[SPARK-5068] [SQL] Fix bug query data when path doesn't exist for HiveContext #4356
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@@ -18,7 +18,7 @@ | |
| package org.apache.spark.sql.hive | ||
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| import org.apache.hadoop.conf.Configuration | ||
| import org.apache.hadoop.fs.{FileSystem, Path, PathFilter} | ||
| import org.apache.hadoop.hive.conf.HiveConf | ||
| import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants._ | ||
| import org.apache.hadoop.hive.ql.exec.Utilities | ||
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@@ -68,6 +68,8 @@ class HadoopTableReader( | |
| math.max(sc.hiveconf.getInt("mapred.map.tasks", 1), sc.sparkContext.defaultMinPartitions) | ||
| } | ||
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| @transient private lazy val fs = FileSystem.get(sc.hiveconf) | ||
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| // TODO: set aws s3 credentials. | ||
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| private val _broadcastedHiveConf = | ||
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@@ -102,24 +104,28 @@ class HadoopTableReader( | |
| val broadcastedHiveConf = _broadcastedHiveConf | ||
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| val tablePath = hiveTable.getPath | ||
| applyFilterIfNeeded(tablePath, filterOpt) match { | ||
| case Some(inputPathStr) => | ||
| // logDebug("Table input: %s".format(tablePath)) | ||
| val ifc = hiveTable.getInputFormatClass | ||
| .asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]] | ||
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| val hadoopRDD = createHadoopRdd (tableDesc, inputPathStr, ifc) | ||
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| val attrsWithIndex = attributes.zipWithIndex | ||
| val mutableRow = new SpecificMutableRow (attributes.map (_.dataType) ) | ||
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| val deserializedHadoopRDD = hadoopRDD.mapPartitions { | ||
| iter => | ||
| val hconf = broadcastedHiveConf.value.value | ||
| val deserializer = deserializerClass.newInstance () | ||
| deserializer.initialize (hconf, tableDesc.getProperties) | ||
| HadoopTableReader.fillObject (iter, deserializer, attrsWithIndex, mutableRow) | ||
| } | ||
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| deserializedHadoopRDD | ||
| case None => new EmptyRDD[Row](sc.sparkContext) | ||
| } | ||
| } | ||
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| override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row] = { | ||
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@@ -142,56 +148,62 @@ class HadoopTableReader( | |
| partitionToDeserializer: Map[HivePartition, | ||
| Class[_ <: Deserializer]], | ||
| filterOpt: Option[PathFilter]): RDD[Row] = { | ||
| val hivePartitionRDDs = partitionToDeserializer.flatMap { case (partition, partDeserializer) => | ||
| val partDesc = Utilities.getPartitionDesc(partition) | ||
| val partPath = HiveShim.getDataLocationPath(partition) | ||
| applyFilterIfNeeded(partPath, filterOpt) match { | ||
| case Some(inputPathStr) => | ||
| val ifc = partDesc.getInputFileFormatClass | ||
| .asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]] | ||
| // Get partition field info | ||
| val partSpec = partDesc.getPartSpec | ||
| val partProps = partDesc.getProperties | ||
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| val partColsDelimited: String = partProps.getProperty(META_TABLE_PARTITION_COLUMNS) | ||
| // Partitioning columns are delimited by "/" | ||
| val partCols = partColsDelimited.trim().split("/").toSeq | ||
| // 'partValues[i]' contains the value for the partitioning column at 'partCols[i]'. | ||
| val partValues = if (partSpec == null) { | ||
| Array.fill(partCols.size)(new String) | ||
| } else { | ||
| partCols.map(col => new String(partSpec.get(col))).toArray | ||
| } | ||
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| // Create local references so that the outer object isn't serialized. | ||
| val tableDesc = relation.tableDesc | ||
| val broadcastedHiveConf = _broadcastedHiveConf | ||
| val localDeserializer = partDeserializer | ||
| val mutableRow = new SpecificMutableRow(attributes.map(_.dataType)) | ||
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| // Splits all attributes into two groups, partition key attributes and those that are not. | ||
| // Attached indices indicate the position of each attribute in the output schema. | ||
| val (partitionKeyAttrs, nonPartitionKeyAttrs) = | ||
| attributes.zipWithIndex.partition { | ||
| case (attr, _) => | ||
| relation.partitionKeys.contains(attr) | ||
| } | ||
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| def fillPartitionKeys(rawPartValues: Array[String], row: MutableRow) = { | ||
| partitionKeyAttrs.foreach { | ||
| case (attr, ordinal) => | ||
| val partOrdinal = relation.partitionKeys.indexOf(attr) | ||
| row(ordinal) = Cast(Literal(rawPartValues(partOrdinal)), attr.dataType).eval(null) | ||
| } | ||
| } | ||
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| // Fill all partition keys to the given MutableRow object | ||
| fillPartitionKeys(partValues, mutableRow) | ||
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| Some(createHadoopRdd(tableDesc, inputPathStr, ifc).mapPartitions { | ||
| iter => | ||
| val hconf = broadcastedHiveConf.value.value | ||
| val deserializer = localDeserializer.newInstance() | ||
| deserializer.initialize(hconf, partProps) | ||
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| // fill the non partition key attributes | ||
| HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs, mutableRow) | ||
| }) | ||
| case None => None | ||
| } | ||
| }.toSeq | ||
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@@ -207,13 +219,21 @@ class HadoopTableReader( | |
| * If `filterOpt` is defined, then it will be used to filter files from `path`. These files are | ||
| * returned in a single, comma-separated string. | ||
| */ | ||
| private def applyFilterIfNeeded(path: Path, filterOpt: Option[PathFilter]): Option[String] = { | ||
| if (fs.exists(path)) { | ||
|
There was a problem hiding this comment. My concern is similar to what @marmbrus mentioned in #3981. It's pretty expensive to check each path in serial for tables with lots of partitions. Especially when the data reside on S3. Can we use |
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| // if the file exists | ||
| filterOpt match { | ||
| case Some(filter) => | ||
| val filteredFiles = fs.listStatus(path, filter).map(_.getPath.toString) | ||
| if (filteredFiles.length > 0) { | ||
| Some(filteredFiles.mkString(",")) | ||
| } else { | ||
| None | ||
| } | ||
| case None => Some(path.toString) | ||
| } | ||
| } else { | ||
| None | ||
| } | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,4 @@ | ||
| 238 val_238 | ||
| 238 val_238 | ||
| 238 val_238 | ||
| 238 val_238 |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,3 @@ | ||
| 238 val_238 | ||
| 238 val_238 | ||
| 238 val_238 |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,81 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| package org.apache.spark.sql.hive | ||
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| import org.scalatest.BeforeAndAfter | ||
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| import org.apache.spark.sql.hive.test.TestHive._ | ||
| import org.apache.spark.util.Utils | ||
| import org.apache.spark.sql.hive.execution.HiveComparisonTest | ||
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| abstract class QueryPartitionSuite extends HiveComparisonTest with BeforeAndAfter { | ||
| protected val tmpDir = Utils.createTempDir() | ||
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| override def beforeAll() { | ||
| sql( | ||
| s"""CREATE TABLE table_with_partition(key int,value string) | ||
| |PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' | ||
| |""".stripMargin) | ||
| sql( | ||
| s"""INSERT OVERWRITE TABLE table_with_partition | ||
| | partition (ds='1') SELECT key,value FROM src LIMIT 1""".stripMargin) | ||
| sql( | ||
| s"""INSERT OVERWRITE TABLE table_with_partition | ||
| | partition (ds='2') SELECT key,value FROM src LIMIT 1""".stripMargin) | ||
| sql( | ||
| s"""INSERT OVERWRITE TABLE table_with_partition | ||
| | partition (ds='3') SELECT key,value FROM src LIMIT 1""".stripMargin) | ||
| sql( | ||
| s"""INSERT OVERWRITE TABLE table_with_partition | ||
| | partition (ds='4') SELECT key,value FROM src LIMIT 1""".stripMargin) | ||
| } | ||
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| override def afterAll() { | ||
| sql("DROP TABLE table_with_partition") | ||
| } | ||
| } | ||
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| class QueryPartitionSuite0 extends QueryPartitionSuite { | ||
| //test for the exist path | ||
| createQueryTest("SPARK-5068 scan partition with existed path", | ||
| "select key,value from table_with_partition", false) | ||
| } | ||
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| class QueryPartitionSuite1 extends QueryPartitionSuite { | ||
| override def beforeAll(): Unit = { | ||
| super.beforeAll() | ||
| //delete the one of the partition manually | ||
| val folders = tmpDir.listFiles.filter(_.isDirectory) | ||
| Utils.deleteRecursively(folders(0)) | ||
| } | ||
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| //test for the after deleting the partition path | ||
| createQueryTest("SPARK-5068 scan partition with non-existed path", | ||
| "select key,value from table_with_partition", false) | ||
| } | ||
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| class QueryPartitionSuite2 extends QueryPartitionSuite { | ||
| override def beforeAll(): Unit = { | ||
| super.beforeAll() | ||
| // delete the path of the table file | ||
| Utils.deleteRecursively(tmpDir) | ||
| } | ||
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| createQueryTest("SPARK-5068 scan table with non-existed path", | ||
| "select key,value from table_with_partition", false) | ||
| } |
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I think we'd better get
fsfrom thepath,because in thehadoop namenode federationwe may get some problems likeWrong FSexception if we use theFileSystem.get(sc.hiveconf)to get fs.