Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/9143#discussion_r44236326
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/util/BatchedWriteAheadLog.scala
---
@@ -0,0 +1,221 @@
+/*
+ * 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.streaming.util
+
+import java.nio.ByteBuffer
+import java.util.concurrent.LinkedBlockingQueue
+import java.util.{Iterator => JIterator}
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.{Await, Promise}
+import scala.concurrent.duration._
+import scala.util.control.NonFatal
+
+import org.apache.spark.{SparkConf, SparkException, Logging}
+import org.apache.spark.util.Utils
+
+/**
+ * A wrapper for a WriteAheadLog that batches records before writing data.
Handles aggregation
+ * during writes, and de-aggregation in the `readAll` method. The end
consumer has to handle
+ * de-aggregation after the `read` method. In addition, the
`WriteAheadLogRecordHandle` returned
+ * after the write will contain the batch of records rather than
individual records.
+ *
+ * When writing a batch of records, the `time` passed to the `wrappedLog`
will be the timestamp
+ * of the latest record in the batch. This is very important in achieving
correctness. Consider the
+ * following example:
+ * We receive records with timestamps 1, 3, 5, 7. We use "log-1" as the
filename. Once we receive
+ * a clean up request for timestamp 3, we would clean up the file "log-1",
and lose data regarding
+ * 5 and 7.
+ *
+ * In addition, notice that the write method is still a blocking call.
This will ensure that a
+ * receiver will not be able to submit multiple `AddBlock` calls,
jeopardizing the ordering of data.
--- End diff --
nit: Make this not refer to the receiver. You can say the following.
This means the caller can assume the same write semantics as any other
WriteAheadLog implementation despite the batching in the background - when the
`write()` returns, the data is written to the WAL and is durable. To take
advantage of the batching, the caller can write from multiple threads, each of
which will stay blocked until the corresponding data has been written.
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