I'm attaching a picture from the streaming UI.
On Sat, Jun 18, 2016 at 7:59 PM, Colin Kincaid Williams <disc...@uw.edu> wrote: > 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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