c1986204e5
### What changes were proposed in this pull request? In the PR, I propose to add legacy date/timestamp formatters based on `SimpleDateFormat` and `FastDateFormat`: - `LegacyFastTimestampFormatter` - uses `FastDateFormat` and supports parsing/formatting in microsecond precision. The code was borrowed from Spark 2.4, see https://github.com/apache/spark/pull/26507 & https://github.com/apache/spark/pull/26582 - `LegacySimpleTimestampFormatter` uses `SimpleDateFormat`, and support the `lenient` mode. When the `lenient` parameter is set to `false`, the parser become much stronger in checking its input. ### Why are the changes needed? Spark 2.4.x uses the following parsers for parsing/formatting date/timestamp strings: - `DateTimeFormat` in CSV/JSON datasource - `SimpleDateFormat` - is used in JDBC datasource, in partitions parsing. - `SimpleDateFormat` in strong mode (`lenient = false`), see https://github.com/apache/spark/blob/branch-2.4/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala#L124. It is used by the `date_format`, `from_unixtime`, `unix_timestamp` and `to_unix_timestamp` functions. The PR aims to make Spark 3.0 compatible with Spark 2.4.x in all those cases when `spark.sql.legacy.timeParser.enabled` is set to `true`. ### Does this PR introduce any user-facing change? This shouldn't change behavior with default settings. If `spark.sql.legacy.timeParser.enabled` is set to `true`, users should observe behavior of Spark 2.4. ### How was this patch tested? - Modified tests in `DateExpressionsSuite` to check the legacy parser - `SimpleDateFormat`. - Added `CSVLegacyTimeParserSuite` and `JsonLegacyTimeParserSuite` to run `CSVSuite` and `JsonSuite` with the legacy parser - `FastDateFormat`. Closes #27524 from MaxGekk/timestamp-formatter-legacy-fallback. Authored-by: Maxim 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 | ||
| 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.