[GitHub] [hudi] garyli1019 commented on a change in pull request #1722: [HUDI-69] Support Spark Datasource for MOR table

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garyli1019 commented on a change in pull request #1722:
URL: https://github.com/apache/hudi/pull/1722#discussion_r438261014



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File path: hudi-spark/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieRealtimeInputFormat.scala
##########
@@ -0,0 +1,197 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import org.apache.hudi.hadoop.realtime.HoodieRealtimeFileSplit
+import org.apache.hudi.realtime.HoodieRealtimeParquetRecordReader
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapred.{FileSplit, JobConf}
+import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType}
+import org.apache.parquet.filter2.compat.FilterCompat
+import org.apache.parquet.filter2.predicate.FilterApi
+import org.apache.parquet.format.converter.ParquetMetadataConverter.SKIP_ROW_GROUPS
+import org.apache.parquet.hadoop.{ParquetFileReader, ParquetInputFormat}
+import org.apache.spark.TaskContext
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
+import org.apache.spark.sql.catalyst.expressions.{JoinedRow, UnsafeRow}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.execution.datasources.PartitionedFile
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.sources.Filter
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.SerializableConfiguration
+
+import java.net.URI
+import scala.collection.JavaConverters._
+
+/**
+ * This class is an extension of ParquetFileFormat from Spark SQL.
+ * The file split, record reader, record reader iterator are customized to read Hudi MOR table.
+ */
+class HoodieRealtimeInputFormat extends ParquetFileFormat {
+  //TODO: Better usage of this short name.
+  override def shortName(): String = "hudi.realtime"
+  override def toString(): String = "hudi.realtime"
+
+  override def buildReaderWithPartitionValues(sparkSession: SparkSession,
+                                               dataSchema: StructType,
+                                               partitionSchema: StructType,
+                                               requiredSchema: StructType,
+                                               filters: Seq[Filter],
+                                               options: Map[String, String],
+                                               hadoopConf: Configuration): (PartitionedFile) => Iterator[InternalRow] = {
+    hadoopConf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[ParquetReadSupport].getName)
+    hadoopConf.set(
+      ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
+      requiredSchema.json)
+    hadoopConf.set(
+      ParquetWriteSupport.SPARK_ROW_SCHEMA,
+      requiredSchema.json)
+    hadoopConf.set(
+      SQLConf.SESSION_LOCAL_TIMEZONE.key,
+      sparkSession.sessionState.conf.sessionLocalTimeZone)
+    hadoopConf.setBoolean(
+      SQLConf.CASE_SENSITIVE.key,
+      sparkSession.sessionState.conf.caseSensitiveAnalysis)
+
+    ParquetWriteSupport.setSchema(requiredSchema, hadoopConf)
+
+    // Sets flags for `ParquetToSparkSchemaConverter`
+    hadoopConf.setBoolean(
+      SQLConf.PARQUET_BINARY_AS_STRING.key,
+      sparkSession.sessionState.conf.isParquetBinaryAsString)
+    hadoopConf.setBoolean(
+      SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
+      sparkSession.sessionState.conf.isParquetINT96AsTimestamp)
+
+    val broadcastedHadoopConf =
+      sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
+
+    // TODO: if you move this into the closure it reverts to the default values.
+    // If true, enable using the custom RecordReader for parquet. This only works for
+    // a subset of the types (no complex types).
+    val resultSchema = StructType(partitionSchema.fields ++ requiredSchema.fields)
+    val sqlConf = sparkSession.sessionState.conf
+    val enableRecordFilter: Boolean = sqlConf.parquetRecordFilterEnabled
+    val timestampConversion: Boolean = sqlConf.isParquetINT96TimestampConversion
+    val enableParquetFilterPushDown: Boolean = sqlConf.parquetFilterPushDown
+    // Whole stage codegen (PhysicalRDD) is able to deal with batches directly
+    val returningBatch = supportBatch(sparkSession, resultSchema)
+    val pushDownDate = sqlConf.parquetFilterPushDownDate
+    val pushDownTimestamp = sqlConf.parquetFilterPushDownTimestamp
+    val pushDownDecimal = sqlConf.parquetFilterPushDownDecimal
+    val pushDownStringStartWith = sqlConf.parquetFilterPushDownStringStartWith
+    val pushDownInFilterThreshold = sqlConf.parquetFilterPushDownInFilterThreshold
+    val isCaseSensitive = sqlConf.caseSensitiveAnalysis
+
+    (file: PartitionedFile) => {
+      assert(file.partitionValues.numFields == partitionSchema.size)
+
+      val sharedConf = broadcastedHadoopConf.value.value
+      val fileSplit =
+        new FileSplit(new Path(new URI(file.filePath)), file.start, file.length, new Array[String](0))
+      val filePath = fileSplit.getPath
+
+      val basePath = sharedConf.get("mapreduce.input.fileinputformat.inputdir")
+      val maxCommitTime = sharedConf.get("hoodie.realtime.last.commit")
+      // Read the log file path from the option
+      val logPathStr = options.getOrElse(fileSplit.getPath.toString, "").split(",")
+      log.debug(s"fileSplit.getPath in HoodieRealtimeInputFormat: ${fileSplit.getPath} and ${fileSplit.getPath.getName}")
+      log.debug(s"logPath in HoodieRealtimeInputFormat: ${logPathStr.toString}")
+      val hoodieRealtimeFileSplit = new HoodieRealtimeFileSplit(fileSplit, basePath, logPathStr.toList.asJava, maxCommitTime)
+
+      lazy val footerFileMetaData =
+        ParquetFileReader.readFooter(sharedConf, filePath, SKIP_ROW_GROUPS).getFileMetaData
+      // Try to push down filters when filter push-down is enabled.
+      val pushed = if (enableParquetFilterPushDown) {
+        val parquetSchema = footerFileMetaData.getSchema
+        val parquetFilters = new ParquetFilters(pushDownDate, pushDownTimestamp, pushDownDecimal,
+          pushDownStringStartWith, pushDownInFilterThreshold, isCaseSensitive)
+        filters
+          // Collects all converted Parquet filter predicates. Notice that not all predicates can be
+          // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
+          // is used here.
+          .flatMap(parquetFilters.createFilter(parquetSchema, _))
+          .reduceOption(FilterApi.and)
+      } else {
+        None
+      }
+
+      // PARQUET_INT96_TIMESTAMP_CONVERSION says to apply timezone conversions to int96 timestamps'
+      // *only* if the file was created by something other than "parquet-mr", so check the actual
+      // writer here for this file.  We have to do this per-file, as each file in the table may
+      // have different writers.
+      // Define isCreatedByParquetMr as function to avoid unnecessary parquet footer reads.
+      def isCreatedByParquetMr: Boolean =
+        footerFileMetaData.getCreatedBy().startsWith("parquet-mr")
+
+      val convertTz =
+        if (timestampConversion && !isCreatedByParquetMr) {
+          Some(DateTimeUtils.getTimeZone(sharedConf.get(SQLConf.SESSION_LOCAL_TIMEZONE.key)))
+        } else {
+          None
+        }
+
+      val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0)
+      val hadoopAttemptContext =
+        new TaskAttemptContextImpl(broadcastedHadoopConf.value.value, attemptId)
+
+      // Try to push down filters when filter push-down is enabled.
+      // Notice: This push-down is RowGroups level, not individual records.
+      if (pushed.isDefined) {
+        ParquetInputFormat.setFilterPredicate(hadoopAttemptContext.getConfiguration, pushed.get)
+      }
+      val taskContext = Option(TaskContext.get())
+      //TODO: Support the vectorized reader.
+      logDebug(s"Falling back to parquet-mr")
+      // ParquetRecordReader returns UnsafeRow
+      val reader = if (pushed.isDefined && enableRecordFilter) {
+        val parquetFilter = FilterCompat.get(pushed.get, null)
+        new HoodieRealtimeParquetRecordReader[UnsafeRow](new ParquetReadSupport(convertTz), parquetFilter, hoodieRealtimeFileSplit, new JobConf(sharedConf))
+      } else {
+        new HoodieRealtimeParquetRecordReader[UnsafeRow](new ParquetReadSupport(convertTz), hoodieRealtimeFileSplit, new JobConf(sharedConf))
+      }
+      val iter = new HoodieParquetRecordReaderIterator(reader)
+      // SPARK-23457 Register a task completion lister before `initialization`.
+      taskContext.foreach(_.addTaskCompletionListener[Unit](_ => iter.close()))
+      reader.initialize(hoodieRealtimeFileSplit, hadoopAttemptContext)
+      iter.init()
+
+      val fullSchema = requiredSchema.toAttributes ++ partitionSchema.toAttributes
+      val joinedRow = new JoinedRow()
+      val appendPartitionColumns = GenerateUnsafeProjection.generate(fullSchema, fullSchema)
+
+      // This is a horrible erasure hack...  if we type the iterator above, then it actually check

Review comment:
       yes this is from Spark 2.4 https://github.com/apache/spark/blob/branch-2.4/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L445
   This horrible erasure hack was updated in the latest master though...




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