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From cfregly <>
Subject [GitHub] spark pull request: [SPARK-1981] Add AWS Kinesis streaming support
Date Tue, 29 Jul 2014 22:04:10 GMT
Github user cfregly commented on a diff in the pull request:
    --- Diff: extras/spark-kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
    @@ -0,0 +1,122 @@
    + * 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
    + *
    + *
    + *
    + * 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.kinesis
    +import java.util.UUID
    +import org.apache.spark.Logging
    +import org.apache.spark.streaming.receiver.Receiver
    +import com.amazonaws.auth.DefaultAWSCredentialsProviderChain
    +import java.nio.ByteBuffer
    +import org.apache.spark.streaming.util.SystemClock
    + * Custom AWS Kinesis-specific implementation of Spark Streaming's Receiver.
    + * This implementation relies on the Kinesis Client Library (KCL) Worker as described
    + *
    + * This is a custom receiver used with StreamingContext.receiverStream(Receiver) as described
    + *
    + * Instances of this class will get shipped to the Spark Streaming Workers to run within
a Spark Executor.
    + *
    + * @param app name
    + * @param Kinesis stream name
    + * @param endpoint url of Kinesis service
    + * @param checkpoint interval (millis) for Kinesis checkpointing (not Spark checkpointing).
    + *   See the Kinesis Spark Streaming documentation for more details on the different
types of checkpoints.
    + * @param in the absence of Kinesis checkpoint info, this is the worker's initial starting
position in the stream.
    + *   The values are either the beginning of the stream per Kinesis' limit of 24 hours
    + *      or the tip of the stream using InitialPositionInStream.LATEST.
    + * @param persistence strategy for RDDs and DStreams.
    + */
    +private[streaming] class KinesisReceiver(
    +  app: String,
    +  stream: String,
    +  endpoint: String,
    +  checkpointIntervalMillis: Long,
    +  initialPositionInStream: InitialPositionInStream,
    +  storageLevel: StorageLevel)
    +  extends Receiver[Array[Byte]](storageLevel) with Logging { receiver =>
    +  /**
    +   *  The lazy val's below will get instantiated in the remote Executor after the closure
is shipped to the Spark Worker. 
    +   *  These are all lazy because they're from third-party Amazon libraries and are not
    +   *  If they're not marked lazy, they will cause NotSerializableExceptions when they're
shipped to the Spark Worker.
    +   */
    +  /**
    +   *  workerId is lazy because we want the address of the actual Worker where the code
runs - not the Driver's ip address.
    +   *  This makes a difference when running in a cluster.
    +   */
    +  lazy val workerId = InetAddress.getLocalHost.getHostAddress() + ":" + UUID.randomUUID()
    --- End diff --
    there can be multiple workers per host, so i can't just use the host address.  but to
answer your question, i guess i don't really need the host address since i'm generating a
random UUID.  
    however, i found it useful when reviewing logs for debugging purposes.  i'll keep for
now unless you have a strong objection.
    good catch.

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