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From "Laing, Michael" <michael.la...@nytimes.com>
Subject Re: OOM at Bootstrap Time
Date Mon, 27 Oct 2014 16:47:12 GMT
Again, from our experience w 2.0.x:

Revert to the defaults - you are manually setting heap way too high IMHO.

On our small nodes we tried LCS - way too much compaction - switch all CFs
to STCS.

We do a major rolling compaction on our small nodes weekly during less busy
hours - works great. Be sure you have enough disk.

We never explicitly delete and only use ttls or truncation. You can set GC
to 0 in that case, so tombstones are more readily expunged. There are a
couple threads in the list that discuss this... also normal rolling repair
becomes optional, reducing load (still repair if something unusual happens
tho...).

In your current situation, you need to kickstart compaction - are there any
CFs you can truncate at least temporarily? Then try compacting a small CF,
then another, etc.

Hopefully you can get enough headroom to add a node.

ml




On Sun, Oct 26, 2014 at 6:24 PM, Maxime <maximelb@gmail.com> wrote:

> Hmm, thanks for the reading.
>
> I initially followed some (perhaps too old) maintenance scripts, which
> included weekly 'nodetool compact'. Is there a way for me to undo the
> damage? Tombstones will be a very important issue for me since the dataset
> is very much a rolling dataset using TTLs heavily.
>
> On Sun, Oct 26, 2014 at 6:04 PM, DuyHai Doan <doanduyhai@gmail.com> wrote:
>
>> "Should doing a major compaction on those nodes lead to a restructuration
>> of the SSTables?" --> Beware of the major compaction on SizeTiered, it will
>> create 2 giant SSTables and the expired/outdated/tombstone columns in this
>> big file will be never cleaned since the SSTable will never get a chance to
>> be compacted again
>>
>> Essentially to reduce the fragmentation of small SSTables you can stay
>> with SizeTiered compaction and play around with compaction properties (the
>> thresholds) to make C* group a bunch of files each time it compacts so that
>> the file number shrinks to a reasonable count
>>
>> Since you're using C* 2.1 and anti-compaction has been introduced, I
>> hesitate advising you to use Leveled compaction as a work-around to reduce
>> SSTable count.
>>
>>  Things are a little bit more complicated because of the incremental
>> repair process (I don't know whether you're using incremental repair or not
>> in production). The Dev blog says that Leveled compaction is performed only
>> on repaired SSTables, the un-repaired ones still use SizeTiered, more
>> details here:
>> http://www.datastax.com/dev/blog/anticompaction-in-cassandra-2-1
>>
>> Regards
>>
>>
>>
>>
>>
>> On Sun, Oct 26, 2014 at 9:44 PM, Jonathan Haddad <jon@jonhaddad.com>
>> wrote:
>>
>>> If the issue is related to I/O, you're going to want to determine if
>>> you're saturated.  Take a look at `iostat -dmx 1`, you'll see avgqu-sz
>>> (queue size) and svctm, (service time).    The higher those numbers
>>> are, the most overwhelmed your disk is.
>>>
>>> On Sun, Oct 26, 2014 at 12:01 PM, DuyHai Doan <doanduyhai@gmail.com>
>>> wrote:
>>> > Hello Maxime
>>> >
>>> > Increasing the flush writers won't help if your disk I/O is not
>>> keeping up.
>>> >
>>> > I've had a look into the log file, below are some remarks:
>>> >
>>> > 1) There are a lot of SSTables on disk for some tables (events for
>>> example,
>>> > but not only). I've seen that some compactions are taking up to 32
>>> SSTables
>>> > (which corresponds to the default max value for SizeTiered compaction).
>>> >
>>> > 2) There is a secondary index that I found suspicious :
>>> loc.loc_id_idx. As
>>> > its name implies I have the impression that it's an index on the id of
>>> the
>>> > loc which would lead to almost an 1-1 relationship between the indexed
>>> value
>>> > and the original loc. Such index should be avoided because they do not
>>> > perform well. If it's not an index on the loc_id, please disregard my
>>> remark
>>> >
>>> > 3) There is a clear imbalance of SSTable count on some nodes. In the
>>> log, I
>>> > saw:
>>> >
>>> > INFO  [STREAM-IN-/xxxx.xxxx.xxxx.20] 2014-10-25 02:21:43,360
>>> > StreamResultFuture.java:166 - [Stream
>>> #a6e54ea0-5bed-11e4-8df5-f357715e1a79
>>> > ID#0] Prepare completed. Receiving 163 files(4 111 187 195 bytes),
>>> sending 0
>>> > files(0 bytes)
>>> >
>>> > INFO  [STREAM-IN-/xxxx.xxxx.xxxx.81] 2014-10-25 02:21:46,121
>>> > StreamResultFuture.java:166 - [Stream
>>> #a6e54ea0-5bed-11e4-8df5-f357715e1a79
>>> > ID#0] Prepare completed. Receiving 154 files(3 332 779 920 bytes),
>>> sending 0
>>> > files(0 bytes)
>>> >
>>> > INFO  [STREAM-IN-/xxxx.xxxx.xxxx.71] 2014-10-25 02:21:50,494
>>> > StreamResultFuture.java:166 - [Stream
>>> #a6e54ea0-5bed-11e4-8df5-f357715e1a79
>>> > ID#0] Prepare completed. Receiving 1315 files(4 606 316 933 bytes),
>>> sending
>>> > 0 files(0 bytes)
>>> >
>>> > INFO  [STREAM-IN-/xxxx.xxxx.xxxx.217] 2014-10-25 02:21:51,036
>>> > StreamResultFuture.java:166 - [Stream
>>> #a6e54ea0-5bed-11e4-8df5-f357715e1a79
>>> > ID#0] Prepare completed. Receiving 1640 files(3 208 023 573 bytes),
>>> sending
>>> > 0 files(0 bytes)
>>> >
>>> >  As you can see, the existing 4 nodes are streaming data to the new
>>> node and
>>> > on average the data set size is about 3.3 - 4.5 Gb. However the number
>>> of
>>> > SSTables is around 150 files for nodes xxxx.xxxx.xxxx.20 and
>>> > xxxx.xxxx.xxxx.81 but goes through the roof to reach 1315 files for
>>> > xxxx.xxxx.xxxx.71 and 1640 files for xxxx.xxxx.xxxx.217
>>> >
>>> >  The total data set size is roughly the same but the file number is
>>> x10,
>>> > which mean that you'll have a bunch of tiny files.
>>> >
>>> >  I guess that upon reception of those files, there will be a massive
>>> flush
>>> > to disk, explaining the behaviour you're facing (flush storm)
>>> >
>>> > I would suggest looking on nodes xxxx.xxxx.xxxx.71 and
>>> xxxx.xxxx.xxxx.217 to
>>> > check for the total SSTable count for each table to confirm this
>>> intuition
>>> >
>>> > Regards
>>> >
>>> >
>>> > On Sun, Oct 26, 2014 at 4:58 PM, Maxime <maximelb@gmail.com> wrote:
>>> >>
>>> >> I've emailed you a raw log file of an instance of this happening.
>>> >>
>>> >> I've been monitoring more closely the timing of events in tpstats and
>>> the
>>> >> logs and I believe this is what is happening:
>>> >>
>>> >> - For some reason, C* decides to provoke a flush storm (I say some
>>> reason,
>>> >> I'm sure there is one but I have had difficulty determining the
>>> behaviour
>>> >> changes between 1.* and more recent releases).
>>> >> - So we see ~ 3000 flush being enqueued.
>>> >> - This happens so suddenly that even boosting the number of flush
>>> writers
>>> >> to 20 does not suffice. I don't even see "all time blocked" numbers
>>> for it
>>> >> before C* stops responding. I suspect this is due to the sudden OOM
>>> and GC
>>> >> occurring.
>>> >> - The last tpstat that comes back before the node goes down indicates
>>> 20
>>> >> active and 3000 pending and the rest 0. It's by far the anomalous
>>> activity.
>>> >>
>>> >> Is there a way to throttle down this generation of Flush? C*
>>> complains if
>>> >> I set the queue_size to any value (deprecated now?) and boosting the
>>> threads
>>> >> does not seem to help since even at 20 we're an order of magnitude
>>> off.
>>> >>
>>> >> Suggestions? Comments?
>>> >>
>>> >>
>>> >> On Sun, Oct 26, 2014 at 2:26 AM, DuyHai Doan <doanduyhai@gmail.com>
>>> wrote:
>>> >>>
>>> >>> Hello Maxime
>>> >>>
>>> >>>  Can you put the complete logs and config somewhere ? It would be
>>> >>> interesting to know what is the cause of the OOM.
>>> >>>
>>> >>> On Sun, Oct 26, 2014 at 3:15 AM, Maxime <maximelb@gmail.com>
wrote:
>>> >>>>
>>> >>>> Thanks a lot that is comforting. We are also small at the moment
so
>>> I
>>> >>>> definitely can relate with the idea of keeping small and simple
at
>>> a level
>>> >>>> where it just works.
>>> >>>>
>>> >>>> I see the new Apache version has a lot of fixes so I will try
to
>>> upgrade
>>> >>>> before I look into downgrading.
>>> >>>>
>>> >>>>
>>> >>>> On Saturday, October 25, 2014, Laing, Michael
>>> >>>> <michael.laing@nytimes.com> wrote:
>>> >>>>>
>>> >>>>> Since no one else has stepped in...
>>> >>>>>
>>> >>>>> We have run clusters with ridiculously small nodes - I have
a
>>> >>>>> production cluster in AWS with 4GB nodes each with 1 CPU
and
>>> disk-based
>>> >>>>> instance storage. It works fine but you can see those little
>>> puppies
>>> >>>>> struggle...
>>> >>>>>
>>> >>>>> And I ran into problems such as you observe...
>>> >>>>>
>>> >>>>> Upgrading Java to the latest 1.7 and - most importantly
-
>>> reverting to
>>> >>>>> the default configuration, esp. for heap, seemed to settle
things
>>> down
>>> >>>>> completely. Also make sure that you are using the 'recommended
>>> production
>>> >>>>> settings' from the docs on your boxen.
>>> >>>>>
>>> >>>>> However we are running 2.0.x not 2.1.0 so YMMV.
>>> >>>>>
>>> >>>>> And we are switching to 15GB nodes w 2 heftier CPUs each
and SSD
>>> >>>>> storage - still a 'small' machine, but much more reasonable
for C*.
>>> >>>>>
>>> >>>>> However I can't say I am an expert, since I deliberately
keep
>>> things so
>>> >>>>> simple that we do not encounter problems - it just works
so I dig
>>> into other
>>> >>>>> stuff.
>>> >>>>>
>>> >>>>> ml
>>> >>>>>
>>> >>>>>
>>> >>>>> On Sat, Oct 25, 2014 at 5:22 PM, Maxime <maximelb@gmail.com>
>>> wrote:
>>> >>>>>>
>>> >>>>>> Hello, I've been trying to add a new node to my cluster
( 4 nodes
>>> )
>>> >>>>>> for a few days now.
>>> >>>>>>
>>> >>>>>> I started by adding a node similar to my current configuration,
4
>>> GB
>>> >>>>>> or RAM + 2 Cores on DigitalOcean. However every time,
I would end
>>> up getting
>>> >>>>>> OOM errors after many log entries of the type:
>>> >>>>>>
>>> >>>>>> INFO  [SlabPoolCleaner] 2014-10-25 13:44:57,240
>>> >>>>>> ColumnFamilyStore.java:856 - Enqueuing flush of mycf:
5383 (0%)
>>> on-heap, 0
>>> >>>>>> (0%) off-heap
>>> >>>>>>
>>> >>>>>> leading to:
>>> >>>>>>
>>> >>>>>> ka-120-Data.db (39291 bytes) for commitlog position
>>> >>>>>> ReplayPosition(segmentId=1414243978538, position=23699418)
>>> >>>>>> WARN  [SharedPool-Worker-13] 2014-10-25 13:48:18,032
>>> >>>>>> AbstractTracingAwareExecutorService.java:167 - Uncaught
exception
>>> on thread
>>> >>>>>> Thread[SharedPool-Worker-13,5,main]: {}
>>> >>>>>> java.lang.OutOfMemoryError: Java heap space
>>> >>>>>>
>>> >>>>>> Thinking it had to do with either compaction somehow
or
>>> streaming, 2
>>> >>>>>> activities I've had tremendous issues with in the past;
I tried
>>> to slow down
>>> >>>>>> the setstreamthroughput to extremely low values all
the way to 5.
>>> I also
>>> >>>>>> tried setting setcompactionthoughput to 0, and then
reading that
>>> in some
>>> >>>>>> cases it might be too fast, down to 8. Nothing worked,
it merely
>>> vaguely
>>> >>>>>> changed the mean time to OOM but not in a way indicating
either
>>> was anywhere
>>> >>>>>> a solution.
>>> >>>>>>
>>> >>>>>> The nodes were configured with 2 GB of Heap initially,
I tried to
>>> >>>>>> crank it up to 3 GB, stressing the host memory to its
limit.
>>> >>>>>>
>>> >>>>>> After doing some exploration (I am considering writing
a
>>> Cassandra Ops
>>> >>>>>> documentation with lessons learned since there seems
to be little
>>> of it in
>>> >>>>>> organized fashions), I read that some people had strange
issues
>>> on lower-end
>>> >>>>>> boxes like that, so I bit the bullet and upgraded my
new node to
>>> a 8GB + 4
>>> >>>>>> Core instance, which was anecdotally better.
>>> >>>>>>
>>> >>>>>> To my complete shock, exact same issues are present,
even raising
>>> the
>>> >>>>>> Heap memory to 6 GB. I figure it can't be a "normal"
situation
>>> anymore, but
>>> >>>>>> must be a bug somehow.
>>> >>>>>>
>>> >>>>>> My cluster is 4 nodes, RF of 2, about 160 GB of data
across all
>>> nodes.
>>> >>>>>> About 10 CF of varying sizes. Runtime writes are between
300 to
>>> 900 /
>>> >>>>>> second. Cassandra 2.1.0, nothing too wild.
>>> >>>>>>
>>> >>>>>> Has anyone encountered these kinds of issues before?
I would
>>> really
>>> >>>>>> enjoy hearing about the experiences of people trying
to run
>>> small-sized
>>> >>>>>> clusters like mine. From everything I read, Cassandra
operations
>>> go very
>>> >>>>>> well on large (16 GB + 8 Cores) machines, but I'm sad
to report
>>> I've had
>>> >>>>>> nothing but trouble trying to run on smaller machines,
perhaps I
>>> can learn
>>> >>>>>> from other's experience?
>>> >>>>>>
>>> >>>>>> Full logs can be provided to anyone interested.
>>> >>>>>>
>>> >>>>>> Cheers
>>> >>>>>
>>> >>>>>
>>> >>>
>>> >>
>>> >
>>>
>>>
>>>
>>> --
>>> Jon Haddad
>>> http://www.rustyrazorblade.com
>>> twitter: rustyrazorblade
>>>
>>
>>
>

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