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## What changes were proposed in this pull request? This PR adds some tests converted from ```outer-join.sql``` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921). <details><summary>Diff comparing to 'outer-join.sql'</summary> <p> ```diff diff --git a/sql/core/src/test/resources/sql-tests/results/outer-join.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-outer-join.sql.out index 5db3bae5d0..819f786070 100644 --- a/sql/core/src/test/resources/sql-tests/results/outer-join.sql.out +++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-outer-join.sql.out -24,17 +24,17 struct<> -- !query 2 SELECT - (SUM(COALESCE(t1.int_col1, t2.int_col0))), - ((COALESCE(t1.int_col1, t2.int_col0)) * 2) + (udf(SUM(udf(COALESCE(t1.int_col1, t2.int_col0))))), + (udf(COALESCE(t1.int_col1, t2.int_col0)) * 2) FROM t1 RIGHT JOIN t2 - ON (t2.int_col0) = (t1.int_col1) -GROUP BY GREATEST(COALESCE(t2.int_col1, 109), COALESCE(t1.int_col1, -449)), + ON udf(t2.int_col0) = udf(t1.int_col1) +GROUP BY udf(GREATEST(COALESCE(udf(t2.int_col1), 109), COALESCE(t1.int_col1, udf(-449)))), COALESCE(t1.int_col1, t2.int_col0) -HAVING (SUM(COALESCE(t1.int_col1, t2.int_col0))) - > ((COALESCE(t1.int_col1, t2.int_col0)) * 2) +HAVING (udf(SUM(COALESCE(udf(t1.int_col1), udf(t2.int_col0))))) + > (udf(COALESCE(t1.int_col1, t2.int_col0)) * 2) -- !query 2 schema -struct<sum(coalesce(int_col1, int_col0)):bigint,(coalesce(int_col1, int_col0) * 2):int> +struct<CAST(udf(cast(sum(cast(cast(udf(cast(coalesce(int_col1, int_col0) as string)) as int) as bigint)) as string)) AS BIGINT):bigint,(CAST(udf(cast(coalesce(int_col1, int_col0) as string)) AS INT) * 2):int> -- !query 2 output -367 -734 -507 -1014 -70,10 +70,10 spark.sql.crossJoin.enabled true SELECT * FROM ( SELECT - COALESCE(t2.int_col1, t1.int_col1) AS int_col + udf(COALESCE(udf(t2.int_col1), udf(t1.int_col1))) AS int_col FROM t1 LEFT JOIN t2 ON false -) t where (t.int_col) is not null +) t where (udf(t.int_col)) is not null -- !query 6 schema struct<int_col:int> -- !query 6 output ``` </p> </details> ## How was this patch tested? Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921). Closes #25103 from huaxingao/spark-28285. Authored-by: Huaxin Gao <huaxing@us.ibm.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-markdown.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 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.