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author | Herman van Hovell <hvanhovell@databricks.com> | 2017-02-10 11:06:57 -0800 |
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committer | Wenchen Fan <wenchen@databricks.com> | 2017-02-10 11:06:57 -0800 |
commit | de8a03e68202647555e30fffba551f65bc77608d (patch) | |
tree | f529ed7b5fe76475226cef8a99061c0bec235198 /sql/hive | |
parent | dadff5f0789cce7cf3728a8adaab42118e5dc019 (diff) | |
download | spark-de8a03e68202647555e30fffba551f65bc77608d.tar.gz spark-de8a03e68202647555e30fffba551f65bc77608d.tar.bz2 spark-de8a03e68202647555e30fffba551f65bc77608d.zip |
[SPARK-19459][SQL] Add Hive datatype (char/varchar) to StructField metadata
## What changes were proposed in this pull request?
Reading from an existing ORC table which contains `char` or `varchar` columns can fail with a `ClassCastException` if the table metadata has been created using Spark. This is caused by the fact that spark internally replaces `char` and `varchar` columns with a `string` column.
This PR fixes this by adding the hive type to the `StructField's` metadata under the `HIVE_TYPE_STRING` key. This is picked up by the `HiveClient` and the ORC reader, see https://github.com/apache/spark/pull/16060 for more details on how the metadata is used.
## How was this patch tested?
Added a regression test to `OrcSourceSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes #16804 from hvanhovell/SPARK-19459.
Diffstat (limited to 'sql/hive')
3 files changed, 39 insertions, 20 deletions
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala index 312ec6776b..13ab4e88e8 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala @@ -61,14 +61,6 @@ private[spark] object HiveUtils extends Logging { /** The version of hive used internally by Spark SQL. */ val hiveExecutionVersion: String = "1.2.1" - /** - * The property key that is used to store the raw hive type string in the metadata of StructField. - * For example, in the case where the Hive type is varchar, the type gets mapped to a string type - * in Spark SQL, but we need to preserve the original type in order to invoke the correct object - * inspector in Hive. - */ - val hiveTypeString: String = "HIVE_TYPE_STRING" - val HIVE_METASTORE_VERSION = buildConf("spark.sql.hive.metastore.version") .doc("Version of the Hive metastore. Available options are " + s"<code>0.12.0</code> through <code>$hiveExecutionVersion</code>.") @@ -465,8 +457,8 @@ private[spark] object HiveUtils extends Logging { /** Converts the native StructField to Hive's FieldSchema. */ private def toHiveColumn(c: StructField): FieldSchema = { - val typeString = if (c.metadata.contains(HiveUtils.hiveTypeString)) { - c.metadata.getString(HiveUtils.hiveTypeString) + val typeString = if (c.metadata.contains(HIVE_TYPE_STRING)) { + c.metadata.getString(HIVE_TYPE_STRING) } else { c.dataType.catalogString } @@ -482,7 +474,7 @@ private[spark] object HiveUtils extends Logging { throw new SparkException("Cannot recognize hive type string: " + hc.getType, e) } - val metadata = new MetadataBuilder().putString(HiveUtils.hiveTypeString, hc.getType).build() + val metadata = new MetadataBuilder().putString(HIVE_TYPE_STRING, hc.getType).build() val field = StructField( name = hc.getName, dataType = columnType, diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala index bf703a5ab6..f0d01ebfcf 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala @@ -47,7 +47,7 @@ import org.apache.spark.sql.catalyst.expressions.Expression import org.apache.spark.sql.catalyst.parser.{CatalystSqlParser, ParseException} import org.apache.spark.sql.execution.QueryExecutionException import org.apache.spark.sql.hive.HiveUtils -import org.apache.spark.sql.types.{MetadataBuilder, StructField, StructType} +import org.apache.spark.sql.types._ import org.apache.spark.util.{CircularBuffer, Utils} /** @@ -790,8 +790,8 @@ private[hive] class HiveClientImpl( .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]] private def toHiveColumn(c: StructField): FieldSchema = { - val typeString = if (c.metadata.contains(HiveUtils.hiveTypeString)) { - c.metadata.getString(HiveUtils.hiveTypeString) + val typeString = if (c.metadata.contains(HIVE_TYPE_STRING)) { + c.metadata.getString(HIVE_TYPE_STRING) } else { c.dataType.catalogString } @@ -806,7 +806,7 @@ private[hive] class HiveClientImpl( throw new SparkException("Cannot recognize hive type string: " + hc.getType, e) } - val metadata = new MetadataBuilder().putString(HiveUtils.hiveTypeString, hc.getType).build() + val metadata = new MetadataBuilder().putString(HIVE_TYPE_STRING, hc.getType).build() val field = StructField( name = hc.getName, dataType = columnType, diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala index fe1e17dd08..59ea8916ef 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala @@ -152,14 +152,41 @@ abstract class OrcSuite extends QueryTest with TestHiveSingleton with BeforeAndA assert(new OrcOptions(Map("Orc.Compress" -> "NONE")).compressionCodec == "NONE") } - test("SPARK-18220: read Hive orc table with varchar column") { + test("SPARK-19459/SPARK-18220: read char/varchar column written by Hive") { val hiveClient = spark.sharedState.externalCatalog.asInstanceOf[HiveExternalCatalog].client + val location = Utils.createTempDir() + val uri = location.toURI try { - hiveClient.runSqlHive("CREATE TABLE orc_varchar(a VARCHAR(10)) STORED AS orc") - hiveClient.runSqlHive("INSERT INTO TABLE orc_varchar SELECT 'a' FROM (SELECT 1) t") - checkAnswer(spark.table("orc_varchar"), Row("a")) + hiveClient.runSqlHive( + """ + |CREATE EXTERNAL TABLE hive_orc( + | a STRING, + | b CHAR(10), + | c VARCHAR(10)) + |STORED AS orc""".stripMargin) + // Hive throws an exception if I assign the location in the create table statement. + hiveClient.runSqlHive( + s"ALTER TABLE hive_orc SET LOCATION '$uri'") + hiveClient.runSqlHive( + "INSERT INTO TABLE hive_orc SELECT 'a', 'b', 'c' FROM (SELECT 1) t") + + // We create a different table in Spark using the same schema which points to + // the same location. + spark.sql( + s""" + |CREATE EXTERNAL TABLE spark_orc( + | a STRING, + | b CHAR(10), + | c VARCHAR(10)) + |STORED AS orc + |LOCATION '$uri'""".stripMargin) + val result = Row("a", "b ", "c") + checkAnswer(spark.table("hive_orc"), result) + checkAnswer(spark.table("spark_orc"), result) } finally { - hiveClient.runSqlHive("DROP TABLE IF EXISTS orc_varchar") + hiveClient.runSqlHive("DROP TABLE IF EXISTS hive_orc") + hiveClient.runSqlHive("DROP TABLE IF EXISTS spark_orc") + Utils.deleteRecursively(location) } } } |