There are 25 nodes in the spark cluster. On Sat, Jun 18, 2016 at 7:53 PM, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > how many nodes are in your cluster? > > --num-executors 6 \ > --driver-memory 4G \ > --executor-memory 2G \ > --total-executor-cores 12 \ > > > Dr Mich Talebzadeh > > > > LinkedIn > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > > > > http://talebzadehmich.wordpress.com > > > > > On 18 June 2016 at 20:40, Colin Kincaid Williams <disc...@uw.edu> wrote: >> >> I updated my app to Spark 1.5.2 streaming so that it consumes from >> Kafka using the direct api and inserts content into an hbase cluster, >> as described in this thread. I was away from this project for awhile >> due to events in my family. >> >> Currently my scheduling delay is high, but the processing time is >> stable around a second. I changed my setup to use 6 kafka partitions >> on a set of smaller kafka brokers, with fewer disks. I've included >> some details below, including the script I use to launch the >> application. I'm using a Spark on Hbase library, whose version is >> relevant to my Hbase cluster. Is it apparent there is something wrong >> with my launch method that could be causing the delay, related to the >> included jars? >> >> Or is there something wrong with the very simple approach I'm taking >> for the application? >> >> Any advice is appriciated. >> >> >> The application: >> >> https://gist.github.com/drocsid/b0efa4ff6ff4a7c3c8bb56767d0b6877 >> >> >> From the streaming UI I get something like: >> >> table Completed Batches (last 1000 out of 27136) >> >> >> Batch Time Input Size Scheduling Delay (?) Processing Time (?) Total >> Delay (?) Output Ops: Succeeded/Total >> >> 2016/06/18 11:21:32 3000 events 1.2 h 1 s 1.2 h 1/1 >> >> 2016/06/18 11:21:31 3000 events 1.2 h 1 s 1.2 h 1/1 >> >> 2016/06/18 11:21:30 3000 events 1.2 h 1 s 1.2 h 1/1 >> >> >> Here's how I'm launching the spark application. >> >> >> #!/usr/bin/env bash >> >> export SPARK_CONF_DIR=/home/colin.williams/spark >> >> export HADOOP_CONF_DIR=/etc/hadoop/conf >> >> export >> HADOOP_CLASSPATH=/home/colin.williams/hbase/conf/:/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/lib/*:/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/lib/hbase-protocol-0.98.6-cdh5.3.0.jar >> >> >> /opt/spark-1.5.2-bin-hadoop2.4/bin/spark-submit \ >> >> --class com.example.KafkaToHbase \ >> >> --master spark://spark_master:7077 \ >> >> --deploy-mode client \ >> >> --num-executors 6 \ >> >> --driver-memory 4G \ >> >> --executor-memory 2G \ >> >> --total-executor-cores 12 \ >> >> --jars >> /home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/zookeeper/zookeeper-3.4.5-cdh5.3.0.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/lib/guava-12.0.1.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/lib/protobuf-java-2.5.0.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/hbase-protocol.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/hbase-client.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/hbase-common.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/hbase-hadoop2-compat.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/hbase-hadoop-compat.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/hbase-server.jar,/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/lib/htrace-core.jar >> \ >> >> --conf spark.app.name="Kafka To Hbase" \ >> >> --conf spark.eventLog.dir="hdfs:///user/spark/applicationHistory" \ >> >> --conf spark.eventLog.enabled=false \ >> >> --conf spark.eventLog.overwrite=true \ >> >> --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \ >> >> --conf spark.streaming.backpressure.enabled=false \ >> >> --conf spark.streaming.kafka.maxRatePerPartition=500 \ >> >> --driver-class-path /home/colin.williams/kafka-hbase.jar \ >> >> --driver-java-options >> >> -Dspark.executor.extraClassPath=/home/colin.williams/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hbase/lib/* >> \ >> >> /home/colin.williams/kafka-hbase.jar "FromTable" "ToTable" >> "broker1:9092,broker2:9092" >> >> On Tue, May 3, 2016 at 8:20 PM, Colin Kincaid Williams <disc...@uw.edu> >> wrote: >> > Thanks Cody, I can see that the partitions are well distributed... >> > Then I'm in the process of using the direct api. >> > >> > On Tue, May 3, 2016 at 6:51 PM, Cody Koeninger <c...@koeninger.org> >> > wrote: >> >> 60 partitions in and of itself shouldn't be a big performance issue >> >> (as long as producers are distributing across partitions evenly). >> >> >> >> On Tue, May 3, 2016 at 1:44 PM, Colin Kincaid Williams <disc...@uw.edu> >> >> wrote: >> >>> Thanks again Cody. Regarding the details 66 kafka partitions on 3 >> >>> kafka servers, likely 8 core systems with 10 disks each. Maybe the >> >>> issue with the receiver was the large number of partitions. I had >> >>> miscounted the disks and so 11*3*2 is how I decided to partition my >> >>> topic on insertion, ( by my own, unjustified reasoning, on a first >> >>> attempt ) . This worked well enough for me, I put 1.7 billion entries >> >>> into Kafka on a map reduce job in 5 and a half hours. >> >>> >> >>> I was concerned using spark 1.5.2 because I'm currently putting my >> >>> data into a CDH 5.3 HDFS cluster, using hbase-spark .98 library jars >> >>> built for spark 1.2 on CDH 5.3. But after debugging quite a bit >> >>> yesterday, I tried building against 1.5.2. So far it's running without >> >>> issue on a Spark 1.5.2 cluster. I'm not sure there was too much >> >>> improvement using the same code, but I'll see how the direct api >> >>> handles it. In the end I can reduce the number of partitions in Kafka >> >>> if it causes big performance issues. >> >>> >> >>> On Tue, May 3, 2016 at 4:08 AM, Cody Koeninger <c...@koeninger.org> >> >>> wrote: >> >>>> print() isn't really the best way to benchmark things, since it calls >> >>>> take(10) under the covers, but 380 records / second for a single >> >>>> receiver doesn't sound right in any case. >> >>>> >> >>>> Am I understanding correctly that you're trying to process a large >> >>>> number of already-existing kafka messages, not keep up with an >> >>>> incoming stream? Can you give any details (e.g. hardware, number of >> >>>> topicpartitions, etc)? >> >>>> >> >>>> Really though, I'd try to start with spark 1.6 and direct streams, or >> >>>> even just kafkacat, as a baseline. >> >>>> >> >>>> >> >>>> >> >>>> On Mon, May 2, 2016 at 7:01 PM, Colin Kincaid Williams >> >>>> <disc...@uw.edu> wrote: >> >>>>> Hello again. I searched for "backport kafka" in the list archives >> >>>>> but >> >>>>> couldn't find anything but a post from Spark 0.7.2 . I was going to >> >>>>> use accumulators to make a counter, but then saw on the Streaming >> >>>>> tab >> >>>>> the Receiver Statistics. Then I removed all other "functionality" >> >>>>> except: >> >>>>> >> >>>>> >> >>>>> JavaPairReceiverInputDStream<byte[], byte[]> dstream = >> >>>>> KafkaUtils >> >>>>> //createStream(JavaStreamingContext jssc,Class<K> >> >>>>> keyTypeClass,Class<V> valueTypeClass, Class<U> keyDecoderClass, >> >>>>> Class<T> valueDecoderClass, java.util.Map<String,String> >> >>>>> kafkaParams, >> >>>>> java.util.Map<String,Integer> topics, StorageLevel storageLevel) >> >>>>> .createStream(jssc, byte[].class, byte[].class, >> >>>>> kafka.serializer.DefaultDecoder.class, >> >>>>> kafka.serializer.DefaultDecoder.class, kafkaParamsMap, topicMap, >> >>>>> StorageLevel.MEMORY_AND_DISK_SER()); >> >>>>> >> >>>>> dstream.print(); >> >>>>> >> >>>>> Then in the Recieiver Stats for the single receiver, I'm seeing >> >>>>> around >> >>>>> 380 records / second. Then to get anywhere near my 10% mentioned >> >>>>> above, I'd need to run around 21 receivers, assuming 380 records / >> >>>>> second, just using the print output. This seems awfully high to me, >> >>>>> considering that I wrote 80000+ records a second to Kafka from a >> >>>>> mapreduce job, and that my bottleneck was likely Hbase. Again using >> >>>>> the 380 estimate, I would need 200+ receivers to reach a similar >> >>>>> amount of reads. >> >>>>> >> >>>>> Even given the issues with the 1.2 receivers, is this the expected >> >>>>> way >> >>>>> to use the Kafka streaming API, or am I doing something terribly >> >>>>> wrong? >> >>>>> >> >>>>> My application looks like >> >>>>> https://gist.github.com/drocsid/b0efa4ff6ff4a7c3c8bb56767d0b6877 >> >>>>> >> >>>>> On Mon, May 2, 2016 at 6:09 PM, Cody Koeninger <c...@koeninger.org> >> >>>>> wrote: >> >>>>>> Have you tested for read throughput (without writing to hbase, just >> >>>>>> deserialize)? >> >>>>>> >> >>>>>> Are you limited to using spark 1.2, or is upgrading possible? The >> >>>>>> kafka direct stream is available starting with 1.3. If you're >> >>>>>> stuck >> >>>>>> on 1.2, I believe there have been some attempts to backport it, >> >>>>>> search >> >>>>>> the mailing list archives. >> >>>>>> >> >>>>>> On Mon, May 2, 2016 at 12:54 PM, Colin Kincaid Williams >> >>>>>> <disc...@uw.edu> wrote: >> >>>>>>> I've written an application to get content from a kafka topic with >> >>>>>>> 1.7 >> >>>>>>> billion entries, get the protobuf serialized entries, and insert >> >>>>>>> into >> >>>>>>> hbase. Currently the environment that I'm running in is Spark 1.2. >> >>>>>>> >> >>>>>>> With 8 executors and 2 cores, and 2 jobs, I'm only getting between >> >>>>>>> 0-2500 writes / second. This will take much too long to consume >> >>>>>>> the >> >>>>>>> entries. >> >>>>>>> >> >>>>>>> I currently believe that the spark kafka receiver is the >> >>>>>>> bottleneck. >> >>>>>>> I've tried both 1.2 receivers, with the WAL and without, and >> >>>>>>> didn't >> >>>>>>> notice any large performance difference. I've tried many different >> >>>>>>> spark configuration options, but can't seem to get better >> >>>>>>> performance. >> >>>>>>> >> >>>>>>> I saw 80000 requests / second inserting these records into kafka >> >>>>>>> using >> >>>>>>> yarn / hbase / protobuf / kafka in a bulk fashion. >> >>>>>>> >> >>>>>>> While hbase inserts might not deliver the same throughput, I'd >> >>>>>>> like to >> >>>>>>> at least get 10%. >> >>>>>>> >> >>>>>>> My application looks like >> >>>>>>> https://gist.github.com/drocsid/b0efa4ff6ff4a7c3c8bb56767d0b6877 >> >>>>>>> >> >>>>>>> This is my first spark application. I'd appreciate any assistance. >> >>>>>>> >> >>>>>>> >> >>>>>>> --------------------------------------------------------------------- >> >>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> >>>>>>> For additional commands, e-mail: user-h...@spark.apache.org >> >>>>>>> >> >>>> >> >>>> --------------------------------------------------------------------- >> >>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> >>>> For additional commands, e-mail: user-h...@spark.apache.org >> >>>> >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >
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