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### What changes were proposed in this pull request? In the PR, I propose to use the `CAST` logic when the pattern is not specified in `DateFormatter` or `TimestampFormatter`. In particular, invoke the `DateTimeUtils.stringToTimestampAnsi()` or `stringToDateAnsi()` in the case. ### Why are the changes needed? 1. This can improve user experience with Spark SQL by making the default date/timestamp parsers more flexible and tolerant to their inputs. 2. We make the default case consistent to the behavior of the `CAST` expression which makes implementation more consistent. ### Does this PR introduce _any_ user-facing change? The changes shouldn't introduce behavior change in regular cases but it can influence on corner cases. New implementation is able to parse more dates/timestamps by default. For instance, old (current) date parses can recognize dates only in the format **yyyy-MM-dd** but new one can handle: * `[+-]yyyy*` * `[+-]yyyy*-[m]m` * `[+-]yyyy*-[m]m-[d]d` * `[+-]yyyy*-[m]m-[d]d ` * `[+-]yyyy*-[m]m-[d]d *` * `[+-]yyyy*-[m]m-[d]dT*` Similarly for timestamps. The old (current) timestamp formatter is able to parse timestamps only in the format **yyyy-MM-dd HH:mm:ss** by default, but new implementation can handle: * `[+-]yyyy*` * `[+-]yyyy*-[m]m` * `[+-]yyyy*-[m]m-[d]d` * `[+-]yyyy*-[m]m-[d]d ` * `[+-]yyyy*-[m]m-[d]d [h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]` * `[+-]yyyy*-[m]m-[d]dT[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]` * `[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]` * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]` ### How was this patch tested? By running the affected test suites: ``` $ build/sbt "test:testOnly *ImageFileFormatSuite" $ build/sbt "test:testOnly *ParquetV2PartitionDiscoverySuite" ``` Closes #33709 from MaxGekk/datetime-cast-default-pattern. Authored-by: Max Gekk <max.gekk@gmail.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.