Hi While I am following this discussion with interest, I am trying to comprehend any architectural benefit of a spark sink.
Is there any feature in flume makes it more suitable to ingest stream data than sppark streaming, so that we should chain them? For example does it help durability or reliability of the source? Or, it is a more tactical choice based on connector availability or such? To me, flume is important component to ingest streams to hdfs or hive directly ie it plays on the batch side of lambda architecture pattern. On 20 Nov 2016 22:30, "Mich Talebzadeh" <mich.talebza...@gmail.com> wrote: > Hi Ian, > > Has this been resolved? > > How about data to Flume and then Kafka and Kafka streaming into Spark? > > Thanks > > Dr Mich Talebzadeh > > > > LinkedIn * > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* > > > > http://talebzadehmich.wordpress.com > > > *Disclaimer:* Use it at your own risk. Any and all responsibility for any > loss, damage or destruction of data or any other property which may arise > from relying on this email's technical content is explicitly disclaimed. > The author will in no case be liable for any monetary damages arising from > such loss, damage or destruction. > > > > On 13 July 2016 at 11:13, Ian Brooks <i.bro...@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 >> >> >> > >
