3f4c32b3ca
### What changes were proposed in this pull request? Handle `YearMonthIntervalType` and `DayTimeIntervalType` in the `sql()` and `toString()` method of `Literal`, and format the ANSI interval in the ANSI style. ### Why are the changes needed? To improve readability and UX with Spark SQL. For example, a test output before the changes: ``` -- !query select timestamp'2011-11-11 11:11:11' - interval '2' day -- !query schema struct<TIMESTAMP '2011-11-11 11:11:11' - 172800000000:timestamp> -- !query output 2011-11-09 11:11:11 ``` ### Does this PR introduce _any_ user-facing change? Should not since the new intervals haven't been released yet. ### How was this patch tested? By running new tests: ``` $ ./build/sbt "test:testOnly *LiteralExpressionSuite" ``` Closes #32196 from MaxGekk/literal-ansi-interval-sql. Authored-by: Max Gekk <max.gekk@gmail.com> Signed-off-by: Max Gekk <max.gekk@gmail.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.