ea525fe8c0
### What changes were proposed in this pull request? With suggestion from cloud-fan https://github.com/apache/spark/pull/28222#issuecomment-620586933 I Checked with both Presto and PostgresSQL, one is implemented intervals with ANSI style year-month/day-time, and the other is mixed and Non-ANSI. They both add the exceeded days in interval time part to the total days of the operation which extracts day from interval values. ```sql presto> SELECT EXTRACT(DAY FROM (cast('2020-01-15 00:00:00' as timestamp) - cast('2020-01-01 00:00:00' as timestamp))); _col0 ------- 14 (1 row) Query 20200428_135239_00000_ahn7x, FINISHED, 1 node Splits: 17 total, 17 done (100.00%) 0:01 [0 rows, 0B] [0 rows/s, 0B/s] presto> SELECT EXTRACT(DAY FROM (cast('2020-01-15 00:00:00' as timestamp) - cast('2020-01-01 00:00:01' as timestamp))); _col0 ------- 13 (1 row) Query 20200428_135246_00001_ahn7x, FINISHED, 1 node Splits: 17 total, 17 done (100.00%) 0:00 [0 rows, 0B] [0 rows/s, 0B/s] presto> ``` ```sql postgres=# SELECT EXTRACT(DAY FROM (cast('2020-01-15 00:00:00' as timestamp) - cast('2020-01-01 00:00:00' as timestamp))); date_part ----------- 14 (1 row) postgres=# SELECT EXTRACT(DAY FROM (cast('2020-01-15 00:00:00' as timestamp) - cast('2020-01-01 00:00:01' as timestamp))); date_part ----------- 13 ``` ``` spark-sql> SELECT EXTRACT(DAY FROM (cast('2020-01-15 00:00:00' as timestamp) - cast('2020-01-01 00:00:01' as timestamp))); 0 spark-sql> SELECT EXTRACT(DAY FROM (cast('2020-01-15 00:00:00' as timestamp) - cast('2020-01-01 00:00:00' as timestamp))); 0 ``` In ANSI standard, the day is exact 24 hours, so we don't need to worry about the conceptual day for interval extraction. The meaning of the conceptual day only takes effect when we add it to a zoned timestamp value. ### Why are the changes needed? Both satisfy the ANSI standard and common use cases in modern SQL platforms ### Does this PR introduce any user-facing change? No, it new in 3.0 ### How was this patch tested? add more uts Closes #28396 from yaooqinn/SPARK-31597. Authored-by: Kent Yao <yaooqinn@hotmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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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.