f44ead89f4
## What changes were proposed in this pull request? This PR contains a tiny update that removes an attribute resolution inconsistency in the Dataset API. The following example is taken from the ticket description: ``` spark.range(1).withColumnRenamed("id", "x").sort(col("id")) // works spark.range(1).withColumnRenamed("id", "x").sort($"id") // works spark.range(1).withColumnRenamed("id", "x").sort('id) // works spark.range(1).withColumnRenamed("id", "x").sort("id") // fails with: org.apache.spark.sql.AnalysisException: Cannot resolve column name "id" among (x); ``` The above `AnalysisException` happens because the last case calls `Dataset.apply()` to convert strings into columns, which triggers attribute resolution. To make the API consistent between overloaded methods, this PR defers the resolution and constructs columns directly. Author: aokolnychyi <anton.okolnychyi@sap.com> Closes #18740 from aokolnychyi/spark-21538. |
||
---|---|---|
.. | ||
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 allows 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
.