Hi Mich,

 

Yes, i managed to resolve this one. The issue was because the way described in the docs doesn't work properly as in order for the Flume part to be notified you need to set the storageLevel on the PollingStream like

 

JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createPollingStream(ssc, addresses, StorageLevel.MEMORY_AND_DISK_SER_2(), 100, 10);

 

 

After setting this, the data is correclty maked as processed by the SPARK reveiver and the Flume sink is notified.

 

-Ian

 

 

> Hi Ian,

>

> Has this been resolved?

>

> How about data to Flume and then Kafka and Kafka streaming into Spark?

>

> Thanks

>

> Dr Mich Talebzadeh

>

>

>

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> On 13 July 2016 at 11:13, Ian Brooks <i.brooks@sensewhere.com> wrote:

> > Hi,

> >

> >

> >

> > I'm currently trying to implement a prototype Spark application that gets

> > data from Flume and processes it. I'm using the pull based method

> > mentioned

> > in https://spark.apache.org/docs/1.6.1/streaming-flume-integration.html

> >

> >

> >

> > The is initially working fine for getting data from Flume, however the

> > Spark client doesn't appear to be letting Flume know that the data has

> > been

> > received, so Flume doesn't remove it from the batch.

> >

> >

> >

> > After 100 requests Flume stops allowing any new data and logs

> >

> >

> >

> > 08 Jul 2016 14:59:00,265 WARN [Spark Sink Processor Thread - 5]

> > (org.apache.spark.streaming.flume.sink.Logging$class.logWarning:80) -

> > Error while processing transaction.

> > org.apache.flume.ChannelException: Take list for MemoryTransaction,

> > capacity 100 full, consider committing more frequently, increasing

> > capacity, or increasing thread count

> >

> > at org.apache.flume.channel.MemoryChannel$MemoryTransaction.doTake(

> >

> > MemoryChannel.java:96)

> >

> >

> >

> > My code to pull the data from Flume is

> >

> >

> >

> > SparkConf sparkConf = new SparkConf(true).setAppName("SLAMSpark");

> >

> > Duration batchInterval = new Duration(10000);

> >

> > final String checkpointDir = "/tmp/";

> >

> >

> >

> > final JavaStreamingContext ssc = new JavaStreamingContext(sparkConf,

> > batchInterval);

> >

> > ssc.checkpoint(checkpointDir);

> >

> > JavaReceiverInputDStream<SparkFlumeEvent> flumeStream =

> > FlumeUtils.createPollingStream(ssc, host, port);

> >

> >

> >

> > // Transform each flume avro event to a process-able format

> >

> > JavaDStream<String> transformedEvents = flumeStream.map(new

> > Function<SparkFlumeEvent, String>() {

> >

> >

> >

> > @Override

> >

> > public String call(SparkFlumeEvent flumeEvent) throws Exception {

> >

> > String flumeEventStr = flumeEvent.event().toString();

> >

> > avroData avroData = new avroData();

> >

> > Gson gson = new GsonBuilder().create();

> >

> > avroData = gson.fromJson(flumeEventStr, avroData.class);

> >

> > HashMap<String,String> body = avroData.getBody();

> >

> > String data = body.get("bytes");

> >

> > return data;

> >

> > }

> >

> > });

> >

> >

> >

> > ...

> >

> >

> >

> > ssc.start();

> >

> > ssc.awaitTermination();

> >

> > ssc.close();

> >

> > }

> >

> >

> >

> > Is there something specific I should be doing to let the Flume server know

> > the batch has been received and processed?

> >

> >

> > --

> >

> > Ian Brooks

 


--

Ian Brooks

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