20a3ef7259
## What changes were proposed in this pull request? A follow-up task from SPARK-25348. To save I/O cost, Spark shouldn't attempt to read the file if users didn't request the `content` column. For example: ``` spark.read.format("binaryFile").load(path).filter($"length" < 1000000).count() ``` ## How was this patch tested? Unit test added. Please review http://spark.apache.org/contributing.html before opening a pull request. Closes #24473 from WeichenXu123/SPARK-27534. Lead-authored-by: Xiangrui Meng <meng@databricks.com> Co-authored-by: WeichenXu <weichen.xu@databricks.com> Signed-off-by: Xiangrui Meng <meng@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
.