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From Jean Carlo <jean.jeancar...@gmail.com>
Subject Re: Slow performance after upgrading from 2.0.9 to 2.1.11
Date Fri, 29 Jan 2016 21:51:09 GMT
I am having the same issue after upgrade cassandra 2.1.12 from 2.0.10. I am
not good on jvm so I would like to know how to do what @CorryOpdenakker
propose with cassandra.

:)

I check concurrent_compactors


Saludos

Jean Carlo

"The best way to predict the future is to invent it" Alan Kay

On Fri, Jan 29, 2016 at 9:24 PM, Corry Opdenakker <corry@bestdata.be> wrote:

> Hi guys,
> Cassandra is still new for me, but I have a lot of java tuning experience.
>
> For root cause detection of performance degradations its always good to
> start with collecting a series of java thread dumps. Take at problem
> occurrence using a loopscript for example 60 thread dumps with an interval
> of 1 or 2 seconds.
> Then load those dumps into IBM thread dump analyzer or in "eclipse mat" or
> any similar tool and see which methods appear to be most active or blocking
> others.
>
> Its really very useful
>
> Same can be be done in a normal situation to compare the difference.
>
> That should give more insights.
>
> Cheers, Corry
>
>
> Op vrijdag 29 januari 2016 heeft Peddi, Praveen <peddi@amazon.com> het
> volgende geschreven:
>
>> Hello,
>> We have another update on performance on 2.1.11. compression_chunk_size
>>  didn’t really help much but We changed concurrent_compactors from default
>> to 64 in 2.1.11 and read latencies improved significantly. However, 2.1.11
>> read latencies are still 1.5 slower than 2.0.9. One thing we noticed in JMX
>> metric that could affect read latencies is that 2.1.11 is running
>> ReadRepairedBackground and ReadRepairedBlocking too frequently compared to
>> 2.0.9 even though our read_repair_chance is same on both. Could anyone
>> shed some light on why 2.1.11 could be running read repair 10 to 50 times
>> more in spite of same configuration on both clusters?
>>
>> dclocal_read_repair_chance=0.100000 AND
>> read_repair_chance=0.000000 AND
>>
>> Here is the table for read repair metrics for both clusters.
>> 2.0.9 2.1.11
>> ReadRepairedBackground 5MinAvg 0.006 0.1
>> 15MinAvg 0.009 0.153
>> ReadRepairedBlocking 5MinAvg 0.002 0.55
>> 15MinAvg 0.007 0.91
>>
>> Thanks
>> Praveen
>>
>> From: Jeff Jirsa <jeff.jirsa@crowdstrike.com>
>> Reply-To: <user@cassandra.apache.org>
>> Date: Thursday, January 14, 2016 at 2:58 PM
>> To: "user@cassandra.apache.org" <user@cassandra.apache.org>
>> Subject: Re: Slow performance after upgrading from 2.0.9 to 2.1.11
>>
>> Sorry I wasn’t as explicit as I should have been
>>
>> The same buffer size is used by compressed reads as well, but tuned with
>> compression_chunk_size table property. It’s likely true that if you lower
>> compression_chunk_size, you’ll see improved read performance.
>>
>> This was covered in the AWS re:Invent youtube link I sent in my original
>> reply.
>>
>>
>>
>> From: "Peddi, Praveen"
>> Reply-To: "user@cassandra.apache.org"
>> Date: Thursday, January 14, 2016 at 11:36 AM
>> To: "user@cassandra.apache.org", Zhiyan Shao
>> Cc: "Agrawal, Pratik"
>> Subject: Re: Slow performance after upgrading from 2.0.9 to 2.1.11
>>
>> Hi,
>> We will try with reduced “rar_buffer_size” to 4KB. However
>> CASSANDRA-10249 <https://issues.apache.org/jira/browse/CASSANDRA-10249> says
>> "this only affects users who have 1. disabled compression, 2. switched to
>> buffered i/o from mmap’d”. None of this is true for us I believe. We use
>> default disk_access_mode which should be mmap. We also used
>> LZ4Compressor when created table.
>>
>> We will let you know if this property had any effect. We were testing
>> with 2.1.11 and this was only fixed in 2.1.12 so we need to play with
>> latest version.
>>
>> Praveen
>>
>>
>>
>> From: Jeff Jirsa <jeff.jirsa@crowdstrike.com>
>> Reply-To: <user@cassandra.apache.org>
>> Date: Thursday, January 14, 2016 at 1:29 PM
>> To: Zhiyan Shao <zhiyan.shao@gmail.com>, "user@cassandra.apache.org" <
>> user@cassandra.apache.org>
>> Cc: "Agrawal, Pratik" <paagrawa@amazon.com>
>> Subject: Re: Slow performance after upgrading from 2.0.9 to 2.1.11
>>
>> This may be due to https://issues.apache.org/jira/browse/CASSANDRA-10249
>>  / https://issues.apache.org/jira/browse/CASSANDRA-8894 - whether or not
>> this is really the case depends on how much of your data is in page cache,
>> and whether or not you’re using mmap. Since the original question was asked
>> by someone using small RAM instances, it’s possible.
>>
>> We mitigate this by dropping compression_chunk_size in order to force a
>> smaller buffer on reads, so we don’t over read very small blocks. This has
>> other side effects (lower compression ratio, more garbage during
>> streaming), but significantly speeds up read workloads for us.
>>
>>
>> From: Zhiyan Shao
>> Date: Thursday, January 14, 2016 at 9:49 AM
>> To: "user@cassandra.apache.org"
>> Cc: Jeff Jirsa, "Agrawal, Pratik"
>> Subject: Re: Slow performance after upgrading from 2.0.9 to 2.1.11
>>
>> Praveen, if you search "Read is slower in 2.1.6 than 2.0.14" in this
>> forum, you can find another thread I sent a while ago. The perf test I did
>> indicated that read is slower for 2.1.6 than 2.0.14 so we stayed with
>> 2.0.14.
>>
>> On Tue, Jan 12, 2016 at 9:35 AM, Peddi, Praveen <peddi@amazon.com> wrote:
>>
>>> Thanks Jeff for your reply. Sorry for delayed response. We were running
>>> some more tests and wanted to wait for the results.
>>>
>>> So basically we saw higher CPU with 2.1.11 was higher compared to 2.0.9
>>> (see below) for the same exact load test. Memory spikes were also
>>> aggressive on 2.1.11.
>>>
>>> So we wanted to rule out any of our custom setting so we ended up doing
>>> some testing with Cassandra stress test and default Cassandra installation.
>>> Here are the results we saw between 2.0.9 and 2.1.11. Both are default
>>> installations and both use Cassandra stress test with same params. This is
>>> the closest apple-apple comparison we can get. As you can see both read and
>>> write latencies are 30 to 50% worse in 2.1.11 than 2.0.9. Since we are
>>> using default installation.
>>>
>>> *Highlights of the test:*
>>> Load: 2x reads and 1x writes
>>> CPU:  2.0.9 (goes upto 25%)  compared to 2.1.11 (goes upto 60%)
>>>
>>> Local read latency: 0.039 ms for 2.0.9 and 0.066 ms for 2.1.11
>>>
>>> Local write Latency: 0.033 ms for 2.0.9 Vs 0.030 ms for 2.1.11
>>>
>>> *One observation is, As the number of threads are increased, 2.1.11 read
>>> latencies are getting worse compared to 2.0.9 (see below table for 24
>>> threads vs 54 threads)*
>>> Not sure if anyone has done this kind of comparison before and what
>>> their thoughts are. I am thinking for this same reason
>>>
>>> 2.0.9 Plain  type       total ops     op/s     pk/s    row/s     mean
>>> med 0.95 0.99 0.999      max    time
>>>  16 threadCount  READ 66854 7205 7205 7205 1.6 1.3 2.8 3.5 9.6 85.3 9.3
>>>  16 threadCount  WRITE 33146 3572 3572 3572 1.3 1 2.6 3.3 7 206.5 9.3
>>>  16 threadCount  total 100000 10777 10777 10777 1.5 1.3 2.7 3.4 7.9
>>> 206.5 9.3
>>> 2.1.11 Plain
>>>  16 threadCount  READ 67096 6818 6818 6818 1.6 1.5 2.6 3.5 7.9 61.7 9.8
>>>  16 threadCount  WRITE 32904 3344 3344 3344 1.4 1.3 2.3 3 6.5 56.7 9.8
>>>  16 threadCount  total 100000 10162 10162 10162 1.6 1.4 2.5 3.2 6 61.7
>>> 9.8
>>> 2.0.9 Plain
>>>  24 threadCount  READ 66414 8167 8167 8167 2 1.6 3.7 7.5 16.7 208 8.1
>>>  24 threadCount  WRITE 33586 4130 4130 4130 1.7 1.3 3.4 5.4 25.6 45.4
>>> 8.1
>>>  24 threadCount  total 100000 12297 12297 12297 1.9 1.5 3.5 6.2 15.2 208
>>> 8.1
>>> 2.1.11 Plain
>>>  24 threadCount  READ 66628 7433 7433 7433 2.2 2.1 3.4 4.3 8.4 38.3 9
>>>  24 threadCount  WRITE 33372 3723 3723 3723 2 1.9 3.1 3.8 21.9 37.2 9
>>>  24 threadCount  total 100000 11155 11155 11155 2.1 2 3.3 4.1 8.8 38.3 9
>>> 2.0.9 Plain
>>>  54 threadCount  READ 67115 13419 13419 13419 2.8 2.6 4.2 6.4 36.9 82.4
>>> 5
>>>  54 threadCount  WRITE 32885 6575 6575 6575 2.5 2.3 3.9 5.6 15.9 81.5 5
>>>  54 threadCount  total 100000 19993 19993 19993 2.7 2.5 4.1 5.7 13.9
>>> 82.4 5
>>> 2.1.11 Plain
>>>  54 threadCount  READ 66780 8951 8951 8951 4.3 3.9 6.8 9.7 49.4 69.9 7.5
>>>  54 threadCount  WRITE 33220 4453 4453 4453 3.5 3.2 5.7 8.2 36.8 68 7.5
>>>  54 threadCount  total 100000 13404 13404 13404 4 3.7 6.6 9.2 48 69.9
>>> 7.5
>>>
>>> From: Jeff Jirsa <jeff.jirsa@crowdstrike.com>
>>> Date: Thursday, January 7, 2016 at 1:01 AM
>>> To: "user@cassandra.apache.org" <user@cassandra.apache.org>, Peddi
>>> Praveen <peddi@amazon.com>
>>> Subject: Re: Slow performance after upgrading from 2.0.9 to 2.1.11
>>>
>>> Anecdotal evidence typically agrees that 2.1 is faster than 2.0 (our
>>> experience was anywhere from 20-60%, depending on workload).
>>>
>>> However, it’s not necessarily true that everything behaves exactly the
>>> same – in particular, memtables are different, commitlog segment handling
>>> is different, and GC params may need to be tuned differently for 2.1 than
>>> 2.0.
>>>
>>> When the system is busy, what’s it actually DOING? Cassandra exposes a
>>> TON of metrics – have you plugged any into a reporting system to see what’s
>>> going on? Is your latency due to pegged cpu, iowait/disk queues or gc
>>> pauses?
>>>
>>> My colleagues spent a lot of time validating different AWS EBS configs
>>> (video from reinvent at https://www.youtube.com/watch?v=1R-mgOcOSd4),
>>> 2.1 was faster in almost every case, but you’re using an instance size I
>>> don’t believe we tried (too little RAM to be viable in production).  c3.2xl
>>> only gives you 15G of ram – most “performance” based systems want 2-4x
that
>>> (people running G1 heaps usually start at 16G heaps and leave another
>>> 16-30G for page cache), you’re running fairly small hardware – it’s
>>> possible that 2.1 isn’t “as good” on smaller hardware.
>>>
>>> (I do see your domain, presumably you know all of this, but just to be
>>> sure):
>>>
>>> You’re using c3, so presumably you’re using EBS – are you using GP2?
>>> Which volume sizes? Are they the same between versions? Are you hitting
>>> your iops limits? Running out of burst tokens? Do you have enhanced
>>> networking enabled? At load, what part of your system is stressed? Are you
>>> cpu bound? Are you seeing GC pauses hurt latency? Have you tried changing
>>> memtable_allocation_type -> offheap objects  (available in 2.1, not in
>>> 2.0)?
>>>
>>> Tuning gc_grace is weird – do you understand what it does? Are you
>>> overwriting or deleting a lot of data in your test (that’d be unusual)? Are
>>> you doing a lot of compaction?
>>>
>>>
>>> From: "Peddi, Praveen"
>>> Reply-To: "user@cassandra.apache.org"
>>> Date: Wednesday, January 6, 2016 at 11:41 AM
>>> To: "user@cassandra.apache.org"
>>> Subject: Slow performance after upgrading from 2.0.9 to 2.1.11
>>>
>>> Hi,
>>> We have upgraded Cassandra from 2.0.9 to 2.1.11 in our loadtest
>>> environment with pretty much same yaml settings in both (removed unused
>>> yaml settings and renamed few others) and we have noticed performance on
>>> 2.1.11 is worse compared to 2.0.9. *After more investigation we found
>>> that the performance gets worse as we increase replication factor on 2.1.11
>>> where as on 2.0.9 performance is more or less same.* Has anything
>>> architecturally changed as far as replication is concerned in 2.1.11?
>>>
>>> All googling only suggested 2.1.11 should be FASTER than 2.0.9 so we are
>>> obviously doing something different. However the client code, load test is
>>> all identical in both cases.
>>>
>>> Details:
>>> Nodes: 3 ec2 c3.2x large
>>> R/W Consistency: QUORUM
>>> Renamed memtable_total_space_in_mb to memtable_heap_space_in_mb and
>>> removed unused properties from yaml file.
>>> We run compaction aggressive compaction with low gc_grace (15 mins) but
>>> this is true for both 2.0.9 and 2.1.11.
>>>
>>> As you can see, all p50, p90 and p99 latencies stayed with in 10%
>>> difference on 2.0.9 when we increased RF from 1 to 3, where as on 2.1.11
>>> latencies almost doubled (especially reads are much slower than writes).
>>>
>>> # Nodes  RF # of rows 2.0.9 2.1.11
>>> READ
>>>       P50 P90 P99 P50 P90 P99
>>> 3 1 450 306 594 747 425 849 1085
>>> 3 3 450 358 634 877 708 1274 2642
>>>
>>> WRITE
>>> 3 1 10 26 80 179 37 131 196
>>> 3 3 10 31 96 184 46 166 468
>>> Any pointers on how to debug performance issues will be appreciated.
>>>
>>> Praveen
>>>
>>
>>
>
> --
> ----------------------------------
> Bestdata.be
> Optimised ict
> Tel:+32(0)496609576
> Corry@BestData.be
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