568db94e0c
## What changes were proposed in this pull request? In the current file source V2 framework, the schema of `FileScan` is not returned correctly if there are overlap columns between `dataSchema` and `partitionSchema`. The actual schema should be `dataSchema - overlapSchema + partitionSchema`, which might have different column order from the pushed down `requiredSchema` in `SupportsPushDownRequiredColumns.pruneColumns`. For example, if the data schema is `[a: String, b: String, c: String]` and the partition schema is `[b: Int, d: Int]`, the result schema is `[a: String, b: Int, c: String, d: Int]` in current `FileTable` and `HadoopFsRelation`. while the actual scan schema is `[a: String, c: String, b: Int, d: Int]` in `FileScan`. To fix the corner case, this PR proposes that the output schema of `FileTable` should be `dataSchema - overlapSchema + partitionSchema`, so that the column order is consistent with `FileScan`. Putting all the partition columns to the end of table schema is more reasonable. ## How was this patch tested? Unit test. Closes #24284 from gengliangwang/FixReadSchema. Authored-by: Gengliang Wang <gengliang.wang@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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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.