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Uses of Class org.apache.crunch.Pair (Apache Crunch 0.10.0 API)
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Uses of Class
org.apache.crunch.Pair

Packages that use Pair
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.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.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
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
<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
 int Pair.compareTo(Pair<K,V> o)
           
 

Method parameters in org.apache.crunch with type arguments of type Pair
 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
 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
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
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
 Pair<S,T> PairMapFn.map(Pair<K,V> input)
           
 Pair<K,V> ExtractKeyFn.map(V input)
           
 

Methods in org.apache.crunch.fn that return types with arguments of type Pair
static
<V1,V2> Aggregator<Pair<V1,V2>>
Aggregators.pairAggregator(Aggregator<V1> a1, Aggregator<V2> a2)
          Apply separate aggregators to each component of a Pair.
 

Methods in org.apache.crunch.fn with parameters of type Pair
 Pair<S,T> PairMapFn.map(Pair<K,V> input)
           
 

Method parameters in org.apache.crunch.fn with type arguments of type Pair
 void PairMapFn.cleanup(Emitter<Pair<S,T>> emitter)
           
 

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
<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
<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
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
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()
           
 

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

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

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

Methods in org.apache.crunch.impl.spark.fn that return Pair
 Pair<K,List<V>> ReduceInputFunction.call(scala.Tuple2<ByteArray,List<byte[]>> kv)
           
 

Methods in org.apache.crunch.impl.spark.fn with parameters of type Pair
 scala.Tuple2<S,Iterable<T>> PairMapIterableFunction.call(Pair<K,List<V>> input)
           
 scala.Tuple2<IntByteArray,byte[]> PartitionedMapOutputFunction.call(Pair<K,V> p)
           
 scala.Tuple2<ByteArray,byte[]> MapOutputFunction.call(Pair<K,V> p)
           
 

Constructor parameters in org.apache.crunch.impl.spark.fn with type arguments of type Pair
FlatMapPairDoFn(DoFn<Pair<K,V>,T> fn, SparkRuntimeContext ctxt)
           
PairFlatMapDoFn(DoFn<T,Pair<K,V>> fn, SparkRuntimeContext ctxt)
           
PairFlatMapPairDoFn(DoFn<Pair<K,V>,Pair<K2,V2>> fn, SparkRuntimeContext ctxt)
           
PairFlatMapPairDoFn(DoFn<Pair<K,V>,Pair<K2,V2>> 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)
           
 

Uses of Pair in org.apache.crunch.lib
 

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

Methods in org.apache.crunch.lib with parameters of type Pair
 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
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
<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
Aggregate.TopKCombineFn(int limit, boolean maximize, PType<Pair<K,V>> pairType)
           
Aggregate.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
 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
 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
 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
static TupleFactory<Pair> TupleFactory.PAIR
           
 

Methods in org.apache.crunch.types that return Pair
 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
 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
 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
static
<V1,V2> AvroType<Pair<V1,V2>>
Avros.pairs(PType<V1> p1, PType<V2> p2)
           
<V1,V2> PType<Pair<V1,V2>>
AvroTypeFamily.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
static
<V1,V2> WritableType<Pair<V1,V2>,TupleWritable>
Writables.pairs(PType<V1> p1, PType<V2> p2)
           
<V1,V2> PType<Pair<V1,V2>>
WritableTypeFamily.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
 Iterator<Pair<S,T>> Tuples.PairIterable.iterator()
           
 

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