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增加 properties map 到 PostQueryExecutionForKylin #70

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Aaaaaaron opened this issue Oct 25, 2019 · 0 comments
Open

增加 properties map 到 PostQueryExecutionForKylin #70

Aaaaaaron opened this issue Oct 25, 2019 · 0 comments

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@Aaaaaaron
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Aaaaaaron commented Oct 25, 2019 •
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KE issue: https://github.com/Kyligence/KAP/issues/14933
Aaaaaaron added a commit that referenced this issue Oct 27, 2019
@Aaaaaaron
#70, use map to pass properites in PostQueryExecutionForKylin.
94a29ac
Aaaaaaron added a commit that referenced this issue Oct 28, 2019
@Aaaaaaron
#70, use map to pass properites in PostQueryExecutionForKylin.
43bec81
lxian pushed a commit to lxian/spark that referenced this issue Sep 24, 2021
@HyukjinKwon
[SPARK-36559][SQL][PYTHON] Create plans dedicated to distributed-sequ…
93cec49 
…ence index for optimization

### What changes were proposed in this pull request?

This PR proposes to move distributed-sequence index implementation to SQL plan to leverage optimizations such as column pruning.

```python
import pyspark.pandas as ps
ps.set_option('compute.default_index_type', 'distributed-sequence')
ps.range(10).id.value_counts().to_frame().spark.explain()
```

**Before:**

```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#51L DESC NULLS LAST], true, 0
   +- Exchange rangepartitioning(count#51L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=Kyligence#70]
      +- HashAggregate(keys=[id#37L], functions=[count(1)], output=[__index_level_0__#48L, count#51L])
         +- Exchange hashpartitioning(id#37L, 200), ENSURE_REQUIREMENTS, [id=Kyligence#67]
            +- HashAggregate(keys=[id#37L], functions=[partial_count(1)], output=[id#37L, count#63L])
               +- Project [id#37L]
                  +- Filter atleastnnonnulls(1, id#37L)
                     +- Scan ExistingRDD[__index_level_0__#36L,id#37L]
                        # ^^^ Base DataFrame created by the output RDD from zipWithIndex (and checkpointed)
```

**After:**

```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#275L DESC NULLS LAST], true, 0
   +- Exchange rangepartitioning(count#275L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=Kyligence#174]
      +- HashAggregate(keys=[id#258L], functions=[count(1)])
         +- HashAggregate(keys=[id#258L], functions=[partial_count(1)])
            +- Filter atleastnnonnulls(1, id#258L)
               +- Range (0, 10, step=1, splits=16)
                  # ^^^ Removed the Spark job execution for `zipWithIndex`
```

### Why are the changes needed?

To leverage optimization of SQL engine and avoid unnecessary shuffle to create default index.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unittests were added. Also, this PR will test all unittests in pandas API on Spark after switching the default index implementation to `distributed-sequence`.

Closes apache#33807 from HyukjinKwon/SPARK-36559.

Authored-by: Hyukjin Kwon <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>
chenzhx pushed a commit to chenzhx/spark that referenced this issue Oct 20, 2021
@HyukjinKwon
[SPARK-36559][SQL][PYTHON] Create plans dedicated to distributed-sequ…
26ae9e9 
…ence index for optimization

### What changes were proposed in this pull request?

This PR proposes to move distributed-sequence index implementation to SQL plan to leverage optimizations such as column pruning.

```python
import pyspark.pandas as ps
ps.set_option('compute.default_index_type', 'distributed-sequence')
ps.range(10).id.value_counts().to_frame().spark.explain()
```

**Before:**

```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#51L DESC NULLS LAST], true, 0
   +- Exchange rangepartitioning(count#51L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=Kyligence#70]
      +- HashAggregate(keys=[id#37L], functions=[count(1)], output=[__index_level_0__#48L, count#51L])
         +- Exchange hashpartitioning(id#37L, 200), ENSURE_REQUIREMENTS, [id=Kyligence#67]
            +- HashAggregate(keys=[id#37L], functions=[partial_count(1)], output=[id#37L, count#63L])
               +- Project [id#37L]
                  +- Filter atleastnnonnulls(1, id#37L)
                     +- Scan ExistingRDD[__index_level_0__#36L,id#37L]
                        # ^^^ Base DataFrame created by the output RDD from zipWithIndex (and checkpointed)
```

**After:**

```bash
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- Sort [count#275L DESC NULLS LAST], true, 0
   +- Exchange rangepartitioning(count#275L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=Kyligence#174]
      +- HashAggregate(keys=[id#258L], functions=[count(1)])
         +- HashAggregate(keys=[id#258L], functions=[partial_count(1)])
            +- Filter atleastnnonnulls(1, id#258L)
               +- Range (0, 10, step=1, splits=16)
                  # ^^^ Removed the Spark job execution for `zipWithIndex`
```

### Why are the changes needed?

To leverage optimization of SQL engine and avoid unnecessary shuffle to create default index.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unittests were added. Also, this PR will test all unittests in pandas API on Spark after switching the default index implementation to `distributed-sequence`.

Closes apache#33807 from HyukjinKwon/SPARK-36559.

Authored-by: Hyukjin Kwon <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>
(cherry picked from commit 93cec49)
Signed-off-by: Hyukjin Kwon <[email protected]>
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@Aaaaaaron