3d2131ab4d
## What changes were proposed in this pull request? One of the common usability problems around reading data in spark (particularly CSV) is that there can often be a conflict between different readers in the classpath. As an example, if someone launches a 2.x spark shell with the spark-csv package in the classpath, Spark currently fails in an extremely unfriendly way (see databricks/spark-csv#367): ```bash ./bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0 scala> val df = spark.read.csv("/foo/bar.csv") java.lang.RuntimeException: Multiple sources found for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat, com.databricks.spark.csv.DefaultSource15), please specify the fully qualified class name. at scala.sys.package$.error(package.scala:27) at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:574) at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:85) at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:85) at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:295) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178) at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:533) at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:412) ... 48 elided ``` This PR proposes a simple way of fixing this error by picking up the internal datasource if there is single (the datasource that has "org.apache.spark" prefix). ```scala scala> spark.range(1).write.format("csv").mode("overwrite").save("/tmp/abc") 17/05/10 09:47:44 WARN DataSource: Multiple sources found for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat, com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat). ``` ```scala scala> spark.range(1).write.format("Csv").mode("overwrite").save("/tmp/abc") 17/05/10 09:47:52 WARN DataSource: Multiple sources found for Csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat, com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat). ``` ## How was this patch tested? Manually tested as below: ```bash ./bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0 ``` ```scala spark.sparkContext.setLogLevel("WARN") ``` **positive cases**: ```scala scala> spark.range(1).write.format("csv").mode("overwrite").save("/tmp/abc") 17/05/10 09:47:44 WARN DataSource: Multiple sources found for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat, com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat). ``` ```scala scala> spark.range(1).write.format("Csv").mode("overwrite").save("/tmp/abc") 17/05/10 09:47:52 WARN DataSource: Multiple sources found for Csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat, com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat). ``` (newlines were inserted for readability). ```scala scala> spark.range(1).write.format("com.databricks.spark.csv").mode("overwrite").save("/tmp/abc") ``` ```scala scala> spark.range(1).write.format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat").mode("overwrite").save("/tmp/abc") ``` **negative cases**: ```scala scala> spark.range(1).write.format("com.databricks.spark.csv.CsvRelation").save("/tmp/abc") java.lang.InstantiationException: com.databricks.spark.csv.CsvRelation ... ``` ```scala scala> spark.range(1).write.format("com.databricks.spark.csv.CsvRelatio").save("/tmp/abc") java.lang.ClassNotFoundException: Failed to find data source: com.databricks.spark.csv.CsvRelatio. Please find packages at http://spark.apache.org/third-party-projects.html ... ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #17916 from HyukjinKwon/datasource-detect. |
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| hive | ||
| hive-thriftserver | ||
| 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.