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[SPARK-17892] [SQL] [2.0] Do Not Optimize Query in CTAS More Than Once #15048 #15502

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What changes were proposed in this pull request?

This PR is to backport #15048 and #15459.

However, in 2.0, we do not have a unified logical node CreateTable and the analyzer rule PreWriteCheck is also different. To minimize the code changes, this PR adds a new rule AnalyzeCreateTableAsSelect. Please treat it as a new PR to review. Thanks!

As explained in #14797:

Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.

We should not optimize the query in CTAS more than once. For example,

spark.range(99, 101).createOrReplaceTempView("tab1")
val sqlStmt = "SELECT id, cast(id as long) * cast('1.0' as decimal(38, 18)) as num FROM tab1"
sql(s"CREATE TABLE tab2 USING PARQUET AS $sqlStmt")
checkAnswer(spark.table("tab2"), sql(sqlStmt))

Before this PR, the results do not match

== Results ==
!== Correct Answer - 2 ==       == Spark Answer - 2 ==
![100,100.000000000000000000]   [100,null]
 [99,99.000000000000000000]     [99,99.000000000000000000]

After this PR, the results match.

+---+----------------------+
|id |num                   |
+---+----------------------+
|99 |99.000000000000000000 |
|100|100.000000000000000000|
+---+----------------------+

In this PR, we do not treat the query in CTAS as a child. Thus, the query will not be optimized when optimizing CTAS statement. However, we still need to analyze it for normalizing and verifying the CTAS in the Analyzer. Thus, we do it in the analyzer rule PreprocessDDL, because so far only this rule needs the analyzed plan of the query.

How was this patch tested?

@SparkQA
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SparkQA commented Oct 15, 2016

Test build #67018 has finished for PR 15502 at commit 9cfebc5.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds the following public classes (experimental):
    • case class AnalyzeCreateTableAsSelect(sparkSession: SparkSession) extends Rule[LogicalPlan]

@gatorsmile
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cc @yhuai @hvanhovell @cloud-fan I guess this needs to be merged to 2.0.2 ASAP?

@@ -510,7 +510,7 @@ private[hive] case class InsertIntoHiveTable(
child: LogicalPlan,
overwrite: Boolean,
ifNotExists: Boolean)
extends LogicalPlan with Command {
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why it's not a command anymore?

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In the Command, this PR requires the child must be empty . Should we convert InsertIntoHiveTable to a non-child Command?

Just FYI, in Spark 2.1, InsertIntoTable is still a LogicalPlan instead of a Command.

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ah i see, this command is gone in 2.1

asfgit pushed a commit that referenced this pull request Oct 17, 2016
…15048

### What changes were proposed in this pull request?
This PR is to backport #15048 and #15459.

However, in 2.0, we do not have a unified logical node `CreateTable` and the analyzer rule `PreWriteCheck` is also different. To minimize the code changes, this PR adds a new rule `AnalyzeCreateTableAsSelect`. Please treat it as a new PR to review. Thanks!

As explained in #14797:
>Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.

We should not optimize the query in CTAS more than once. For example,
```Scala
spark.range(99, 101).createOrReplaceTempView("tab1")
val sqlStmt = "SELECT id, cast(id as long) * cast('1.0' as decimal(38, 18)) as num FROM tab1"
sql(s"CREATE TABLE tab2 USING PARQUET AS $sqlStmt")
checkAnswer(spark.table("tab2"), sql(sqlStmt))
```
Before this PR, the results do not match
```
== Results ==
!== Correct Answer - 2 ==       == Spark Answer - 2 ==
![100,100.000000000000000000]   [100,null]
 [99,99.000000000000000000]     [99,99.000000000000000000]
```
After this PR, the results match.
```
+---+----------------------+
|id |num                   |
+---+----------------------+
|99 |99.000000000000000000 |
|100|100.000000000000000000|
+---+----------------------+
```

In this PR, we do not treat the `query` in CTAS as a child. Thus, the `query` will not be optimized when optimizing CTAS statement. However, we still need to analyze it for normalizing and verifying the CTAS in the Analyzer. Thus, we do it in the analyzer rule `PreprocessDDL`, because so far only this rule needs the analyzed plan of the `query`.

### How was this patch tested?

Author: gatorsmile <gatorsmile@gmail.com>

Closes #15502 from gatorsmile/ctasOptimize2.0.
@cloud-fan
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LGTM, merging to 2.0!

@gatorsmile
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Thanks! Close it now.

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3 participants