diff options
author | Sameer Agarwal <sameer@databricks.com> | 2016-01-26 07:50:37 -0800 |
---|---|---|
committer | Yin Huai <yhuai@databricks.com> | 2016-01-26 07:50:37 -0800 |
commit | 08c781ca672820be9ba32838bbe40d2643c4bde4 (patch) | |
tree | 7d77b7fb5a18967125ddaf4736fb98ecd83ec88f /sql/hive | |
parent | ae0309a8812a4fade3a0ea67d8986ca870aeb9eb (diff) | |
download | spark-08c781ca672820be9ba32838bbe40d2643c4bde4.tar.gz spark-08c781ca672820be9ba32838bbe40d2643c4bde4.tar.bz2 spark-08c781ca672820be9ba32838bbe40d2643c4bde4.zip |
[SPARK-12682][SQL] Add support for (optionally) not storing tables in hive metadata format
This PR adds a new table option (`skip_hive_metadata`) that'd allow the user to skip storing the table metadata in hive metadata format. While this could be useful in general, the specific use-case for this change is that Hive doesn't handle wide schemas well (see https://issues.apache.org/jira/browse/SPARK-12682 and https://issues.apache.org/jira/browse/SPARK-6024) which in turn prevents such tables from being queried in SparkSQL.
Author: Sameer Agarwal <sameer@databricks.com>
Closes #10826 from sameeragarwal/skip-hive-metadata.
Diffstat (limited to 'sql/hive')
-rw-r--r-- | sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala | 7 | ||||
-rw-r--r-- | sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala | 32 |
2 files changed, 39 insertions, 0 deletions
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 0cfe03ba91..80e45d5162 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -327,7 +327,14 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive // TODO: Support persisting partitioned data source relations in Hive compatible format val qualifiedTableName = tableIdent.quotedString + val skipHiveMetadata = options.getOrElse("skipHiveMetadata", "false").toBoolean val (hiveCompatibleTable, logMessage) = (maybeSerDe, dataSource.relation) match { + case _ if skipHiveMetadata => + val message = + s"Persisting partitioned data source relation $qualifiedTableName into " + + "Hive metastore in Spark SQL specific format, which is NOT compatible with Hive." + (None, message) + case (Some(serde), relation: HadoopFsRelation) if relation.paths.length == 1 && relation.partitionColumns.isEmpty => val hiveTable = newHiveCompatibleMetastoreTable(relation, serde) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index 211932fea0..d9e4b020fd 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -900,4 +900,36 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv sqlContext.sql("""use default""") sqlContext.sql("""drop database if exists testdb8156 CASCADE""") } + + test("skip hive metadata on table creation") { + val schema = StructType((1 to 5).map(i => StructField(s"c_$i", StringType))) + + catalog.createDataSourceTable( + tableIdent = TableIdentifier("not_skip_hive_metadata"), + userSpecifiedSchema = Some(schema), + partitionColumns = Array.empty[String], + bucketSpec = None, + provider = "parquet", + options = Map("path" -> "just a dummy path", "skipHiveMetadata" -> "false"), + isExternal = false) + + // As a proxy for verifying that the table was stored in Hive compatible format, we verify that + // each column of the table is of native type StringType. + assert(catalog.client.getTable("default", "not_skip_hive_metadata").schema + .forall(column => HiveMetastoreTypes.toDataType(column.hiveType) == StringType)) + + catalog.createDataSourceTable( + tableIdent = TableIdentifier("skip_hive_metadata"), + userSpecifiedSchema = Some(schema), + partitionColumns = Array.empty[String], + bucketSpec = None, + provider = "parquet", + options = Map("path" -> "just a dummy path", "skipHiveMetadata" -> "true"), + isExternal = false) + + // As a proxy for verifying that the table was stored in SparkSQL format, we verify that + // the table has a column type as array of StringType. + assert(catalog.client.getTable("default", "skip_hive_metadata").schema + .forall(column => HiveMetastoreTypes.toDataType(column.hiveType) == ArrayType(StringType))) + } } |