can be specified as key-selector functions or field position keys (see Reduce examples for how to specify keys).
DataSet<Tuple2<String, Integer>> in = // [...]
// Return the first five (arbitrary) elements of the DataSet
DataSet<Tuple2<String, Integer>> out1 = in.first(5);
// Return the first two (arbitrary) elements of each String group
DataSet<Tuple2<String, Integer>> out2 = in.groupBy(0)
.first(2);
// Return the first three elements of each String group ordered by the Integer field
DataSet<Tuple2<String, Integer>> out3 = in.groupBy(0)
.sortGroup(1, Order.ASCENDING)
.first(3);val in: DataSet[(String, Int)] = // [...]
// Return the first five (arbitrary) elements of the DataSet
val out1 = in.first(5)
// Return the first two (arbitrary) elements of each String group
val out2 = in.groupBy(0).first(2)
// Return the first three elements of each String group ordered by the Integer field
val out3 = in.groupBy(0).sortGroup(1, Order.ASCENDING).first(3)