Uses of Class
org.apache.crunch.Pair

Packages that use Pair

Package	Description
org.apache.crunch	Client-facing API and core abstractions.
org.apache.crunch.contrib.bloomfilter	Support for creating Bloom Filters.
org.apache.crunch.contrib.text
org.apache.crunch.fn	Commonly used functions for manipulating collections.
org.apache.crunch.impl.dist
org.apache.crunch.impl.dist.collect
org.apache.crunch.impl.mem	In-memory Pipeline implementation for rapid prototyping and testing.
org.apache.crunch.impl.spark
org.apache.crunch.impl.spark.collect
org.apache.crunch.impl.spark.fn
org.apache.crunch.lambda	Alternative Crunch API using Java 8 features to allow construction of pipelines using lambda functions and method references.
org.apache.crunch.lib	Joining, sorting, aggregating, and other commonly used functionality.
org.apache.crunch.lib.join	Inner and outer joins on collections.
org.apache.crunch.types	Common functionality for business object serialization.
org.apache.crunch.types.avro	Business object serialization using Apache Avro.
org.apache.crunch.types.writable	Business object serialization using Hadoop's Writables framework.
org.apache.crunch.util	An assorted set of utilities.

Uses of Pair in org.apache.crunch

Methods in org.apache.crunch that return Pair

Modifier and Type	Method and Description
static <T,U> Pair<T,U>	Pair.of(T first, U second)

Methods in org.apache.crunch that return types with arguments of type Pair

Modifier and Type	Method and Description
<U> PTable<K,Pair<Collection<V>,Collection<U>>>	PTable.cogroup(PTable<K,U> other) Co-group operation with the given table on common keys.
<U> PTable<K,Pair<V,U>>	PTable.join(PTable<K,U> other) Perform an inner join on this table and the one passed in as an argument on their common keys.

Methods in org.apache.crunch with parameters of type Pair

Modifier and Type	Method and Description
int	Pair.compareTo(Pair<K,V> o)

Method parameters in org.apache.crunch with type arguments of type Pair

Modifier and Type	Method and Description
<K,V> PTable<K,V>	Pipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype) Creates a PTable containing the values found in the given Iterable using an implementation-specific distribution mechanism.
<K,V> PTable<K,V>	Pipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype, CreateOptions options) Creates a PTable containing the values found in the given Iterable using an implementation-specific distribution mechanism.
PTable<K,V>	PTable.filter(FilterFn<Pair<K,V>> filterFn) Apply the given filter function to this instance and return the resulting PTable.
PTable<K,V>	PTable.filter(String name, FilterFn<Pair<K,V>> filterFn) Apply the given filter function to this instance and return the resulting PTable.
<K,V> PTable<K,V>	PCollection.parallelDo(DoFn<S,Pair<K,V>> doFn, PTableType<K,V> type) Similar to the other parallelDo instance, but returns a PTable instance instead of a PCollection.
<K,V> PTable<K,V>	PCollection.parallelDo(String name, DoFn<S,Pair<K,V>> doFn, PTableType<K,V> type) Similar to the other parallelDo instance, but returns a PTable instance instead of a PCollection.
<K,V> PTable<K,V>	PCollection.parallelDo(String name, DoFn<S,Pair<K,V>> doFn, PTableType<K,V> type, ParallelDoOptions options) Similar to the other parallelDo instance, but returns a PTable instance instead of a PCollection.

Uses of Pair in org.apache.crunch.contrib.bloomfilter

Method parameters in org.apache.crunch.contrib.bloomfilter with type arguments of type Pair

Modifier and Type	Method and Description
void	BloomFilterFn.cleanup(Emitter<Pair<String,org.apache.hadoop.util.bloom.BloomFilter>> emitter)
void	BloomFilterFn.process(S input, Emitter<Pair<String,org.apache.hadoop.util.bloom.BloomFilter>> emitter)

Uses of Pair in org.apache.crunch.contrib.text

Methods in org.apache.crunch.contrib.text that return types with arguments of type Pair

Modifier and Type	Method and Description
static <K,V> Extractor<Pair<K,V>>	Extractors.xpair(TokenizerFactory scannerFactory, Extractor<K> one, Extractor<V> two) Returns an Extractor for pairs of the given types that uses the given TokenizerFactory for parsing the sub-fields.

Method parameters in org.apache.crunch.contrib.text with type arguments of type Pair

Modifier and Type	Method and Description
static <K,V> PTable<K,V>	Parse.parseTable(String groupName, PCollection<String> input, Extractor<Pair<K,V>> extractor) Parses the lines of the input PCollection<String> and returns a PTable<K, V> using the given Extractor<Pair<K, V>>.
static <K,V> PTable<K,V>	Parse.parseTable(String groupName, PCollection<String> input, PTypeFamily ptf, Extractor<Pair<K,V>> extractor) Parses the lines of the input PCollection<String> and returns a PTable<K, V> using the given Extractor<Pair<K, V>> that uses the given PTypeFamily.

Uses of Pair in org.apache.crunch.fn

Methods in org.apache.crunch.fn that return Pair

Modifier and Type	Method and Description
Pair<S,T>	PairMapFn.map(Pair<K,V> input)
Pair<V2,V1>	SwapFn.map(Pair<V1,V2> input)
Pair<K,V>	SPairFunction.map(T input)
Pair<K,V>	ExtractKeyFn.map(V input)

Methods in org.apache.crunch.fn that return types with arguments of type Pair

Modifier and Type	Method and Description
static <V1,V2> Aggregator<Pair<V1,V2>>	Aggregators.pairAggregator(Aggregator<V1> a1, Aggregator<V2> a2) Apply separate aggregators to each component of a Pair.
static <V1,V2> PType<Pair<V2,V1>>	SwapFn.ptype(PType<Pair<V1,V2>> pt)

Methods in org.apache.crunch.fn with parameters of type Pair

Modifier and Type	Method and Description
R	SFunction2.map(Pair<K,V> input)
Pair<S,T>	PairMapFn.map(Pair<K,V> input)
Pair<V2,V1>	SwapFn.map(Pair<V1,V2> input)
void	SFlatMapFunction2.process(Pair<K,V> input, Emitter<R> emitter)

Method parameters in org.apache.crunch.fn with type arguments of type Pair

Modifier and Type	Method and Description
void	PairMapFn.cleanup(Emitter<Pair<S,T>> emitter)
void	SPairFlatMapFunction.process(T input, Emitter<Pair<K,V>> emitter)
static <V1,V2> PType<Pair<V2,V1>>	SwapFn.ptype(PType<Pair<V1,V2>> pt)

Uses of Pair in org.apache.crunch.impl.dist

Method parameters in org.apache.crunch.impl.dist with type arguments of type Pair

Modifier and Type	Method and Description
<K,V> PTable<K,V>	DistributedPipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype)
<K,V> PTable<K,V>	DistributedPipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype, CreateOptions options)

Uses of Pair in org.apache.crunch.impl.dist.collect

Methods in org.apache.crunch.impl.dist.collect that return types with arguments of type Pair

Modifier and Type	Method and Description
<U> PTable<K,Pair<Collection<V>,Collection<U>>>	PTableBase.cogroup(PTable<K,U> other)
PType<Pair<K,V>>	EmptyPTable.getPType()
PType<Pair<K,V>>	BaseUnionTable.getPType()
PType<Pair<K,V>>	BaseInputTable.getPType()
PType<Pair<K,Iterable<V>>>	BaseGroupedTable.getPType()
PType<Pair<K,V>>	BaseDoTable.getPType()
<U> PTable<K,Pair<V,U>>	PTableBase.join(PTable<K,U> other)

Method parameters in org.apache.crunch.impl.dist.collect with type arguments of type Pair

Modifier and Type	Method and Description
<S,K,V> BaseDoTable<K,V>	PCollectionFactory.createDoTable(String name, PCollectionImpl<S> chainingCollection, CombineFn<K,V> combineFn, DoFn<S,Pair<K,V>> fn, PTableType<K,V> type)
<S,K,V> BaseDoTable<K,V>	PCollectionFactory.createDoTable(String name, PCollectionImpl<S> chainingCollection, DoFn<S,Pair<K,V>> fn, PTableType<K,V> type, ParallelDoOptions options)
PTable<K,V>	PTableBase.filter(FilterFn<Pair<K,V>> filterFn)
PTable<K,V>	PTableBase.filter(String name, FilterFn<Pair<K,V>> filterFn)
<K,V> PTable<K,V>	PCollectionImpl.parallelDo(DoFn<S,Pair<K,V>> fn, PTableType<K,V> type)
<K,V> PTable<K,V>	PCollectionImpl.parallelDo(String name, DoFn<S,Pair<K,V>> fn, PTableType<K,V> type)
<K,V> PTable<K,V>	PCollectionImpl.parallelDo(String name, DoFn<S,Pair<K,V>> fn, PTableType<K,V> type, ParallelDoOptions options)

Uses of Pair in org.apache.crunch.impl.mem

Method parameters in org.apache.crunch.impl.mem with type arguments of type Pair

Modifier and Type	Method and Description
<K,V> PTable<K,V>	MemPipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype)
<K,V> PTable<K,V>	MemPipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype, CreateOptions options)
static <S,T> PTable<S,T>	MemPipeline.tableOf(Iterable<Pair<S,T>> pairs)
static <S,T> PTable<S,T>	MemPipeline.typedTableOf(PTableType<S,T> ptype, Iterable<Pair<S,T>> pairs)

Uses of Pair in org.apache.crunch.impl.spark

Methods in org.apache.crunch.impl.spark that return types with arguments of type Pair

Modifier and Type	Method and Description
static <K,V> com.google.common.base.Function<Pair<K,V>,scala.Tuple2<K,V>>	GuavaUtils.pair2tupleFunc()
static <K,V> com.google.common.base.Function<scala.Tuple2<K,V>,Pair<K,V>>	GuavaUtils.tuple2PairFunc()

Method parameters in org.apache.crunch.impl.spark with type arguments of type Pair

Modifier and Type	Method and Description
<K,V> PTable<K,V>	SparkPipeline.create(Iterable<Pair<K,V>> contents, PTableType<K,V> ptype, CreateOptions options)

Uses of Pair in org.apache.crunch.impl.spark.collect

Methods in org.apache.crunch.impl.spark.collect that return types with arguments of type Pair

Modifier and Type	Method and Description
PType<Pair<K,V>>	CreatedTable.getPType()

Method parameters in org.apache.crunch.impl.spark.collect with type arguments of type Pair

Modifier and Type	Method and Description
<S,K,V> BaseDoTable<K,V>	SparkCollectFactory.createDoTable(String name, PCollectionImpl<S> parent, CombineFn<K,V> combineFn, DoFn<S,Pair<K,V>> fn, PTableType<K,V> type)
<S,K,V> BaseDoTable<K,V>	SparkCollectFactory.createDoTable(String name, PCollectionImpl<S> parent, DoFn<S,Pair<K,V>> fn, PTableType<K,V> type, ParallelDoOptions options)

Constructor parameters in org.apache.crunch.impl.spark.collect with type arguments of type Pair

Constructor and Description
CreatedTable(SparkPipeline pipeline, Iterable<Pair<K,V>> contents, PTableType<K,V> ptype, CreateOptions options)

Uses of Pair in org.apache.crunch.impl.spark.fn

Methods in org.apache.crunch.impl.spark.fn that return Pair

Modifier and Type	Method and Description
Pair<K,Iterable<V>>	ReduceInputFunction.call(scala.Tuple2<ByteArray,Iterable<byte[]>> kv)

Methods in org.apache.crunch.impl.spark.fn with parameters of type Pair

Modifier and Type	Method and Description
scala.Tuple2<S,Iterable<T>>	PairMapIterableFunction.call(Pair<K,List<V>> input)
scala.Tuple2<K,V>	Tuple2MapFunction.call(Pair<K,V> p)
scala.Tuple2<IntByteArray,byte[]>	PartitionedMapOutputFunction.call(Pair<K,V> p)
scala.Tuple2<ByteArray,byte[]>	MapOutputFunction.call(Pair<K,V> p)

Method parameters in org.apache.crunch.impl.spark.fn with type arguments of type Pair

Modifier and Type	Method and Description
Iterable<scala.Tuple2<K,V>>	CrunchPairTuple2.call(Iterator<Pair<K,V>> iterator)

Constructor parameters in org.apache.crunch.impl.spark.fn with type arguments of type Pair

Constructor and Description
FlatMapPairDoFn(DoFn<Pair<K,V>,T> fn, SparkRuntimeContext ctxt)
PairFlatMapDoFn(DoFn<T,Pair<K,V>> fn, SparkRuntimeContext ctxt)
PairMapFunction(MapFn<Pair<K,V>,S> fn, SparkRuntimeContext ctxt)
PairMapIterableFunction(MapFn<Pair<K,List<V>>,Pair<S,Iterable<T>>> fn, SparkRuntimeContext runtimeContext)
PairMapIterableFunction(MapFn<Pair<K,List<V>>,Pair<S,Iterable<T>>> fn, SparkRuntimeContext runtimeContext)
Tuple2MapFunction(MapFn<Pair<K,V>,Pair<K,V>> fn, SparkRuntimeContext ctxt)
Tuple2MapFunction(MapFn<Pair<K,V>,Pair<K,V>> fn, SparkRuntimeContext ctxt)

Uses of Pair in org.apache.crunch.lambda

Methods in org.apache.crunch.lambda that return types with arguments of type Pair

Modifier and Type	Method and Description
default <U> LTable<K,Pair<Collection<V>,Collection<U>>>	LTable.cogroup(LTable<K,U> other) Cogroup this table with another LTable with the same key type, collecting the set of values from each side.
default <U> LTable<K,Pair<V,U>>	LTable.join(LTable<K,U> other) Inner join this table to another LTable which has the same key type using a reduce-side join
default <U> LTable<K,Pair<V,U>>	LTable.join(LTable<K,U> other, JoinType joinType) Join this table to another LTable which has the same key type using the provide JoinType and the DefaultJoinStrategy (reduce-side join).
default <U> LTable<K,Pair<V,U>>	LTable.join(LTable<K,U> other, JoinType joinType, JoinStrategy<K,V,U> joinStrategy) Join this table to another LTable which has the same key type using the provided JoinType and JoinStrategy

Method parameters in org.apache.crunch.lambda with type arguments of type Pair

Modifier and Type	Method and Description
default LTable<K,V>	LTable.filter(SPredicate<Pair<K,V>> predicate) Filter the rows of the table using the supplied predicate.
default <K,V> LTable<K,V>	LCollection.filterMap(SFunction<S,Optional<Pair<K,V>>> fn, PTableType<K,V> pType) Combination of a filter and map operation by using a function with Optional return type.
default <K,V> LTable<K,V>	LCollection.flatMap(SFunction<S,java.util.stream.Stream<Pair<K,V>>> fn, PTableType<K,V> pType) Map each element to zero or more output elements using the provided stream-returning function to yield an LTable
default LTable<K,V>	LTable.incrementIf(Enum<?> counter, SPredicate<Pair<K,V>> condition) Increment a counter for every element satisfying the conditional predicate supplied.
default LTable<K,V>	LTable.incrementIf(String counterGroup, String counterName, SPredicate<Pair<K,V>> condition) Increment a counter for every element satisfying the conditional predicate supplied.
default <K,V> LTable<K,V>	LCollection.map(SFunction<S,Pair<K,V>> fn, PTableType<K,V> pType) Map the elements of this collection 1-1 through the supplied function to yield an LTable
default <K,V> LTable<K,V>	LCollection.parallelDo(DoFn<S,Pair<K,V>> fn, PTableType<K,V> pType) Transform this LCollection to an LTable using a standard Crunch DoFn
default <K,V> LTable<K,V>	LCollection.parallelDo(LDoFn<S,Pair<K,V>> fn, PTableType<K,V> pType) Transform this LCollection using a Lambda-friendly LDoFn.

Uses of Pair in org.apache.crunch.lib

Methods in org.apache.crunch.lib that return Pair

Modifier and Type	Method and Description
static <K,V> Pair<K,V>	PTables.getDetachedValue(PTableType<K,V> tableType, Pair<K,V> value) Create a detached value for a table Pair.
static <K,V> Pair<K,Iterable<V>>	PTables.getGroupedDetachedValue(PGroupedTableType<K,V> groupedTableType, Pair<K,Iterable<V>> value) Created a detached value for a PGroupedTable value.
static <T,U> Pair<PCollection<T>,PCollection<U>>	Channels.split(PCollection<Pair<T,U>> pCollection) Splits a PCollection of any Pair of objects into a Pair of PCollection}, to allow for the output of a DoFn to be handled using separate channels.
static <T,U> Pair<PCollection<T>,PCollection<U>>	Channels.split(PCollection<Pair<T,U>> pCollection, PType<T> firstPType, PType<U> secondPType) Splits a PCollection of any Pair of objects into a Pair of PCollection}, to allow for the output of a DoFn to be handled using separate channels.

Methods in org.apache.crunch.lib that return types with arguments of type Pair

Modifier and Type	Method and Description
static <K,U,V> PTable<K,Pair<Collection<U>,Collection<V>>>	Cogroup.cogroup(int numReducers, PTable<K,U> left, PTable<K,V> right) Co-groups the two PTable arguments with a user-specified degree of parallelism (a.k.a, number of reducers.)
static <K,U,V> PTable<K,Pair<Collection<U>,Collection<V>>>	Cogroup.cogroup(PTable<K,U> left, PTable<K,V> right) Co-groups the two PTable arguments.
static <U,V> PCollection<Pair<U,V>>	Cartesian.cross(PCollection<U> left, PCollection<V> right) Performs a full cross join on the specified PCollections (using the same strategy as Pig's CROSS operator).
static <U,V> PCollection<Pair<U,V>>	Cartesian.cross(PCollection<U> left, PCollection<V> right, int parallelism) Performs a full cross join on the specified PCollections (using the same strategy as Pig's CROSS operator).
static <K1,K2,U,V> PTable<Pair<K1,K2>,Pair<U,V>>	Cartesian.cross(PTable<K1,U> left, PTable<K2,V> right) Performs a full cross join on the specified PTables (using the same strategy as Pig's CROSS operator).
static <K1,K2,U,V> PTable<Pair<K1,K2>,Pair<U,V>>	Cartesian.cross(PTable<K1,U> left, PTable<K2,V> right) Performs a full cross join on the specified PTables (using the same strategy as Pig's CROSS operator).
static <K1,K2,U,V> PTable<Pair<K1,K2>,Pair<U,V>>	Cartesian.cross(PTable<K1,U> left, PTable<K2,V> right, int parallelism) Performs a full cross join on the specified PTables (using the same strategy as Pig's CROSS operator).
static <K1,K2,U,V> PTable<Pair<K1,K2>,Pair<U,V>>	Cartesian.cross(PTable<K1,U> left, PTable<K2,V> right, int parallelism) Performs a full cross join on the specified PTables (using the same strategy as Pig's CROSS operator).
static <K,V,T> DoFn<Pair<K,Iterable<V>>,T>	DoFns.detach(DoFn<Pair<K,Iterable<V>>,T> reduceFn, PType<V> valueType) "Reduce" DoFn wrapper which detaches the values in the iterable, preventing the unexpected behaviour related to object reuse often observed when using Avro.
static <K,U,V> PTable<K,Pair<U,V>>	Join.fullJoin(PTable<K,U> left, PTable<K,V> right) Performs a full outer join on the specified PTables.
static <T,N extends Number> PCollection<Pair<Integer,T>>	Sample.groupedWeightedReservoirSample(PTable<Integer,Pair<T,N>> input, int[] sampleSizes) The most general purpose of the weighted reservoir sampling patterns that allows us to choose a random sample of elements for each of N input groups.
static <T,N extends Number> PCollection<Pair<Integer,T>>	Sample.groupedWeightedReservoirSample(PTable<Integer,Pair<T,N>> input, int[] sampleSizes, Long seed) Same as the other groupedWeightedReservoirSample method, but include a seed for testing purposes.
static <K,U,V> PTable<K,Pair<U,V>>	Join.innerJoin(PTable<K,U> left, PTable<K,V> right) Performs an inner join on the specified PTables.
static <K,U,V> PTable<K,Pair<U,V>>	Join.join(PTable<K,U> left, PTable<K,V> right) Performs an inner join on the specified PTables.
static <K,U,V> PTable<K,Pair<U,V>>	Join.leftJoin(PTable<K,U> left, PTable<K,V> right) Performs a left outer join on the specified PTables.
static <K,U,V> PTable<K,Pair<U,V>>	Join.rightJoin(PTable<K,U> left, PTable<K,V> right) Performs a right outer join on the specified PTables.
static <U,V> PCollection<Pair<U,V>>	Sort.sortPairs(PCollection<Pair<U,V>> collection, Sort.ColumnOrder... columnOrders) Sorts the PCollection of Pairs using the specified column ordering.
static <X,Y> PTable<X,Collection<Pair<Long,Y>>>	TopList.topNYbyX(PTable<X,Y> input, int n) Create a top-list of elements in the provided PTable, categorised by the key of the input table and using the count of the value part of the input table.

Methods in org.apache.crunch.lib with parameters of type Pair

Modifier and Type	Method and Description
int	Aggregate.PairValueComparator.compare(Pair<K,V> left, Pair<K,V> right)
int	Aggregate.PairValueComparator.compare(Pair<K,V> left, Pair<K,V> right)
static <K,V> Pair<K,V>	PTables.getDetachedValue(PTableType<K,V> tableType, Pair<K,V> value) Create a detached value for a table Pair.
static <K,V> Pair<K,Iterable<V>>	PTables.getGroupedDetachedValue(PGroupedTableType<K,V> groupedTableType, Pair<K,Iterable<V>> value) Created a detached value for a PGroupedTable value.
void	Aggregate.TopKCombineFn.process(Pair<Integer,Iterable<Pair<K,V>>> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)
void	Aggregate.TopKFn.process(Pair<K,V> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)

Method parameters in org.apache.crunch.lib with type arguments of type Pair

Modifier and Type	Method and Description
static <K,V> PTable<K,V>	PTables.asPTable(PCollection<Pair<K,V>> pcollect) Convert the given PCollection<Pair<K, V>> to a PTable<K, V>.
void	Aggregate.TopKFn.cleanup(Emitter<Pair<Integer,Pair<K,V>>> emitter)
void	Aggregate.TopKFn.cleanup(Emitter<Pair<Integer,Pair<K,V>>> emitter)
static <K,V,T> DoFn<Pair<K,Iterable<V>>,T>	DoFns.detach(DoFn<Pair<K,Iterable<V>>,T> reduceFn, PType<V> valueType) "Reduce" DoFn wrapper which detaches the values in the iterable, preventing the unexpected behaviour related to object reuse often observed when using Avro.
static <T,N extends Number> PCollection<Pair<Integer,T>>	Sample.groupedWeightedReservoirSample(PTable<Integer,Pair<T,N>> input, int[] sampleSizes) The most general purpose of the weighted reservoir sampling patterns that allows us to choose a random sample of elements for each of N input groups.
static <T,N extends Number> PCollection<Pair<Integer,T>>	Sample.groupedWeightedReservoirSample(PTable<Integer,Pair<T,N>> input, int[] sampleSizes, Long seed) Same as the other groupedWeightedReservoirSample method, but include a seed for testing purposes.
void	Aggregate.TopKCombineFn.process(Pair<Integer,Iterable<Pair<K,V>>> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)
void	Aggregate.TopKCombineFn.process(Pair<Integer,Iterable<Pair<K,V>>> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)
void	Aggregate.TopKCombineFn.process(Pair<Integer,Iterable<Pair<K,V>>> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)
void	Aggregate.TopKFn.process(Pair<K,V> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)
void	Aggregate.TopKFn.process(Pair<K,V> input, Emitter<Pair<Integer,Pair<K,V>>> emitter)
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>, using the given number of reducers.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>, using the given number of reducers.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>, using the given number of reducers.
static <K,V1,V2,U,V> PTable<U,V>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,Pair<U,V>> doFn, PTableType<U,V> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PTable<U, V>, using the given number of reducers.
static <K,V1,V2,T> PCollection<T>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,T> doFn, PType<T> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PCollection<T>.
static <K,V1,V2,T> PCollection<T>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,T> doFn, PType<T> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PCollection<T>.
static <K,V1,V2,T> PCollection<T>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,T> doFn, PType<T> ptype) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PCollection<T>.
static <K,V1,V2,T> PCollection<T>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,T> doFn, PType<T> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PCollection<T>, using the given number of reducers.
static <K,V1,V2,T> PCollection<T>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,T> doFn, PType<T> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PCollection<T>, using the given number of reducers.
static <K,V1,V2,T> PCollection<T>	SecondarySort.sortAndApply(PTable<K,Pair<V1,V2>> input, DoFn<Pair<K,Iterable<Pair<V1,V2>>>,T> doFn, PType<T> ptype, int numReducers) Perform a secondary sort on the given PTable instance and then apply a DoFn to the resulting sorted data to yield an output PCollection<T>, using the given number of reducers.
static <U,V> PCollection<Pair<U,V>>	Sort.sortPairs(PCollection<Pair<U,V>> collection, Sort.ColumnOrder... columnOrders) Sorts the PCollection of Pairs using the specified column ordering.
static <T,U> Pair<PCollection<T>,PCollection<U>>	Channels.split(PCollection<Pair<T,U>> pCollection) Splits a PCollection of any Pair of objects into a Pair of PCollection}, to allow for the output of a DoFn to be handled using separate channels.
static <T,U> Pair<PCollection<T>,PCollection<U>>	Channels.split(PCollection<Pair<T,U>> pCollection, PType<T> firstPType, PType<U> secondPType) Splits a PCollection of any Pair of objects into a Pair of PCollection}, to allow for the output of a DoFn to be handled using separate channels.
static <T,N extends Number> PCollection<T>	Sample.weightedReservoirSample(PCollection<Pair<T,N>> input, int sampleSize) Selects a weighted sample of the elements of the given PCollection, where the second term in the input Pair is a numerical weight.
static <T,N extends Number> PCollection<T>	Sample.weightedReservoirSample(PCollection<Pair<T,N>> input, int sampleSize, Long seed) The weighted reservoir sampling function with the seed term exposed for testing purposes.

Constructor parameters in org.apache.crunch.lib with type arguments of type Pair

Constructor and Description
Result(long count, Iterable<Pair<Double,V>> quantiles)
TopKCombineFn(int limit, boolean maximize, PType<Pair<K,V>> pairType)
TopKFn(int limit, boolean ascending, PType<Pair<K,V>> pairType)

Uses of Pair in org.apache.crunch.lib.join

Methods in org.apache.crunch.lib.join that return types with arguments of type Pair

Modifier and Type	Method and Description
PTable<K,Pair<U,V>>	DefaultJoinStrategy.join(PTable<K,U> left, PTable<K,V> right, JoinFn<K,U,V> joinFn) Perform a default join on the given PTable instances using a user-specified JoinFn.
PTable<K,Pair<U,V>>	ShardedJoinStrategy.join(PTable<K,U> left, PTable<K,V> right, JoinType joinType)
PTable<K,Pair<U,V>>	MapsideJoinStrategy.join(PTable<K,U> left, PTable<K,V> right, JoinType joinType)
PTable<K,Pair<U,V>>	JoinStrategy.join(PTable<K,U> left, PTable<K,V> right, JoinType joinType) Join two tables with the given join type.
PTable<K,Pair<U,V>>	DefaultJoinStrategy.join(PTable<K,U> left, PTable<K,V> right, JoinType joinType)
PTable<K,Pair<U,V>>	BloomFilterJoinStrategy.join(PTable<K,U> left, PTable<K,V> right, JoinType joinType)

Methods in org.apache.crunch.lib.join with parameters of type Pair

Modifier and Type	Method and Description
void	JoinFn.process(Pair<Pair<K,Integer>,Iterable<Pair<U,V>>> input, Emitter<Pair<K,Pair<U,V>>> emitter) Split up the input record to make coding a bit more manageable.

Method parameters in org.apache.crunch.lib.join with type arguments of type Pair

Modifier and Type	Method and Description
void	LeftOuterJoinFn.cleanup(Emitter<Pair<K,Pair<U,V>>> emitter) Called during the cleanup of the MapReduce job this DoFn is associated with.
void	LeftOuterJoinFn.cleanup(Emitter<Pair<K,Pair<U,V>>> emitter) Called during the cleanup of the MapReduce job this DoFn is associated with.
void	FullOuterJoinFn.cleanup(Emitter<Pair<K,Pair<U,V>>> emitter) Called during the cleanup of the MapReduce job this DoFn is associated with.
void	FullOuterJoinFn.cleanup(Emitter<Pair<K,Pair<U,V>>> emitter) Called during the cleanup of the MapReduce job this DoFn is associated with.
void	RightOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	RightOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	RightOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	LeftOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	LeftOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	LeftOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
abstract void	JoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
abstract void	JoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
abstract void	JoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	InnerJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter)
void	InnerJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter)
void	InnerJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter)
void	FullOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	FullOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
void	FullOuterJoinFn.join(K key, int id, Iterable<Pair<U,V>> pairs, Emitter<Pair<K,Pair<U,V>>> emitter) Performs the actual joining.
static <K,U,V,T> PCollection<T>	OneToManyJoin.oneToManyJoin(PTable<K,U> left, PTable<K,V> right, DoFn<Pair<U,Iterable<V>>,T> postProcessFn, PType<T> ptype) Performs a join on two tables, where the left table only contains a single value per key.
static <K,U,V,T> PCollection<T>	OneToManyJoin.oneToManyJoin(PTable<K,U> left, PTable<K,V> right, DoFn<Pair<U,Iterable<V>>,T> postProcessFn, PType<T> ptype, int numReducers) Supports a user-specified number of reducers for the one-to-many join.
void	JoinFn.process(Pair<Pair<K,Integer>,Iterable<Pair<U,V>>> input, Emitter<Pair<K,Pair<U,V>>> emitter) Split up the input record to make coding a bit more manageable.
void	JoinFn.process(Pair<Pair<K,Integer>,Iterable<Pair<U,V>>> input, Emitter<Pair<K,Pair<U,V>>> emitter) Split up the input record to make coding a bit more manageable.
void	JoinFn.process(Pair<Pair<K,Integer>,Iterable<Pair<U,V>>> input, Emitter<Pair<K,Pair<U,V>>> emitter) Split up the input record to make coding a bit more manageable.
void	JoinFn.process(Pair<Pair<K,Integer>,Iterable<Pair<U,V>>> input, Emitter<Pair<K,Pair<U,V>>> emitter) Split up the input record to make coding a bit more manageable.

Uses of Pair in org.apache.crunch.types

Fields in org.apache.crunch.types with type parameters of type Pair

Modifier and Type	Field and Description
static TupleFactory<Pair>	TupleFactory.PAIR

Methods in org.apache.crunch.types that return Pair

Modifier and Type	Method and Description
Pair<K,Iterable<V>>	PGroupedTableType.PairIterableMapFn.map(Pair<Object,Iterable<Object>> input)

Methods in org.apache.crunch.types that return types with arguments of type Pair

Modifier and Type	Method and Description
ReadableSourceTarget<Pair<K,Iterable<V>>>	PGroupedTableType.getDefaultFileSource(org.apache.hadoop.fs.Path path)
<V1,V2> PType<Pair<V1,V2>>	PTypeFamily.pairs(PType<V1> p1, PType<V2> p2)

Methods in org.apache.crunch.types with parameters of type Pair

Modifier and Type	Method and Description
Pair<K,Iterable<V>>	PGroupedTableType.PairIterableMapFn.map(Pair<Object,Iterable<Object>> input)

Uses of Pair in org.apache.crunch.types.avro

Methods in org.apache.crunch.types.avro that return types with arguments of type Pair

Modifier and Type	Method and Description
<V1,V2> PType<Pair<V1,V2>>	AvroTypeFamily.pairs(PType<V1> p1, PType<V2> p2)
static <V1,V2> AvroType<Pair<V1,V2>>	Avros.pairs(PType<V1> p1, PType<V2> p2)

Uses of Pair in org.apache.crunch.types.writable

Methods in org.apache.crunch.types.writable that return types with arguments of type Pair

Modifier and Type	Method and Description
<V1,V2> PType<Pair<V1,V2>>	WritableTypeFamily.pairs(PType<V1> p1, PType<V2> p2)
static <V1,V2> WritableType<Pair<V1,V2>,TupleWritable>	Writables.pairs(PType<V1> p1, PType<V2> p2)

Uses of Pair in org.apache.crunch.util

Methods in org.apache.crunch.util that return types with arguments of type Pair

Modifier and Type	Method and Description
Iterator<Pair<S,T>>	Tuples.PairIterable.iterator()