[SQL] Add test case with workaround for reading partitioned Avro files

In order to read from partitioned Avro files we need to also set the `SERDEPROPERTIES` since `TBLPROPERTIES` are not passed to the initialization.  This PR simply adds a test to make sure we don't break this workaround.

Author: Michael Armbrust <michael@databricks.com>

Closes #2340 from marmbrus/avroPartitioned and squashes the following commits:

6b969d6 [Michael Armbrust] fix style
fea2124 [Michael Armbrust] Add test case with workaround for reading partitioned avro files.
[SPARK-3447][SQL] Remove explicit conversion with JListWrapper to avoid NPE

Author: Michael Armbrust <michael@databricks.com>

Closes #2323 from marmbrus/kryoJListNPE and squashes the following commits:

9634f11 [Michael Armbrust] Rollback JSON RDD changes
4d4d93c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into kryoJListNPE
646976b [Michael Armbrust] Fix JSON RDD Conversion too
59065bc [Michael Armbrust] Remove explicit conversion to avoid NPE
[SPARK-2781][SQL] Check resolution of LogicalPlans in Analyzer.

LogicalPlan contains a ‘resolved’ attribute indicating that all of its execution requirements have been resolved. This attribute is not checked before query execution. The analyzer contains a step to check that all Expressions are resolved, but this is not equivalent to checking all LogicalPlans. In particular, the Union plan’s implementation of ‘resolved’ verifies that the types of its children’s columns are compatible. Because the analyzer does not check that a Union plan is resolved, it is possible to execute a Union plan that outputs different types in the same column.  See SPARK-2781 for an example.

This patch adds two checks to the analyzer’s CheckResolution rule. First, each logical plan is checked to see if it is not resolved despite its children being resolved. This allows the ‘problem’ unresolved plan to be included in the TreeNodeException for reporting. Then as a backstop the root plan is checked to see if it is resolved, which recursively checks that the entire plan tree is resolved. Note that the resolved attribute is implemented recursively, and this patch also explicitly checks the resolved attribute on each logical plan in the tree. I assume the query plan trees will not be large enough for this redundant checking to meaningfully impact performance.

Because this patch starts validating that LogicalPlans are resolved before execution, I had to fix some cases where unresolved plans were passing through the analyzer as part of the implementation of the hive query system. In particular, HiveContext applies the CreateTables and PreInsertionCasts, and ExtractPythonUdfs rules manually after the analyzer runs. I moved these rules to the analyzer stage (for hive queries only), in the process completing a code TODO indicating the rules should be moved to the analyzer.

It’s worth noting that moving the CreateTables rule means introducing an analyzer rule with a significant side effect - in this case the side effect is creating a hive table. The rule will only attempt to create a table once even if its batch is executed multiple times, because it converts the InsertIntoCreatedTable plan it matches against into an InsertIntoTable. Additionally, these hive rules must be added to the Resolution batch rather than as a separate batch because hive rules rules may be needed to resolve non-root nodes, leaving the root to be resolved on a subsequent batch iteration. For example, the hive compatibility test auto_smb_mapjoin_14, and others, make use of a query plan where the root is a Union and its children are each a hive InsertIntoTable.

Mixing the custom hive rules with standard analyzer rules initially resulted in an additional failure because of policy differences between spark sql and hive when casting a boolean to a string. Hive casts booleans to strings as “true” / “false” while spark sql casts booleans to strings as “1” / “0” (causing the cast1.q test to fail). This behavior is a result of the BooleanCasts rule in HiveTypeCoercion.scala, and from looking at the implementation of BooleanCasts I think converting to to “1”/“0” is potentially a programming mistake. (If the BooleanCasts rule is disabled, casting produces “true”/“false” instead.) I believe “true” / “false” should be the behavior for spark sql - I changed the behavior so bools are converted to “true”/“false” to be consistent with hive, and none of the existing spark tests failed.

Finally, in some initial testing with hive it appears that an implicit type coercion of boolean to string results in a lowercase string, e.g. CONCAT( TRUE, “” ) -> “true” while an explicit cast produces an all caps string, e.g. CAST( TRUE AS STRING ) -> “TRUE”.  The change I’ve made just converts to lowercase strings in all cases.  I believe it is at least more correct than the existing spark sql implementation where all Cast expressions become “1” / “0”.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #1706 from staple/SPARK-2781 and squashes the following commits:

32683c4 [Aaron Staple] Fix compilation failure due to merge.
7c77fda [Aaron Staple] Move ExtractPythonUdfs to Analyzer's extendedRules in HiveContext.
d49bfb3 [Aaron Staple] Address review comments.
915b690 [Aaron Staple] Fix merge issue causing compilation failure.
701dcd2 [Aaron Staple] [SPARK-2781][SQL] Check resolution of LogicalPlans in Analyzer.