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From Jean-Daniel Cryans <jdcry...@apache.org>
Subject Re: Performance Question
Date Wed, 27 Jul 2016 18:12:06 GMT
Hey Ben,

I fixed a few hangs in the Java client over the past few weeks, so you
might be hitting that. To confirm if it's the case, set a timeout that's
way higher, like minutes. If it still times out, might be the hang in which
case there are some workarounds.

Otherwise, it might be that your cluster is getting slammed? Have you
checked the usuals like high iowait, swapping, etc? Also take a look at the
WARNING log from the tservers and see if they complain about long Write
RPCs.

FWIW I've been testing non-stop inserts on a 6 nodes cluster (of which one
is just a master) here and I have 318B (318,852,472,816) rows inserted,
43TB on disk post-replication and compression, so I'm not too worried about
800M rows unless they're hundreds of KB each :P

J-D

On Tue, Jul 26, 2016 at 5:15 PM, Benjamin Kim <bbuild11@gmail.com> wrote:

> I have reached over 800M rows (813,997,990), and now it’s starting to
> timeout when UPSERTing data.
>
> 16/07/27 00:04:58 WARN scheduler.TaskSetManager: Lost task 0.0 in stage
> 17.0 (TID 87, prod-dc1-datanode163.pdc1i.gradientx.com):
> com.stumbleupon.async.TimeoutException: Timed out after 30000ms when
> joining Deferred@1592877776(state=PENDING, result=null,
> callback=org.kududb.client.AsyncKuduSession$ConvertBatchToListOfResponsesCB@154c94f8
> -> wakeup thread Executor task launch worker-2, errback=passthrough ->
> wakeup thread Executor task launch worker-2)
> at com.stumbleupon.async.Deferred.doJoin(Deferred.java:1177)
> at com.stumbleupon.async.Deferred.join(Deferred.java:1045)
> at org.kududb.client.KuduSession.close(KuduSession.java:110)
> at org.kududb.spark.kudu.KuduContext.writeRows(KuduContext.scala:181)
> at
> org.kududb.spark.kudu.KuduContext$$anonfun$writeRows$1.apply(KuduContext.scala:131)
> at
> org.kududb.spark.kudu.KuduContext$$anonfun$writeRows$1.apply(KuduContext.scala:130)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1869)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1869)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
>
>
> Thanks,
> Ben
>
>
> On Jul 18, 2016, at 10:32 AM, Todd Lipcon <todd@cloudera.com> wrote:
>
> On Mon, Jul 18, 2016 at 10:31 AM, Benjamin Kim <bbuild11@gmail.com> wrote:
>
>> Todd,
>>
>> Thanks for the info. I was going to upgrade after the testing, but now,
>> it looks like I will have to do it earlier than expected.
>>
>> I will do the upgrade, then resume.
>>
>
> OK, sounds good. The upgrade shouldn't invalidate any performance testing
> or anything -- just fixes this important bug.
>
> -Todd
>
>
>> On Jul 18, 2016, at 10:29 AM, Todd Lipcon <todd@cloudera.com> wrote:
>>
>> Hi Ben,
>>
>> Any chance that you are running Kudu 0.9.0 instead of 0.9.1? There's a
>> known serious bug in 0.9.0 which can cause this kind of corruption.
>>
>> Assuming that you are running with replication count 3 this time, you
>> should be able to move aside that tablet metadata file and start the
>> server. It will recreate a new repaired replica automatically.
>>
>> -Todd
>>
>> On Mon, Jul 18, 2016 at 10:28 AM, Benjamin Kim <bbuild11@gmail.com>
>> wrote:
>>
>>> During my re-population of the Kudu table, I am getting this error
>>> trying to restart a tablet server after it went down. The job that
>>> populates this table has been running for over a week.
>>>
>>> [libprotobuf ERROR google/protobuf/message_lite.cc:123] Can't parse
>>> message of type "kudu.tablet.TabletSuperBlockPB" because it is missing
>>> required fields: rowsets[2324].columns[15].block
>>> F0718 17:01:26.783571   468 tablet_server_main.cc:55] Check failed:
>>> _s.ok() Bad status: IO error: Could not init Tablet Manager: Failed to open
>>> tablet metadata for tablet: 24637ee6f3e5440181ce3f20b1b298ba: Failed to
>>> load tablet metadata for tablet id 24637ee6f3e5440181ce3f20b1b298ba: Could
>>> not load tablet metadata from
>>> /mnt/data1/kudu/data/tablet-meta/24637ee6f3e5440181ce3f20b1b298ba: Unable
>>> to parse PB from path:
>>> /mnt/data1/kudu/data/tablet-meta/24637ee6f3e5440181ce3f20b1b298ba
>>> *** Check failure stack trace: ***
>>>     @           0x7d794d  google::LogMessage::Fail()
>>>     @           0x7d984d  google::LogMessage::SendToLog()
>>>     @           0x7d7489  google::LogMessage::Flush()
>>>     @           0x7da2ef  google::LogMessageFatal::~LogMessageFatal()
>>>     @           0x78172b  (unknown)
>>>     @       0x344d41ed5d  (unknown)
>>>     @           0x7811d1  (unknown)
>>>
>>> Does anyone know what this means?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Jul 11, 2016, at 10:47 AM, Todd Lipcon <todd@cloudera.com> wrote:
>>>
>>> On Mon, Jul 11, 2016 at 10:40 AM, Benjamin Kim <bbuild11@gmail.com>
>>> wrote:
>>>
>>>> Todd,
>>>>
>>>> I had it at one replica. Do I have to recreate?
>>>>
>>>
>>> We don't currently have the ability to "accept data loss" on a tablet
>>> (or set of tablets). If the machine is gone for good, then currently the
>>> only easy way to recover is to recreate the table. If this sounds really
>>> painful, though, maybe we can work up some kind of tool you could use to
>>> just recreate the missing tablets (with those rows lost).
>>>
>>> -Todd
>>>
>>>>
>>>> On Jul 11, 2016, at 10:37 AM, Todd Lipcon <todd@cloudera.com> wrote:
>>>>
>>>> Hey Ben,
>>>>
>>>> Is the table that you're querying replicated? Or was it created with
>>>> only one replica per tablet?
>>>>
>>>> -Todd
>>>>
>>>> On Mon, Jul 11, 2016 at 10:35 AM, Benjamin Kim <bkim@amobee.com> wrote:
>>>>
>>>>> Over the weekend, a tablet server went down. It’s not coming back up.
>>>>> So, I decommissioned it and removed it from the cluster. Then, I restarted
>>>>> Kudu because I was getting a timeout  exception trying to do counts on
the
>>>>> table. Now, when I try again. I get the same error.
>>>>>
>>>>> 16/07/11 17:32:36 WARN scheduler.TaskSetManager: Lost task 468.3 in
>>>>> stage 0.0 (TID 603, prod-dc1-datanode167.pdc1i.gradientx.com):
>>>>> com.stumbleupon.async.TimeoutException: Timed out after 30000ms when
>>>>> joining Deferred@712342716(state=PAUSED, result=Deferred@1765902299,
>>>>> callback=passthrough -> scanner opened -> wakeup thread Executor
task
>>>>> launch worker-2, errback=openScanner errback -> passthrough ->
wakeup
>>>>> thread Executor task launch worker-2)
>>>>> at com.stumbleupon.async.Deferred.doJoin(Deferred.java:1177)
>>>>> at com.stumbleupon.async.Deferred.join(Deferred.java:1045)
>>>>> at org.kududb.client.KuduScanner.nextRows(KuduScanner.java:57)
>>>>> at
>>>>> org.kududb.spark.kudu.RowResultIteratorScala.hasNext(KuduRDD.scala:99)
>>>>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>> at
>>>>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:88)
>>>>> at
>>>>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
>>>>> at
>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>>>>> at
>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>>>>> at
>>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>>>>> at
>>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>> at
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>> at java.lang.Thread.run(Thread.java:745)
>>>>>
>>>>> Does anyone know how to recover from this?
>>>>>
>>>>> Thanks,
>>>>> *Benjamin Kim*
>>>>> *Data Solutions Architect*
>>>>>
>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>
>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>> 3250 Ocean Park Blvd, Suite 200  |  Santa Monica, CA 90405  |
>>>>> www.amobee.com
>>>>>
>>>>> On Jul 6, 2016, at 9:46 AM, Dan Burkert <dan@cloudera.com> wrote:
>>>>>
>>>>>
>>>>>
>>>>> On Wed, Jul 6, 2016 at 7:05 AM, Benjamin Kim <bbuild11@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Over the weekend, the row count is up to <500M. I will give it
>>>>>> another few days to get to 1B rows. I still get consistent times
~15s for
>>>>>> doing row counts despite the amount of data growing.
>>>>>>
>>>>>> On another note, I got a solicitation email from SnappyData to
>>>>>> evaluate their product. They claim to be the “Spark Data Store”
with tight
>>>>>> integration with Spark executors. It claims to be an OLTP and OLAP
system
>>>>>> with being an in-memory data store first then to disk. After going
to
>>>>>> several Spark events, it would seem that this is the new “hot”
area for
>>>>>> vendors. They all (MemSQL, Redis, Aerospike, Datastax, etc.) claim
to be
>>>>>> the best "Spark Data Store”. I’m wondering if Kudu will become
this too?
>>>>>> With the performance I’ve seen so far, it would seem that it can
be a
>>>>>> contender. All that is needed is a hardened Spark connector package,
I
>>>>>> would think. The next evaluation I will be conducting is to see if
>>>>>> SnappyData’s claims are valid by doing my own tests.
>>>>>>
>>>>>
>>>>> It's hard to compare Kudu against any other data store without a lot
>>>>> of analysis and thorough benchmarking, but it is certainly a goal of
Kudu
>>>>> to be a great platform for ingesting and analyzing data through Spark.
 Up
>>>>> till this point most of the Spark work has been community driven, but
more
>>>>> thorough integration testing of the Spark connector is going to be a
focus
>>>>> going forward.
>>>>>
>>>>> - Dan
>>>>>
>>>>>
>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Jun 15, 2016, at 12:47 AM, Todd Lipcon <todd@cloudera.com>
wrote:
>>>>>>
>>>>>> Hi Benjamin,
>>>>>>
>>>>>> What workload are you using for benchmarks? Using spark or something
>>>>>> more custom? rdd or data frame or SQL, etc? Maybe you can share the
schema
>>>>>> and some queries
>>>>>>
>>>>>> Todd
>>>>>>
>>>>>> Todd
>>>>>> On Jun 15, 2016 8:10 AM, "Benjamin Kim" <bbuild11@gmail.com>
wrote:
>>>>>>
>>>>>>> Hi Todd,
>>>>>>>
>>>>>>> Now that Kudu 0.9.0 is out. I have done some tests. Already,
I am
>>>>>>> impressed. Compared to HBase, read and write performance are
better. Write
>>>>>>> performance has the greatest improvement (> 4x), while read
is > 1.5x.
>>>>>>> Albeit, these are only preliminary tests. Do you know of a way
to really do
>>>>>>> some conclusive tests? I want to see if I can match your results
on my 50
>>>>>>> node cluster.
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>> On May 30, 2016, at 10:33 AM, Todd Lipcon <todd@cloudera.com>
wrote:
>>>>>>>
>>>>>>> On Sat, May 28, 2016 at 7:12 AM, Benjamin Kim <bbuild11@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Todd,
>>>>>>>>
>>>>>>>> It sounds like Kudu can possibly top or match those numbers
put out
>>>>>>>> by Aerospike. Do you have any performance statistics published
or any
>>>>>>>> instructions as to measure them myself as good way to test?
In addition,
>>>>>>>> this will be a test using Spark, so should I wait for Kudu
version 0.9.0
>>>>>>>> where support will be built in?
>>>>>>>>
>>>>>>>
>>>>>>> We don't have a lot of benchmarks published yet, especially on
the
>>>>>>> write side. I've found that thorough cross-system benchmarks
are very
>>>>>>> difficult to do fairly and accurately, and often times users
end up
>>>>>>> misguided if they pay too much attention to them :) So, given
a finite
>>>>>>> number of developers working on Kudu, I think we've tended to
spend more
>>>>>>> time on the project itself and less time focusing on "competition".
I'm
>>>>>>> sure there are use cases where Kudu will beat out Aerospike,
and probably
>>>>>>> use cases where Aerospike will beat Kudu as well.
>>>>>>>
>>>>>>> From my perspective, it would be great if you can share some
details
>>>>>>> of your workload, especially if there are some areas you're finding
Kudu
>>>>>>> lacking. Maybe we can spot some easy code changes we could make
to improve
>>>>>>> performance, or suggest a tuning variable you could change.
>>>>>>>
>>>>>>> -Todd
>>>>>>>
>>>>>>>
>>>>>>>> On May 27, 2016, at 9:19 PM, Todd Lipcon <todd@cloudera.com>
wrote:
>>>>>>>>
>>>>>>>> On Fri, May 27, 2016 at 8:20 PM, Benjamin Kim <bbuild11@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Mike,
>>>>>>>>>
>>>>>>>>> First of all, thanks for the link. It looks like an interesting
>>>>>>>>> read. I checked that Aerospike is currently at version
3.8.2.3, and in the
>>>>>>>>> article, they are evaluating version 3.5.4. The main
thing that impressed
>>>>>>>>> me was their claim that they can beat Cassandra and HBase
by 8x for writing
>>>>>>>>> and 25x for reading. Their big claim to fame is that
Aerospike can write 1M
>>>>>>>>> records per second with only 50 nodes. I wanted to see
if this is real.
>>>>>>>>>
>>>>>>>>
>>>>>>>> 1M records per second on 50 nodes is pretty doable by Kudu
as well,
>>>>>>>> depending on the size of your records and the insertion order.
I've been
>>>>>>>> playing with a ~70 node cluster recently and seen 1M+ writes/second
>>>>>>>> sustained, and bursting above 4M. These are 1KB rows with
11 columns, and
>>>>>>>> with pretty old HDD-only nodes. I think newer flash-based
nodes could do
>>>>>>>> better.
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> To answer your questions, we have a DMP with user profiles
with
>>>>>>>>> many attributes. We create segmentation information off
of these attributes
>>>>>>>>> to classify them. Then, we can target advertising appropriately
for our
>>>>>>>>> sales department. Much of the data processing is for
applying models on all
>>>>>>>>> or if not most of every profile’s attributes to find
similarities (nearest
>>>>>>>>> neighbor/clustering) over a large number of rows when
batch processing or a
>>>>>>>>> small subset of rows for quick online scoring. So, our
use case is a
>>>>>>>>> typical advanced analytics scenario. We have tried HBase,
but it doesn’t
>>>>>>>>> work well for these types of analytics.
>>>>>>>>>
>>>>>>>>> I read, that Aerospike in the release notes, they did
do many
>>>>>>>>> improvements for batch and scan operations.
>>>>>>>>>
>>>>>>>>> I wonder what your thoughts are for using Kudu for this.
>>>>>>>>>
>>>>>>>>
>>>>>>>> Sounds like a good Kudu use case to me. I've heard great
things
>>>>>>>> about Aerospike for the low latency random access portion,
but I've also
>>>>>>>> heard that it's _very_ expensive, and not particularly suited
to the
>>>>>>>> columnar scan workload. Lastly, I think the Apache license
of Kudu is much
>>>>>>>> more appealing than the AGPL3 used by Aerospike. But, that's
not really a
>>>>>>>> direct answer to the performance question :)
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On May 27, 2016, at 6:21 PM, Mike Percy <mpercy@cloudera.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> Have you considered whether you have a scan heavy or
a random
>>>>>>>>> access heavy workload? Have you considered whether you
always access /
>>>>>>>>> update a whole row vs only a partial row? Kudu is a column
store so has
>>>>>>>>> some awesome performance characteristics when you are
doing a lot of
>>>>>>>>> scanning of just a couple of columns.
>>>>>>>>>
>>>>>>>>> I don't know the answer to your question but if your
concern is
>>>>>>>>> performance then I would be interested in seeing comparisons
from a perf
>>>>>>>>> perspective on certain workloads.
>>>>>>>>>
>>>>>>>>> Finally, a year ago Aerospike did quite poorly in a Jepsen
test:
>>>>>>>>> https://aphyr.com/posts/324-jepsen-aerospike
>>>>>>>>>
>>>>>>>>> I wonder if they have addressed any of those issues.
>>>>>>>>>
>>>>>>>>> Mike
>>>>>>>>>
>>>>>>>>> On Friday, May 27, 2016, Benjamin Kim <bbuild11@gmail.com>
wrote:
>>>>>>>>>
>>>>>>>>>> I am just curious. How will Kudu compare with Aerospike
(
>>>>>>>>>> http://www.aerospike.com)? I went to a Spark Roadshow
and found
>>>>>>>>>> out about this piece of software. It appears to fit
our use case perfectly
>>>>>>>>>> since we are an ad-tech company trying to leverage
our user profiles data.
>>>>>>>>>> Plus, it already has a Spark connector and has a
SQL-like client. The
>>>>>>>>>> tables can be accessed using Spark SQL DataFrames
and, also, made into SQL
>>>>>>>>>> tables for direct use with Spark SQL ODBC/JDBC Thriftserver.
I see from the
>>>>>>>>>> work done here http://gerrit.cloudera.org:8080/#/c/2992/
that
>>>>>>>>>> the Spark integration is well underway and, from
the looks of it lately,
>>>>>>>>>> almost complete. I would prefer to use Kudu since
we are already a Cloudera
>>>>>>>>>> shop, and Kudu is easy to deploy and configure using
Cloudera Manager. I
>>>>>>>>>> also hope that some of Aerospike’s speed optimization
techniques can make
>>>>>>>>>> it into Kudu in the future, if they have not been
already thought of or
>>>>>>>>>> included.
>>>>>>>>>>
>>>>>>>>>> Just some thoughts…
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> --
>>>>>>>>> Mike Percy
>>>>>>>>> Software Engineer, Cloudera
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Todd Lipcon
>>>>>>>> Software Engineer, Cloudera
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Todd Lipcon
>>>>>>> Software Engineer, Cloudera
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Todd Lipcon
>>>> Software Engineer, Cloudera
>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Todd Lipcon
>>> Software Engineer, Cloudera
>>>
>>>
>>>
>>
>>
>> --
>> Todd Lipcon
>> Software Engineer, Cloudera
>>
>>
>>
>
>
> --
> Todd Lipcon
> Software Engineer, Cloudera
>
>
>

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