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## What changes were proposed in this pull request? The reader schema is said to be evolved (or projected) when it changed after the data is written. The followings are already supported in file-based data sources. Note that partition columns are not maintained in files. In this PR, `column` means `non-partition column`. 1. Add a column 2. Hide a column 3. Change a column position 4. Change a column type (upcast) This issue aims to guarantee users a backward-compatible read-schema test coverage on file-based data sources and to prevent future regressions by *adding read schema tests explicitly*. Here, we consider safe changes without data loss. For example, data type change should be from small types to larger types like `int`-to-`long`, not vice versa. As of today, in the master branch, file-based data sources have the following coverage. File Format | Coverage | Note ----------- | ---------- | ------------------------------------------------ TEXT | N/A | Schema consists of a single string column. CSV | 1, 2, 4 | JSON | 1, 2, 3, 4 | ORC | 1, 2, 3, 4 | Native vectorized ORC reader has the widest coverage among ORC formats. PARQUET | 1, 2, 3 | ## How was this patch tested? Pass the Jenkins with newly added test suites. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #20208 from dongjoon-hyun/SPARK-SCHEMA-EVOLUTION. |
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| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-markdown.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 an extension of SQLContext called HiveContext that allows 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.