b49aaa33e1
### What changes were proposed in this pull request?
This PR intends to fix a minor bug when normalizing floats for struct types;
```
scala> import org.apache.spark.sql.execution.aggregate.HashAggregateExec
scala> val df = Seq(Tuple1(Tuple1(-0.0d)), Tuple1(Tuple1(0.0d))).toDF("k")
scala> val agg = df.distinct()
scala> agg.explain()
== Physical Plan ==
*(2) HashAggregate(keys=[k#40], functions=[])
+- Exchange hashpartitioning(k#40, 200), true, [id=#62]
+- *(1) HashAggregate(keys=[knownfloatingpointnormalized(if (isnull(k#40)) null else named_struct(col1, knownfloatingpointnormalized(normalizenanandzero(k#40._1)))) AS k#40], functions=[])
+- *(1) LocalTableScan [k#40]
scala> val aggOutput = agg.queryExecution.sparkPlan.collect { case a: HashAggregateExec => a.output.head }
scala> aggOutput.foreach { attr => println(attr.prettyJson) }
### Final Aggregate ###
[ {
"class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference",
"num-children" : 0,
"name" : "k",
"dataType" : {
"type" : "struct",
"fields" : [ {
"name" : "_1",
^^^
"type" : "double",
"nullable" : false,
"metadata" : { }
} ]
},
"nullable" : true,
"metadata" : { },
"exprId" : {
"product-class" : "org.apache.spark.sql.catalyst.expressions.ExprId",
"id" : 40,
"jvmId" : "a824e83f-933e-4b85-a1ff-577b5a0e2366"
},
"qualifier" : [ ]
} ]
### Partial Aggregate ###
[ {
"class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference",
"num-children" : 0,
"name" : "k",
"dataType" : {
"type" : "struct",
"fields" : [ {
"name" : "col1",
^^^^
"type" : "double",
"nullable" : true,
"metadata" : { }
} ]
},
"nullable" : true,
"metadata" : { },
"exprId" : {
"product-class" : "org.apache.spark.sql.catalyst.expressions.ExprId",
"id" : 40,
"jvmId" : "a824e83f-933e-4b85-a1ff-577b5a0e2366"
},
"qualifier" : [ ]
} ]
```
### Why are the changes needed?
bugfix.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests.
Closes #29780 from maropu/FixBugInNormalizedFloatingNumbers.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
|
||
|---|---|---|
| .. | ||
| 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.