Simplify _createFromPandasWithArrow().
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HyukjinKwon
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Oct 20, 2017
| return None | ||
| arrow_types = [to_arrow_type(f.dataType) for f in schema.fields] | ||
| batches = [_create_batch([(c, t) | ||
| for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)]) |
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BTW, looks this is only the place using zip. Not a big deal but I think we are safe to replace
from itertools import imap as mapfrom itertools import izip as zip, imap as mapWe could change this after merging this first too.
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merged, thanks! |
BryanCutler
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Sep 8, 2020
…or its output partitioning
### What changes were proposed in this pull request?
Currently, the `BroadcastHashJoinExec`'s `outputPartitioning` only uses the streamed side's `outputPartitioning`. However, if the join type of `BroadcastHashJoinExec` is an inner-like join, the build side's info (the join keys) can be added to `BroadcastHashJoinExec`'s `outputPartitioning`.
For example,
```Scala
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "500")
val t1 = (0 until 100).map(i => (i % 5, i % 13)).toDF("i1", "j1")
val t2 = (0 until 100).map(i => (i % 5, i % 13)).toDF("i2", "j2")
val t3 = (0 until 20).map(i => (i % 7, i % 11)).toDF("i3", "j3")
val t4 = (0 until 100).map(i => (i % 5, i % 13)).toDF("i4", "j4")
// join1 is a sort merge join.
val join1 = t1.join(t2, t1("i1") === t2("i2"))
// join2 is a broadcast join where t3 is broadcasted.
val join2 = join1.join(t3, join1("i1") === t3("i3"))
// Join on the column from the broadcasted side (i3).
val join3 = join2.join(t4, join2("i3") === t4("i4"))
join3.explain
```
You see that `Exchange hashpartitioning(i2#103, 200)` is introduced because there is no output partitioning info from the build side.
```
== Physical Plan ==
*(6) SortMergeJoin [i3#29], [i4#40], Inner
:- *(4) Sort [i3#29 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(i3#29, 200), true, [id=apache#55]
: +- *(3) BroadcastHashJoin [i1#7], [i3#29], Inner, BuildRight
: :- *(3) SortMergeJoin [i1#7], [i2#18], Inner
: : :- *(1) Sort [i1#7 ASC NULLS FIRST], false, 0
: : : +- Exchange hashpartitioning(i1#7, 200), true, [id=#28]
: : : +- LocalTableScan [i1#7, j1#8]
: : +- *(2) Sort [i2#18 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(i2#18, 200), true, [id=#29]
: : +- LocalTableScan [i2#18, j2#19]
: +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=apache#34]
: +- LocalTableScan [i3#29, j3#30]
+- *(5) Sort [i4#40 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(i4#40, 200), true, [id=apache#39]
+- LocalTableScan [i4#40, j4#41]
```
This PR proposes to introduce output partitioning for the build side for `BroadcastHashJoinExec` if the streamed side has a `HashPartitioning` or a collection of `HashPartitioning`s.
There is a new internal config `spark.sql.execution.broadcastHashJoin.outputPartitioningExpandLimit`, which can limit the number of partitioning a `HashPartitioning` can expand to. It can be set to "0" to disable this feature.
### Why are the changes needed?
To remove unnecessary shuffle.
### Does this PR introduce _any_ user-facing change?
Yes, now the shuffle in the above example can be eliminated:
```
== Physical Plan ==
*(5) SortMergeJoin [i3#108], [i4#119], Inner
:- *(3) Sort [i3#108 ASC NULLS FIRST], false, 0
: +- *(3) BroadcastHashJoin [i1#86], [i3#108], Inner, BuildRight
: :- *(3) SortMergeJoin [i1#86], [i2#97], Inner
: : :- *(1) Sort [i1#86 ASC NULLS FIRST], false, 0
: : : +- Exchange hashpartitioning(i1#86, 200), true, [id=apache#120]
: : : +- LocalTableScan [i1#86, j1#87]
: : +- *(2) Sort [i2#97 ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(i2#97, 200), true, [id=apache#121]
: : +- LocalTableScan [i2#97, j2#98]
: +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=apache#126]
: +- LocalTableScan [i3#108, j3#109]
+- *(4) Sort [i4#119 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(i4#119, 200), true, [id=apache#130]
+- LocalTableScan [i4#119, j4#120]
```
### How was this patch tested?
Added new tests.
Closes apache#28676 from imback82/broadcast_join_output.
Authored-by: Terry Kim <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
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Simplify
_createFromPandasWithArrow()if we won't reach schema check block. (https://github.com/apache/spark/pull/19459/files#r145603645).