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From Keith Turner <ke...@deenlo.com>
Subject Re: maximize usage of cluster resources during ingestion
Date Wed, 19 Jul 2017 16:25:41 GMT
On Thu, Jul 13, 2017 at 10:56 AM,  <dlmarion@comcast.net> wrote:
> Regarding the referenced paper, pre-splitting the tables, using an optimized zookeeper
deployment, and increasing concurrent minor / major compactions are good things. I'm not sure
that we want to recommend turning off the write ahead logs and replication for production
deployments.


I wouldn't recommend turning off write ahead logs either.   However,
it can be useful to turn them off for performance testing to
understand their impact.

I noticed you set "table.durability": "flush" which is good for
performance.  However the metdata table may still be set to sync which
can cause performance problems.   The following article describes how
to set the metdata table to flush. The article also describes the
consequences for regular tables of having the metadata table set to
sync.

http://accumulo.apache.org/blog/2016/11/02/durability-performance.html

>
> -----Original Message-----
> From: Jeremy Kepner [mailto:kepner@ll.mit.edu]
> Sent: Thursday, July 13, 2017 10:05 AM
> To: user@accumulo.apache.org
> Subject: Re: maximize usage of cluster resources during ingestion
>
> https://arxiv.org/abs/1406.4923  contains a number of tricks for maximizing ingest performance.
>
> On Thu, Jul 13, 2017 at 08:13:40AM -0400, Jonathan Wonders wrote:
>> Keep in mind that Accumulo puts a much different kind of load on HDFS
>> than the DFSIO benchmark.  It might be more appropriate to use a tool
>> like dstat to monitor HDD utilization and queue depth.  HDD throughput
>> benchmarks usually will involve high queue depths as disks are much
>> more effective when they can pipeline and batch updates. Accumulo's
>> WAL workload will typically call hflush or hsync periodically which
>> interrupts the IO pipeline much like memory barriers can interrupt CPU
>> pipelining except more severe.  This is necessary to provide
>> durability guarantees, but definitely comes at a cost to throughput.
>> Any database that has these durability guarantees will suffer
>> similarly to an extent.  For Accumulo, it is probably worse than for
>> non-distributed databases because the flush or sync must happen at
>> each replica prior to the mutation being added into the in-memory map.
>>
>> I think one of the reasons the recommendation was made to add more
>> tablet servers is because each tablet server only writes to one WAL at
>> a time and each block will live on N disk based on replication factor.
>> If you have a replication factor of 3, there will be 10x3 blocks being
>> appended to at any given time (excluding compactions).  Since you have
>> 120 disks, not all will be participating in write-ahead-logging, so
>> you should not count the IO capacity of these extra disks towards
>> expected ingest throughput.  10 tablet servers per node is probably
>> too many because there would likely be a lot of contention
>> flushing/syncing WALs.  I'm not sure how smart HDFS is about how it
>> distributes the WAL load.  You might see more benefit with 2-4
>> tservers per node.  This would mostly likely require more batch writer threads in
the client as well.
>>
>> I'm not too surprised that snappy did not help because the WALs are
>> not compressed and are likely a bigger bottleneck than compaction
>> since you have many disks not participating in WAL.
>>
>>
>> On Wed, Jul 12, 2017 at 11:16 AM, Josh Elser <elserj@apache.org> wrote:
>>
>> > You probably want to split the table further than just 4 tablets per
>> > tablet server. Try 10's of tablets per server.
>> >
>> > Also, merging the content from (who I assume is) your coworker on
>> > this stackoverflow post[1], I don't believe the suggestion[2] to
>> > verify WAL max size, minc threshold, and native maps size was brought up yet.
>> >
>> > Also, did you look at the JVM GC logs for the TabletServers like was
>> > previously suggested to you?
>> >
>> > [1] https://stackoverflow.com/questions/44928354/accumulo-tablet
>> > -server-doesnt-utilize-all-available-resources-on-host-machine/
>> > [2] https://accumulo.apache.org/1.8/accumulo_user_manual.html#_n
>> > ative_maps_configuration
>> >
>> > On 7/12/17 10:12 AM, Massimilian Mattetti wrote:
>> >
>> >> Hi all,
>> >>
>> >> I ran a few experiments in the last days trying to identify what is
>> >> the bottleneck for the ingestion process.
>> >> - Running 10 tservers per node instead of only one gave me a very
>> >> neglectable performance improvement of about 15%.
>> >> - Running the ingestor processes from the two masters give the same
>> >> performance as running one ingestor process in each tablet server
>> >> (10
>> >> ingestors)
>> >> - neither the network limit (10 Gb network) nor the disk throughput
>> >> limit has been reached (1GB/s per node reached while running the
>> >> TestDFSIO benchmark on HDFS)
>> >> - CPU is always around 20% on each tserver
>> >> - changing compression from GZ to snappy did not provide any
>> >> benefit
>> >> - increasing the tserver.total.mutation.queue.maxto 200MB actually
>> >> decreased the performance I am going to run some ingestion
>> >> experiment with Kudu over the next few days, but any other
>> >> suggestion on how improve the performance on Accumulo is very
>> >> welcome.
>> >> Thanks.
>> >>
>> >> Best Regards,
>> >> Massimiliano
>> >>
>> >>
>> >>
>> >> From: Jonathan Wonders <jwonders88@gmail.com>
>> >> To: user@accumulo.apache.org, Dave Marion <dlmarion@comcast.net>
>> >> Date: 07/07/2017 04:02
>> >> Subject: Re: maximize usage of cluster resources during ingestion
>> >> -------------------------------------------------------------------
>> >> -----
>> >>
>> >>
>> >>
>> >> I've personally never seen full CPU utilization during pure ingest.
>> >> Typically the bottleneck has been I/O related. The majority of
>> >> steady-state CPU utilization under a heavy ingest load is probably
>> >> due to compression unless you have custom constraints running. This
>> >> can depend on the compression algorithm you have selected.  There
>> >> is probably a measurable contribution from inserting into the
>> >> in-memory map.  Otherwise, not much computation occurs during ingest per
mutation.
>> >>
>> >> On Thu, Jul 6, 2017 at 8:18 AM, Dave Marion <_dlmarion@comcast.net_
>> >> <mailto:dlmarion@comcast.net>> wrote:
>> >> That's a good point. I would also look at increasing
>> >> tserver.total.mutation.queue.max. Are you seeing hold times? If
>> >> not, I would keep pushing harder until you do, then move to
>> >> multiple tablet servers. Do you have any GC logs?
>> >>
>> >>
>> >> On July 6, 2017 at 4:47 AM Cyrille Savelief <_csavelief@gmail.com_
>> >> <mailto:csavelief@gmail.com>> wrote:
>> >>
>> >> Are you sure Accumulo is not waiting for your app's data? There
>> >> might be GC pauses in your ingest code (we have already experienced that).
>> >>
>> >> Le jeu. 6 juil. 2017 à 10:32, Massimilian Mattetti
>> >> <_MASSIMIL@il.ibm.com_ <mailto:MASSIMIL@il.ibm.com>> a écrit
:
>> >> Thank you all for the suggestions.
>> >>
>> >> About the native memory map I checked the logs on each tablet
>> >> server and it was loaded correctly (of course the
>> >> tserver.memory.maps.native.enabled
>> >> was set to true), so the GC pauses should not be the problem
>> >> eventually. I managed to get much better ingestion graph by
>> >> reducing the native map size to *2GB* and increasing the Batch
>> >> Writer threads number from the default (3 was really bad for my
>> >> configuration) to *10* (I think it does not make sense having more threads
than tablet servers, am I right?).
>> >>
>> >> The configuration that I used for the table is:
>> >> "table.file.replication": "2",
>> >> "table.compaction.minor.logs.threshold": "3",
>> >> "table.durability": "flush",
>> >> "table.split.threshold": "1G"
>> >>
>> >> while for the tablet servers is:
>> >> "tserver.wal.blocksize": "1G",
>> >>   "tserver.walog.max.size": "2G",
>> >> "tserver.memory.maps.max": "2G",
>> >> "tserver.compaction.minor.concurrent.max": "50",
>> >> "tserver.compaction.major.concurrent.max": "20",
>> >> "tserver.wal.replication": "2",
>> >>   "tserver.compaction.major.thread.files.open.max": "15"
>> >>
>> >> The new graph:
>> >>
>> >>
>> >> I still have the problem of a CPU usage that is less than*20%.* So
>> >> I am thinking to run multiple tablet servers per node (like 5 or
>> >> 10) in order to maximize the CPU usage. Besides that I do not have
>> >> any other idea on how to stress those servers with ingestion.
>> >> Any suggestions are very welcome. Meanwhile, thank you all again
>> >> for your help.
>> >>
>> >>
>> >> Best Regards,
>> >> Massimiliano
>> >>
>> >>
>> >>
>> >> From: Jonathan Wonders <_jwonders88@gmail.com_ <mailto:
>> >> jwonders88@gmail.com>>
>> >> To: _user@accumulo.apache.org_ <mailto:user@accumulo.apache.org>
>> >> Date: 06/07/2017 04:01
>> >> Subject: Re: maximize usage of cluster resources during ingestion
>> >> -------------------------------------------------------------------
>> >> -----
>> >>
>> >>
>> >>
>> >> Hi Massimilian,
>> >>
>> >> Are you seeing held commits during the ingest pauses?  Just based
>> >> on having looked at many similar graphs in the past, this might be
>> >> one of the major culprits.  A tablet server has a memory region
>> >> with a bounded size
>> >> (tserver.memory.maps.max) where it buffers data that has not yet
>> >> been written to RFiles (through the process of minor compaction).
>> >> The region is segmented by tablet and each tablet can have a buffer
>> >> that is undergoing ingest as well as a buffer that is undergoing
>> >> minor compaction. A memory manager decides when to initiate minor
>> >> compactions for the tablet buffers and the default implementation
>> >> tries to keep the memory region 80-90% full while preferring to
>> >> compact the largest tablet buffers. Creating larger RFiles during minor
compaction should lead to less major compactions.
>> >> During a minor compaction, the tablet buffer still "consumes"
>> >> memory within the in memory map and high ingest rates can lead to
>> >> exhausing the remaining capacity.  The default memory manage uses
>> >> an adaptive strategy to predict the expected memory usage and makes
>> >> compaction decisions that should maintain some free memory.  Batch
>> >> writers can be bursty and a bit unpredictable which could throw off
>> >> these estimates.  Also, depending on the ingest profile, sometimes
>> >> an in-memory tablet buffer will consume a large percentage of the
>> >> total buffer.  This leads to long minor compactions when the buffer
>> >> size is large which can allow ingest enough time to exhaust the
>> >> buffer before that memory can be reclaimed. When a tablet server
>> >> has to block ingest, it can affect client ingest rates to other
>> >> tablet servers due to the way that batch writers work.  This can
>> >> lead to other tablet servers underestimating future ingest rates which can
further exacerbate the problem.
>> >>
>> >> There are some configuration changes that could reduce the severity
>> >> of held commits, although they might reduce peak ingest rates.
>> >> Reducing the in memory map size can reduce the maximum pause time due to
held commits.
>> >> Adding additional tablets should help avoid the problem of a single
>> >> tablet buffer consuming a large percentage of the memory region.
>> >> It might be better to aim for ~20 tablets per server if your
>> >> problem allows for it.  It is also possible to replace the memory
>> >> manager with a custom one.  I've tried this in the past and have
>> >> seen stability improvements by making the memory thresholds less
>> >> aggressive (50-75% full).  This did reduce peak ingest rate in some cases,
but that was a reasonable tradeoff.
>> >>
>> >> Based on your current configuration, if a tablet server is serving
>> >> 4 tablets and has a 32GB buffer, your first minor compactions will
>> >> be at least 8GB and they will probably grow larger over time until
>> >> the tablets naturally split.  Consider how long it would take to
>> >> write this RFile compared to your peak ingest rate.  As others have
>> >> suggested, make sure to use the native maps.  Based on your current
>> >> JVM heap size, using the Java in-memory map would probably lead to OOME
or very bad GC performance.
>> >>
>> >> Accumulo can trace minor compaction durations so you can get a feel
>> >> for max pause times or measure the effect of configuration changes.
>> >>
>> >> Cheers,
>> >> --Jonathan
>> >>
>> >> On Wed, Jul 5, 2017 at 7:16 PM, Dave Marion <_dlmarion@comcast.net_
>> >> <mailto:dlmarion@comcast.net>> wrote:
>> >>
>> >> Based on what Cyrille said, I would look at garbage collection,
>> >> specifically I would look at how much of your newly allocated
>> >> objects spill into the old generation before they are flushed to
>> >> disk. Additionally, I would turn off the debug log or log to SSD’s
>> >> if you have them. Another thought, seeing that you have 256GB RAM /
>> >> node, is to run multiple tablet servers per node. Do you have 10
>> >> threads on your Batch Writers? What about the Batch Writer latency,
>> >> is it too low such that you are not filling the buffer?
>> >>
>> >> *From:* Massimilian Mattetti [mailto:_MASSIMIL@il.ibm.com_ <mailto:
>> >> MASSIMIL@il.ibm.com>] *
>> >> Sent:* Wednesday, July 05, 2017 8:37 AM*
>> >> To:* _user@accumulo.apache.org_ <mailto:user@accumulo.apache.org>*
>> >> Subject:* maximize usage of cluster resources during ingestion
>> >>
>> >> Hi all,
>> >>
>> >> I have an Accumulo 1.8.1 cluster made by 12 bare metal servers.
>> >> Each server has 256GB of Ram and 2 x 10 cores CPU. 2 machines are
>> >> used as masters (running HDFS NameNodes, Accumulo Master and
>> >> Monitor). The other 10 machines has 12 Disks of 1 TB (11 used by
>> >> HDFS DataNode process) and are running Accumulo TServer processes.
>> >> All the machines are connected via a 10Gb network and 3 of them are
>> >> running ZooKeeper. I have run some heavy ingestion test on this
>> >> cluster but I have never been able to reach more than *20% *CPU
>> >> usage on each Tablet Server. I am running an ingestion process
>> >> (using batch writers) on each data node. The table is pre-split in
>> >> order to have 4 tablets per tablet server. Monitoring the network I
>> >> have seen that data is received/sent from each node with a peak
>> >> rate of about 120MB/s / 100MB/s while the aggregated disk write throughput
on each tablet servers is around 120MB/s.
>> >>
>> >> The table configuration I am playing with are:
>> >> "table.file.replication": "2",
>> >> "table.compaction.minor.logs.threshold": "10",
>> >> "table.durability": "flush",
>> >> "table.file.max": "30",
>> >> "table.compaction.major.ratio": "9",
>> >> "table.split.threshold": "1G"
>> >>
>> >> while the tablet server configuration is:
>> >> "tserver.wal.blocksize": "2G",
>> >> "tserver.walog.max.size": "8G",
>> >> "tserver.memory.maps.max": "32G",
>> >> "tserver.compaction.minor.concurrent.max": "50",
>> >> "tserver.compaction.major.concurrent.max": "8",
>> >> "tserver.total.mutation.queue.max": "50M",
>> >> "tserver.wal.replication": "2",
>> >> "tserver.compaction.major.thread.files.open.max": "15"
>> >>
>> >> the tablet server heap has been set to 32GB
>> >>
>> >>  From Monitor UI
>> >>
>> >>
>> >> As you can see I have a lot of valleys in which the ingestion rate
>> >> reaches 0.
>> >> What would be a good procedure to identify the bottleneck which
>> >> causes the 0 ingestion rate periods?
>> >> Thanks.
>> >>
>> >> Best Regards,
>> >> Max
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>

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