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com.tresata.spark.datasetops.package.scala Maven / Gradle / Ivy
package com.tresata.spark.datasetops
import org.apache.spark.sql.{ Dataset, Encoder, TypedColumn }
import org.apache.spark.sql.expressions.Aggregator
object `package` {
private def aggregatorFromFunctions[A, B, C](zero: () => B, reduce: (B, A) => B, merge: (B, B) => B, finish: B => C)(implicit encB: Encoder[B], encC: Encoder[C]): Aggregator[A, B, C] = {
val (z, r, m, f) = (zero, reduce, merge, finish)
new Aggregator[A, B, C]{
def zero: B = z()
def reduce(b: B, a: A): B = r(b, a)
def merge(b1: B, b2: B): B = m(b1, b2)
def finish(b: B): C = f(b)
def bufferEncoder: Encoder[B] = encB
def outputEncoder: Encoder[C] = encC
}
}
implicit class RichPairDataset[K, V](val ds: Dataset[(K, V)]) extends AnyVal {
private[datasetops] def withDefaultFieldNames(implicit encKV: Encoder[(K, V)]): Dataset[(K, V)] = ds.toDF("_1", "_2").as[(K, V)]
/** Apply a provided function to the values of the key-value Dataset.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3)).toDS.mapValues(_ + 2).show
+---+---+
| _1| _2|
+---+---+
| 1| 4|
| 1| 5|
+---+---+
* }}}
*/
def mapValues[U](f: V => U)(implicit encKU: Encoder[(K, U)]): Dataset[(K, U)] = ds.map{ kv => (kv._1, f(kv._2)) }
/** Flat-map the key-value Dataset's values for each key, with the provided function.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2)).toDS.flatMapValues{ case x => List(x, x + 1) }.show
+---+---+
| _1| _2|
+---+---+
| 1| 2|
| 1| 3|
+---+---+
* }}}
*/
def flatMapValues[U](f: V => TraversableOnce[U])(implicit encKU: Encoder[(K, U)]): Dataset[(K, U)] = ds.flatMap{ kv => f(kv._2).map(v1 => (kv._1, v1)) }
/** Reduce the key-value Dataset's values for each key, with the provided function.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3)).toDS.reduceByKey(_ + _).show
+-----+---------------------+
|value|ReduceAggregator(int)|
+-----+---------------------+
| 1| 5|
+-----+---------------------+
* }}}
*/
def reduceByKey(f: (V, V) => V)(implicit encK: Encoder[K], encV: Encoder[V]): Dataset[(K, V)] = ds.groupByKey(_._1).mapValues(_._2).reduceGroups(f)
/** Use a TypedColumn to aggregate the key-value Dataset's values for each key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.aggByKey(typed.avg(x => x + 2)).show
+-----+-----------------+
|value|TypedAverage(int)|
+-----+-----------------+
| 1| 4.5|
| 2| 6.0|
+-----+-----------------+
* }}}
*/
def aggByKey[U1](col1: TypedColumn[V, U1])(implicit encK: Encoder[K], encV: Encoder[V]): Dataset[(K, U1)] =
ds.groupByKey(_._1).mapValues(_._2).agg(col1)
/** Use two TypedColumns to aggregate the key-value Dataset's values for each key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.aggByKey(typed.avg(x => x + 2), typed.sum(x => x)).show
+-----+-----------------+-------------------+
|value|TypedAverage(int)|TypedSumDouble(int)|
+-----+-----------------+-------------------+
| 1| 4.5| 5.0|
| 2| 6.0| 4.0|
+-----+-----------------+-------------------+
* }}}
*/
def aggByKey[U1, U2](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2])(implicit encK: Encoder[K], encV: Encoder[V]): Dataset[(K, U1, U2)] =
ds.groupByKey(_._1).mapValues(_._2).agg(col1, col2)
/** Use three TypedColumns to aggregate the key-value Dataset's values for each key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.aggByKey(typed.avg(x => x + 2), typed.sum(x => x), typed.avg(x => x - 2)).show
+-----+-----------------+-------------------+-----------------+
|value|TypedAverage(int)|TypedSumDouble(int)|TypedAverage(int)|
+-----+-----------------+-------------------+-----------------+
| 1| 4.5| 5.0| 0.5|
| 2| 6.0| 4.0| 2.0|
+-----+-----------------+-------------------+-----------------+
* }}}
*/
def aggByKey[U1, U2, U3](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3])(implicit encK: Encoder[K], encV: Encoder[V]): Dataset[(K, U1, U2, U3)] =
ds.groupByKey(_._1).mapValues(_._2).agg(col1, col2, col3)
/** Use four TypedColumns to aggregate the key-value Dataset's values for each key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> val agg = typed.sum((x: Int) => x)
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.aggByKey(agg, agg, agg, agg).show
+-----+-------------------+-------------------+-------------------+-------------------+
|value|TypedSumDouble(int)|TypedSumDouble(int)|TypedSumDouble(int)|TypedSumDouble(int)|
+-----+-------------------+-------------------+-------------------+-------------------+
| 2| 4.0| 4.0| 4.0| 4.0|
| 1| 5.0| 5.0| 5.0| 5.0|
+-----+-------------------+-------------------+-------------------+-------------------+
* }}}
*/
def aggByKey[U1, U2, U3, U4](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4])(implicit encK: Encoder[K], encV: Encoder[V]):
Dataset[(K, U1, U2, U3, U4)] =
ds.groupByKey(_._1).mapValues(_._2).agg(col1, col2, col3, col4)
/** Use scala functions to aggregate the key-value Dataset's values for each key: zero, reduce, merge, and finish.
*
* {{{
scala> val zero = () => 1.0
scala> val reduce = (x: Double, y: Int) => x / y
scala> val merge = (x: Double, y: Double) => x + y
scala> val finish = (x: Double) => x * 10
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3)).toDS.aggByKey(zero, reduce, merge, finish).show
+-----+------------------+
|value| anon$1(int)|
+-----+------------------+
| 1|18.333333333333332|
+-----+------------------+
* }}}
*/
def aggByKey[B, U](zero: () => B, reduce: (B, V) => B, merge: (B, B) => B, finish: B => U)(implicit envK: Encoder[K], envV: Encoder[V], encB: Encoder[B], encU: Encoder[U]): Dataset[(K, U)] = {
val sqlAgg = aggregatorFromFunctions(zero, reduce, merge, finish)
ds.groupByKey(_._1).mapValues(_._2).agg(sqlAgg.toColumn)
}
/** Use a zero element and scala functions (reduce, merge, and finish) to aggregate the key-value Dataset's values for each key.
*
* {{{
scala> val zero = 1.0
scala> val reduce = (x: Double, y: Int) => x / y
scala> val merge = (x: Double, y: Double) => x + y
scala> val finish = (x: Double) => x * 10
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3)).toDS.aggByKey(zero, reduce, merge, finish).show
+-----+------------------+
|value| anon$1(int)|
+-----+------------------+
| 1|18.333333333333332|
+-----+------------------+
* }}}
*/
def aggByKey[B, U](zero: B, reduce: (B, V) => B, merge: (B, B) => B, finish: B => U)(implicit envK: Encoder[K], envV: Encoder[V], encB: Encoder[B], encU: Encoder[U]): Dataset[(K, U)] =
aggByKey(() => zero, reduce, merge, finish)
/** Count the number rows in the key-value Dataset with each key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.countByKey.show
+-----+--------+
|value|count(1)|
+-----+--------+
| 1| 2|
| 2| 1|
+-----+--------+
* }}}
*/
def countByKey()(implicit encK: Encoder[K]): Dataset[(K, Long)] = ds.groupByKey(_._1).count
/** Inner join with another key-value Dataset on their keys.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.joinByKey(Seq((1,4)).toDS).show
+---+-----+
| _1| _2|
+---+-----+
| 1|[2,4]|
| 1|[3,4]|
+---+-----+
* }}}
*/
def joinOnKey[V1](other: Dataset[(K, V1)])(implicit encKV: Encoder[(K, V)], encKV1: Encoder[(K, V1)], encKVV1: Encoder[(K, (V, V1))]): Dataset[(K, (V, V1))] = {
val ds1 = ds.withDefaultFieldNames
val other1 = other.withDefaultFieldNames
ds1.joinWith(other1, ds1("_1") === other1("_1"), "inner").map{ x => (x._1._1, (x._1._2, x._2._2)) }
}
/** Left outer join with another key-value Dataset on their keys.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.leftOuterJoinByKey(Seq((1,4)).toDS).show
+---+--------+
| _1| _2|
+---+--------+
| 1| [2,4]|
| 1| [3,4]|
| 2|[4,null]|
+---+--------+
* }}}
*/
def leftOuterJoinOnKey[V1](other: Dataset[(K, V1)])(implicit encKV: Encoder[(K, V)], encKV1: Encoder[(K, V1)], encKVOptV1: Encoder[(K, (V, Option[V1]))]): Dataset[(K, (V, Option[V1]))] = {
val ds1 = ds.withDefaultFieldNames
val other1 = other.withDefaultFieldNames
ds1.joinWith(other1, ds1("_1") === other1("_1"), "left_outer").map{ x => (x._1._1, (x._1._2, Option(x._2).map(_._2))) }
}
/** Right outer join with another key-value Dataset on their keys.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.rightOuterJoinByKey(Seq((1,4)).toDS).show
+---+-----+
| _1| _2|
+---+-----+
| 1|[3,4]|
| 1|[2,4]|
+---+-----+
* }}}
*/
def rightOuterJoinOnKey[V1](other: Dataset[(K, V1)])(implicit encKV: Encoder[(K, V)], encKV1: Encoder[(K, V1)], encKVOptV1: Encoder[(K, (Option[V], V1))]): Dataset[(K, (Option[V], V1))] = {
val ds1 = ds.withDefaultFieldNames
val other1 = other.withDefaultFieldNames
ds1.joinWith(other1, ds1("_1") === other1("_1"), "right_outer").map{ x => (x._2._1, (Option(x._1).map(_._2), x._2._2)) }
}
/** Full outer join with another key-value Dataset on their keys.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.fullOuterJoinByKey(Seq((1, 4), (1, 5)).toDS).show
+---+--------+
| _1| _2|
+---+--------+
| 1| [2,4]|
| 1| [2,5]|
| 1| [3,4]|
| 1| [3,5]|
| 2|[4,null]|
+---+--------+
* }}}
*/
def fullOuterJoinOnKey[V1](other: Dataset[(K, V1)])(implicit encKV: Encoder[(K, V)], encKV1: Encoder[(K, V1)], encKVOptV1: Encoder[(K, (Option[V], Option[V1]))]):
Dataset[(K, (Option[V], Option[V1]))] = {
val ds1 = ds.withDefaultFieldNames
val other1 = other.withDefaultFieldNames
ds1.joinWith(other1, ds1("_1") === other1("_1"), "full_outer").map{ x => (if (x._1 == null) x._2._1 else x._1._1, (Option(x._1).map(_._2), Option(x._2).map(_._2))) }
}
/** Discard the key-value Dataset's values, leaving only the keys.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.keys.show
+-----+
|value|
+-----+
| 1|
| 1|
| 2|
+-----+
* }}}
*/
def keys(implicit encK: Encoder[K]): Dataset[K] = ds.map(_._1)
/** Discard the key-value Dataset's keys, leaving only the values.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> Seq((1, 2), (1, 3), (2, 4)).toDS.values.show
+-----+
|value|
+-----+
| 2|
| 3|
| 4|
+-----+
* }}}
*/
def values(implicit encV: Encoder[V]): Dataset[V] = ds.map(_._2)
/** Partition the key-value Dataset by key, up to the maximum given number of partitions.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> val ds = Seq((1, 2), (1, 3), (2, 4)).toDS
scala> ds.rdd.partitions
res1: Array[org.apache.spark.Partition] = Array(org.apache.spark.rdd.ParallelCollectionPartition@20fe, org.apache.spark.rdd.ParallelCollectionPartition@20ff, org.apache.spark.rdd.ParallelCollectionPartition@2100)
scala> ds.partitionByKey(1).rdd.partitions
res1: Array[org.apache.spark.Partition] = Array(org.apache.spark.sql.execution.ShuffledRowRDDPartition@0)
* }}}
*/
def partitionByKey(numPartitions: Int)(implicit encKV: Encoder[(K, V)]): Dataset[(K, V)] = {
val ds1 = ds.withDefaultFieldNames
ds1.repartition(numPartitions, ds1("_1"))
}
/** Partition the key-value Dataset by key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> val ds = Seq((1, 2), (1, 3), (2, 4)).toDS
scala> ds.rdd.partitions
res1: Array[org.apache.spark.Partition] = Array(org.apache.spark.rdd.ParallelCollectionPartition@20fe, org.apache.spark.rdd.ParallelCollectionPartition@20ff, org.apache.spark.rdd.ParallelCollectionPartition@2100)
scala> ds.partitionByKey.rdd.partitions
res2: Array[org.apache.spark.Partition] = Array(org.apache.spark.sql.execution.ShuffledRowRDDPartition@0, org.apache.spark.sql.execution.ShuffledRowRDDPartition@1, org.apache.spark.sql.execution.ShuffledRowRDDPartition@2, org.apache.spark.sql.execution.ShuffledRowRDDPartition@3, org.apache.spark.sql.execution.ShuffledRowRDDPartition@4, org.apache.spark.sql.execution.ShuffledRowRDDPartition@5, org.apache.spark.sql.execution.ShuffledRowRDDPartition@6, org.apache.spark.sql.execution.ShuffledRowRDDPartition@7)
* }}}
*/
def partitionByKey(implicit encKV: Encoder[(K, V)]): Dataset[(K, V)] = {
val ds1 = ds.withDefaultFieldNames
ds1.repartition(ds1("_1"))
}
/** Sort the key-value Dataset within partitions by key.
*
* {{{
scala> import com.tresata.spark.datasetops.RichPairDataset
scala> val ds = Seq((1, 2), (3, 1), (2, 2), (2, 6), (1, 1)).toDS
scala> ds.partitionByKey(2).rdd.glom.map(_.map(_._1).toSeq).collect
res56: Array[Seq[Int]] = Array(WrappedArray(2, 2), WrappedArray(1, 3, 1))
scala> ds.partitionByKey(2).sortWithinPartitionsByKey.rdd.glom.map(_.map(_._1).toSeq).collect
res57: Array[Seq[Int]] = Array(WrappedArray(2, 2), WrappedArray(1, 1, 3))
* }}}
*/
def sortWithinPartitionsByKey(implicit encKV: Encoder[(K, V)]): Dataset[(K, V)] = {
val ds1 = ds.withDefaultFieldNames
ds1.sortWithinPartitions(ds1("_1"))
}
}
}
