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From Todd Lipcon <t...@cloudera.com>
Subject Re: Performance Question
Date Mon, 18 Jul 2016 17:32:49 GMT
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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