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## What changes were proposed in this pull request? If we refresh a InMemoryFileIndex with a FileStatusCache, it will first use the FileStatusCache to re-generate the cachedLeafFiles etc, then call FileStatusCache.invalidateAll. While the order to do these two actions is wrong, this lead to the refresh action does not take effect. ``` override def refresh(): Unit = { refresh0() fileStatusCache.invalidateAll() } private def refresh0(): Unit = { val files = listLeafFiles(rootPaths) cachedLeafFiles = new mutable.LinkedHashMap[Path, FileStatus]() ++= files.map(f => f.getPath -> f) cachedLeafDirToChildrenFiles = files.toArray.groupBy(_.getPath.getParent) cachedPartitionSpec = null } ``` ## How was this patch tested? unit test added Author: windpiger <songjun@outlook.com> Closes #17079 from windpiger/fixInMemoryFileIndexRefresh. |
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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.