ebd1431a5a
## What changes were proposed in this pull request? InMemoryFileIndex.listLeafFiles should use listLocatedStatus for DistributedFileSystem. DistributedFileSystem overrides the listLocatedStatus method in order to do it with 1 single namenode call thus saving thousands of calls to getBlockLocations. Currently in InMemoryFileIndex, all directory listings are done using FileSystem.listStatus following by individual calls to FileSystem.getFileBlockLocations. This is painstakingly slow for folders that have large numbers of files because this process happens serially and parallelism is only applied at the folder level, not the file level. FileSystem also provides another API listLocatedStatus which returns the LocatedFileStatus objects that already have the block locations. In FileSystem main class this just delegates to listStatus and getFileBlockLocations similarly to the way Spark does it. However when HDFS specifically is the backing file system, DistributedFileSystem overrides this method and simply makes one single call to the namenode to retrieve the directory listing with the block locations. This avoids potentially thousands or more calls to namenode and also is more consistent because files will either exist with locations or not exist instead of having the FileNotFoundException exception case. For our example directory with 6500 files, the load time of spark.read.parquet was reduced 96x from 76 seconds to .8 seconds. This savings only goes up with the number of files in the directory. In the pull request instead of using this method always which could lead to a FileNotFoundException that could be tough to decipher in the default FileSystem implementation, this method is only used when the FileSystem is a DistributedFileSystem and otherwise the old logic still applies. ## How was this patch tested? test suite ran Closes #24672 from rrusso2007/master. Authored-by: rrusso2007 <rrusso2007@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.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.