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Posted to dev@cassandra.apache.org by "techpyaasa ." <te...@gmail.com> on 2017/05/04 14:21:26 UTC
Re: Slow performance after upgrading from 2.0.9 to 2.1.11
Hi guys,
Has anybody got fix for this issue?
Recently we upgraded our c* cluster from 2.0.17 to 2.1.17. And we saw
increase in read latency on few tables, read latency increased almost 2 to
3 times more than what it was in 2.0.17.
Kindly let me know the fix for it if anybody knows it.
Thanks
TechPyaasa
On Wed, Nov 9, 2016 at 1:28 AM, Dikang Gu <di...@gmail.com> wrote:
> 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(IntegerT
>> ype.java:29)
>> at org.apache.cassandra.db.composites.AbstractSimpleCellNameTyp
>> e.compare(AbstractSimpleCellNameType.java:98)
>> at org.apache.cassandra.db.composites.AbstractSimpleCellNameTyp
>> e.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.SliceByNamesReadCommandSerializer.
>> 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.receiveMessag
>> e(IncomingTcpConnection.java:195)
>> at org.apache.cassandra.net.IncomingTcpConnection.receiveMessag
>> es(IncomingTcpConnection.java:172)
>> at org.apache.cassandra.net.IncomingTcpConnection.run(IncomingT
>> cpConnection.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
>> <ma...@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/marsh
>> al/IntegerType;.compare
>> 2.39% perf-196410.map [.]
>> Ljava/nio/Buffer;.checkIndex in Lorg/apache/cassandra/db/marsh
>> al/IntegerType;.findMostSignificantByte
>> 2.03% perf-196410.map [.]
>> Ljava/math/BigInteger;.compareTo in Lorg/apache/cassandra/db/Decor
>> atedKey;.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-libsna
>> ppyjava.so/> [.] 0x0000000000003804
>> 0.87% perf-196410.map [.]
>> Ljava/io/DataInputStream;.readFully in Lorg/apache/cassandra/db/Abstr
>> actCell$1;.computeNext
>> 0.82% snappy-1.0.5.2-libsnappyjava.so<http://snappy-1.0.5.2-libsna
>> ppyjava.so/> [.] 0x00000000000036dc
>> 0.79% [kernel] [k]
>> copy_user_generic_string
>> 0.73% perf-196410.map [.] vtable chunks
>> 0.71% perf-196410.map [.]
>> Lorg/apache/cassandra/db/OnDiskAtom$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-libsna
>> ppyjava.so/> [.] 0x00000000000036df
>>
>> On Fri, Jan 29, 2016 at 2:11 PM, Corry Opdenakker <corry@bestdata.be
>> <ma...@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
>> <ma...@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 <co...@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 <pe...@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 <je...@crowdstrike.com>
>> Reply-To: <us...@cassandra.apache.org>
>> Date: Thursday, January 14, 2016 at 2:58 PM
>> To: "user@cassandra.apache.org" <us...@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 <je...@crowdstrike.com>
>> Reply-To: <us...@cassandra.apache.org>
>> Date: Thursday, January 14, 2016 at 1:29 PM
>> To: Zhiyan Shao <zh...@gmail.com>, "user@cassandra.apache.org" <
>> user@cassandra.apache.org>
>> Cc: "Agrawal, Pratik" <pa...@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 <pe...@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 <je...@crowdstrike.com>
>> Date: Thursday, January 7, 2016 at 1:01 AM
>> To: "user@cassandra.apache.org" <us...@cassandra.apache.org>, Peddi
>> Praveen <pe...@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<ma...@BestData.be>
>> ----------------------------------
>>
>>
>>
>>
>> --
>> ----------------------------------
>> Bestdata.be<http://Bestdata.be>
>> Optimised ict
>> Tel:+32(0)496609576<tel:%2B32%280%29496609576>
>> Corry@BestData.be<ma...@BestData.be>
>> ----------------------------------
>>
>>
>>
>>
>> --
>> Dikang
>>
>>
>>
>>
>>
>> --
>> Dikang
>>
>>
>>
>
>
> --
> Dikang
>
>