aed8dbab1d
### What changes were proposed in this pull request? This is a follow-up of https://github.com/apache/spark/pull/29160. We already removed the indeterministicity. This PR aims the following for the existing code base. 1. Add an explicit document to `DataFrameReader/DataFrameWriter`. 2. Add `toMap` to `CaseInsensitiveMap` in order to return `originalMap: Map[String, T]` because it's more consistent with the existing `case-sensitive key names` behavior for the existing code pattern like `AppendData.byName(..., extraOptions.toMap)`. Previously, it was `HashMap.toMap`. 3. During (2), we need to change the following to keep the original logic using `CaseInsensitiveMap.++`. ```scala - val params = extraOptions.toMap ++ connectionProperties.asScala.toMap + val params = extraOptions ++ connectionProperties.asScala ``` 4. Additionally, use `.toMap` in the following because `dsOptions.asCaseSensitiveMap()` is used later. ```scala - val options = sessionOptions ++ extraOptions + val options = sessionOptions.filterKeys(!extraOptions.contains(_)) ++ extraOptions.toMap val dsOptions = new CaseInsensitiveStringMap(options.asJava) ``` ### Why are the changes needed? `extraOptions.toMap` is used in several places (e.g. `DataFrameReader`) to hand over `Map[String, T]`. In this case, `CaseInsensitiveMap[T] private (val originalMap: Map[String, T])` had better return `originalMap`. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Pass the Jenkins or GitHub Action with the existing tests and newly add test case at `JDBCSuite`. Closes #29191 from dongjoon-hyun/SPARK-32364-3. Authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> |
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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.