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author | Yin Huai <yhuai@databricks.com> | 2015-09-22 13:29:39 -0700 |
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committer | Yin Huai <yhuai@databricks.com> | 2015-09-22 13:29:39 -0700 |
commit | 2204cdb28483b249616068085d4e88554fe6acef (patch) | |
tree | 1789dc690f1fb2076ed5be8e9d81e811503d4421 | |
parent | 5017c685f484ec256101d1d33bad11d9e0c0f641 (diff) | |
download | spark-2204cdb28483b249616068085d4e88554fe6acef.tar.gz spark-2204cdb28483b249616068085d4e88554fe6acef.tar.bz2 spark-2204cdb28483b249616068085d4e88554fe6acef.zip |
[SPARK-10672] [SQL] Do not fail when we cannot save the metadata of a data source table in a hive compatible way
https://issues.apache.org/jira/browse/SPARK-10672
With changes in this PR, we will fallback to same the metadata of a table in Spark SQL specific way if we fail to save it in a hive compatible way (Hive throws an exception because of its internal restrictions, e.g. binary and decimal types cannot be saved to parquet if the metastore is running Hive 0.13). I manually tested the fix with the following test in `DataSourceWithHiveMetastoreCatalogSuite` (`spark.sql.hive.metastore.version=0.13` and `spark.sql.hive.metastore.jars`=`maven`).
```
test(s"fail to save metadata of a parquet table in hive 0.13") {
withTempPath { dir =>
withTable("t") {
val path = dir.getCanonicalPath
sql(
s"""CREATE TABLE t USING $provider
|OPTIONS (path '$path')
|AS SELECT 1 AS d1, cast("val_1" as binary) AS d2
""".stripMargin)
sql(
s"""describe formatted t
""".stripMargin).collect.foreach(println)
sqlContext.table("t").show
}
}
}
}
```
Without this fix, we will fail with the following error.
```
org.apache.hadoop.hive.ql.metadata.HiveException: java.lang.UnsupportedOperationException: Unknown field type: binary
at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:619)
at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:576)
at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$createTable$1.apply$mcV$sp(ClientWrapper.scala:359)
at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$createTable$1.apply(ClientWrapper.scala:357)
at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$createTable$1.apply(ClientWrapper.scala:357)
at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$withHiveState$1.apply(ClientWrapper.scala:256)
at org.apache.spark.sql.hive.client.ClientWrapper.retryLocked(ClientWrapper.scala:211)
at org.apache.spark.sql.hive.client.ClientWrapper.withHiveState(ClientWrapper.scala:248)
at org.apache.spark.sql.hive.client.ClientWrapper.createTable(ClientWrapper.scala:357)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.createDataSourceTable(HiveMetastoreCatalog.scala:358)
at org.apache.spark.sql.hive.execution.CreateMetastoreDataSourceAsSelect.run(commands.scala:285)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:58)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:58)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:144)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:129)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:725)
at org.apache.spark.sql.test.SQLTestUtils$$anonfun$sql$1.apply(SQLTestUtils.scala:56)
at org.apache.spark.sql.test.SQLTestUtils$$anonfun$sql$1.apply(SQLTestUtils.scala:56)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcV$sp$2$$anonfun$apply$2.apply$mcV$sp(HiveMetastoreCatalogSuite.scala:165)
at org.apache.spark.sql.test.SQLTestUtils$class.withTable(SQLTestUtils.scala:150)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.withTable(HiveMetastoreCatalogSuite.scala:52)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcV$sp$2.apply(HiveMetastoreCatalogSuite.scala:162)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcV$sp$2.apply(HiveMetastoreCatalogSuite.scala:161)
at org.apache.spark.sql.test.SQLTestUtils$class.withTempPath(SQLTestUtils.scala:125)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.withTempPath(HiveMetastoreCatalogSuite.scala:52)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1.apply$mcV$sp(HiveMetastoreCatalogSuite.scala:161)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1.apply(HiveMetastoreCatalogSuite.scala:161)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1.apply(HiveMetastoreCatalogSuite.scala:161)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:42)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
at org.scalatest.FunSuite.runTest(FunSuite.scala:1555)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483)
at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1555)
at org.scalatest.Suite$class.run(Suite.scala:1424)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
at org.scalatest.SuperEngine.runImpl(Engine.scala:545)
at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.org$scalatest$BeforeAndAfterAll$$super$run(HiveMetastoreCatalogSuite.scala:52)
at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257)
at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.run(HiveMetastoreCatalogSuite.scala:52)
at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:462)
at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:671)
at sbt.ForkMain$Run$2.call(ForkMain.java:294)
at sbt.ForkMain$Run$2.call(ForkMain.java:284)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.UnsupportedOperationException: Unknown field type: binary
at org.apache.hadoop.hive.ql.io.parquet.serde.ArrayWritableObjectInspector.getObjectInspector(ArrayWritableObjectInspector.java:108)
at org.apache.hadoop.hive.ql.io.parquet.serde.ArrayWritableObjectInspector.<init>(ArrayWritableObjectInspector.java:60)
at org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe.initialize(ParquetHiveSerDe.java:113)
at org.apache.hadoop.hive.metastore.MetaStoreUtils.getDeserializer(MetaStoreUtils.java:339)
at org.apache.hadoop.hive.ql.metadata.Table.getDeserializerFromMetaStore(Table.java:288)
at org.apache.hadoop.hive.ql.metadata.Table.checkValidity(Table.java:194)
at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:597)
... 76 more
```
Author: Yin Huai <yhuai@databricks.com>
Closes #8824 from yhuai/datasourceMetadata.
-rw-r--r-- | sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala | 101 |
1 files changed, 50 insertions, 51 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 0c1b41e337..012634cb5a 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 @@ -309,69 +309,68 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive } // TODO: Support persisting partitioned data source relations in Hive compatible format - val hiveTable = (maybeSerDe, dataSource.relation) match { + val qualifiedTableName = tableIdent.quotedString + val (hiveCompitiableTable, logMessage) = (maybeSerDe, dataSource.relation) match { case (Some(serde), relation: HadoopFsRelation) - if relation.paths.length == 1 && relation.partitionColumns.isEmpty => - // Hive ParquetSerDe doesn't support decimal type until 1.2.0. - val isParquetSerDe = serde.inputFormat.exists(_.toLowerCase.contains("parquet")) - val hasDecimalFields = relation.schema.existsRecursively(_.isInstanceOf[DecimalType]) - - val hiveParquetSupportsDecimal = client.version match { - case org.apache.spark.sql.hive.client.hive.v1_2 => true - case _ => false - } - - if (isParquetSerDe && !hiveParquetSupportsDecimal && hasDecimalFields) { - // If Hive version is below 1.2.0, we cannot save Hive compatible schema to - // metastore when the file format is Parquet and the schema has DecimalType. - logWarning { - "Persisting Parquet relation with decimal field(s) into Hive metastore in Spark SQL " + - "specific format, which is NOT compatible with Hive. Because ParquetHiveSerDe in " + - s"Hive ${client.version.fullVersion} doesn't support decimal type. See HIVE-6384." - } - newSparkSQLSpecificMetastoreTable() - } else { - logInfo { - "Persisting data source relation with a single input path into Hive metastore in " + - s"Hive compatible format. Input path: ${relation.paths.head}" - } - newHiveCompatibleMetastoreTable(relation, serde) - } + if relation.paths.length == 1 && relation.partitionColumns.isEmpty => + val hiveTable = newHiveCompatibleMetastoreTable(relation, serde) + val message = + s"Persisting data source relation $qualifiedTableName with a single input path " + + s"into Hive metastore in Hive compatible format. Input path: ${relation.paths.head}." + (Some(hiveTable), message) case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty => - logWarning { - "Persisting partitioned data source relation into Hive metastore in " + - s"Spark SQL specific format, which is NOT compatible with Hive. Input path(s): " + - relation.paths.mkString("\n", "\n", "") - } - newSparkSQLSpecificMetastoreTable() + val message = + s"Persisting partitioned data source relation $qualifiedTableName into " + + "Hive metastore in Spark SQL specific format, which is NOT compatible with Hive. " + + "Input path(s): " + relation.paths.mkString("\n", "\n", "") + (None, message) case (Some(serde), relation: HadoopFsRelation) => - logWarning { - "Persisting data source relation with multiple input paths into Hive metastore in " + - s"Spark SQL specific format, which is NOT compatible with Hive. Input paths: " + - relation.paths.mkString("\n", "\n", "") - } - newSparkSQLSpecificMetastoreTable() + val message = + s"Persisting data source relation $qualifiedTableName with multiple input paths into " + + "Hive metastore in Spark SQL specific format, which is NOT compatible with Hive. " + + s"Input paths: " + relation.paths.mkString("\n", "\n", "") + (None, message) case (Some(serde), _) => - logWarning { - s"Data source relation is not a ${classOf[HadoopFsRelation].getSimpleName}. " + - "Persisting it into Hive metastore in Spark SQL specific format, " + - "which is NOT compatible with Hive." - } - newSparkSQLSpecificMetastoreTable() + val message = + s"Data source relation $qualifiedTableName is not a " + + s"${classOf[HadoopFsRelation].getSimpleName}. Persisting it into Hive metastore " + + "in Spark SQL specific format, which is NOT compatible with Hive." + (None, message) case _ => - logWarning { + val message = s"Couldn't find corresponding Hive SerDe for data source provider $provider. " + - "Persisting data source relation into Hive metastore in Spark SQL specific format, " + - "which is NOT compatible with Hive." - } - newSparkSQLSpecificMetastoreTable() + s"Persisting data source relation $qualifiedTableName into Hive metastore in " + + s"Spark SQL specific format, which is NOT compatible with Hive." + (None, message) } - client.createTable(hiveTable) + (hiveCompitiableTable, logMessage) match { + case (Some(table), message) => + // We first try to save the metadata of the table in a Hive compatiable way. + // If Hive throws an error, we fall back to save its metadata in the Spark SQL + // specific way. + try { + logInfo(message) + client.createTable(table) + } catch { + case throwable: Throwable => + val warningMessage = + s"Could not persist $qualifiedTableName in a Hive compatible way. Persisting " + + s"it into Hive metastore in Spark SQL specific format." + logWarning(warningMessage, throwable) + val sparkSqlSpecificTable = newSparkSQLSpecificMetastoreTable() + client.createTable(sparkSqlSpecificTable) + } + + case (None, message) => + logWarning(message) + val hiveTable = newSparkSQLSpecificMetastoreTable() + client.createTable(hiveTable) + } } def hiveDefaultTableFilePath(tableName: String): String = { |