PairRDDFunctions
Related Doc:
package rdd
Perform a full outer join of this and other.
Perform a full outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or
the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each
element (k, w) in other, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements
in this have key k. Uses the default Partitioner to partition the output RDD.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.
For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average,
should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.
Perform a full outer join of this and other.
Perform a full outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or
the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each
element (k, w) in other, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements
in this have key k. Uses the org.apache.spark.HashPartitioner to partition the results.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.
For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average,
should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.
Perform a full outer join of this and other.
Perform a full outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or
the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each
element (k, w) in other, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements
in this have key k. Uses the given Partitioner to partition the output RDD.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.
For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average,
should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.
Perform a left outer join of this and other.
Perform a left outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the
pair (k, (v, None)) if no elements in other have key k. Uses the default Partitioner
to partition the results.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, consider using other.rightOuterMergeJoin(this) if other is the larger of the two
RDDs being joined.
Perform a left outer join of this and other.
Perform a left outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the
pair (k, (v, None)) if no elements in other have key k. Uses org.apache.spark.HashPartitioner
to partition the results into {partitions} partitions.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, consider using other.rightOuterMergeJoin(this) if other is the larger of the two
RDDs being joined.
Perform a left outer join of this and other.
Perform a left outer join of this and other. For each element (k, v) in this, the
resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other, or the
pair (k, (v, None)) if no elements in other have key k. Uses the given Partitioner to
partition the output RDD.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, consider using other.rightOuterMergeJoin(this) if other is the larger of the two
RDDs being joined.
Return an RDD containing all pairs of elements with matching keys in this and other.
Return an RDD containing all pairs of elements with matching keys in this and other. Each
pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and
(k, v2) is in other. Uses the default Partitioner to partition the results.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average,
should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.
Return an RDD containing all pairs of elements with matching keys in this and other.
Return an RDD containing all pairs of elements with matching keys in this and other. Each
pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and
(k, v2) is in other. Uses org.apache.spark.HashPartitioner to partition the results into {partitions}
partitions.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average,
should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.
Return an RDD containing all pairs of elements with matching keys in this and other.
Return an RDD containing all pairs of elements with matching keys in this and other. Each
pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this and
(k, v2) is in other. Uses the given Partitioner to partition the output RDD.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and spilled to disk, if necessary, so they may be iterated across for every value of (k) in this.
For performance reasons, the side of the join that has the largest number of values per unique key grouping, on average,
should be this and joined against other so that the likelihood of a spill occurring with other will be reduced.
Perform a right outer join of this and other.
Perform a right outer join of this and other. For each element (k, w) in other, the
resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the
pair (k, (None, w)) if no elements in this have key k. Uses the default Partitioner to
partition the results.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, consider using other.leftOuterMergeJoin(this) if other is the larger of the two
RDDs being joined.
Perform a right outer join of this and other.
Perform a right outer join of this and other. For each element (k, w) in other, the
resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the
pair (k, (None, w)) if no elements in this have key k. Uses the org.apache.spark.HashPartitioner to
partition the results.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, consider using other.leftOuterMergeJoin(this) if other is the larger of the two
RDDs being joined.
Perform a right outer join of this and other.
Perform a right outer join of this and other. For each element (k, w) in other, the
resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this, or the
pair (k, (None, w)) if no elements in this have key k. Uses the given Partitioner to
partition the output RDD.
During the join, values for any given (k) that is present in the right side will accumulate into memory
and (if necessary) spilled to disk so they may be iterated across for every value of (k) in this.
There is no accumulation of values for this, only other.
For performance reasons, consider using other.leftOuterMergeJoin(this) if other is the larger of the two
RDDs being joined.
Merge-join operators that provide scalable equivalents to the existing Spark RDD
join,leftOuterJoin,rightOuterJoin,fullOuterJoinoperators.Refer to the documentation for MergeJoin for implementation details.