Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/8125#discussion_r38167285
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/sources/CombineSmallFile.scala ---
@@ -0,0 +1,43 @@
+/*
+ * 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.sources
+
+import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SQLContext
+
+object CombineSmallFile {
+ def combineWithFiles[T](rdd: RDD[T], sqlContext: SQLContext, inputFiles:
Array[FileStatus])
+ : RDD[T] = {
+ if (sqlContext.conf.combineSmallFile) {
+ val totalLen = inputFiles.map { file =>
+ if (file.isDir) 0L else file.getLen
+ }.sum
+ val numPartitions = (totalLen / sqlContext.conf.splitSize + 1).toInt
+ rdd.coalesce(numPartitions)
--- End diff --
I think this is a very hack way to solve this problem. As we can not tell
how the the data source to be split, even for Hadoop, the split size just a
hint, use that for computing the partition number probably too risky for a
generic data process framework.
And the `RDD.coalesce` actually will combine the splits in a arbitrary way,
it's probably causes the data skew, as we most likely combine the large
partitions into a a single task.
IMO, I'd like to deep investigate how Hive to combine the small partitions,
by using the `CombineHiveInputFormat` or `HiveInputFormat`, which seems has a
strategy to select the partitions according to both input format, and also keep
the balance.
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