History

Sunitha Kambhampati 4161c62429 [SPARK-28067][SQL] Fix incorrect results for decimal aggregate sum by returning null on decimal overflow ### What changes were proposed in this pull request? JIRA SPARK-28067: Wrong results are returned for aggregate sum with decimals with whole stage codegen enabled Repro: WholeStage enabled enabled -> Wrong results WholeStage disabled -> Returns exception Decimal precision 39 exceeds max precision 38 Issues: 1. Wrong results are returned which is bad 2. Inconsistency between whole stage enabled and disabled. Cause: Sum does not take care of possibility of overflow for the intermediate steps. ie the updateExpressions and mergeExpressions. This PR makes the following changes: - Add changes to check if overflow occurs for decimal in aggregate Sum and if there is an overflow, it will return null for the Sum operation when spark.sql.ansi.enabled is false. - When spark.sql.ansi.enabled is true, then the sum operation will return an exception if an overflow occurs for the decimal operation in Sum. - This is keeping it consistent with the behavior defined in spark.sql.ansi.enabled property Before the fix: Scenario 1: - WRONG RESULTS ``` scala> val df = Seq( \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2)).toDF("decNum", "intNum") df: org.apache.spark.sql.DataFrame = [decNum: decimal(38,18), intNum: int] scala> val df2 = df.withColumnRenamed("decNum", "decNum2").join(df, "intNum").agg(sum("decNum")) df2: org.apache.spark.sql.DataFrame = [sum(decNum): decimal(38,18)] scala> df2.show(40,false) +---------------------------------------+ \|sum(decNum) \| +---------------------------------------+ \|20000000000000000000.000000000000000000\| +---------------------------------------+ ``` -- Before fix: Scenario2: Setting spark.sql.ansi.enabled to true - WRONG RESULTS ``` scala> spark.conf.set("spark.sql.ansi.enabled", "true") scala> val df = Seq( \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2)).toDF("decNum", "intNum") df: org.apache.spark.sql.DataFrame = [decNum: decimal(38,18), intNum: int] scala> val df2 = df.withColumnRenamed("decNum", "decNum2").join(df, "intNum").agg(sum("decNum")) df2: org.apache.spark.sql.DataFrame = [sum(decNum): decimal(38,18)] scala> df2.show(40,false) +---------------------------------------+ \|sum(decNum) \| +---------------------------------------+ \|20000000000000000000.000000000000000000\| +---------------------------------------+ ``` After the fix: Scenario1: ``` scala> val df = Seq( \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2)).toDF("decNum", "intNum") df: org.apache.spark.sql.DataFrame = [decNum: decimal(38,18), intNum: int] scala> val df2 = df.withColumnRenamed("decNum", "decNum2").join(df, "intNum").agg(sum("decNum")) df2: org.apache.spark.sql.DataFrame = [sum(decNum): decimal(38,18)] scala> df2.show(40,false) +-----------+ \|sum(decNum)\| +-----------+ \|null \| +-----------+ ``` After fix: Scenario2: Setting the spark.sql.ansi.enabled to true: ``` scala> spark.conf.set("spark.sql.ansi.enabled", "true") scala> val df = Seq( \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 1), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2), \| (BigDecimal("10000000000000000000"), 2)).toDF("decNum", "intNum") df: org.apache.spark.sql.DataFrame = [decNum: decimal(38,18), intNum: int] scala> val df2 = df.withColumnRenamed("decNum", "decNum2").join(df, "intNum").agg(sum("decNum")) df2: org.apache.spark.sql.DataFrame = [sum(decNum): decimal(38,18)] scala> df2.show(40,false) 20/02/18 10:57:43 ERROR Executor: Exception in task 5.0 in stage 4.0 (TID 30) java.lang.ArithmeticException: Decimal(expanded,100000000000000000000.000000000000000000,39,18}) cannot be represented as Decimal(38, 18). ``` ### Why are the changes needed? The changes are needed in order to fix the wrong results that are returned for decimal aggregate sum. ### Does this PR introduce any user-facing change? User would see wrong results on aggregate sum that involved decimal overflow prior to this change, but now the user will see null. But if user enables the spark.sql.ansi.enabled flag to true, then the user will see an exception and not incorrect results. ### How was this patch tested? New test has been added and existing tests for sql, catalyst and hive suites were run ok. Closes #27627 from skambha/decaggfixwrongresults. Lead-authored-by: Sunitha Kambhampati <skambha@us.ibm.com> Co-authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>		2020-06-02 11:30:30 +00:00
..
catalyst	[SPARK-28067][SQL] Fix incorrect results for decimal aggregate sum by returning null on decimal overflow	2020-06-02 11:30:30 +00:00
core	[SPARK-28067][SQL] Fix incorrect results for decimal aggregate sum by returning null on decimal overflow	2020-06-02 11:30:30 +00:00
hive	[SPARK-31862][SQL] Remove exception wrapping in AQE	2020-05-29 04:23:38 +00:00
hive-thriftserver	[SPARK-31859][SPARK-31861][SPARK-31863] Fix Thriftserver session timezone issues	2020-05-30 06:14:32 +00:00
create-docs.sh	[SPARK-31550][SQL][DOCS] Set nondeterministic configurations with general meanings in sql configuration doc	2020-04-27 17:08:52 +09:00
gen-sql-api-docs.py	[SPARK-31474][SQL][FOLLOWUP] Replace _FUNC_ placeholder with functionname in the note field of expression info	2020-04-23 13:33:04 +09:00
gen-sql-config-docs.py	[SPARK-31550][SQL][DOCS] Set nondeterministic configurations with general meanings in sql configuration doc	2020-04-27 17:08:52 +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	[SPARK-30731] Update deprecated Mkdocs option	2020-02-19 17:28:58 +09:00
README.md	[SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options	2020-02-09 19:20:47 +09:00

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 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.