Add aarch64 support leveldbjni #34
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This change adds a aarch64 profile to using the org.openlabtesting.leveldbjni which supports aarch64 platform.
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huangtianhua
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Jun 24, 2020
### What changes were proposed in this pull request?
To support formatted explain for AQE.
### Why are the changes needed?
AQE does not support formatted explain yet. It's good to support it for better user experience, debugging, etc.
Before:
```
== Physical Plan ==
AdaptiveSparkPlan (1)
+- * HashAggregate (unknown)
+- CustomShuffleReader (unknown)
+- ShuffleQueryStage (unknown)
+- Exchange (unknown)
+- * HashAggregate (unknown)
+- * Project (unknown)
+- * BroadcastHashJoin Inner BuildRight (unknown)
:- * LocalTableScan (unknown)
+- BroadcastQueryStage (unknown)
+- BroadcastExchange (unknown)
+- LocalTableScan (unknown)
(1) AdaptiveSparkPlan
Output [4]: [k#7, count(v1)#32L, sum(v1)#33L, avg(v2)#34]
Arguments: HashAggregate(keys=[k#7], functions=[count(1), sum(cast(v1#8 as bigint)), avg(cast(v2#19 as bigint))]), AdaptiveExecutionContext(org.apache.spark.sql.SparkSession104ab57b), [PlanAdaptiveSubqueries(Map())], false
```
After:
```
== Physical Plan ==
AdaptiveSparkPlan (14)
+- * HashAggregate (13)
+- CustomShuffleReader (12)
+- ShuffleQueryStage (11)
+- Exchange (10)
+- * HashAggregate (9)
+- * Project (8)
+- * BroadcastHashJoin Inner BuildRight (7)
:- * Project (2)
: +- * LocalTableScan (1)
+- BroadcastQueryStage (6)
+- BroadcastExchange (5)
+- * Project (4)
+- * LocalTableScan (3)
(1) LocalTableScan [codegen id : 2]
Output [2]: [_1#x, _2#x]
Arguments: [_1#x, _2#x]
(2) Project [codegen id : 2]
Output [2]: [_1#x AS k#x, _2#x AS v1#x]
Input [2]: [_1#x, _2#x]
(3) LocalTableScan [codegen id : 1]
Output [2]: [_1#x, _2#x]
Arguments: [_1#x, _2#x]
(4) Project [codegen id : 1]
Output [2]: [_1#x AS k#x, _2#x AS v2#x]
Input [2]: [_1#x, _2#x]
(5) BroadcastExchange
Input [2]: [k#x, v2#x]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=#x]
(6) BroadcastQueryStage
Output [2]: [k#x, v2#x]
Arguments: 0
(7) BroadcastHashJoin [codegen id : 2]
Left keys [1]: [k#x]
Right keys [1]: [k#x]
Join condition: None
(8) Project [codegen id : 2]
Output [3]: [k#x, v1#x, v2#x]
Input [4]: [k#x, v1#x, k#x, v2#x]
(9) HashAggregate [codegen id : 2]
Input [3]: [k#x, v1#x, v2#x]
Keys [1]: [k#x]
Functions [3]: [partial_count(1), partial_sum(cast(v1#x as bigint)), partial_avg(cast(v2#x as bigint))]
Aggregate Attributes [4]: [count#xL, sum#xL, sum#x, count#xL]
Results [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
(10) Exchange
Input [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
Arguments: hashpartitioning(k#x, 5), true, [id=#x]
(11) ShuffleQueryStage
Output [5]: [sum#xL, k#x, sum#x, count#xL, count#xL]
Arguments: 1
(12) CustomShuffleReader
Input [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
Arguments: coalesced
(13) HashAggregate [codegen id : 3]
Input [5]: [k#x, count#xL, sum#xL, sum#x, count#xL]
Keys [1]: [k#x]
Functions [3]: [count(1), sum(cast(v1#x as bigint)), avg(cast(v2#x as bigint))]
Aggregate Attributes [3]: [count(1)#xL, sum(cast(v1#x as bigint))#xL, avg(cast(v2#x as bigint))#x]
Results [4]: [k#x, count(1)#xL AS count(v1)#xL, sum(cast(v1#x as bigint))#xL AS sum(v1)#xL, avg(cast(v2#x as bigint))#x AS avg(v2)#x]
(14) AdaptiveSparkPlan
Output [4]: [k#x, count(v1)#xL, sum(v1)#xL, avg(v2)#x]
Arguments: isFinalPlan=true
```
### Does this PR introduce any user-facing change?
No, this should be new feature along with AQE in Spark 3.0.
### How was this patch tested?
Added a query file: `explain-aqe.sql` and a unit test.
Closes apache#28271 from Ngone51/support_formatted_explain_for_aqe.
Authored-by: yi.wu <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
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Add leveldbjni exclusion test