DataSet (flink-java 0.8.1 API)

java.lang.Object
- org.apache.flink.api.java.DataSet<T>

Type Parameters:
T - The type of the DataSet, i.e., the type of the elements of the DataSet.

Direct Known Subclasses:

BulkIterationResultSet, DeltaIteration.SolutionSetPlaceHolder, DeltaIteration.WorksetPlaceHolder, DeltaIterationResultSet, Operator
```
public abstract class DataSet<T>
extends Object
```
A DataSet represents a collection of elements of the same type.
A DataSet can be transformed into another DataSet by applying a transformation as for example

Constructor Summary

Constructors
Modifier Constructor and Description

protected DataSet(ExecutionEnvironment context, TypeInformation<T> type)

Constructors
Modifier	Constructor and Description
`protected`	`DataSet(ExecutionEnvironment context, TypeInformation<T> type)`

Method Summary

Methods
Modifier and Type	Method and Description
`AggregateOperator<T>`	`aggregate(Aggregations agg, int field)` Applies an Aggregate transformation on a non-grouped `Tuple` `DataSet`. Note: Only Tuple DataSets can be aggregated. The transformation applies a built-in `Aggregation` on a specified field of a Tuple DataSet.
`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. A CoGroup transformation combines the elements of two `DataSets` into one DataSet.
`<R> CrossOperator.DefaultCross<T,R>`	`cross(DataSet<R> other)` Initiates a Cross transformation. A Cross transformation combines the elements of two `DataSets` into one DataSet.
`<R> CrossOperator.DefaultCross<T,R>`	`crossWithHuge(DataSet<R> other)` Initiates a Cross transformation. A Cross transformation combines the elements of two `DataSets` into one DataSet.
`<R> CrossOperator.DefaultCross<T,R>`	`crossWithTiny(DataSet<R> other)` Initiates a Cross transformation. A Cross transformation combines the elements of two `DataSets` into one DataSet.
`DistinctOperator<T>`	`distinct()` Returns a distinct set of a `Tuple` `DataSet` using all fields of the tuple.
`DistinctOperator<T>`	`distinct(int... fields)` Returns a distinct set of a `Tuple` `DataSet` using field position keys.
`<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 `Tuple` `DataSet` using expression keys.
`FilterOperator<T>`	`filter(FilterFunction<T> filter)` Applies a Filter transformation on a `DataSet`. The transformation calls a `RichFilterFunction` for each element of the DataSet and retains only those element for which the function returns true.
`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`. The transformation calls a `RichFlatMapFunction` for each element of the DataSet.
`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)` Groups a `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.
`<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. 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.
`<R> JoinOperator.JoinOperatorSets<T,R>`	`joinWithTiny(DataSet<R> other)` Initiates a Join transformation.
`<R> MapOperator<T,R>`	`map(MapFunction<T,R> mapper)` Applies a Map transformation on a `DataSet`. The transformation calls a `RichMapFunction` for each element of the 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>> PartitionOperator<T>`	`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.
`<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>> PartitionOperator<T>`	`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.
`DataSink<T>`	`print()` Writes a DataSet to the standard output stream (stdout). For each element of the DataSet the result of `Object.toString()` is written.
`DataSink<T>`	`printToErr()` Writes a DataSet to the standard error stream (stderr). For each element of the DataSet the result of `Object.toString()` is written.
`<OUT extends Tuple> ProjectOperator<?,OUT>`	`project(int... fieldIndexes)` Applies a Project transformation on a `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 `ProjectOperator.project(int[])`.
`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`. The transformation consecutively calls a `RichReduceFunction` until only a single element remains which is the result of the transformation.
`<R> GroupReduceOperator<T,R>`	`reduceGroup(GroupReduceFunction<T,R> reducer)` Applies a GroupReduce transformation on a non-grouped `DataSet`. The transformation calls a `RichGroupReduceFunction` once with the full DataSet.
`<X> DataSet<X>`	`runOperation(CustomUnaryOperation<T,X> operation)` Runs a `CustomUnaryOperation` on the data set.
`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 a CSV file 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.
`DataSink<T>`	`writeAsCsv(String filePath, FileSystem.WriteMode writeMode)` Writes a `Tuple` DataSet as a CSV file 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.
`DataSink<T>`	`writeAsCsv(String filePath, String rowDelimiter, String fieldDelimiter)` Writes a `Tuple` DataSet as a CSV file 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.
`DataSink<T>`	`writeAsCsv(String filePath, String rowDelimiter, String fieldDelimiter, FileSystem.WriteMode writeMode)` Writes a `Tuple` DataSet as a CSV file 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.
`DataSink<String>`	`writeAsFormattedText(String filePath, FileSystem.WriteMode writeMode, TextOutputFormat.TextFormatter<T> formatter)` Writes a DataSet as a text file to the specified location. For each element of the DataSet the result of `TextOutputFormat.TextFormatter.format(Object)` is written.
`DataSink<String>`	`writeAsFormattedText(String filePath, TextOutputFormat.TextFormatter<T> formatter)` Writes a DataSet as a text file to the specified location. For each element of the DataSet the result of `TextOutputFormat.TextFormatter.format(Object)` is written.
`DataSink<T>`	`writeAsText(String filePath)` Writes a DataSet as a text file to the specified location. For each element of the DataSet the result of `Object.toString()` is written.
`DataSink<T>`	`writeAsText(String filePath, FileSystem.WriteMode writeMode)` Writes a DataSet as a text file to the specified location. For each element of the DataSet the result of `Object.toString()` is written.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - DataSet
```
protected DataSet(ExecutionEnvironment context,
       TypeInformation<T> type)
```
- Method Detail
  - getExecutionEnvironment
```
public ExecutionEnvironment getExecutionEnvironment()
```
    Returns the ExecutionEnvironment in which this DataSet is registered.
    
    Returns:
    The ExecutionEnvironment in which this DataSet is registered.
    See Also:
    ExecutionEnvironment
  - getType
```
public TypeInformation<T> getType()
```
    Returns the TypeInformation for the type of this DataSet.
    
    Returns:
    The TypeInformation for the type of this DataSet.
    See Also:
    TypeInformation
  - clean
```
public <F> F clean(F f)
```
  - map
```
public <R> MapOperator<T,R> map(MapFunction<T,R> mapper)
```
    Applies a Map transformation on a DataSet.
    The transformation calls a RichMapFunction for each element of the DataSet. Each MapFunction call returns exactly one element.
    
    Parameters:
    mapper - The MapFunction that is called for each element of the DataSet.
    
    Returns:
    A MapOperator that represents the transformed DataSet.
    See Also:
    RichMapFunction, MapOperator, DataSet
  - mapPartition
```
public <R> MapPartitionOperator<T,R> mapPartition(MapPartitionFunction<T,R> mapPartition)
```
    Applies a Map-style operation to the entire partition of the data. The function is called once per parallel partition of the data, and the entire partition is available through the given Iterator. The number of elements that each instance of the MapPartition function sees is non deterministic and depends on the degree of parallelism of the operation. 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.
    
    Parameters:
    mapPartition - The MapPartitionFunction that is called for the full DataSet.
    
    Returns:
    A MapPartitionOperator that represents the transformed DataSet.
    See Also:
    MapPartitionFunction, MapPartitionOperator, DataSet
  - flatMap
```
public <R> FlatMapOperator<T,R> flatMap(FlatMapFunction<T,R> flatMapper)
```
    Applies a FlatMap transformation on a DataSet.
    The transformation calls a RichFlatMapFunction for each element of the DataSet. Each FlatMapFunction call can return any number of elements including none.
    
    Parameters:
    flatMapper - The FlatMapFunction that is called for each element of the DataSet.
    
    Returns:
    A FlatMapOperator that represents the transformed DataSet.
    See Also:
    RichFlatMapFunction, FlatMapOperator, DataSet
  - filter
```
public FilterOperator<T> filter(FilterFunction<T> filter)
```
    Applies a Filter transformation on a 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.
    
    Parameters:
    filter - The FilterFunction that is called for each element of the DataSet.
    
    Returns:
    A FilterOperator that represents the filtered DataSet.
    See Also:
    RichFilterFunction, FilterOperator, DataSet
  - project
```
public <OUT extends Tuple> ProjectOperator<?,OUT> project(int... fieldIndexes)
```
    Applies a Project transformation on a 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 ProjectOperator.project(int[]). Note: With the current implementation, the Project transformation looses type information.
    
    Parameters:
    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.
    
    Returns:
    A ProjectOperator that represents the projected DataSet.
    See Also:
    Tuple, DataSet, ProjectOperator
  - aggregate
```
public AggregateOperator<T> aggregate(Aggregations agg,
                             int field)
```
    Applies an Aggregate transformation on a non-grouped 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).
    
    Parameters:
    agg - The built-in aggregation function that is computed.
    field - The index of the Tuple field on which the aggregation function is applied.
    
    Returns:
    An AggregateOperator that represents the aggregated DataSet.
    See Also:
    Tuple, Aggregations, AggregateOperator, DataSet
  - sum
```
public AggregateOperator<T> sum(int field)
```
    Syntactic sugar for aggregate (SUM, field)
    
    Parameters:
    field - The index of the Tuple field on which the aggregation function is applied.
    
    Returns:
    An AggregateOperator that represents the summed DataSet.
    See Also:
    AggregateOperator
  - max
```
public AggregateOperator<T> max(int field)
```
    Syntactic sugar for 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.
    
    Parameters:
    field - The index of the Tuple field on which the aggregation function is applied.
    
    Returns:
    An AggregateOperator that represents the max'ed DataSet.
    See Also:
    aggregate(Aggregations, int), maxBy(int...)
  - min
```
public AggregateOperator<T> min(int field)
```
    Syntactic sugar for 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.
    
    Parameters:
    field - The index of the Tuple field on which the aggregation function is applied.
    
    Returns:
    An AggregateOperator that represents the min'ed DataSet.
    See Also:
    aggregate(Aggregations, int), minBy(int...)
  - reduce
```
public ReduceOperator<T> reduce(ReduceFunction<T> reducer)
```
    Applies a Reduce transformation on a non-grouped 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.
    
    Parameters:
    reducer - The ReduceFunction that is applied on the DataSet.
    
    Returns:
    A ReduceOperator that represents the reduced DataSet.
    See Also:
    RichReduceFunction, ReduceOperator, DataSet
  - reduceGroup
```
public <R> GroupReduceOperator<T,R> reduceGroup(GroupReduceFunction<T,R> reducer)
```
    Applies a GroupReduce transformation on a non-grouped 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.
    
    Parameters:
    reducer - The GroupReduceFunction that is applied on the DataSet.
    
    Returns:
    A GroupReduceOperator that represents the reduced DataSet.
    See Also:
    RichGroupReduceFunction, GroupReduceOperator, DataSet
  - minBy
```
public ReduceOperator<T> minBy(int... fields)
```
    Selects an element with minimum value.
    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.
    Parameters:
    fields - Field positions to compute the minimum over
    
    Returns:
    A ReduceOperator representing the minimum
  - maxBy
```
public ReduceOperator<T> maxBy(int... fields)
```
    Selects an element with maximum value.
    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.
    Parameters:
    fields - Field positions to compute the maximum over
    
    Returns:
    A ReduceOperator representing the maximum
  - first
```
public GroupReduceOperator<T,T> first(int n)
```
    Returns a new set containing the first n elements in this DataSet.
    
    Parameters:
    n - The desired number of elements.
    
    Returns:
    A ReduceGroupOperator that represents the DataSet containing the elements.
  - distinct
```
public <K> DistinctOperator<T> distinct(KeySelector<T,K> keyExtractor)
```
    Returns a distinct set of a 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.
    
    Parameters:
    keyExtractor - The KeySelector function which extracts the key values from the DataSet on which the distinction of the DataSet is decided.
    
    Returns:
    A DistinctOperator that represents the distinct DataSet.
  - distinct
```
public DistinctOperator<T> distinct(int... fields)
```
    Returns a distinct set of a 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.
    
    Parameters:
    fields - One or more field positions on which the distinction of the DataSet is decided.
    
    Returns:
    A DistinctOperator that represents the distinct DataSet.
  - distinct
```
public DistinctOperator<T> distinct(String... fields)
```
    Returns a distinct set of a Tuple DataSet using expression keys.
    The field position keys specify the fields of Tuples or Pojos on which the decision is made if two elements are distinct or not.
    
    Parameters:
    fields - One or more field positions on which the distinction of the DataSet is decided.
    
    Returns:
    A DistinctOperator that represents the distinct DataSet.
  - distinct
```
public DistinctOperator<T> distinct()
```
    Returns a distinct set of a Tuple DataSet using all fields of the tuple.
    Note: This operator can only be applied to Tuple DataSets.
    
    Returns:
    A DistinctOperator that represents the distinct DataSet.
  - groupBy
```
public <K> UnsortedGrouping<T> groupBy(KeySelector<T,K> keyExtractor)
```
    Groups a 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.
    Parameters:
    keyExtractor - The KeySelector function which extracts the key values from the DataSet on which it is grouped.
    
    Returns:
    An UnsortedGrouping on which a transformation needs to be applied to obtain a transformed DataSet.
    See Also:
    KeySelector, UnsortedGrouping, AggregateOperator, ReduceOperator, GroupReduceOperator, DataSet
  - groupBy
```
public UnsortedGrouping<T> groupBy(int... fields)
```
    Groups a 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.
    Parameters:
    fields - One or more field positions on which the DataSet will be grouped.
    
    Returns:
    A Grouping on which a transformation needs to be applied to obtain a transformed DataSet.
    See Also:
    Tuple, UnsortedGrouping, AggregateOperator, ReduceOperator, GroupReduceOperator, DataSet
  - groupBy
```
public UnsortedGrouping<T> groupBy(String... fields)
```
    Groups a 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.
    Parameters:
    fields - One or more field expressions on which the DataSet will be grouped.
    
    Returns:
    A Grouping on which a transformation needs to be applied to obtain a transformed DataSet.
    See Also:
    Tuple, UnsortedGrouping, AggregateOperator, ReduceOperator, GroupReduceOperator, DataSet
  - join
```
public <R> JoinOperator.JoinOperatorSets<T,R> join(DataSet<R> other)
```
    Initiates a Join transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is joined.
    
    Returns:
    A JoinOperatorSets to continue the definition of the Join transformation.
    See Also:
    JoinOperator.JoinOperatorSets, DataSet
  - join
```
public <R> JoinOperator.JoinOperatorSets<T,R> join(DataSet<R> other,
                                          JoinOperatorBase.JoinHint strategy)
```
    Initiates a Join transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is joined.
    strategy - The strategy that should be used execute the join. If null is give, then the optimizer will pick the join strategy.
    
    Returns:
    A JoinOperatorSets to continue the definition of the Join transformation.
    See Also:
    JoinOperator.JoinOperatorSets, DataSet
  - joinWithTiny
```
public <R> JoinOperator.JoinOperatorSets<T,R> joinWithTiny(DataSet<R> other)
```
    Initiates a Join transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is joined.
    
    Returns:
    A JoinOperatorSets to continue the definition of the Join transformation.
    See Also:
    JoinOperator.JoinOperatorSets, DataSet
  - joinWithHuge
```
public <R> JoinOperator.JoinOperatorSets<T,R> joinWithHuge(DataSet<R> other)
```
    Initiates a Join transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is joined.
    
    Returns:
    A JoinOperatorSet to continue the definition of the Join transformation.
    See Also:
    JoinOperator.JoinOperatorSets, DataSet
  - coGroup
```
public <R> CoGroupOperator.CoGroupOperatorSets<T,R> coGroup(DataSet<R> other)
```
    Initiates a CoGroup transformation.
    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.
    
    Parameters:
    other - The other DataSet of the CoGroup transformation.
    
    Returns:
    A CoGroupOperatorSets to continue the definition of the CoGroup transformation.
    See Also:
    CoGroupOperator.CoGroupOperatorSets, CoGroupOperator, DataSet
  - cross
```
public <R> CrossOperator.DefaultCross<T,R> cross(DataSet<R> other)
```
    Initiates a Cross transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is crossed.
    
    Returns:
    A DefaultCross that returns a Tuple2 for each pair of crossed elements.
    See Also:
    CrossOperator.DefaultCross, CrossFunction, DataSet, Tuple2
  - crossWithTiny
```
public <R> CrossOperator.DefaultCross<T,R> crossWithTiny(DataSet<R> other)
```
    Initiates a Cross transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is crossed.
    
    Returns:
    A DefaultCross that returns a Tuple2 for each pair of crossed elements.
    See Also:
    CrossOperator.DefaultCross, CrossFunction, DataSet, Tuple2
  - crossWithHuge
```
public <R> CrossOperator.DefaultCross<T,R> crossWithHuge(DataSet<R> other)
```
    Initiates a Cross transformation.
    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.
    
    Parameters:
    other - The other DataSet with which this DataSet is crossed.
    
    Returns:
    A DefaultCross that returns a Tuple2 for each pair of crossed elements.
    See Also:
    CrossOperator.DefaultCross, CrossFunction, DataSet, Tuple2
  - iterate
```
public IterativeDataSet<T> iterate(int maxIterations)
```
    Initiates an iterative part of the program that executes multiple times and feeds back data sets. The iterative part needs to be closed by calling 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)).
    Parameters:
    maxIterations - The maximum number of times that the iteration is executed.
    
    Returns:
    An IterativeDataSet that marks the start of the iterative part and needs to be closed by IterativeDataSet.closeWith(DataSet).
    See Also:
    IterativeDataSet
  - iterateDelta
```
public <R> DeltaIteration<T,R> iterateDelta(DataSet<R> workset,
                                   int maxIterations,
                                   int... keyPositions)
```
    Initiates a delta iteration. A delta iteration is similar to a regular iteration (as started by 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(initialFeedbakSet, 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);
 
 
```
    Parameters:
    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.
    
    Returns:
    The DeltaIteration that marks the start of a delta iteration.
    See Also:
    DeltaIteration
  - runOperation
```
public <X> DataSet<X> runOperation(CustomUnaryOperation<T,X> operation)
```
    Runs a CustomUnaryOperation on the data set. Custom operations are typically complex operators that are composed of multiple steps.
    
    Parameters:
    operation - The operation to run.
    
    Returns:
    The data set produced by the operation.
  - union
```
public UnionOperator<T> union(DataSet<T> other)
```
    Creates a union of this DataSet with an other DataSet. The other DataSet must be of the same data type.
    
    Parameters:
    other - The other DataSet which is unioned with the current DataSet.
    
    Returns:
    The resulting DataSet.
  - partitionByHash
```
public PartitionOperator<T> partitionByHash(int... fields)
```
    Hash-partitions a DataSet on the specified key fields.
    Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
    
    Parameters:
    fields - The field indexes on which the DataSet is hash-partitioned.
    
    Returns:
    The partitioned DataSet.
  - partitionByHash
```
public PartitionOperator<T> partitionByHash(String... fields)
```
    Hash-partitions a DataSet on the specified key fields.
    Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
    
    Parameters:
    fields - The field expressions on which the DataSet is hash-partitioned.
    
    Returns:
    The partitioned DataSet.
  - partitionByHash
```
public <K extends Comparable<K>> PartitionOperator<T> partitionByHash(KeySelector<T,K> keyExtractor)
```
    Partitions a DataSet using the specified KeySelector.
    Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
    
    Parameters:
    keyExtractor - The KeyExtractor with which the DataSet is hash-partitioned.
    
    Returns:
    The partitioned DataSet.
    See Also:
    KeySelector
  - partitionCustom
```
public <K> PartitionOperator<T> partitionCustom(Partitioner<K> partitioner,
                                       int field)
```
    Partitions a tuple DataSet on the specified key fields using a custom partitioner. This method takes the key position to partition on, and a partitioner that accepts the key type.
    Note: This method works only on single field keys.
    
    Parameters:
    partitioner - The partitioner to assign partitions to keys.
    field - The field index on which the DataSet is to partitioned.
    
    Returns:
    The partitioned DataSet.
  - partitionCustom
```
public <K> PartitionOperator<T> partitionCustom(Partitioner<K> partitioner,
                                       String field)
```
    Partitions a POJO DataSet on the specified key fields using a custom partitioner. This method takes the key expression to partition on, and a partitioner that accepts the key type.
    Note: This method works only on single field keys.
    
    Parameters:
    partitioner - The partitioner to assign partitions to keys.
    field - The field index on which the DataSet is to partitioned.
    
    Returns:
    The partitioned DataSet.
  - partitionCustom
```
public <K extends Comparable<K>> PartitionOperator<T> partitionCustom(Partitioner<K> partitioner,
                                                             KeySelector<T,K> keyExtractor)
```
    Partitions a DataSet on the key returned by the selector, using a custom partitioner. This method takes the key selector t get the key to partition on, and a partitioner that accepts the key type.
    Note: This method works only on single field keys, i.e. the selector cannot return tuples of fields.
    
    Parameters:
    partitioner - The partitioner to assign partitions to keys.
    keyExtractor - The KeyExtractor with which the DataSet is hash-partitioned.
    
    Returns:
    The partitioned DataSet.
    See Also:
    KeySelector
  - rebalance
```
public 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. This can help to improve performance in case of heavy data skew and compute intensive operations.
    Important:This operation shuffles the whole DataSet over the network and can take significant amount of time.
    
    Returns:
    The re-balanced DataSet.
  - writeAsText
```
public DataSink<T> writeAsText(String filePath)
```
    Writes a DataSet as a text file to the specified location.
    For each element of the DataSet the result of Object.toString() is written.
    
    Parameters:
    filePath - The path pointing to the location the text file is written to.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    TextOutputFormat
  - writeAsText
```
public DataSink<T> writeAsText(String filePath,
                      FileSystem.WriteMode writeMode)
```
    Writes a DataSet as a text file to the specified location.
    For each element of the DataSet the result of Object.toString() is written.
    
    Parameters:
    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.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    TextOutputFormat
  - writeAsFormattedText
```
public DataSink<String> writeAsFormattedText(String filePath,
                                    TextOutputFormat.TextFormatter<T> formatter)
```
    Writes a DataSet as a text file to the specified location.
    For each element of the DataSet the result of TextOutputFormat.TextFormatter.format(Object) is written.
    
    Parameters:
    filePath - The path pointing to the location the text file is written to.
    formatter - formatter that is applied on every element of the DataSet.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    TextOutputFormat
  - writeAsFormattedText
```
public DataSink<String> writeAsFormattedText(String filePath,
                                    FileSystem.WriteMode writeMode,
                                    TextOutputFormat.TextFormatter<T> formatter)
```
    Writes a DataSet as a text file to the specified location.
    For each element of the DataSet the result of TextOutputFormat.TextFormatter.format(Object) is written.
    
    Parameters:
    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.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    TextOutputFormat
  - writeAsCsv
```
public DataSink<T> writeAsCsv(String filePath)
```
    Writes a Tuple DataSet as a CSV file 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).
    
    Parameters:
    filePath - The path pointing to the location the CSV file is written to.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    Tuple, CsvOutputFormat
  - writeAsCsv
```
public DataSink<T> writeAsCsv(String filePath,
                     FileSystem.WriteMode writeMode)
```
    Writes a Tuple DataSet as a CSV file 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).
    
    Parameters:
    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.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    Tuple, CsvOutputFormat
  - writeAsCsv
```
public DataSink<T> writeAsCsv(String filePath,
                     String rowDelimiter,
                     String fieldDelimiter)
```
    Writes a Tuple DataSet as a CSV file 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.
    
    Parameters:
    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.
    See Also:
    Tuple, CsvOutputFormat
  - writeAsCsv
```
public DataSink<T> writeAsCsv(String filePath,
                     String rowDelimiter,
                     String fieldDelimiter,
                     FileSystem.WriteMode writeMode)
```
    Writes a Tuple DataSet as a CSV file 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.
    
    Parameters:
    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.
    See Also:
    Tuple, CsvOutputFormat
  - print
```
public DataSink<T> print()
```
    Writes a DataSet to the standard output stream (stdout).
    For each element of the DataSet the result of Object.toString() is written.
    
    Returns:
    The DataSink that writes the DataSet.
  - printToErr
```
public DataSink<T> printToErr()
```
    Writes a DataSet to the standard error stream (stderr).
    For each element of the DataSet the result of Object.toString() is written.
    
    Returns:
    The DataSink that writes the DataSet.
  - write
```
public DataSink<T> write(FileOutputFormat<T> outputFormat,
                String filePath)
```
    Writes a DataSet using a FileOutputFormat to a specified location. This method adds a data sink to the program.
    
    Parameters:
    outputFormat - The FileOutputFormat to write the DataSet.
    filePath - The path to the location where the DataSet is written.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    FileOutputFormat
  - write
```
public DataSink<T> write(FileOutputFormat<T> outputFormat,
                String filePath,
                FileSystem.WriteMode writeMode)
```
    Writes a DataSet using a FileOutputFormat to a specified location. This method adds a data sink to the program.
    
    Parameters:
    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.
    
    Returns:
    The DataSink that writes the DataSet.
    See Also:
    FileOutputFormat
  - output
```
public DataSink<T> output(OutputFormat<T> outputFormat)
```
    Emits a DataSet using an 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.
    
    Parameters:
    outputFormat - The OutputFormat to process the DataSet.
    
    Returns:
    The DataSink that processes the DataSet.
    See Also:
    OutputFormat, DataSink
  - checkSameExecutionContext
```
protected static void checkSameExecutionContext(DataSet<?> set1,
                             DataSet<?> set2)
```

Class DataSet<T>

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

DataSet

Method Detail

getExecutionEnvironment

getType

clean

map

mapPartition

flatMap

filter

project

aggregate

sum

max

min

reduce

reduceGroup

minBy

maxBy

first

distinct

distinct

distinct

distinct

groupBy

groupBy

groupBy

join

join

joinWithTiny

joinWithHuge

coGroup

cross

crossWithTiny

crossWithHuge

iterate

iterateDelta

runOperation

union

partitionByHash

partitionByHash

partitionByHash

partitionCustom

partitionCustom

partitionCustom

rebalance

writeAsText

writeAsText

writeAsFormattedText

writeAsFormattedText

writeAsCsv

writeAsCsv

writeAsCsv

writeAsCsv

print

printToErr

write

write

output

checkSameExecutionContext