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From Dikang Gu <dikan...@gmail.com>
Subject Re: Slow performance after upgrading from 2.0.9 to 2.1.11
Date Tue, 08 Nov 2016 19:58:44 GMT
Michael, thanks for the info. It sounds to me a very serious performance
regression. :(

On Tue, Nov 8, 2016 at 11:39 AM, Michael Kjellman <
mkjellman@internalcircle.com> wrote:

> Yes, We hit this as well. We have a internal patch that I wrote to mostly
> revert the behavior back to ByteBuffers with as small amount of code change
> as possible. Performance of our build is now even with 2.0.x and we've also
> forward ported it to 3.x (although the 3.x patch was even more complicated
> due to Bounds, RangeTombstoneBound, ClusteringPrefix which actually
> increases the number of allocations to somewhere between 11 and 13
> depending on how I count it per indexed block -- making it even worse than
> what you're observing in 2.1.
>
> We haven't upstreamed it as 2.1 is obviously not taking any changes at
> this point and the longer term solution is https://issues.apache.org/
> jira/browse/CASSANDRA-9754 (which also includes the changes to go back to
> ByteBuffers and remove as much of the Composites from the storage engine as
> possible.) Also, the solution is a bit of a hack -- although it was a
> blocker from us deploying 2.1 -- so i'm not sure how "hacky" it is if it
> works..
>
> best,
> kjellman
>
>
> On Nov 8, 2016, at 11:31 AM, Dikang Gu <dikang85@gmail.com<mailto:dik
> ang85@gmail.com>> wrote:
>
> This is very expensive:
>
> "MessagingService-Incoming-/2401:db00:21:1029:face:0:9:0" prio=10
> tid=0x00007f2fd57e1800 nid=0x1cc510 runnable [0x00007f2b971b0000]
>    java.lang.Thread.State: RUNNABLE
>         at org.apache.cassandra.db.marshal.IntegerType.compare(
> IntegerType.java:29)
>         at org.apache.cassandra.db.composites.AbstractSimpleCellNameType.
> compare(AbstractSimpleCellNameType.java:98)
>         at org.apache.cassandra.db.composites.AbstractSimpleCellNameType.
> compare(AbstractSimpleCellNameType.java:31)
>         at java.util.TreeMap.put(TreeMap.java:545)
>         at java.util.TreeSet.add(TreeSet.java:255)
>         at org.apache.cassandra.db.filter.NamesQueryFilter$
> Serializer.deserialize(NamesQueryFilter.java:254)
>         at org.apache.cassandra.db.filter.NamesQueryFilter$
> Serializer.deserialize(NamesQueryFilter.java:228)
>         at org.apache.cassandra.db.SliceByNamesReadCommandSeriali
> zer.deserialize(SliceByNamesReadCommand.java:104)
>         at org.apache.cassandra.db.ReadCommandSerializer.
> deserialize(ReadCommand.java:156)
>         at org.apache.cassandra.db.ReadCommandSerializer.
> deserialize(ReadCommand.java:132)
>         at org.apache.cassandra.net.MessageIn.read(MessageIn.java:99)
>         at org.apache.cassandra.net.IncomingTcpConnection.receiveMessage(
> IncomingTcpConnection.java:195)
>         at org.apache.cassandra.net.IncomingTcpConnection.receiveMessages(
> IncomingTcpConnection.java:172)
>         at org.apache.cassandra.net.IncomingTcpConnection.run(
> IncomingTcpConnection.java:88)
>
>
> Checked the git history, it comes from this jira:
> https://issues.apache.org/jira/browse/CASSANDRA-5417
>
> Any thoughts?
> ​
>
> On Fri, Oct 28, 2016 at 10:32 AM, Paulo Motta <pauloricardomg@gmail.com<
> mailto:pauloricardomg@gmail.com>> wrote:
> Haven't seen this before, but perhaps it's related to CASSANDRA-10433?
> This is just a wild guess as it's in a related codepath, but maybe worth
> trying out the patch available to see if it helps anything...
>
> 2016-10-28 15:03 GMT-02:00 Dikang Gu <dikang85@gmail.com<mailto:dik
> ang85@gmail.com>>:
> We are seeing huge cpu regression when upgrading one of our 2.0.16 cluster
> to 2.1.14 as well. The 2.1.14 node is not able to handle the same amount of
> read traffic as the 2.0.16 node, actually, it's less than 50%.
>
> And in the perf results, the first line could go as high as 50%, as we
> turn up the read traffic, which never appeared in 2.0.16.
>
> Any thoughts?
> Thanks
>
>
> Samples: 952K of event 'cycles', Event count (approx.): 229681774560
> Overhead  Shared Object                          Symbol
>    6.52%  perf-196410.map                        [.]
> Lorg/apache/cassandra/db/marshal/IntegerType;.compare in
> Lorg/apache/cassandra/db/composites/AbstractSimpleCellNameType;.compare
>    4.84%  libzip.so                              [.] adler32
>    2.88%  perf-196410.map                        [.]
> Ljava/nio/HeapByteBuffer;.get in Lorg/apache/cassandra/db/
> marshal/IntegerType;.compare
>    2.39%  perf-196410.map                        [.]
> Ljava/nio/Buffer;.checkIndex in Lorg/apache/cassandra/db/
> marshal/IntegerType;.findMostSignificantByte
>    2.03%  perf-196410.map                        [.]
> Ljava/math/BigInteger;.compareTo in Lorg/apache/cassandra/db/
> DecoratedKey;.compareTo
>    1.65%  perf-196410.map                        [.] vtable chunks
>    1.44%  perf-196410.map                        [.]
> Lorg/apache/cassandra/db/DecoratedKey;.compareTo in Ljava/util/concurrent/
> ConcurrentSkipListMap;.findNode
>    1.02%  perf-196410.map                        [.]
> Lorg/apache/cassandra/db/composites/AbstractSimpleCellNameType;.compare
>    1.00%  snappy-1.0.5.2-libsnappyjava.so<http://snappy-1.0.5.2-
> libsnappyjava.so/>        [.] 0x0000000000003804
>    0.87%  perf-196410.map                        [.]
> Ljava/io/DataInputStream;.readFully in Lorg/apache/cassandra/db/
> AbstractCell$1;.computeNext
>    0.82%  snappy-1.0.5.2-libsnappyjava.so<http://snappy-1.0.5.2-
> libsnappyjava.so/>        [.] 0x00000000000036dc
>    0.79%  [kernel]                               [k]
> copy_user_generic_string
>    0.73%  perf-196410.map                        [.] vtable chunks
>    0.71%  perf-196410.map                        [.]
> Lorg/apache/cassandra/db/OnDiskAtom$Serializer;.deserializeFromSSTable in
> Lorg/apache/cassandra/db/AbstractCell$1;.computeNext
>    0.70%  [kernel]                               [k] find_busiest_group
>    0.69%  perf-196410.map                        [.] <80>H<F4>3<AE>^?
>    0.68%  perf-196410.map                        [.]
> Lorg/apache/cassandra/db/DecoratedKey;.compareTo
>    0.65%  perf-196410.map                        [.]
> jbyte_disjoint_arraycopy
>    0.64%  [kernel]                               [k] _raw_spin_lock
>    0.63%  [kernel]                               [k] __schedule
>    0.45%  snappy-1.0.5.2-libsnappyjava.so<http://snappy-1.0.5.2-
> libsnappyjava.so/>        [.] 0x00000000000036df
>
> On Fri, Jan 29, 2016 at 2:11 PM, Corry Opdenakker <corry@bestdata.be
> <mailto:corry@bestdata.be>> wrote:
> @JC, Get the pid of your target java process (something like "ps -ef |
> grep -i cassandra") .
> Then do a kill -3 <pid> (at unix/linux)
> Check the stdout logfile of the process.
>  it should contain the threaddump.
> If you found it, then great!
> Let that kill -3 loop for about 2 or 3 minutes.
> Herafter copy paste and load the stdout file into one if the mentioned
> tools.
> If you are not familiar with the java internals, then those threaddumps
> will learn you a lot:)
>
>
>
>
> Op vrijdag 29 januari 2016 heeft Jean Carlo <jean.jeancarl48@gmail.com<
> mailto:jean.jeancarl48@gmail.com>> het volgende geschreven:
> 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<http://Bestdata.be>
> Optimised ict
> Tel:+32(0)496609576<tel:%2B32%280%29496609576>
> Corry@BestData.be<mailto:Corry@BestData.be>
> ----------------------------------
>
>
>
>
> --
> ----------------------------------
> Bestdata.be<http://Bestdata.be>
> Optimised ict
> Tel:+32(0)496609576<tel:%2B32%280%29496609576>
> Corry@BestData.be<mailto:Corry@BestData.be>
> ----------------------------------
>
>
>
>
> --
> Dikang
>
>
>
>
>
> --
> Dikang
>
>
>


-- 
Dikang

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