0603913c66
### What changes were proposed in this pull request?
Currently when enable parquet vectorized reader, use binary type as partition col will return incorrect value as below UT
```scala
test("Parquet vector reader incorrect with binary partition value") {
Seq(false, true).foreach(tag => {
withSQLConf("spark.sql.parquet.enableVectorizedReader" -> tag.toString) {
withTable("t1") {
sql(
"""CREATE TABLE t1(name STRING, id BINARY, part BINARY)
| USING PARQUET PARTITIONED BY (part)""".stripMargin)
sql(s"INSERT INTO t1 PARTITION(part = 'Spark SQL') VALUES('a', X'537061726B2053514C')")
if (tag) {
checkAnswer(sql("SELECT name, cast(id as string), cast(part as string) FROM t1"),
Row("a", "Spark SQL", ""))
} else {
checkAnswer(sql("SELECT name, cast(id as string), cast(part as string) FROM t1"),
Row("a", "Spark SQL", "Spark SQL"))
}
}
}
})
}
```
### Why are the changes needed?
Fix data incorrect issue
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes #30824 from AngersZhuuuu/SPARK-33593.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
|
||
|---|---|---|
| .. | ||
| 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.