T - The type of the DataSet, i.e., the type of the elements of the DataSet.@Public public abstract class DataSet<T> extends Object
A DataSet can be transformed into another DataSet by applying a transformation as for example
| Modifier and Type | Field and Description |
|---|---|
protected ExecutionEnvironment |
context |
| Modifier | Constructor and Description |
|---|---|
protected |
DataSet(ExecutionEnvironment context,
TypeInformation<T> typeInfo) |
| Modifier and Type | Method and Description |
|---|---|
AggregateOperator<T> |
aggregate(Aggregations agg,
int field)
|
protected static void |
checkSameExecutionContext(DataSet<?> set1,
DataSet<?> set2) |
<F> F |
clean(F f) |
<R> CoGroupOperator.CoGroupOperatorSets<T,R> |
coGroup(DataSet<R> other)
Initiates a CoGroup transformation.
|
List<T> |
collect()
Convenience method to get the elements of a DataSet as a List.
|
<R> GroupCombineOperator<T,R> |
combineGroup(GroupCombineFunction<T,R> combiner)
Applies a GroupCombineFunction on a non-grouped
DataSet. |
long |
count()
Convenience method to get the count (number of elements) of a DataSet.
|
<R> CrossOperator.DefaultCross<T,R> |
cross(DataSet<R> other)
Initiates a Cross transformation.
|
<R> CrossOperator.DefaultCross<T,R> |
crossWithHuge(DataSet<R> other)
Initiates a Cross transformation.
|
<R> CrossOperator.DefaultCross<T,R> |
crossWithTiny(DataSet<R> other)
Initiates a Cross transformation.
|
DistinctOperator<T> |
distinct()
Returns a distinct set of a
DataSet. |
DistinctOperator<T> |
distinct(int... fields)
|
<K> DistinctOperator<T> |
distinct(KeySelector<T,K> keyExtractor)
Returns a distinct set of a
DataSet using a KeySelector function. |
DistinctOperator<T> |
distinct(String... fields)
Returns a distinct set of a
DataSet using expression keys. |
protected void |
fillInType(TypeInformation<T> typeInfo)
Tries to fill in the type information.
|
FilterOperator<T> |
filter(FilterFunction<T> filter)
Applies a Filter transformation on a
DataSet. |
GroupReduceOperator<T,T> |
first(int n)
Returns a new set containing the first n elements in this
DataSet. |
<R> FlatMapOperator<T,R> |
flatMap(FlatMapFunction<T,R> flatMapper)
Applies a FlatMap transformation on a
DataSet. |
<R> JoinOperatorSetsBase<T,R> |
fullOuterJoin(DataSet<R> other)
Initiates a Full Outer Join transformation.
|
<R> JoinOperatorSetsBase<T,R> |
fullOuterJoin(DataSet<R> other,
JoinOperatorBase.JoinHint strategy)
Initiates a Full Outer Join transformation.
|
ExecutionEnvironment |
getExecutionEnvironment()
Returns the
ExecutionEnvironment in which this DataSet is registered. |
TypeInformation<T> |
getType()
Returns the
TypeInformation for the type of this DataSet. |
UnsortedGrouping<T> |
groupBy(int... fields)
|
<K> UnsortedGrouping<T> |
groupBy(KeySelector<T,K> keyExtractor)
Groups a
DataSet using a KeySelector function. |
UnsortedGrouping<T> |
groupBy(String... fields)
Groups a
DataSet using field expressions. |
IterativeDataSet<T> |
iterate(int maxIterations)
Initiates an iterative part of the program that executes multiple times and feeds back data sets.
|
<R> DeltaIteration<T,R> |
iterateDelta(DataSet<R> workset,
int maxIterations,
int... keyPositions)
Initiates a delta iteration.
|
<R> JoinOperator.JoinOperatorSets<T,R> |
join(DataSet<R> other)
Initiates a Join transformation.
|
<R> JoinOperator.JoinOperatorSets<T,R> |
join(DataSet<R> other,
JoinOperatorBase.JoinHint strategy)
Initiates a Join transformation.
|
<R> JoinOperator.JoinOperatorSets<T,R> |
joinWithHuge(DataSet<R> other)
Initiates a Join transformation.
|
<R> JoinOperator.JoinOperatorSets<T,R> |
joinWithTiny(DataSet<R> other)
Initiates a Join transformation.
|
<R> JoinOperatorSetsBase<T,R> |
leftOuterJoin(DataSet<R> other)
Initiates a Left Outer Join transformation.
|
<R> JoinOperatorSetsBase<T,R> |
leftOuterJoin(DataSet<R> other,
JoinOperatorBase.JoinHint strategy)
Initiates a Left Outer Join transformation.
|
<R> MapOperator<T,R> |
map(MapFunction<T,R> mapper)
Applies a Map transformation on this DataSet.
|
<R> MapPartitionOperator<T,R> |
mapPartition(MapPartitionFunction<T,R> mapPartition)
Applies a Map-style operation to the entire partition of the data.
|
AggregateOperator<T> |
max(int field)
Syntactic sugar for
aggregate(Aggregations, int) using Aggregations.MAX as
the aggregation function. |
ReduceOperator<T> |
maxBy(int... fields)
Selects an element with maximum value.
|
AggregateOperator<T> |
min(int field)
Syntactic sugar for
aggregate(Aggregations, int) using Aggregations.MIN as
the aggregation function. |
ReduceOperator<T> |
minBy(int... fields)
Selects an element with minimum value.
|
DataSink<T> |
output(OutputFormat<T> outputFormat)
Emits a DataSet using an
OutputFormat. |
PartitionOperator<T> |
partitionByHash(int... fields)
Hash-partitions a DataSet on the specified key fields.
|
<K extends Comparable<K>> |
partitionByHash(KeySelector<T,K> keyExtractor)
Partitions a DataSet using the specified KeySelector.
|
PartitionOperator<T> |
partitionByHash(String... fields)
Hash-partitions a DataSet on the specified key fields.
|
PartitionOperator<T> |
partitionByRange(int... fields)
Range-partitions a DataSet on the specified key fields.
|
<K extends Comparable<K>> |
partitionByRange(KeySelector<T,K> keyExtractor)
Range-partitions a DataSet using the specified KeySelector.
|
PartitionOperator<T> |
partitionByRange(String... fields)
Range-partitions a DataSet on the specified key fields.
|
<K> PartitionOperator<T> |
partitionCustom(Partitioner<K> partitioner,
int field)
Partitions a tuple DataSet on the specified key fields using a custom partitioner.
|
<K extends Comparable<K>> |
partitionCustom(Partitioner<K> partitioner,
KeySelector<T,K> keyExtractor)
Partitions a DataSet on the key returned by the selector, using a custom partitioner.
|
<K> PartitionOperator<T> |
partitionCustom(Partitioner<K> partitioner,
String field)
Partitions a POJO DataSet on the specified key fields using a custom partitioner.
|
void |
print()
Prints the elements in a DataSet to the standard output stream
System.out of the JVM that calls
the print() method. |
DataSink<T> |
print(String sinkIdentifier)
Deprecated.
Use
printOnTaskManager(String) instead. |
DataSink<T> |
printOnTaskManager(String prefix)
Writes a DataSet to the standard output streams (stdout) of the TaskManagers that execute
the program (or more specifically, the data sink operators).
|
void |
printToErr()
Prints the elements in a DataSet to the standard error stream
System.err of the JVM that calls
the print() method. |
DataSink<T> |
printToErr(String sinkIdentifier)
Deprecated.
Use
printOnTaskManager(String) instead, or the PrintingOutputFormat. |
<OUT extends Tuple> |
project(int... fieldIndexes)
|
PartitionOperator<T> |
rebalance()
Enforces a re-balancing of the DataSet, i.e., the DataSet is evenly distributed over all parallel instances of the
following task.
|
ReduceOperator<T> |
reduce(ReduceFunction<T> reducer)
Applies a Reduce transformation on a non-grouped
DataSet. |
<R> GroupReduceOperator<T,R> |
reduceGroup(GroupReduceFunction<T,R> reducer)
Applies a GroupReduce transformation on a non-grouped
DataSet. |
<R> JoinOperatorSetsBase<T,R> |
rightOuterJoin(DataSet<R> other)
Initiates a Right Outer Join transformation.
|
<R> JoinOperatorSetsBase<T,R> |
rightOuterJoin(DataSet<R> other,
JoinOperatorBase.JoinHint strategy)
Initiates a Right Outer Join transformation.
|
<X> DataSet<X> |
runOperation(CustomUnaryOperation<T,X> operation)
Runs a
CustomUnaryOperation on the data set. |
SortPartitionOperator<T> |
sortPartition(int field,
Order order)
Locally sorts the partitions of the DataSet on the specified field in the specified order.
|
<K> SortPartitionOperator<T> |
sortPartition(KeySelector<T,K> keyExtractor,
Order order)
Locally sorts the partitions of the DataSet on the extracted key in the specified order.
|
SortPartitionOperator<T> |
sortPartition(String field,
Order order)
Locally sorts the partitions of the DataSet on the specified field in the specified order.
|
AggregateOperator<T> |
sum(int field)
Syntactic sugar for aggregate (SUM, field).
|
UnionOperator<T> |
union(DataSet<T> other)
Creates a union of this DataSet with an other DataSet.
|
DataSink<T> |
write(FileOutputFormat<T> outputFormat,
String filePath)
Writes a DataSet using a
FileOutputFormat to a specified location. |
DataSink<T> |
write(FileOutputFormat<T> outputFormat,
String filePath,
FileSystem.WriteMode writeMode)
Writes a DataSet using a
FileOutputFormat to a specified location. |
DataSink<T> |
writeAsCsv(String filePath)
Writes a
Tuple DataSet as CSV file(s) to the specified location. |
DataSink<T> |
writeAsCsv(String filePath,
FileSystem.WriteMode writeMode)
Writes a
Tuple DataSet as CSV file(s) to the specified location. |
DataSink<T> |
writeAsCsv(String filePath,
String rowDelimiter,
String fieldDelimiter)
Writes a
Tuple DataSet as CSV file(s) to the specified location with the specified field and line delimiters. |
DataSink<T> |
writeAsCsv(String filePath,
String rowDelimiter,
String fieldDelimiter,
FileSystem.WriteMode writeMode)
Writes a
Tuple DataSet as CSV file(s) to the specified location with the specified field and line delimiters. |
DataSink<String> |
writeAsFormattedText(String filePath,
FileSystem.WriteMode writeMode,
TextOutputFormat.TextFormatter<T> formatter)
Writes a DataSet as text file(s) to the specified location.
|
DataSink<String> |
writeAsFormattedText(String filePath,
TextOutputFormat.TextFormatter<T> formatter)
Writes a DataSet as text file(s) to the specified location.
|
DataSink<T> |
writeAsText(String filePath)
Writes a DataSet as text file(s) to the specified location.
|
DataSink<T> |
writeAsText(String filePath,
FileSystem.WriteMode writeMode)
Writes a DataSet as text file(s) to the specified location.
|
protected final ExecutionEnvironment context
protected DataSet(ExecutionEnvironment context, TypeInformation<T> typeInfo)
public ExecutionEnvironment getExecutionEnvironment()
ExecutionEnvironment in which this DataSet is registered.ExecutionEnvironmentprotected void fillInType(TypeInformation<T> typeInfo)
typeInfo - The type information to fill in.IllegalStateException - Thrown, if the type information has been accessed before.public TypeInformation<T> getType()
TypeInformation for the type of this DataSet.TypeInformationpublic <F> F clean(F f)
public <R> MapOperator<T,R> map(MapFunction<T,R> mapper)
The transformation calls a MapFunction for each element of the DataSet.
Each MapFunction call returns exactly one element.
mapper - The MapFunction that is called for each element of the DataSet.MapFunction,
RichMapFunction,
MapOperatorpublic <R> MapPartitionOperator<T,R> mapPartition(MapPartitionFunction<T,R> mapPartition)
This function is intended for operations that cannot transform individual elements,
requires no grouping of elements. To transform individual elements,
the use of map() and flatMap() is preferable.
mapPartition - The MapPartitionFunction that is called for the full DataSet.MapPartitionFunction,
MapPartitionOperatorpublic <R> FlatMapOperator<T,R> flatMap(FlatMapFunction<T,R> flatMapper)
DataSet.
The transformation calls a RichFlatMapFunction for each element of the DataSet.
Each FlatMapFunction call can return any number of elements including none.
flatMapper - The FlatMapFunction that is called for each element of the DataSet.RichFlatMapFunction,
FlatMapOperator,
DataSetpublic FilterOperator<T> filter(FilterFunction<T> filter)
DataSet.
The transformation calls a RichFilterFunction for each element of the DataSet
and retains only those element for which the function returns true. Elements for
which the function returns false are filtered.
filter - The FilterFunction that is called for each element of the DataSet.RichFilterFunction,
FilterOperator,
DataSetpublic <OUT extends Tuple> ProjectOperator<?,OUT> project(int... fieldIndexes)
Tuple DataSet.
Note: Only Tuple DataSets can be projected using field indexes.
The transformation projects each Tuple of the DataSet onto a (sub)set of fields.
Additional fields can be added to the projection by calling project(int[]).
Note: With the current implementation, the Project transformation looses type information.
fieldIndexes - The field indexes of the input tuple that are retained.
The order of fields in the output tuple corresponds to the order of field indexes.Tuple,
DataSet,
ProjectOperatorpublic AggregateOperator<T> aggregate(Aggregations agg, int field)
Tuple DataSet.
Note: Only Tuple DataSets can be aggregated.
The transformation applies a built-in Aggregation on a specified field
of a Tuple DataSet. Additional aggregation functions can be added to the resulting
AggregateOperator by calling AggregateOperator.and(Aggregations, int).
agg - The built-in aggregation function that is computed.field - The index of the Tuple field on which the aggregation function is applied.Tuple,
Aggregations,
AggregateOperator,
DataSetpublic AggregateOperator<T> sum(int field)
field - The index of the Tuple field on which the aggregation function is applied.AggregateOperatorpublic AggregateOperator<T> max(int field)
aggregate(Aggregations, int) using Aggregations.MAX as
the aggregation function.
Note: This operation is not to be confused with maxBy(int...),
which selects one element with maximum value at the specified field positions.
field - The index of the Tuple field on which the aggregation function is applied.aggregate(Aggregations, int),
maxBy(int...)public AggregateOperator<T> min(int field)
aggregate(Aggregations, int) using Aggregations.MIN as
the aggregation function.
Note: This operation is not to be confused with minBy(int...),
which selects one element with the minimum value at the specified field positions.
field - The index of the Tuple field on which the aggregation function is applied.aggregate(Aggregations, int),
minBy(int...)public long count()
throws Exception
Exceptionpublic List<T> collect() throws Exception
Exceptionpublic ReduceOperator<T> reduce(ReduceFunction<T> reducer)
DataSet.
The transformation consecutively calls a RichReduceFunction
until only a single element remains which is the result of the transformation.
A ReduceFunction combines two elements into one new element of the same type.
reducer - The ReduceFunction that is applied on the DataSet.RichReduceFunction,
ReduceOperator,
DataSetpublic <R> GroupReduceOperator<T,R> reduceGroup(GroupReduceFunction<T,R> reducer)
DataSet.
The transformation calls a RichGroupReduceFunction once with the full DataSet.
The GroupReduceFunction can iterate over all elements of the DataSet and emit any
number of output elements including none.
reducer - The GroupReduceFunction that is applied on the DataSet.RichGroupReduceFunction,
GroupReduceOperator,
DataSetpublic <R> GroupCombineOperator<T,R> combineGroup(GroupCombineFunction<T,R> combiner)
DataSet.
A CombineFunction is similar to a GroupReduceFunction but does not perform a full data exchange. Instead, the
CombineFunction calls the combine method once per partition for combining a group of results. This
operator is suitable for combining values into an intermediate format before doing a proper groupReduce where
the data is shuffled across the node for further reduction. The GroupReduce operator can also be supplied with
a combiner by implementing the RichGroupReduce function. The combine method of the RichGroupReduce function
demands input and output type to be the same. The CombineFunction, on the other side, can have an arbitrary
output type.combiner - The GroupCombineFunction that is applied on the DataSet.public ReduceOperator<T> minBy(int... fields)
The minimum is computed over the specified fields in lexicographical order.
Example 1: Given a data set with elements [0, 1], [1, 0], the
results will be:
minBy(0): [0, 1]minBy(1): [1, 0]Example 2: Given a data set with elements [0, 0], [0, 1], the
results will be:
minBy(0, 1): [0, 0]If multiple values with minimum value at the specified fields exist, a random one will be picked.
Internally, this operation is implemented as a ReduceFunction.
fields - Field positions to compute the minimum overReduceOperator representing the minimumpublic ReduceOperator<T> maxBy(int... fields)
The maximum is computed over the specified fields in lexicographical order.
Example 1: Given a data set with elements [0, 1], [1, 0], the
results will be:
maxBy(0): [1, 0]maxBy(1): [0, 1]Example 2: Given a data set with elements [0, 0], [0, 1], the
results will be:
maxBy(0, 1): [0, 1]If multiple values with maximum value at the specified fields exist, a random one will be picked.
Internally, this operation is implemented as a ReduceFunction.
fields - Field positions to compute the maximum overReduceOperator representing the maximumpublic GroupReduceOperator<T,T> first(int n)
DataSet.n - The desired number of elements.public <K> DistinctOperator<T> distinct(KeySelector<T,K> keyExtractor)
DataSet using a KeySelector function.
The KeySelector function is called for each element of the DataSet and extracts a single key value on which the decision is made if two items are distinct or not.
keyExtractor - The KeySelector function which extracts the key values from the DataSet on which the
distinction of the DataSet is decided.public DistinctOperator<T> distinct(int... fields)
Tuple DataSet using field position keys.
The field position keys specify the fields of Tuples on which the decision is made if two Tuples are distinct or not.
Note: Field position keys can only be specified for Tuple DataSets.
fields - One or more field positions on which the distinction of the DataSet is decided.public DistinctOperator<T> distinct(String... fields)
DataSet using expression keys.
The field expression keys specify the fields of a CompositeType
(e.g., Tuple or Pojo type) on which the decision is made if two elements are distinct or not.
In case of a AtomicType, only the wildcard expression ("*") is valid.
fields - One or more field expressions on which the distinction of the DataSet is decided.public DistinctOperator<T> distinct()
DataSet.
If the input is a CompositeType (Tuple or Pojo type),
distinct is performed on all fields and each field must be a key type
public <K> UnsortedGrouping<T> groupBy(KeySelector<T,K> keyExtractor)
DataSet using a KeySelector function.
The KeySelector function is called for each element of the DataSet and extracts a single
key value on which the DataSet is grouped.
This method returns an UnsortedGrouping on which one of the following grouping transformation
can be applied.
UnsortedGrouping.sortGroup(int, org.apache.flink.api.common.operators.Order) to get a SortedGrouping.
UnsortedGrouping.aggregate(Aggregations, int) to apply an Aggregate transformation.
UnsortedGrouping.reduce(org.apache.flink.api.common.functions.ReduceFunction) to apply a Reduce transformation.
UnsortedGrouping.reduceGroup(org.apache.flink.api.common.functions.GroupReduceFunction) to apply a GroupReduce transformation.
keyExtractor - The KeySelector function which extracts the key values from the DataSet on which it is grouped.KeySelector,
UnsortedGrouping,
AggregateOperator,
ReduceOperator,
GroupReduceOperator,
DataSetpublic UnsortedGrouping<T> groupBy(int... fields)
Tuple DataSet using field position keys.
Note: Field position keys only be specified for Tuple DataSets.
The field position keys specify the fields of Tuples on which the DataSet is grouped.
This method returns an UnsortedGrouping on which one of the following grouping transformation
can be applied.
UnsortedGrouping.sortGroup(int, org.apache.flink.api.common.operators.Order) to get a SortedGrouping.
UnsortedGrouping.aggregate(Aggregations, int) to apply an Aggregate transformation.
UnsortedGrouping.reduce(org.apache.flink.api.common.functions.ReduceFunction) to apply a Reduce transformation.
UnsortedGrouping.reduceGroup(org.apache.flink.api.common.functions.GroupReduceFunction) to apply a GroupReduce transformation.
fields - One or more field positions on which the DataSet will be grouped.Tuple,
UnsortedGrouping,
AggregateOperator,
ReduceOperator,
GroupReduceOperator,
DataSetpublic UnsortedGrouping<T> groupBy(String... fields)
DataSet using field expressions. A field expression is either the name of a public field
or a getter method with parentheses of the DataSetS underlying type. A dot can be used to drill down
into objects, as in "field1.getInnerField2()" .
This method returns an UnsortedGrouping on which one of the following grouping transformation
can be applied.
UnsortedGrouping.sortGroup(int, org.apache.flink.api.common.operators.Order) to get a SortedGrouping.
UnsortedGrouping.aggregate(Aggregations, int) to apply an Aggregate transformation.
UnsortedGrouping.reduce(org.apache.flink.api.common.functions.ReduceFunction) to apply a Reduce transformation.
UnsortedGrouping.reduceGroup(org.apache.flink.api.common.functions.GroupReduceFunction) to apply a GroupReduce transformation.
fields - One or more field expressions on which the DataSet will be grouped.Tuple,
UnsortedGrouping,
AggregateOperator,
ReduceOperator,
GroupReduceOperator,
DataSetpublic <R> JoinOperator.JoinOperatorSets<T,R> join(DataSet<R> other)
A Join transformation joins the elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
This method returns a JoinOperator.JoinOperatorSets on which one of the where methods
can be called to define the join key of the first joining (i.e., this) DataSet.
other - The other DataSet with which this DataSet is joined.JoinOperator.JoinOperatorSets,
DataSetpublic <R> JoinOperator.JoinOperatorSets<T,R> join(DataSet<R> other, JoinOperatorBase.JoinHint strategy)
A Join transformation joins the elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
This method returns a JoinOperator.JoinOperatorSets on which one of the where methods
can be called to define the join key of the first joining (i.e., this) DataSet.
other - The other DataSet with which this DataSet is joined.strategy - The strategy that should be used execute the join. If null is given, then the
optimizer will pick the join strategy.JoinOperator.JoinOperatorSets,
DataSetpublic <R> JoinOperator.JoinOperatorSets<T,R> joinWithTiny(DataSet<R> other)
A Join transformation joins the elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
This method also gives the hint to the optimizer that the second DataSet to join is much smaller than the first one.
This method returns a JoinOperator.JoinOperatorSets on which
JoinOperator.JoinOperatorSets.where(String...) needs to be called to define the join key of the first
joining (i.e., this) DataSet.
other - The other DataSet with which this DataSet is joined.JoinOperator.JoinOperatorSets,
DataSetpublic <R> JoinOperator.JoinOperatorSets<T,R> joinWithHuge(DataSet<R> other)
A Join transformation joins the elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
This method also gives the hint to the optimizer that the second DataSet to join is much larger than the first one.
This method returns a JoinOperator.JoinOperatorSets on which one of the where methods
can be called to define the join key of the first joining (i.e., this) DataSet.
other - The other DataSet with which this DataSet is joined.JoinOperator.JoinOperatorSets,
DataSetpublic <R> JoinOperatorSetsBase<T,R> leftOuterJoin(DataSet<R> other)
An Outer Join transformation joins two elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
Elements of the left DataSet (i.e. this) that do not have a matching
element on the other side are joined with null and emitted to the
resulting DataSet.
other - The other DataSet with which this DataSet is joined.JoinOperatorSetsBase,
DataSetpublic <R> JoinOperatorSetsBase<T,R> leftOuterJoin(DataSet<R> other, JoinOperatorBase.JoinHint strategy)
An Outer Join transformation joins two elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
Elements of the left DataSet (i.e. this) that do not have a matching
element on the other side are joined with null and emitted to the
resulting DataSet.
other - The other DataSet with which this DataSet is joined.strategy - The strategy that should be used execute the join. If null is given, then the
optimizer will pick the join strategy.JoinOperatorSetsBase,
DataSetpublic <R> JoinOperatorSetsBase<T,R> rightOuterJoin(DataSet<R> other)
An Outer Join transformation joins two elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
Elements of the right DataSet (i.e. other) that do not have a matching
element on this side are joined with null and emitted to the
resulting DataSet.
other - The other DataSet with which this DataSet is joined.JoinOperatorSetsBase,
DataSetpublic <R> JoinOperatorSetsBase<T,R> rightOuterJoin(DataSet<R> other, JoinOperatorBase.JoinHint strategy)
An Outer Join transformation joins two elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
Elements of the right DataSet (i.e. other) that do not have a matching
element on this side are joined with null and emitted to the
resulting DataSet.
other - The other DataSet with which this DataSet is joined.strategy - The strategy that should be used execute the join. If null is given, then the
optimizer will pick the join strategy.JoinOperatorSetsBase,
DataSetpublic <R> JoinOperatorSetsBase<T,R> fullOuterJoin(DataSet<R> other)
An Outer Join transformation joins two elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
Elements of both DataSets that do not have a matching
element on the opposing side are joined with null and emitted to the
resulting DataSet.
other - The other DataSet with which this DataSet is joined.JoinOperatorSetsBase,
DataSetpublic <R> JoinOperatorSetsBase<T,R> fullOuterJoin(DataSet<R> other, JoinOperatorBase.JoinHint strategy)
An Outer Join transformation joins two elements of two
DataSets on key equality and provides multiple ways to combine
joining elements into one DataSet.
Elements of both DataSets that do not have a matching
element on the opposing side are joined with null and emitted to the
resulting DataSet.
other - The other DataSet with which this DataSet is joined.strategy - The strategy that should be used execute the join. If null is given, then the
optimizer will pick the join strategy.JoinOperatorSetsBase,
DataSetpublic <R> CoGroupOperator.CoGroupOperatorSets<T,R> coGroup(DataSet<R> other)
A CoGroup transformation combines the elements of
two DataSets into one DataSet. It groups each DataSet individually on a key and
gives groups of both DataSets with equal keys together into a RichCoGroupFunction.
If a DataSet has a group with no matching key in the other DataSet, the CoGroupFunction
is called with an empty group for the non-existing group.
The CoGroupFunction can iterate over the elements of both groups and return any number of elements including none.
This method returns a CoGroupOperator.CoGroupOperatorSets on which one of the where methods
can be called to define the join key of the first joining (i.e., this) DataSet.
other - The other DataSet of the CoGroup transformation.CoGroupOperator.CoGroupOperatorSets,
CoGroupOperator,
DataSetpublic <R> CrossOperator.DefaultCross<T,R> cross(DataSet<R> other)
A Cross transformation combines the elements of two
DataSets into one DataSet. It builds all pair combinations of elements of
both DataSets, i.e., it builds a Cartesian product.
The resulting CrossOperator.DefaultCross wraps each pair of crossed elements into a Tuple2, with
the element of the first input being the first field of the tuple and the element of the
second input being the second field of the tuple.
Call CrossOperator.DefaultCross.with(org.apache.flink.api.common.functions.CrossFunction) to define a
CrossFunction which is called for
each pair of crossed elements. The CrossFunction returns a exactly one element for each pair of input elements.
other - The other DataSet with which this DataSet is crossed.CrossOperator.DefaultCross,
CrossFunction,
DataSet,
Tuple2public <R> CrossOperator.DefaultCross<T,R> crossWithTiny(DataSet<R> other)
A Cross transformation combines the elements of two
DataSets into one DataSet. It builds all pair combinations of elements of
both DataSets, i.e., it builds a Cartesian product.
This method also gives the hint to the optimizer that the second DataSet to cross is much
smaller than the first one.
The resulting CrossOperator.DefaultCross wraps each pair of crossed elements into a Tuple2, with
the element of the first input being the first field of the tuple and the element of the
second input being the second field of the tuple.
Call CrossOperator.DefaultCross.with(org.apache.flink.api.common.functions.CrossFunction) to define a
CrossFunction which is called for
each pair of crossed elements. The CrossFunction returns a exactly one element for each pair of input elements.
other - The other DataSet with which this DataSet is crossed.CrossOperator.DefaultCross,
CrossFunction,
DataSet,
Tuple2public <R> CrossOperator.DefaultCross<T,R> crossWithHuge(DataSet<R> other)
A Cross transformation combines the elements of two
DataSets into one DataSet. It builds all pair combinations of elements of
both DataSets, i.e., it builds a Cartesian product.
This method also gives the hint to the optimizer that the second DataSet to cross is much
larger than the first one.
The resulting CrossOperator.DefaultCross wraps each pair of crossed elements into a Tuple2, with
the element of the first input being the first field of the tuple and the element of the
second input being the second field of the tuple.
Call CrossOperator.DefaultCross.with(org.apache.flink.api.common.functions.CrossFunction) to define a
CrossFunction which is called for
each pair of crossed elements. The CrossFunction returns a exactly one element for each pair of input elements.
other - The other DataSet with which this DataSet is crossed.CrossOperator.DefaultCross,
CrossFunction,
DataSet,
Tuple2public IterativeDataSet<T> iterate(int maxIterations)
IterativeDataSet.closeWith(DataSet). The data set
given to the closeWith(DataSet) method is the data set that will be fed back and used as the input
to the next iteration. The return value of the closeWith(DataSet) method is the resulting
data set after the iteration has terminated.
An example of an iterative computation is as follows:
DataSet<Double> input = ...;
DataSet<Double> startOfIteration = input.iterate(10);
DataSet<Double> toBeFedBack = startOfIteration
.map(new MyMapper())
.groupBy(...).reduceGroup(new MyReducer());
DataSet<Double> result = startOfIteration.closeWith(toBeFedBack);
The iteration has a maximum number of times that it executes. A dynamic termination can be realized by using a
termination criterion (see IterativeDataSet.closeWith(DataSet, DataSet)).
maxIterations - The maximum number of times that the iteration is executed.IterativeDataSet.closeWith(DataSet).IterativeDataSetpublic <R> DeltaIteration<T,R> iterateDelta(DataSet<R> workset, int maxIterations, int... keyPositions)
iterate(int),
but maintains state across the individual iteration steps. The Solution set, which represents the current state
at the beginning of each iteration can be obtained via DeltaIteration.getSolutionSet() ()}.
It can be be accessed by joining (or CoGrouping) with it. The DataSet that represents the workset of an iteration
can be obtained via DeltaIteration.getWorkset().
The solution set is updated by producing a delta for it, which is merged into the solution set at the end of each
iteration step.
The delta iteration must be closed by calling DeltaIteration.closeWith(DataSet, DataSet). The two
parameters are the delta for the solution set and the new workset (the data set that will be fed back).
The return value of the closeWith(DataSet, DataSet) method is the resulting
data set after the iteration has terminated. Delta iterations terminate when the feed back data set
(the workset) is empty. In addition, a maximum number of steps is given as a fall back termination guard.
Elements in the solution set are uniquely identified by a key. When merging the solution set delta, contained elements with the same key are replaced.
NOTE: Delta iterations currently support only tuple valued data types. This restriction will be removed in the future. The key is specified by the tuple position.
A code example for a delta iteration is as follows
DeltaIteration<Tuple2<Long, Long>, Tuple2<Long, Long>> iteration =
initialState.iterateDelta(initialFeedbackSet, 100, 0);
DataSet<Tuple2<Long, Long>> delta = iteration.groupBy(0).aggregate(Aggregations.AVG, 1)
.join(iteration.getSolutionSet()).where(0).equalTo(0)
.flatMap(new ProjectAndFilter());
DataSet<Tuple2<Long, Long>> feedBack = delta.join(someOtherSet).where(...).equalTo(...).with(...);
// close the delta iteration (delta and new workset are identical)
DataSet<Tuple2<Long, Long>> result = iteration.closeWith(delta, feedBack);
workset - The initial version of the data set that is fed back to the next iteration step (the workset).maxIterations - The maximum number of iteration steps, as a fall back safeguard.keyPositions - The position of the tuple fields that is used as the key of the solution set.DeltaIterationpublic <X> DataSet<X> runOperation(CustomUnaryOperation<T,X> operation)
CustomUnaryOperation on the data set. Custom operations are typically complex
operators that are composed of multiple steps.operation - The operation to run.public UnionOperator<T> union(DataSet<T> other)
other - The other DataSet which is unioned with the current DataSet.public PartitionOperator<T> partitionByHash(int... fields)
Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
fields - The field indexes on which the DataSet is hash-partitioned.public PartitionOperator<T> partitionByHash(String... fields)
Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
fields - The field expressions on which the DataSet is hash-partitioned.public <K extends Comparable<K>> PartitionOperator<T> partitionByHash(KeySelector<T,K> keyExtractor)
Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
keyExtractor - The KeyExtractor with which the DataSet is hash-partitioned.KeySelectorpublic PartitionOperator<T> partitionByRange(int... fields)
Important:This operation requires an extra pass over the DataSet to compute the range boundaries and shuffles the whole DataSet over the network. This can take significant amount of time.
fields - The field indexes on which the DataSet is range-partitioned.public PartitionOperator<T> partitionByRange(String... fields)
Important:This operation requires an extra pass over the DataSet to compute the range boundaries and shuffles the whole DataSet over the network. This can take significant amount of time.
fields - The field expressions on which the DataSet is range-partitioned.public <K extends Comparable<K>> PartitionOperator<T> partitionByRange(KeySelector<T,K> keyExtractor)
Important:This operation requires an extra pass over the DataSet to compute the range boundaries and shuffles the whole DataSet over the network. This can take significant amount of time.
keyExtractor - The KeyExtractor with which the DataSet is range-partitioned.KeySelectorpublic <K> PartitionOperator<T> partitionCustom(Partitioner<K> partitioner, int field)
Note: This method works only on single field keys.
partitioner - The partitioner to assign partitions to keys.field - The field index on which the DataSet is to partitioned.public <K> PartitionOperator<T> partitionCustom(Partitioner<K> partitioner, String field)
Note: This method works only on single field keys.
partitioner - The partitioner to assign partitions to keys.field - The field index on which the DataSet is to partitioned.public <K extends Comparable<K>> PartitionOperator<T> partitionCustom(Partitioner<K> partitioner, KeySelector<T,K> keyExtractor)
Note: This method works only on single field keys, i.e. the selector cannot return tuples of fields.
partitioner - The partitioner to assign partitions to keys.keyExtractor - The KeyExtractor with which the DataSet is partitioned.KeySelectorpublic PartitionOperator<T> rebalance()
Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
public SortPartitionOperator<T> sortPartition(int field, Order order)
field - The field index on which the DataSet is sorted.order - The order in which the DataSet is sorted.public SortPartitionOperator<T> sortPartition(String field, Order order)
field - The field expression referring to the field on which the DataSet is sorted.order - The order in which the DataSet is sorted.public <K> SortPartitionOperator<T> sortPartition(KeySelector<T,K> keyExtractor, Order order)
Note that no additional sort keys can be appended to a KeySelector sort keys. To sort the partitions by multiple values using KeySelector, the KeySelector must return a tuple consisting of the values.
keyExtractor - The KeySelector function which extracts the key values from the DataSet
on which the DataSet is sorted.order - The order in which the DataSet is sorted.public DataSink<T> writeAsText(String filePath)
For each element of the DataSet the result of Object.toString() is written.
Output files and directories
What output how writeAsText() method produces is depending on other circumstance
.
└── path1/
├── 1
├── 2
└── ... dataset.writeAsText("file:///path1"); .
└── path1 // Parallelism is set to only this particular operation
dataset.writeAsText("file:///path1").setParallelism(1);
// This will creates the same effect but note all operators' parallelism are set to one
env.setParallelism(1);
...
dataset.writeAsText("file:///path1"); .
└── path1/
└── 1 // fs.output.always-create-directory = true
dataset.writeAsText("file:///path1").setParallelism(1); filePath - The path pointing to the location the text file or files under the directory is written to.TextOutputFormatpublic DataSink<T> writeAsText(String filePath, FileSystem.WriteMode writeMode)
For each element of the DataSet the result of Object.toString() is written.
filePath - The path pointing to the location the text file is written to.writeMode - Control the behavior for existing files. Options are NO_OVERWRITE and OVERWRITE.TextOutputFormat,
Output files and directoriespublic DataSink<String> writeAsFormattedText(String filePath, TextOutputFormat.TextFormatter<T> formatter)
For each element of the DataSet the result of TextOutputFormat.TextFormatter.format(Object) is written.
filePath - The path pointing to the location the text file is written to.formatter - formatter that is applied on every element of the DataSet.TextOutputFormat,
Output files and directoriespublic DataSink<String> writeAsFormattedText(String filePath, FileSystem.WriteMode writeMode, TextOutputFormat.TextFormatter<T> formatter)
For each element of the DataSet the result of TextOutputFormat.TextFormatter.format(Object) is written.
filePath - The path pointing to the location the text file is written to.writeMode - Control the behavior for existing files. Options are NO_OVERWRITE and OVERWRITE.formatter - formatter that is applied on every element of the DataSet.TextOutputFormat,
Output files and directoriespublic DataSink<T> writeAsCsv(String filePath)
Tuple DataSet as CSV file(s) to the specified location.
Note: Only a Tuple DataSet can written as a CSV file.
For each Tuple field the result of Object.toString() is written.
Tuple fields are separated by the default field delimiter "comma" (,).
Tuples are are separated by the newline character (\n).
filePath - The path pointing to the location the CSV file is written to.Tuple,
CsvOutputFormat,
Output files and directoriespublic DataSink<T> writeAsCsv(String filePath, FileSystem.WriteMode writeMode)
Tuple DataSet as CSV file(s) to the specified location.
Note: Only a Tuple DataSet can written as a CSV file.
For each Tuple field the result of Object.toString() is written.
Tuple fields are separated by the default field delimiter "comma" (,).
Tuples are are separated by the newline character (\n).
filePath - The path pointing to the location the CSV file is written to.writeMode - The behavior regarding existing files. Options are NO_OVERWRITE and OVERWRITE.Tuple,
CsvOutputFormat,
Output files and directoriespublic DataSink<T> writeAsCsv(String filePath, String rowDelimiter, String fieldDelimiter)
Tuple DataSet as CSV file(s) to the specified location with the specified field and line delimiters.
Note: Only a Tuple DataSet can written as a CSV file.
For each Tuple field the result of Object.toString() is written.
filePath - The path pointing to the location the CSV file is written to.rowDelimiter - The row delimiter to separate Tuples.fieldDelimiter - The field delimiter to separate Tuple fields.Tuple,
CsvOutputFormat,
Output files and directoriespublic DataSink<T> writeAsCsv(String filePath, String rowDelimiter, String fieldDelimiter, FileSystem.WriteMode writeMode)
Tuple DataSet as CSV file(s) to the specified location with the specified field and line delimiters.
Note: Only a Tuple DataSet can written as a CSV file.
For each Tuple field the result of Object.toString() is written.
filePath - The path pointing to the location the CSV file is written to.rowDelimiter - The row delimiter to separate Tuples.fieldDelimiter - The field delimiter to separate Tuple fields.writeMode - The behavior regarding existing files. Options are NO_OVERWRITE and OVERWRITE.Tuple,
CsvOutputFormat,
Output files and directoriespublic void print()
throws Exception
System.out of the JVM that calls
the print() method. For programs that are executed in a cluster, this method needs
to gather the contents of the DataSet back to the client, to print it there.
The string written for each element is defined by the Object.toString() method.
This method immediately triggers the program execution, similar to the
collect() and count() methods.
ExceptionprintToErr(),
printOnTaskManager(String)public void printToErr()
throws Exception
System.err of the JVM that calls
the print() method. For programs that are executed in a cluster, this method needs
to gather the contents of the DataSet back to the client, to print it there.
The string written for each element is defined by the Object.toString() method.
This method immediately triggers the program execution, similar to the
collect() and count() methods.
Exceptionprint(),
printOnTaskManager(String)public DataSink<T> printOnTaskManager(String prefix)
To print the data to the console or stdout stream of the client process instead, use the
print() method.
For each element of the DataSet the result of Object.toString() is written.
prefix - The string to prefix each line of the output with. This helps identifying outputs
from different printing sinks.print()@Deprecated @PublicEvolving public DataSink<T> print(String sinkIdentifier)
printOnTaskManager(String) instead.For each element of the DataSet the result of Object.toString() is written.
sinkIdentifier - The string to prefix the output with.@Deprecated @PublicEvolving public DataSink<T> printToErr(String sinkIdentifier)
printOnTaskManager(String) instead, or the PrintingOutputFormat.For each element of the DataSet the result of Object.toString() is written.
sinkIdentifier - The string to prefix the output with.public DataSink<T> write(FileOutputFormat<T> outputFormat, String filePath)
FileOutputFormat to a specified location.
This method adds a data sink to the program.outputFormat - The FileOutputFormat to write the DataSet.filePath - The path to the location where the DataSet is written.FileOutputFormatpublic DataSink<T> write(FileOutputFormat<T> outputFormat, String filePath, FileSystem.WriteMode writeMode)
FileOutputFormat to a specified location.
This method adds a data sink to the program.outputFormat - The FileOutputFormat to write the DataSet.filePath - The path to the location where the DataSet is written.writeMode - The mode of writing, indicating whether to overwrite existing files.FileOutputFormatpublic DataSink<T> output(OutputFormat<T> outputFormat)
OutputFormat. This method adds a data sink to the program.
Programs may have multiple data sinks. A DataSet may also have multiple consumers (data sinks
or transformations) at the same time.outputFormat - The OutputFormat to process the DataSet.OutputFormat,
DataSinkCopyright © 2014–2018 The Apache Software Foundation. All rights reserved.