Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/7279#discussion_r36039556
--- Diff:
core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala ---
@@ -0,0 +1,83 @@
+/*
+ * 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.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.{Logging, SparkEnv, SparkException, TaskContext}
+import org.apache.spark.storage.{RDDBlockId, StorageLevel}
+
+/**
+ * An implementation of checkpointing implemented on top of Spark's
caching layer.
+ *
+ * Local checkpointing trades off fault tolerance for performance by
skipping the expensive
+ * step of saving the RDD data to a reliable and fault-tolerant storage.
Instead, the data
+ * is written to the local, ephemeral block storage that lives in each
executor. This is useful
+ * for use cases where RDDs build up long lineages that need to be
truncated often (e.g. GraphX).
+ */
+private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient rdd:
RDD[T])
+ extends RDDCheckpointData[T](rdd) with Logging {
+
+ /**
+ * Ensure the RDD is fully cached so the partitions can be recovered
later.
+ */
+ protected override def doCheckpoint(): CheckpointRDD[T] = {
+ val cm = SparkEnv.get.cacheManager
+ val bmm = SparkEnv.get.blockManager.master
+ val level = rdd.getStorageLevel
+
+ // Assume storage level uses disk; otherwise memory eviction may cause
data loss
+ assume(level.useDisk, s"Storage level $level is not appropriate for
local checkpointing")
+
+ // Not all RDD actions compute all partitions of the RDD (e.g. take)
+ // We must compute and cache any missing partitions for correctness
reasons
+ rdd.partitions.foreach { p =>
--- End diff --
I think you're right. We can probably replace this whole block with just an
`rdd.count()` actually, since we assume that it's already marked for caching.
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