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From Eduardo Costa Alfaia <e.costaalf...@unibs.it>
Subject Re: Spark's behavior
Date Fri, 02 May 2014 21:14:17 GMT
Hi TD,

I got the another information today using Spark 1.0 RC3 and the situation remain the same:


The lines begin after 17 sec:

14/05/02 21:52:25 INFO SparkDeploySchedulerBackend: Granted executor ID app-20140502215225-0005/0
on hostPort computer8.ant-net:57229 with 2 cores, 2.0 GB RAM
14/05/02 21:52:25 INFO AppClient$ClientActor: Executor updated: app-20140502215225-0005/0
is now RUNNING
14/05/02 21:52:25 INFO ReceiverTracker: ReceiverTracker started
14/05/02 21:52:26 INFO ForEachDStream: metadataCleanupDelay = -1
14/05/02 21:52:26 INFO SocketInputDStream: metadataCleanupDelay = -1
14/05/02 21:52:26 INFO SocketInputDStream: Slide time = 1000 ms
14/05/02 21:52:26 INFO SocketInputDStream: Storage level = StorageLevel(false, false, false,
false, 1)
14/05/02 21:52:26 INFO SocketInputDStream: Checkpoint interval = null
14/05/02 21:52:26 INFO SocketInputDStream: Remember duration = 1000 ms
14/05/02 21:52:26 INFO SocketInputDStream: Initialized and validated org.apache.spark.streaming.dstream.SocketInputDStream@5433868e
14/05/02 21:52:26 INFO ForEachDStream: Slide time = 1000 ms
14/05/02 21:52:26 INFO ForEachDStream: Storage level = StorageLevel(false, false, false, false,
1)
14/05/02 21:52:26 INFO ForEachDStream: Checkpoint interval = null
14/05/02 21:52:26 INFO ForEachDStream: Remember duration = 1000 ms
14/05/02 21:52:26 INFO ForEachDStream: Initialized and validated org.apache.spark.streaming.dstream.ForEachDStream@1ebdbc05
14/05/02 21:52:26 INFO SparkContext: Starting job: collect at ReceiverTracker.scala:270
14/05/02 21:52:26 INFO RecurringTimer: Started timer for JobGenerator at time 1399060346000
14/05/02 21:52:26 INFO JobGenerator: Started JobGenerator at 1399060346000 ms
14/05/02 21:52:26 INFO JobScheduler: Started JobScheduler
14/05/02 21:52:26 INFO DAGScheduler: Registering RDD 3 (reduceByKey at ReceiverTracker.scala:270)
14/05/02 21:52:26 INFO ReceiverTracker: Stream 0 received 0 blocks
14/05/02 21:52:26 INFO DAGScheduler: Got job 0 (collect at ReceiverTracker.scala:270) with
20 output partitions (allowLocal=false)
14/05/02 21:52:26 INFO DAGScheduler: Final stage: Stage 0(collect at ReceiverTracker.scala:270)
14/05/02 21:52:26 INFO DAGScheduler: Parents of final stage: List(Stage 1)
14/05/02 21:52:26 INFO JobScheduler: Added jobs for time 1399060346000 ms
14/05/02 21:52:26 INFO JobScheduler: Starting job streaming job 1399060346000 ms.0 from job
set of time 1399060346000 ms
14/05/02 21:52:26 INFO JobGenerator: Checkpointing graph for time 1399060346000 ms
-------------------------------------------14/05/02 21:52:26 INFO DStreamGraph: Updating checkpoint
data for time 1399060346000 ms

Time: 1399060346000 ms
-------------------------------------------

14/05/02 21:52:26 INFO JobScheduler: Finished job streaming job 1399060346000 ms.0 from job
set of time 1399060346000 ms
14/05/02 21:52:26 INFO JobScheduler: Total delay: 0.325 s for time 1399060346000 ms (execution:
0.024 s)



14/05/02 21:52:42 INFO JobScheduler: Added jobs for time 1399060362000 ms
14/05/02 21:52:42 INFO JobGenerator: Checkpointing graph for time 1399060362000 ms
14/05/02 21:52:42 INFO DStreamGraph: Updating checkpoint data for time 1399060362000 ms
14/05/02 21:52:42 INFO DStreamGraph: Updated checkpoint data for time 1399060362000 ms
14/05/02 21:52:42 INFO JobScheduler: Starting job streaming job 1399060362000 ms.0 from job
set of time 1399060362000 ms
14/05/02 21:52:42 INFO SparkContext: Starting job: take at DStream.scala:593
14/05/02 21:52:42 INFO DAGScheduler: Got job 2 (take at DStream.scala:593) with 1 output partitions
(allowLocal=true)
14/05/02 21:52:42 INFO DAGScheduler: Final stage: Stage 3(take at DStream.scala:593)
14/05/02 21:52:42 INFO DAGScheduler: Parents of final stage: List()
14/05/02 21:52:42 INFO DAGScheduler: Missing parents: List()
14/05/02 21:52:42 INFO DAGScheduler: Computing the requested partition locally
14/05/02 21:52:42 INFO BlockManager: Found block input-0-1399060360400 locally
14/05/02 21:52:42 INFO CheckpointWriter: Checkpoint for time 1399060361000 ms saved to file
'hdfs://computer8:54310/user/root/INPUT/checkpoint-1399060361000', took 2457 
bytes and 507 ms
14/05/02 21:52:42 INFO CheckpointWriter: Saving checkpoint for time 1399060362000 ms to file
'hdfs://computer8:54310/user/root/INPUT/checkpoint-1399060362000'
14/05/02 21:52:42 INFO DStreamGraph: Clearing checkpoint data for time 1399060361000 ms
14/05/02 21:52:42 INFO DStreamGraph: Cleared checkpoint data for time 1399060361000 ms
14/05/02 21:52:42 INFO BlockManagerInfo: Added input-0-1399060360800 in memory on computer8.ant-net:50052
(size: 238.8 KB, free: 1177.0 MB)
14/05/02 21:52:42 INFO SparkContext: Job finished: take at DStream.scala:593, took 0.107033025
s
-------------------------------------------
Time: 1399060362000 ms
-------------------------------------------
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14/05/02 21:52:42 INFO JobScheduler: Finished job streaming job 1399060362000 ms.0 from job
set of time 1399060362000 ms



On Apr 30, 2014, at 0:56, Tathagata Das <tathagata.das1565@gmail.com> wrote:

> Strange! Can you just do lines.print() to print the raw data instead of doing word count.
Beyond that we can do two things. 
> 
> 1. Can see the Spark stage UI to see whether there are stages running during the 30 second
period you referred to?
> 2. If you upgrade to using Spark master branch (or Spark 1.0 RC3, see different thread
by Patrick), it has a streaming UI, which shows the number of records received, the state
of the receiver, etc. That may be more useful in debugging whats going on .
> 
> TD 
> 
> 
> On Tue, Apr 29, 2014 at 3:31 PM, Eduardo Costa Alfaia <e.costaalfaia@unibs.it>
wrote:
> Hi TD,
> We are not using stream context with master local, we have 1 Master and 8 Workers and
1 word source. The command line that we are using is:
> bin/run-example org.apache.spark.streaming.examples.JavaNetworkWordCount spark://192.168.0.13:7077
>      
> On Apr 30, 2014, at 0:09, Tathagata Das <tathagata.das1565@gmail.com> wrote:
> 
>> Is you batch size 30 seconds by any chance? 
>> 
>> Assuming not, please check whether you are creating the streaming context with master
"local[n]" where n > 2. With "local" or "local[1]", the system only has one processing
slot, which is occupied by the receiver leaving no room for processing the received data.
It could be that after 30 seconds, the server disconnects, the receiver terminates, releasing
the single slot for the processing to proceed. 
>> 
>> TD
>> 
>> 
>> On Tue, Apr 29, 2014 at 2:28 PM, Eduardo Costa Alfaia <e.costaalfaia@unibs.it>
wrote:
>> Hi TD,
>> 
>> In my tests with spark streaming, I'm using JavaNetworkWordCount(modified) code and
a program that I wrote that sends words to the Spark worker, I use TCP as transport. I verified
that after starting Spark, it connects to my source which actually starts sending, but the
first word count is advertised approximately 30 seconds after the context creation. So I'm
wondering where is stored the 30 seconds data already sent by the source. Is this a normal
spark’s behaviour? I saw the same behaviour using the shipped JavaNetworkWordCount application.
>> 
>> Many thanks.
>> --
>> Informativa sulla Privacy: http://www.unibs.it/node/8155
>> 
> 
> 
> Informativa sulla Privacy: http://www.unibs.it/node/8155
> 


-- 
Informativa sulla Privacy: http://www.unibs.it/node/8155

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