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[SPARK-4050][SQL] Fix caching of temporary tables with projections.

Michael Armbrust authored 10 years ago

Previously cached data was found by `sameResult` plan matching on optimized plans.  This technique however fails to locate the cached data when a temporary table with a projection is queried with a further reduced projection.  The failure is due to the fact that optimization will collapse the projections, producing a plan that no longer produces the sameResult as the cached data (though the cached data still subsumes the desired data).  For example consider the following previously failing test case.

```scala
sql("CACHE TABLE tempTable AS SELECT key FROM testData")
assertCached(sql("SELECT COUNT(*) FROM tempTable"))
```

In this PR I change the matching to occur after analysis instead of optimization, so that in the case of temporary tables, the plans will always match.  I think this should work generally, however, this error does raise questions about the need to do more thorough subsumption checking when locating cached data.

Another question is what sort of semantics we want to provide when uncaching data from temporary tables.  For example consider the following sequence of commands:

```scala
testData.select('key).registerTempTable("tempTable1")
testData.select('key).registerTempTable("tempTable2")
cacheTable("tempTable1")

// This obviously works.
assertCached(sql("SELECT COUNT(*) FROM tempTable1"))

// It seems good that this works ...
assertCached(sql("SELECT COUNT(*) FROM tempTable2"))

// ... but is this valid?
uncacheTable("tempTable2")

// Should this still be cached?
assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0)
```

Author: Michael Armbrust <michael@databricks.com>

Closes #2912 from marmbrus/cachingBug and squashes the following commits:

9c822d4 [Michael Armbrust] remove commented out code
5c72fb7 [Michael Armbrust] Add a test case / question about uncaching semantics.
63a23e4 [Michael Armbrust] Perform caching on analyzed instead of optimized plan.
03f1cfe [Michael Armbrust] Clean-up / add tests to SameResult suite.

0e886610

0e886610 10 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

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scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

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./bin/pyspark

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>>> sc.parallelize(range(1000)).count()

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Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

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