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[GitHub] [spark] viirya opened a new pull request #26087: [SPARK-29427][SQL]
Create KeyValueGroupedDataset from existing columns in DataFrame
viirya opened a new pull request #26087: [SPARK-29427][SQL] Create KeyValueGroupedDataset from existing columns in DataFrame
URL: https://github.com/apache/spark/pull/26087
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### What changes were proposed in this pull request?
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This PR proposes to add groupByRelationKey API to Dataset. It creates KeyValueGroupedDataset instance using existing relational columns, instead of a typed function in groupByKey API. Because it leverages existing columns, it can use existing data partition, if any, when doing operations like cogroup.
### Why are the changes needed?
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1. If you propose a new API, clarify the use case for a new API.
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Currently if users want to do cogroup on DataFrames, there is no good way to do except for KeyValueGroupedDataset. KeyValueGroupedDataset ignores existing data partition if any. That is a problem.
```scala
// df1 and df2 are certainly partitioned and sorted.
val df1 = Seq((1, 2, 3), (2, 3, 4)).toDF("a", "b", "c")
.repartition($"a", $"b").sortWithinPartitions("a", "b")
val df2 = Seq((1, 2, 4), (2, 3, 5)).toDF("a", "b", "c")
.repartition($"a", $"b").sortWithinPartitions("a", "b")
```
```scala
// This groupByRelationKey won't unnecessarily repartition the data
val df3 = df1.groupByRelationKey("a", "b")
.cogroup(df2.groupByRelationKey("a", "b")) { case (key, data1, data2) =>
data1.zip(data2).map { p =>
p._1.getInt(2) + p._2.getInt(2)
}
}
```
```
== Physical Plan ==
*(5) SerializeFromObject [input[0, int, false] AS value#11206]
+- CoGroup org.apache.spark.sql.DataFrameSuite$$Lambda$4888/206084072@4601674e, createexternalrow(a#11172, b#11173, StructField(a,IntegerType,false), StructField(b,IntegerTy
pe,false)), createexternalrow(a#11172, b#11173, c#11174, StructField(a,IntegerType,false), StructField(b,IntegerType,false), StructField(c,IntegerType,false)), createexterna
lrow(a#11188, b#11189, c#11190, StructField(a,IntegerType,false), StructField(b,IntegerType,false), StructField(c,IntegerType,false)), [a#11172, b#11173], [a#11188, b#11189]
, [a#11172, b#11173, c#11174], [a#11188, b#11189, c#11190], obj#11205: int
:- *(2) Sort [a#11172 ASC NULLS FIRST, b#11173 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(a#11172, b#11173, 5), false, [id=#10174]
: +- *(1) Project [_1#11165 AS a#11172, _2#11166 AS b#11173, _3#11167 AS c#11174]
: +- *(1) LocalTableScan [_1#11165, _2#11166, _3#11167]
+- *(4) Sort [a#11188 ASC NULLS FIRST, b#11189 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(a#11188, b#11189, 5), false, [id=#10179]
+- *(3) Project [_1#11181 AS a#11188, _2#11182 AS b#11189, _3#11183 AS c#11190]
+- *(3) LocalTableScan [_1#11181, _2#11182, _3#11183]
```
```scala
// Current approach creates additional AppendColumns and repartition data again
df1.groupByKey(r => r.getInt(0)).cogroup(df2.groupByKey(r => r.getInt(0))) {
case (key, data1, data2) =>
data1.zip(data2).map { p =>
p._1.getInt(2) + p._2.getInt(2)
}
}
```
```
== Physical Plan ==
*(7) SerializeFromObject [input[0, int, false] AS value#11216]
+- CoGroup org.apache.spark.sql.DataFrameSuite$$Lambda$4892/905560656@19f7e6c5, value#11211: int, createexternalrow(a#11172, b#11173, c#11174, StructField(a,IntegerType,false), StructField(b,IntegerType,false), StructField(c,IntegerType,false)), createexternalrow(a#11188, b#11189, c#11190, StructField(a,IntegerType,false), StructField(b,IntegerType,false), StructField(c,IntegerType,false)), [value#11211], [value#11213], [a#11172, b#11173, c#11174], [a#11188, b#11189, c#11190], obj#11215: int
:- *(3) Sort [value#11211 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(value#11211, 5), true, [id=#10442]
: +- AppendColumns org.apache.spark.sql.DataFrameSuite$$Lambda$4889/2021090091@6396e053, createexternalrow(a#11172, b#11173, c#11174, StructField(a,IntegerType,false), StructField(b,IntegerType,false), StructField(c,IntegerType,false)), [input[0, int, false] AS value#11211]
: +- *(2) Sort [a#11172 ASC NULLS FIRST, b#11173 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(a#11172, b#11173, 5), false, [id=#10437]
: +- *(1) Project [_1#11165 AS a#11172, _2#11166 AS b#11173, _3#11167 AS c#11174]
: +- *(1) LocalTableScan [_1#11165, _2#11166, _3#11167]
+- *(6) Sort [value#11213 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(value#11213, 5), true, [id=#10452]
+- AppendColumns org.apache.spark.sql.DataFrameSuite$$Lambda$4891/1736834504@798dbf14, createexternalrow(a#11188, b#11189, c#11190, StructField(a,IntegerType,false), StructField(b,IntegerType,false), StructField(c,IntegerType,false)), [input[0, int, false] AS value#11213]
+- *(5) Sort [a#11188 ASC NULLS FIRST, b#11189 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(a#11188, b#11189, 5), false, [id=#10447]
+- *(4) Project [_1#11181 AS a#11188, _2#11182 AS b#11189, _3#11183 AS c#11190]
+- *(4) LocalTableScan [_1#11181, _2#11182, _3#11183]
```
### Does this PR introduce any user-facing change?
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If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If no, write 'No'.
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Yes, this adds a new groupByRelationKey API to Dataset.
### How was this patch tested?
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Unit test.
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