Re: Spark Streaming Write Ahead Log (WAL) not replaying data after restart

[Patrick McGloin]

Thank you Shixiong, that is what I was missing.

On 26 January 2016 at 00:27, Shixiong(Ryan) Zhu 
wrote:

> You need to define a create function and use StreamingContext.getOrCreate.
> See the example here:
> http://spark.apache.org/docs/latest/streaming-programming-guide.html#how-to-configure-checkpointing
>
> On Thu, Jan 21, 2016 at 3:32 AM, Patrick McGloin <
> mcgloin.patr...@gmail.com> wrote:
>
>> Hi all,
>>
>> To have a simple way of testing the Spark Streaming Write Ahead Log I
>> created a very simple Custom Input Receiver, which will generate strings
>> and store those:
>>
>> class InMemoryStringReceiver extends 
>> Receiver[String](StorageLevel.MEMORY_AND_DISK_SER) {
>>
>>   val batchID = System.currentTimeMillis()
>>
>>   def onStart() {
>> new Thread("InMemoryStringReceiver") {
>>   override def run(): Unit = {
>> var i = 0
>> while(true) {
>>   
>> //http://spark.apache.org/docs/latest/streaming-custom-receivers.html
>>   //To implement a reliable receiver, you have to use 
>> store(multiple-records) to store data.
>>   store(ArrayBuffer(s"$batchID-$i"))
>>   println(s"Stored => [$batchID-$i)]")
>>   Thread.sleep(1000L)
>>   i = i + 1
>> }
>>   }
>> }.start()
>>   }
>>
>>   def onStop() {}
>> }
>>
>> I then created a simple Application which will use the Custom Receiver to
>> stream the data and process it:
>>
>> object DStreamResilienceTest extends App {
>>
>>   val conf = new 
>> SparkConf().setMaster("local[*]").setAppName("DStreamResilienceTest").set("spark.streaming.receiver.writeAheadLog.enable",
>>  "true")
>>   val ssc = new StreamingContext(conf, Seconds(1))
>>   
>> ssc.checkpoint("hdfs://myhdfsserver/user/spark/checkpoint/DStreamResilienceTest")
>>   val customReceiverStream: ReceiverInputDStream[String] = 
>> ssc.receiverStream(new InMemoryStringReceiver())
>>   customReceiverStream.foreachRDD { (rdd: RDD[String]) =>
>> println(s"processed => [${rdd.collect().toList}]")
>> Thread.sleep(2000L)
>>   }
>>   ssc.start()
>>   ssc.awaitTermination()
>>
>> }
>>
>> As you can see the processing of each received RDD has sleep of 2 seconds
>> while the Strings are stored every second. This creates a backlog and the
>> new strings pile up, and should be stored in the WAL. Indeed, I can see the
>> files in the checkpoint dirs getting updated. Running the app I get output
>> like this:
>>
>> [info] Stored => [1453374654941-0)]
>> [info] processed => [List(1453374654941-0)]
>> [info] Stored => [1453374654941-1)]
>> [info] Stored => [1453374654941-2)]
>> [info] processed => [List(1453374654941-1)]
>> [info] Stored => [1453374654941-3)]
>> [info] Stored => [1453374654941-4)]
>> [info] processed => [List(1453374654941-2)]
>> [info] Stored => [1453374654941-5)]
>> [info] Stored => [1453374654941-6)]
>> [info] processed => [List(1453374654941-3)]
>> [info] Stored => [1453374654941-7)]
>> [info] Stored => [1453374654941-8)]
>> [info] processed => [List(1453374654941-4)]
>> [info] Stored => [1453374654941-9)]
>> [info] Stored => [1453374654941-10)]
>>
>> As you would expect, the storing is out pacing the processing. So I kill
>> the application and restart it. This time I commented out the sleep in the
>> foreachRDD so that the processing can clear any backlog:
>>
>> [info] Stored => [1453374753946-0)]
>> [info] processed => [List(1453374753946-0)]
>> [info] Stored => [1453374753946-1)]
>> [info] processed => [List(1453374753946-1)]
>> [info] Stored => [1453374753946-2)]
>> [info] processed => [List(1453374753946-2)]
>> [info] Stored => [1453374753946-3)]
>> [info] processed => [List(1453374753946-3)]
>> [info] Stored => [1453374753946-4)]
>> [info] processed => [List(1453374753946-4)]
>>
>> As you can see the new events are processed but none from the previous
>> batch. The old WAL logs are cleared and I see log messages like this but
>> the old data does not get processed.
>>
>> INFO WriteAheadLogManager : Recovered 1 write ahead log files from 
>> hdfs://myhdfsserver/user/spark/checkpoint/DStreamResilienceTest/receivedData/0
>>
>> What am I doing wrong? I am using Spark 1.5.2.
>>
>> Best regards,
>>
>> Patrick
>>
>
>

Re: Spark Streaming Write Ahead Log (WAL) not replaying data after restart

[Shixiong(Ryan) Zhu]

You need to define a create function and use StreamingContext.getOrCreate.
See the example here:
http://spark.apache.org/docs/latest/streaming-programming-guide.html#how-to-configure-checkpointing

On Thu, Jan 21, 2016 at 3:32 AM, Patrick McGloin 
wrote:

> Hi all,
>
> To have a simple way of testing the Spark Streaming Write Ahead Log I
> created a very simple Custom Input Receiver, which will generate strings
> and store those:
>
> class InMemoryStringReceiver extends 
> Receiver[String](StorageLevel.MEMORY_AND_DISK_SER) {
>
>   val batchID = System.currentTimeMillis()
>
>   def onStart() {
> new Thread("InMemoryStringReceiver") {
>   override def run(): Unit = {
> var i = 0
> while(true) {
>   
> //http://spark.apache.org/docs/latest/streaming-custom-receivers.html
>   //To implement a reliable receiver, you have to use 
> store(multiple-records) to store data.
>   store(ArrayBuffer(s"$batchID-$i"))
>   println(s"Stored => [$batchID-$i)]")
>   Thread.sleep(1000L)
>   i = i + 1
> }
>   }
> }.start()
>   }
>
>   def onStop() {}
> }
>
> I then created a simple Application which will use the Custom Receiver to
> stream the data and process it:
>
> object DStreamResilienceTest extends App {
>
>   val conf = new 
> SparkConf().setMaster("local[*]").setAppName("DStreamResilienceTest").set("spark.streaming.receiver.writeAheadLog.enable",
>  "true")
>   val ssc = new StreamingContext(conf, Seconds(1))
>   
> ssc.checkpoint("hdfs://myhdfsserver/user/spark/checkpoint/DStreamResilienceTest")
>   val customReceiverStream: ReceiverInputDStream[String] = 
> ssc.receiverStream(new InMemoryStringReceiver())
>   customReceiverStream.foreachRDD { (rdd: RDD[String]) =>
> println(s"processed => [${rdd.collect().toList}]")
> Thread.sleep(2000L)
>   }
>   ssc.start()
>   ssc.awaitTermination()
>
> }
>
> As you can see the processing of each received RDD has sleep of 2 seconds
> while the Strings are stored every second. This creates a backlog and the
> new strings pile up, and should be stored in the WAL. Indeed, I can see the
> files in the checkpoint dirs getting updated. Running the app I get output
> like this:
>
> [info] Stored => [1453374654941-0)]
> [info] processed => [List(1453374654941-0)]
> [info] Stored => [1453374654941-1)]
> [info] Stored => [1453374654941-2)]
> [info] processed => [List(1453374654941-1)]
> [info] Stored => [1453374654941-3)]
> [info] Stored => [1453374654941-4)]
> [info] processed => [List(1453374654941-2)]
> [info] Stored => [1453374654941-5)]
> [info] Stored => [1453374654941-6)]
> [info] processed => [List(1453374654941-3)]
> [info] Stored => [1453374654941-7)]
> [info] Stored => [1453374654941-8)]
> [info] processed => [List(1453374654941-4)]
> [info] Stored => [1453374654941-9)]
> [info] Stored => [1453374654941-10)]
>
> As you would expect, the storing is out pacing the processing. So I kill
> the application and restart it. This time I commented out the sleep in the
> foreachRDD so that the processing can clear any backlog:
>
> [info] Stored => [1453374753946-0)]
> [info] processed => [List(1453374753946-0)]
> [info] Stored => [1453374753946-1)]
> [info] processed => [List(1453374753946-1)]
> [info] Stored => [1453374753946-2)]
> [info] processed => [List(1453374753946-2)]
> [info] Stored => [1453374753946-3)]
> [info] processed => [List(1453374753946-3)]
> [info] Stored => [1453374753946-4)]
> [info] processed => [List(1453374753946-4)]
>
> As you can see the new events are processed but none from the previous
> batch. The old WAL logs are cleared and I see log messages like this but
> the old data does not get processed.
>
> INFO WriteAheadLogManager : Recovered 1 write ahead log files from 
> hdfs://myhdfsserver/user/spark/checkpoint/DStreamResilienceTest/receivedData/0
>
> What am I doing wrong? I am using Spark 1.5.2.
>
> Best regards,
>
> Patrick
>