7f9ec19eae
## What changes were proposed in this pull request? Currently each data source OutputWriter is responsible for specifying the entire file name for each file output. This, however, does not make any sense because we rely on file naming schemes for certain behaviors in Spark SQL, e.g. bucket id. The current approach allows individual data sources to break the implementation of bucketing. On the flip side, we also don't want to move file naming entirely out of data sources, because different data sources do want to specify different extensions. This patch divides file name specification into two parts: the first part is a prefix specified by the caller of OutputWriter (in WriteOutput), and the second part is the suffix that can be specified by the OutputWriter itself. Note that a side effect of this change is that now all file based data sources also support bucketing automatically. There are also some other minor cleanups: - Removed the UUID passed through generic Configuration string - Some minor rewrites for better clarity - Renamed "path" in multiple places to "stagingDir", to more accurately reflect its meaning ## How was this patch tested? This should be covered by existing data source tests. Author: Reynold Xin <rxin@databricks.com> Closes #15562 from rxin/SPARK-18021. |
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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.