1b416a0c77
## What changes were proposed in this pull request? Hive using incorrect **InputFormat**(`org.apache.hadoop.mapred.SequenceFileInputFormat`) to read Spark's **Parquet** bucketed data source table. Spark side: ```sql spark-sql> CREATE TABLE t (c1 INT, c2 INT) USING parquet CLUSTERED BY (c1) SORTED BY (c1) INTO 2 BUCKETS; 2019-04-29 17:52:05 WARN HiveExternalCatalog:66 - Persisting bucketed data source table `default`.`t` into Hive metastore in Spark SQL specific format, which is NOT compatible with Hive. spark-sql> DESC FORMATTED t; c1 int NULL c2 int NULL # Detailed Table Information Database default Table t Owner yumwang Created Time Mon Apr 29 17:52:05 CST 2019 Last Access Thu Jan 01 08:00:00 CST 1970 Created By Spark 2.4.0 Type MANAGED Provider parquet Num Buckets 2 Bucket Columns [`c1`] Sort Columns [`c1`] Table Properties [transient_lastDdlTime=1556531525] Location file:/user/hive/warehouse/t Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe InputFormat org.apache.hadoop.mapred.SequenceFileInputFormat OutputFormat org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat Storage Properties [serialization.format=1] ``` Hive side: ```sql hive> DESC FORMATTED t; OK # col_name data_type comment c1 int c2 int # Detailed Table Information Database: default Owner: root CreateTime: Wed May 08 03:38:46 GMT-07:00 2019 LastAccessTime: UNKNOWN Retention: 0 Location: file:/user/hive/warehouse/t Table Type: MANAGED_TABLE Table Parameters: bucketing_version spark spark.sql.create.version 3.0.0-SNAPSHOT spark.sql.sources.provider parquet spark.sql.sources.schema.bucketCol.0 c1 spark.sql.sources.schema.numBucketCols 1 spark.sql.sources.schema.numBuckets 2 spark.sql.sources.schema.numParts 1 spark.sql.sources.schema.numSortCols 1 spark.sql.sources.schema.part.0 {\"type\":\"struct\",\"fields\":[{\"name\":\"c1\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"c2\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}}]} spark.sql.sources.schema.sortCol.0 c1 transient_lastDdlTime 1557311926 # Storage Information SerDe Library: org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe InputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat OutputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat Compressed: No Num Buckets: -1 Bucket Columns: [] Sort Columns: [] Storage Desc Params: path file:/user/hive/warehouse/t serialization.format 1 ``` So it's non-bucketed table at Hive side. This pr set the `SerDe` correctly so Hive can read these tables. Related code: |
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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
.