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From Cody Koeninger <c...@koeninger.org>
Subject Re: Improving performance of a kafka spark streaming app
Date Fri, 24 Jun 2016 14:35:38 GMT
Unless I'm misreading the image you posted, it does show event counts
for the single batch that is still running, with 1.7 billion events in
it.  The recent batches do show 0 events, but I'm guessing that's
because they're actually empty.

When you said you had a kafka topic with 1.7 billion events in it, did
you mean it just statically contains that many events, and no new
events are coming in currently?  If that's the case, you may be better
off just generating RDDs of an appropriate range of offsets, one after
the other, rather than using streaming.

I'm also still not clear if you have tried benchmarking a job that
simply reads from your topic, without inserting into hbase.

On Thu, Jun 23, 2016 at 12:09 AM, Colin Kincaid Williams <discord@uw.edu> wrote:
> Streaming UI tab showing empty events and very different metrics than on 1.5.2
>
> On Thu, Jun 23, 2016 at 5:06 AM, Colin Kincaid Williams <discord@uw.edu> wrote:
>> After a bit of effort I moved from a Spark cluster running 1.5.2, to a
>> Yarn cluster running 1.6.1 jars. I'm still setting the maxRPP. The
>> completed batches are no longer showing the number of events processed
>> in the Streaming UI tab . I'm getting around 4k inserts per second in
>> hbase, but I haven't yet tried to remove or reset the mRPP.  I will
>> attach a screenshot of the UI tab. It shows significantly lower
>> figures for processing and delay times, than the previous posted shot.
>> It also shows the batches as empty, however I see the requests hitting
>> hbase.
>>
>> Then it's possible my issues were related to running on the Spark
>> 1.5.2 cluster. Also is the missing event count in the completed
>> batches a bug? Should I file an issue?
>>
>> On Tue, Jun 21, 2016 at 9:04 PM, Colin Kincaid Williams <discord@uw.edu> wrote:
>>> Thanks @Cody, I will try that out. In the interm, I tried to validate
>>> my Hbase cluster by running a random write test and see 30-40K writes
>>> per second. This suggests there is noticeable room for improvement.
>>>
>>> On Tue, Jun 21, 2016 at 8:32 PM, Cody Koeninger <cody@koeninger.org> wrote:
>>>> Take HBase out of the equation and just measure what your read
>>>> performance is by doing something like
>>>>
>>>> createDirectStream(...).foreach(_.println)
>>>>
>>>> not take() or print()
>>>>
>>>> On Tue, Jun 21, 2016 at 3:19 PM, Colin Kincaid Williams <discord@uw.edu>
wrote:
>>>>> @Cody I was able to bring my processing time down to a second by
>>>>> setting maxRatePerPartition as discussed. My bad that I didn't
>>>>> recognize it as the cause of my scheduling delay.
>>>>>
>>>>> Since then I've tried experimenting with a larger Spark Context
>>>>> duration. I've been trying to get some noticeable improvement
>>>>> inserting messages from Kafka -> Hbase using the above application.
>>>>> I'm currently getting around 3500 inserts / second on a 9 node hbase
>>>>> cluster. So far, I haven't been able to get much more throughput. Then
>>>>> I'm looking for advice here how I should tune Kafka and Spark for this
>>>>> job.
>>>>>
>>>>> I can create a kafka topic with as many partitions that I want. I can
>>>>> set the Duration and maxRatePerPartition. I have 1.7 billion messages
>>>>> that I can insert rather quickly into the Kafka queue, and I'd like to
>>>>> get them into Hbase as quickly as possible.
>>>>>
>>>>> I'm looking for advice regarding # Kafka Topic Partitions / Streaming
>>>>> Duration / maxRatePerPartition / any other spark settings or code
>>>>> changes that I should make to try to get a better consumption rate.
>>>>>
>>>>> Thanks for all the help so far, this is the first Spark application I
>>>>> have written.
>>>>>
>>>>> On Mon, Jun 20, 2016 at 12:32 PM, Colin Kincaid Williams <discord@uw.edu>
wrote:
>>>>>> I'll try dropping the maxRatePerPartition=400, or maybe even lower.
>>>>>> However even at application starts up I have this large scheduling
>>>>>> delay. I will report my progress later on.
>>>>>>
>>>>>> On Mon, Jun 20, 2016 at 2:12 PM, Cody Koeninger <cody@koeninger.org>
wrote:
>>>>>>> If your batch time is 1 second and your average processing time
is
>>>>>>> 1.16 seconds, you're always going to be falling behind.  That
would
>>>>>>> explain why you've built up an hour of scheduling delay after
eight
>>>>>>> hours of running.
>>>>>>>
>>>>>>> On Sat, Jun 18, 2016 at 4:40 PM, Colin Kincaid Williams <discord@uw.edu>
wrote:
>>>>>>>> Hi Mich again,
>>>>>>>>
>>>>>>>> Regarding batch window, etc. I have provided the sources,
but I'm not
>>>>>>>> currently calling the window function. Did you see the program
source?
>>>>>>>> It's only 100 lines.
>>>>>>>>
>>>>>>>> https://gist.github.com/drocsid/b0efa4ff6ff4a7c3c8bb56767d0b6877
>>>>>>>>
>>>>>>>> Then I would expect I'm using defaults, other than what has
been shown
>>>>>>>> in the configuration.
>>>>>>>>
>>>>>>>> For example:
>>>>>>>>
>>>>>>>> In the launcher configuration I set --conf
>>>>>>>> spark.streaming.kafka.maxRatePerPartition=500 \ and I believe
there
>>>>>>>> are 500 messages for the duration set in the application:
>>>>>>>> JavaStreamingContext jssc = new JavaStreamingContext(jsc,
new
>>>>>>>> Duration(1000));
>>>>>>>>
>>>>>>>>
>>>>>>>> Then with the --num-executors 6 \ submit flag, and the
>>>>>>>> spark.streaming.kafka.maxRatePerPartition=500 I think that's
how we
>>>>>>>> arrive at the 3000 events per batch in the UI, pasted above.
>>>>>>>>
>>>>>>>> Feel free to correct me if I'm wrong.
>>>>>>>>
>>>>>>>> Then are you suggesting that I set the window?
>>>>>>>>
>>>>>>>> Maybe following this as reference:
>>>>>>>>
>>>>>>>> https://databricks.gitbooks.io/databricks-spark-reference-applications/content/logs_analyzer/chapter1/windows.html
>>>>>>>>
>>>>>>>> On Sat, Jun 18, 2016 at 8:08 PM, Mich Talebzadeh
>>>>>>>> <mich.talebzadeh@gmail.com> wrote:
>>>>>>>>> Ok
>>>>>>>>>
>>>>>>>>> What is the set up for these please?
>>>>>>>>>
>>>>>>>>> batch window
>>>>>>>>> window length
>>>>>>>>> sliding interval
>>>>>>>>>
>>>>>>>>> And also in each batch window how much data do you get
in (no of messages in
>>>>>>>>> the topic whatever)?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Dr Mich Talebzadeh
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> LinkedIn
>>>>>>>>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> http://talebzadehmich.wordpress.com
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 18 June 2016 at 21:01, Mich Talebzadeh <mich.talebzadeh@gmail.com>
wrote:
>>>>>>>>>>
>>>>>>>>>> I believe you have an issue with performance?
>>>>>>>>>>
>>>>>>>>>> have you checked spark GUI (default 4040) for details
including shuffles
>>>>>>>>>> etc?
>>>>>>>>>>
>>>>>>>>>> HTH
>>>>>>>>>>
>>>>>>>>>> Dr Mich Talebzadeh
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> LinkedIn
>>>>>>>>>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> http://talebzadehmich.wordpress.com
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 18 June 2016 at 20:59, Colin Kincaid Williams
<discord@uw.edu> wrote:
>>>>>>>>>>>
>>>>>>>>>>> There are 25 nodes in the spark cluster.
>>>>>>>>>>>
>>>>>>>>>>> On Sat, Jun 18, 2016 at 7:53 PM, Mich Talebzadeh
>>>>>>>>>>> <mich.talebzadeh@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 <discord@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
>>>>>>>>>>> >> <discord@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 <cody@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
>>>>>>>>>>> >> >> <discord@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
>>>>>>>>>>> >> >>> <cody@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
>>>>>>>>>>> >> >>>> <discord@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
>>>>>>>>>>> >> >>>>> <cody@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
>>>>>>>>>>> >> >>>>>> <discord@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-unsubscribe@spark.apache.org
>>>>>>>>>>> >> >>>>>>> For additional
commands, e-mail: user-help@spark.apache.org
>>>>>>>>>>> >> >>>>>>>
>>>>>>>>>>> >> >>>>
>>>>>>>>>>> >> >>>>
>>>>>>>>>>> >> >>>> ---------------------------------------------------------------------
>>>>>>>>>>> >> >>>> To unsubscribe, e-mail:
user-unsubscribe@spark.apache.org
>>>>>>>>>>> >> >>>> For additional commands,
e-mail: user-help@spark.apache.org
>>>>>>>>>>> >> >>>>
>>>>>>>>>>> >>
>>>>>>>>>>> >> ---------------------------------------------------------------------
>>>>>>>>>>> >> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>>>>>>>>> >> For additional commands, e-mail: user-help@spark.apache.org
>>>>>>>>>>> >>
>>>>>>>>>>> >
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>

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