garyli1019 commented on a change in pull request #1702:
URL: https://github.com/apache/hudi/pull/1702#discussion_r449934912
##########
File path: hudi-spark/src/main/scala/org/apache/hudi/DefaultSource.scala
##########
@@ -54,29 +58,54 @@ class DefaultSource extends RelationProvider
val parameters = Map(QUERY_TYPE_OPT_KEY -> DEFAULT_QUERY_TYPE_OPT_VAL) ++
translateViewTypesToQueryTypes(optParams)
val path = parameters.get("path")
- if (path.isEmpty) {
- throw new HoodieException("'path' must be specified.")
- }
if (parameters(QUERY_TYPE_OPT_KEY).equals(QUERY_TYPE_SNAPSHOT_OPT_VAL)) {
- // this is just effectively RO view only, where `path` can contain a mix
of
- // non-hoodie/hoodie path files. set the path filter up
- sqlContext.sparkContext.hadoopConfiguration.setClass(
- "mapreduce.input.pathFilter.class",
- classOf[HoodieROTablePathFilter],
- classOf[org.apache.hadoop.fs.PathFilter])
-
- log.info("Constructing hoodie (as parquet) data source with options :" +
parameters)
- log.warn("Snapshot view not supported yet via data source, for
MERGE_ON_READ tables. " +
- "Please query the Hive table registered using Spark SQL.")
- // simply return as a regular parquet relation
- DataSource.apply(
- sparkSession = sqlContext.sparkSession,
- userSpecifiedSchema = Option(schema),
- className = "parquet",
- options = parameters)
- .resolveRelation()
+ val readPathsStr =
parameters.get(DataSourceReadOptions.READ_PATHS_OPT_KEY)
Review comment:
Are these additional paths on top of the `path`? Any example of the use
cases?
##########
File path: hudi-spark/src/main/scala/org/apache/hudi/HudiBootstrapRDD.scala
##########
@@ -0,0 +1,131 @@
+/*
+ * 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.hudi
+
+import org.apache.spark.{Partition, TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.datasources.PartitionedFile
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.vectorized.ColumnarBatch
+
+class HudiBootstrapRDD(@transient spark: SparkSession,
+ dataReadFunction: PartitionedFile => Iterator[Any],
+ skeletonReadFunction: PartitionedFile => Iterator[Any],
+ regularReadFunction: PartitionedFile => Iterator[Any],
+ dataSchema: StructType,
+ skeletonSchema: StructType,
+ requiredColumns: Array[String],
+ tableState: HudiBootstrapTableState)
+ extends RDD[InternalRow](spark.sparkContext, Nil) {
+
+ override def compute(split: Partition, context: TaskContext):
Iterator[InternalRow] = {
+ val bootstrapPartition = split.asInstanceOf[HudiBootstrapPartition]
+
+ if (log.isDebugEnabled) {
+ if (bootstrapPartition.split.skeletonFile.isDefined) {
+ logDebug("Got Split => Index: " + bootstrapPartition.index + ", Data
File: "
+ + bootstrapPartition.split.dataFile.filePath + ", Skeleton File: "
+ + bootstrapPartition.split.skeletonFile.get.filePath)
+ } else {
+ logDebug("Got Split => Index: " + bootstrapPartition.index + ", Data
File: "
+ + bootstrapPartition.split.dataFile.filePath)
+ }
+ }
+
+ var partitionedFileIterator: Iterator[InternalRow] = null
+
+ if (bootstrapPartition.split.skeletonFile.isDefined) {
+ // It is a bootstrap split. Check both skeleton and data files.
+ if (dataSchema.isEmpty) {
+ // No data column to fetch, hence fetch only from skeleton file
+ partitionedFileIterator =
read(bootstrapPartition.split.skeletonFile.get, skeletonReadFunction)
+ } else if (skeletonSchema.isEmpty) {
+ // No metadata column to fetch, hence fetch only from data file
+ partitionedFileIterator = read(bootstrapPartition.split.dataFile,
dataReadFunction)
+ } else {
+ // Fetch from both data and skeleton file, and merge
+ val dataFileIterator = read(bootstrapPartition.split.dataFile,
dataReadFunction)
+ val skeletonFileIterator =
read(bootstrapPartition.split.skeletonFile.get, skeletonReadFunction)
+ partitionedFileIterator = merge(skeletonFileIterator, dataFileIterator)
+ }
+ } else {
+ partitionedFileIterator = read(bootstrapPartition.split.dataFile,
regularReadFunction)
+ }
+ partitionedFileIterator
+ }
+
+ def merge(skeletonFileIterator: Iterator[InternalRow], dataFileIterator:
Iterator[InternalRow])
+ : Iterator[InternalRow] = {
+ new Iterator[InternalRow] {
+ override def hasNext: Boolean = dataFileIterator.hasNext &&
skeletonFileIterator.hasNext
+ override def next(): InternalRow = {
+ mergeInternalRow(skeletonFileIterator.next(), dataFileIterator.next())
+ }
+ }
+ }
+
+ def mergeInternalRow(skeletonRow: InternalRow, dataRow: InternalRow):
InternalRow = {
+ val skeletonArr = skeletonRow.copy().toSeq(skeletonSchema)
+ val dataArr = dataRow.copy().toSeq(dataSchema)
+ // We need to return it in the order requested
+ val mergedArr = requiredColumns.map(col => {
+ if (skeletonSchema.fieldNames.contains(col)) {
+ val idx = skeletonSchema.fieldIndex(col)
+ skeletonArr(idx)
+ } else {
+ val idx = dataSchema.fieldIndex(col)
+ dataArr(idx)
+ }
+ })
+
+ logDebug("Merged data and skeleton values => " + mergedArr.mkString(","))
+ val mergedRow = InternalRow.fromSeq(mergedArr)
+ mergedRow
+ }
+
+ def read(partitionedFile: PartitionedFile, readFileFunction: PartitionedFile
=> Iterator[Any])
+ : Iterator[InternalRow] = {
+ val fileIterator = readFileFunction(partitionedFile)
+
+ import scala.collection.JavaConverters._
+
+ val rows = fileIterator.flatMap(_ match {
+ case r: InternalRow => Seq(r)
+ case b: ColumnarBatch => b.rowIterator().asScala
Review comment:
We probably have to use `rowIterator` since we will need to merge on row
level anyway, same for MOR table too. Agree that Spark will convert
`ColumnBatch` to `row` at some point and it is very difficult to locate.
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