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Vladimir Kuriatkov e5b8136f47 [SPARK-21402][SQL] Fix java array of structs deserialization 
When deserializing values of ArrayType with struct elements in java beans, fields of structs get mixed up.
I suggest using struct data types retrieved from resolved input data instead of inferring them from java beans.

## What changes were proposed in this pull request?

MapObjects expression is used to map array elements to java beans. Struct type of elements is inferred from java bean structure and ends up with mixed up field order.
I used UnresolvedMapObjects instead of MapObjects, which allows to provide element type for MapObjects during analysis based on the resolved input data, not on the java bean.

## How was this patch tested?

Added a test case.
Built complete project on travis.

michalsenkyr cloud-fan marmbrus liancheng

Closes #22708 from vofque/SPARK-21402.

Lead-authored-by: Vladimir Kuriatkov <vofque@gmail.com>
Co-authored-by: Vladimir Kuriatkov <Vladimir_Kuriatkov@epam.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2018-10-17 22:13:05 +08:00
..
catalyst [SPARK-21402][SQL] Fix java array of structs deserialization 2018-10-17 22:13:05 +08:00
core [SPARK-21402][SQL] Fix java array of structs deserialization 2018-10-17 22:13:05 +08:00
hive [SQL][CATALYST][MINOR] update some error comments 2018-10-17 12:45:13 +08:00
hive-thriftserver Revert "[SPARK-25408] Move to mode ideomatic Java8" 2018-10-05 11:03:41 +08:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation 2017-08-05 10:10:56 -07:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [MINOR][DOC] Fix some typos and grammar issues 2018-04-06 13:37:08 +08:00

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 an extension of SQLContext called HiveContext that allows 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.