03e3006312
## What changes were proposed in this pull request? I did a post-hoc review of https://github.com/apache/spark/pull/25008 , and would like to propose some cleanups/fixes/improvements: 1. Do not track the scanTime metrics in `ColumnarToRowExec`. This metrics is specific to file scan, and doesn't make sense for a general batch-to-row operator. 2. Because of 2, we need to track scanTime when building RDDs in the file scan node. 3. use `RDD#mapPartitionsInternal` instead of `flatMap` in several places, as `mapPartitionsInternal` is created for Spark SQL and we use it in almost all the SQL operators. 4. Add `limitNotReachedCond` in `ColumnarToRowExec`. This was in the `ColumnarBatchScan` before and is critical for performance. 5. Clear the relationship between codegen stage and columnar stage. The whole-stage-codegen framework is completely row-based, so these 2 kinds of stages can NEVER overlap. When they are adjacent, it's either a `RowToColumnarExec` above `WholeStageExec`, or a `ColumnarToRowExec` above the `InputAdapter`. 6. Reuse the `ColumnarBatch` in `RowToColumnarExec`. We don't need to create a new one every time, just need to reset it. 7. Do not skip testing full scan node in `LogicalPlanTagInSparkPlanSuite` 8. Add back the removed tests in `WholeStageCodegenSuite`. ## How was this patch tested? existing tests Closes #25264 from cloud-fan/minor. Authored-by: Wenchen Fan <wenchen@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.