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[SPARK-11412][SQL] Support merge schema for ORC
## What changes were proposed in this pull request?
Currently, ORC's `inferSchema` is implemented as randomly choosing one ORC file and reading its schema.
This PR follows the behavior of Parquet, it implements merge schemas logic by reading all ORC files in parallel through a spark job.
Users can enable merge schema by `spark.read.orc("xxx").option("mergeSchema", "true")` or by setting `spark.sql.orc.mergeSchema` to `true`, the prior one has higher priority.
## How was this patch tested?
tested by UT OrcUtilsSuite.scala
Closes #24043 from WangGuangxin/SPARK-11412.
Lead-authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Co-authored-by: wangguangxin.cn <wangguangxin.cn@bytedance.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>- Loading branch information
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SchemaMergeUtils.scala
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| /* | ||
| * 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.execution.datasources | ||
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| import org.apache.hadoop.conf.Configuration | ||
| import org.apache.hadoop.fs.{FileStatus, Path} | ||
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| import org.apache.spark.SparkException | ||
| import org.apache.spark.internal.Logging | ||
| import org.apache.spark.sql.SparkSession | ||
| import org.apache.spark.sql.types.StructType | ||
| import org.apache.spark.util.SerializableConfiguration | ||
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| object SchemaMergeUtils extends Logging { | ||
| /** | ||
| * Figures out a merged Parquet/ORC schema with a distributed Spark job. | ||
| */ | ||
| def mergeSchemasInParallel( | ||
| sparkSession: SparkSession, | ||
| files: Seq[FileStatus], | ||
| schemaReader: (Seq[FileStatus], Configuration, Boolean) => Seq[StructType]) | ||
| : Option[StructType] = { | ||
| val serializedConf = new SerializableConfiguration(sparkSession.sessionState.newHadoopConf()) | ||
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| // !! HACK ALERT !! | ||
| // Here is a hack for Parquet, but it can be used by Orc as well. | ||
| // | ||
| // Parquet requires `FileStatus`es to read footers. | ||
| // Here we try to send cached `FileStatus`es to executor side to avoid fetching them again. | ||
| // However, `FileStatus` is not `Serializable` | ||
| // but only `Writable`. What makes it worse, for some reason, `FileStatus` doesn't play well | ||
| // with `SerializableWritable[T]` and always causes a weird `IllegalStateException`. These | ||
| // facts virtually prevents us to serialize `FileStatus`es. | ||
| // | ||
| // Since Parquet only relies on path and length information of those `FileStatus`es to read | ||
| // footers, here we just extract them (which can be easily serialized), send them to executor | ||
| // side, and resemble fake `FileStatus`es there. | ||
| val partialFileStatusInfo = files.map(f => (f.getPath.toString, f.getLen)) | ||
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| // Set the number of partitions to prevent following schema reads from generating many tasks | ||
| // in case of a small number of orc files. | ||
| val numParallelism = Math.min(Math.max(partialFileStatusInfo.size, 1), | ||
| sparkSession.sparkContext.defaultParallelism) | ||
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| val ignoreCorruptFiles = sparkSession.sessionState.conf.ignoreCorruptFiles | ||
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| // Issues a Spark job to read Parquet/ORC schema in parallel. | ||
| val partiallyMergedSchemas = | ||
| sparkSession | ||
| .sparkContext | ||
| .parallelize(partialFileStatusInfo, numParallelism) | ||
| .mapPartitions { iterator => | ||
| // Resembles fake `FileStatus`es with serialized path and length information. | ||
| val fakeFileStatuses = iterator.map { case (path, length) => | ||
| new FileStatus(length, false, 0, 0, 0, 0, null, null, null, new Path(path)) | ||
| }.toSeq | ||
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| val schemas = schemaReader(fakeFileStatuses, serializedConf.value, ignoreCorruptFiles) | ||
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| if (schemas.isEmpty) { | ||
| Iterator.empty | ||
| } else { | ||
| var mergedSchema = schemas.head | ||
| schemas.tail.foreach { schema => | ||
| try { | ||
| mergedSchema = mergedSchema.merge(schema) | ||
| } catch { case cause: SparkException => | ||
| throw new SparkException( | ||
| s"Failed merging schema:\n${schema.treeString}", cause) | ||
| } | ||
| } | ||
| Iterator.single(mergedSchema) | ||
| } | ||
| }.collect() | ||
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| if (partiallyMergedSchemas.isEmpty) { | ||
| None | ||
| } else { | ||
| var finalSchema = partiallyMergedSchemas.head | ||
| partiallyMergedSchemas.tail.foreach { schema => | ||
| try { | ||
| finalSchema = finalSchema.merge(schema) | ||
| } catch { case cause: SparkException => | ||
| throw new SparkException( | ||
| s"Failed merging schema:\n${schema.treeString}", cause) | ||
| } | ||
| } | ||
| Some(finalSchema) | ||
| } | ||
| } | ||
| } |
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