c7a68a920d
### What changes were proposed in this pull request?
This PR intends to fix a bug of distinct FIRST/LAST aggregates in v2.4.6/v3.0.0/master;
```
scala> sql("SELECT FIRST(DISTINCT v) FROM VALUES 1, 2, 3 t(v)").show()
...
Caused by: java.lang.UnsupportedOperationException: Cannot evaluate expression: false#37
at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.eval(Expression.scala:258)
at org.apache.spark.sql.catalyst.expressions.AttributeReference.eval(namedExpressions.scala:226)
at org.apache.spark.sql.catalyst.expressions.aggregate.First.ignoreNulls(First.scala:68)
at org.apache.spark.sql.catalyst.expressions.aggregate.First.updateExpressions$lzycompute(First.scala:82)
at org.apache.spark.sql.catalyst.expressions.aggregate.First.updateExpressions(First.scala:81)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$15.apply(HashAggregateExec.scala:268)
```
A root cause of this bug is that the `Aggregation` strategy replaces a foldable boolean `ignoreNullsExpr` expr with a `Unevaluable` expr (`AttributeReference`) for distinct FIRST/LAST aggregate functions. But, this operation cannot be allowed because the `Analyzer` has checked that it must be foldabe;
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.. | ||
catalyst | ||
core | ||
hive | ||
hive-thriftserver | ||
create-docs.sh | ||
gen-sql-api-docs.py | ||
gen-sql-config-docs.py | ||
gen-sql-functions-docs.py | ||
mkdocs.yml | ||
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.