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Tracking changes #8

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Tracking changes #8

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What changes were proposed in this pull request?

...

How was this patch tested?

....

@ifilonenko ifilonenko closed this Oct 15, 2018
ifilonenko pushed a commit that referenced this pull request Mar 26, 2019
Proposes the following changes to the API:

- closeAndGetLength() is split into separate close() and getNumBytesWritten() operations.
- openChannel and openStream renamed to toChannel and toStream

Proposes the following changes to the implementation:

- close() in the default implementation now persists the length in the partitionLengths array
- getNumBytesWritten() doesn't necessitate the writer's resources to be closed ahead of it
- Don't close the stream in BypassMergeSortShuffleWriter - only close it in DefaultShufflePartitionWriter#close (for consistency with how we treat channels)
ifilonenko pushed a commit that referenced this pull request Oct 11, 2019
## What changes were proposed in this pull request?
This PR aims at improving the way physical plans are explained in spark.

Currently, the explain output for physical plan may look very cluttered and each operator's
string representation can be very wide and wraps around in the display making it little
hard to follow. This especially happens when explaining a query 1) Operating on wide tables
2) Has complex expressions etc.

This PR attempts to split the output into two sections. In the header section, we display
the basic operator tree with a number associated with each operator. In this section, we strictly
control what we output for each operator. In the footer section, each operator is verbosely
displayed. Based on the feedback from Maryann, the uncorrelated subqueries (SubqueryExecs) are not included in the main plan. They are printed separately after the main plan and can be
correlated by the originating expression id from its parent plan.

To illustrate, here is a simple plan displayed in old vs new way.

Example query1 :
```
EXPLAIN SELECT key, Max(val) FROM explain_temp1 WHERE key > 0 GROUP BY key HAVING max(val) > 0
```

Old :
```
*(2) Project [key#2, max(val)#15]
+- *(2) Filter (isnotnull(max(val#3)apache-spark-on-k8s#18) AND (max(val#3)apache-spark-on-k8s#18 > 0))
   +- *(2) HashAggregate(keys=[key#2], functions=[max(val#3)], output=[key#2, max(val)#15, max(val#3)apache-spark-on-k8s#18])
      +- Exchange hashpartitioning(key#2, 200)
         +- *(1) HashAggregate(keys=[key#2], functions=[partial_max(val#3)], output=[key#2, max#21])
            +- *(1) Project [key#2, val#3]
               +- *(1) Filter (isnotnull(key#2) AND (key#2 > 0))
                  +- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), (key#2 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), GreaterThan(key,0)], ReadSchema: struct<key:int,val:int>
```
New :
```
Project (8)
+- Filter (7)
   +- HashAggregate (6)
      +- Exchange (5)
         +- HashAggregate (4)
            +- Project (3)
               +- Filter (2)
                  +- Scan parquet default.explain_temp1 (1)

(1) Scan parquet default.explain_temp1 [codegen id : 1]
Output: [key#2, val#3]

(2) Filter [codegen id : 1]
Input     : [key#2, val#3]
Condition : (isnotnull(key#2) AND (key#2 > 0))

(3) Project [codegen id : 1]
Output    : [key#2, val#3]
Input     : [key#2, val#3]

(4) HashAggregate [codegen id : 1]
Input: [key#2, val#3]

(5) Exchange
Input: [key#2, max#11]

(6) HashAggregate [codegen id : 2]
Input: [key#2, max#11]

(7) Filter [codegen id : 2]
Input     : [key#2, max(val)#5, max(val#3)#8]
Condition : (isnotnull(max(val#3)#8) AND (max(val#3)#8 > 0))

(8) Project [codegen id : 2]
Output    : [key#2, max(val)#5]
Input     : [key#2, max(val)#5, max(val#3)#8]
```

Example Query2 (subquery):
```
SELECT * FROM   explain_temp1 WHERE  KEY = (SELECT Max(KEY) FROM   explain_temp2 WHERE  KEY = (SELECT Max(KEY) FROM   explain_temp3 WHERE  val > 0) AND val = 2) AND val > 3
```
Old:
```
*(1) Project [key#2, val#3]
+- *(1) Filter (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#39)) AND (val#3 > 3))
   :  +- Subquery scalar-subquery#39
   :     +- *(2) HashAggregate(keys=[], functions=[max(KEY#26)], output=[max(KEY)apache-spark-on-k8s#45])
   :        +- Exchange SinglePartition
   :           +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#26)], output=[max#47])
   :              +- *(1) Project [key#26]
   :                 +- *(1) Filter (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#38)) AND (val#27 = 2))
   :                    :  +- Subquery scalar-subquery#38
   :                    :     +- *(2) HashAggregate(keys=[], functions=[max(KEY#28)], output=[max(KEY)apache-spark-on-k8s#43])
   :                    :        +- Exchange SinglePartition
   :                    :           +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#28)], output=[max#49])
   :                    :              +- *(1) Project [key#28]
   :                    :                 +- *(1) Filter (isnotnull(val#29) AND (val#29 > 0))
   :                    :                    +- *(1) FileScan parquet default.explain_temp3[key#28,val#29] Batched: true, DataFilters: [isnotnull(val#29), (val#29 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp3], PartitionFilters: [], PushedFilters: [IsNotNull(val), GreaterThan(val,0)], ReadSchema: struct<key:int,val:int>
   :                    +- *(1) FileScan parquet default.explain_temp2[key#26,val#27] Batched: true, DataFilters: [isnotnull(key#26), isnotnull(val#27), (val#27 = 2)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp2], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), EqualTo(val,2)], ReadSchema: struct<key:int,val:int>
   +- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), isnotnull(val#3), (val#3 > 3)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), GreaterThan(val,3)], ReadSchema: struct<key:int,val:int>
```
New:
```
Project (3)
+- Filter (2)
   +- Scan parquet default.explain_temp1 (1)

(1) Scan parquet default.explain_temp1 [codegen id : 1]
Output: [key#2, val#3]

(2) Filter [codegen id : 1]
Input     : [key#2, val#3]
Condition : (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#23)) AND (val#3 > 3))

(3) Project [codegen id : 1]
Output    : [key#2, val#3]
Input     : [key#2, val#3]
===== Subqueries =====

Subquery:1 Hosting operator id = 2 Hosting Expression = Subquery scalar-subquery#23
HashAggregate (9)
+- Exchange (8)
   +- HashAggregate (7)
      +- Project (6)
         +- Filter (5)
            +- Scan parquet default.explain_temp2 (4)

(4) Scan parquet default.explain_temp2 [codegen id : 1]
Output: [key#26, val#27]

(5) Filter [codegen id : 1]
Input     : [key#26, val#27]
Condition : (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#22)) AND (val#27 = 2))

(6) Project [codegen id : 1]
Output    : [key#26]
Input     : [key#26, val#27]

(7) HashAggregate [codegen id : 1]
Input: [key#26]

(8) Exchange
Input: [max#35]

(9) HashAggregate [codegen id : 2]
Input: [max#35]

Subquery:2 Hosting operator id = 5 Hosting Expression = Subquery scalar-subquery#22
HashAggregate (15)
+- Exchange (14)
   +- HashAggregate (13)
      +- Project (12)
         +- Filter (11)
            +- Scan parquet default.explain_temp3 (10)

(10) Scan parquet default.explain_temp3 [codegen id : 1]
Output: [key#28, val#29]

(11) Filter [codegen id : 1]
Input     : [key#28, val#29]
Condition : (isnotnull(val#29) AND (val#29 > 0))

(12) Project [codegen id : 1]
Output    : [key#28]
Input     : [key#28, val#29]

(13) HashAggregate [codegen id : 1]
Input: [key#28]

(14) Exchange
Input: [max#37]

(15) HashAggregate [codegen id : 2]
Input: [max#37]
```

Note:
I opened this PR as a WIP to start getting feedback. I will be on vacation starting tomorrow
would not be able to immediately incorporate the feedback. I will start to
work on them as soon as i can. Also, currently this PR provides a basic infrastructure
for explain enhancement. The details about individual operators will be implemented
in follow-up prs
## How was this patch tested?
Added a new test `explain.sql` that tests basic scenarios. Need to add more tests.

Closes apache#24759 from dilipbiswal/explain_feature.

Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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