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Emil Ejbyfeldt e2e3fe7782 [SPARK-35653][SQL] Fix CatalystToExternalMap interpreted path fails for Map with case classes as keys or values 
### What changes were proposed in this pull request?
Use the key/value LambdaFunction to convert the elements instead of
using CatalystTypeConverters.createToScalaConverter. This is how it is
done in MapObjects and that correctly handles Arrays with case classes.

### Why are the changes needed?
Before these changes the added test cases would fail with the following:
```
[info] - encode/decode for map with case class as value: Map(1 -> IntAndString(1,a)) (interpreted path) *** FAILED *** (64 milliseconds)
[info]   Encoded/Decoded data does not match input data
[info]
[info]   in:  Map(1 -> IntAndString(1,a))
[info]   out: Map(1 -> [1,a])
[info]   types: scala.collection.immutable.Map$Map1 [info]
[info]   Encoded Data: [org.apache.spark.sql.catalyst.expressions.UnsafeMapData5ecf5d9e]
[info]   Schema: value#823
[info]   root
[info]   -- value: map (nullable = true)
[info]       |-- key: integer
[info]       |-- value: struct (valueContainsNull = true)
[info]       |    |-- i: integer (nullable = false)
[info]       |    |-- s: string (nullable = true)
[info]
[info]
[info]   fromRow Expressions:
[info]   catalysttoexternalmap(lambdavariable(CatalystToExternalMap_key, IntegerType, false, 178), lambdavariable(CatalystToExternalMap_key, IntegerType, false, 178), lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179), if (isnull(lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179))) null else newInstance(class org.apache.spark.sql.catalyst.encoders.IntAndString), input[0, map<int,struct<i:int,s:string>>, true], interface scala.collection.immutable.Map
[info]   :- lambdavariable(CatalystToExternalMap_key, IntegerType, false, 178)
[info]   :- lambdavariable(CatalystToExternalMap_key, IntegerType, false, 178)
[info]   :- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179)
[info]   :- if (isnull(lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179))) null else newInstance(class org.apache.spark.sql.catalyst.encoders.IntAndString)
[info]   :  :- isnull(lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179))
[info]   :  :  +- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179)
[info]   :  :- null
[info]   :  +- newInstance(class org.apache.spark.sql.catalyst.encoders.IntAndString)
[info]   :     :- assertnotnull(lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179).i)
[info]   :     :  +- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179).i
[info]   :     :     +- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179)
[info]   :     +- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179).s.toString
[info]   :        +- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179).s
[info]   :           +- lambdavariable(CatalystToExternalMap_value, StructField(i,IntegerType,false), StructField(s,StringType,true), true, 179)
[info]   +- input[0, map<int,struct<i:int,s:string>>, true] (ExpressionEncoderSuite.scala:627)
```
So using a map with cases classes for keys or values and using the interpreted path would incorrect deserialize data from the catalyst representation.

### Does this PR introduce _any_ user-facing change?
Yes, it fixes the bug.

### How was this patch tested?
Existing and new unit tests in the ExpressionEncoderSuite

Closes #32783 from eejbyfeldt/fix-interpreted-path-for-map-with-case-classes.

Authored-by: Emil Ejbyfeldt <eejbyfeldt@liveintent.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
2021-06-10 09:37:27 -07:00
..
catalyst [SPARK-35653][SQL] Fix CatalystToExternalMap interpreted path fails for Map with case classes as keys or values 2021-06-10 09:37:27 -07:00
core [SPARK-35673][SQL] Fix user-defined hint and unrecognized hint in subquery 2021-06-10 15:32:10 +08:00
hive [SPARK-35058][SQL] Group exception messages in hive/client 2021-06-09 08:23:09 +00:00
hive-thriftserver [SPARK-21957][SQL][FOLLOWUP] Support CURRENT_USER without tailing parentheses 2021-06-04 13:32:56 +00:00
create-docs.sh [SPARK-34010][SQL][DODCS] Use python3 instead of python in SQL documentation build 2021-01-05 19:48:10 +09:00
gen-sql-api-docs.py [SPARK-34747][SQL][DOCS] Add virtual operators to the built-in function document 2021-03-19 10:19:26 +09:00
gen-sql-config-docs.py [SPARK-32194][PYTHON] Use proper exception classes instead of plain Exception 2021-05-26 11:54:40 +09:00
gen-sql-functions-docs.py [SPARK-31562][SQL] Update ExpressionDescription for substring, current_date, and current_timestamp 2020-04-26 11:46:52 -07:00
mkdocs.yml
README.md

README.md

Spark SQL

This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.

Spark SQL is broken up into four subprojects:

  • Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
  • Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
  • Hive Support (sql/hive) - Includes extensions that allow users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
  • HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.

Running ./sql/create-docs.sh generates SQL documentation for built-in functions under sql/site, and SQL configuration documentation that gets included as part of configuration.md in the main docs directory.