fluo-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Keith Turner <ke...@deenlo.com>
Subject Re: fluo accumulo table tablet servers not keeping up with application
Date Tue, 31 Oct 2017 21:14:17 GMT
On Tue, Oct 31, 2017 at 2:22 PM, Meier, Caleb <Caleb.Meier@parsons.com> wrote:
> Hey Keith,
>
> Just following up on your last message.  After looking at the worker ScanTask logs, it
seems like the workers are conducting scans as frequently as the min sleep time permits. 
That is, if the min sleep time is set to 10s, a ScanTask is being executed every 10s.  In
addition, running the Fluo wait command indicates that the number of outstanding notifications
steadily increases or is held constant (depending on the number of workers).  Based on your
comments below, it seems like the workers should be scanning at a lower rate given that the
notification work queue is constantly increasing in size.  Another thing that we tried was
reducing the number of workers and increasing the min sleep time.  This lowered the scan burden
on the tablet server, but unsurprisingly our processing rate plummeted.  We also tried lowering
the ingest rate for a fixed number of workers (lowering the notification rate for each worker
thread).  While it took longer for the TabletServer to become saturated, it still became overwhelmed.
>
> In general, for the queries that we are benchmarking, our notification:data ratio is
about 7:1 (i.e. each piece of ingested data generates about 7 notifications on the way to
being entirely processed).  I think that this is our primary culprit, but I think that our
application specific scans are also part of the problem (I'm still in the process of trying
to determine what portion of the scans that we are seeing is specific to our observers and
what portion is specific to notification discovery - any suggestions here would be appreciated).
 One reason that I think notification discovery is the culprit is that we implemented an in
memory cache for the metadata, and that didn't seem to affect the scan rate too much (metadata
seeks constitute about 30% of our seeks/scans).
>
> Going forward, we're going to shard our data and look into ways to cut down on scans.
 Any other suggestions about how to improve performance would be appreciated.

In 1.0.0 each worker scans all tablets for notifications.  In 1.1.0
tablets and workers split into groups, you can adjust the worker group
size[1], it defaults to 7.  If you are using 1.1.0, I would recommend
experimenting with this.  If you have 70 workers, then you will have
10 groups.  The tablets will also be divided into 10 groups.  Each
worker will scan all of the tablets in its group.  Notifications are
hash partitioned within a group.  If you lower the group size, then
you will have less scanning.  But as you lower the group size you
increase the chance of work being unevenly spread.  For example with a
group size of 7 that means at most 7 workers will scan a tablet.  It
also means the notifications in  tablet can only be processed by 7
workers.  In the worst case if one tablet has all of the
notifications, then only only 7 workers will process those
notifications.  If the notifications in the table are evenly spread
across tablets, then you could probably decrease the group size to 2
or 3.

There are two possible ways to get sense of what scans are up to via
sampling.  One is to sample listscans commands in the accumulo shell
and see what iterators are in use.  Transactions and notification
scanning will use different iterators.  Could also sample scan jstacks
in some tservers and look at which iterators are used.

Another thing to look into would be to see how many deleted
notifications there are.  Using the command

  fluo scan --raw -c ntfy

Should be able to see notifications and deletes for notifications.  I
am curious how many deletes there are.  When a table if flushed/minor
compacted some notifications will be GC by an iterator.  A full
compaction will do more.  These deletes have to be filtered at scan
time.  If you have a chance I would be interested in the following
numbers (or ratios for the three numbers).

 * How many deleted notifications are there? How many notifications are there?
 * Flush table
 * How many deleted notifications are there? How many notifications are there?
 * compact table
 * How many deleted notifications are there? How many notifications are there?

Keith

[1]: https://github.com/apache/fluo/blob/rel/fluo-1.1.0-incubating/modules/core/src/main/java/org/apache/fluo/core/impl/FluoConfigurationImpl.java#L30

>
> Thanks,
> Caleb
>
> Caleb A. Meier, Ph.D.
> Senior Software Engineer ♦ Analyst
> Parsons Corporation
> 1911 N. Fort Myer Drive, Suite 800 ♦ Arlington, VA 22209
> Office:  (703)797-3066
> Caleb.Meier@Parsons.com ♦ www.parsons.com
>
> -----Original Message-----
> From: Keith Turner [mailto:keith@deenlo.com]
> Sent: Friday, October 27, 2017 12:17 PM
> To: fluo-dev <dev@fluo.apache.org>
> Subject: Re: fluo accumulo table tablet servers not keeping up with application
>
> On Fri, Oct 27, 2017 at 11:03 AM, Meier, Caleb <Caleb.Meier@parsons.com> wrote:
>> Hey Keith,
>>
>> Our benchmark consists of a single query that is a join of two statement patterns
(essentially patterns that incoming data matches, where a unit of data is a statement).  We
are ingesting 50 pairs of statements a minute (100 total), where each statement in the pair
matches one of the statement patterns.  Because the data is being ingested at a constant rate,
the statement pattern Observers and Join Observers are constantly working.  One thing that
is worth mentioning is that we changed the property fluo.implScanTask.maxSleep from 5 min
to 10 seconds.  Based on the constant ingest rate, your comments below, and our low maxSleep,
it seems like the workers would constantly be scanning for new notifications.
>>
>>> Once a worker scans all tablets and finds a list of notifications, it does not
scan again until half of those notifications are processed.
>>
>> How does the maxSleep property work in conjunction with this?  If the max sleep time
elapses before a worker processes half of the notifications, will it scan?
>
> I don't think it will scan again until the # of queued notifications is cut in half.
 I looked in 1.0.0 and 1.1.0 and I think while loops linked below should hold off on the scan
until the queue halves.
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_fluo_blob_rel_fluo-2D1.0.0-2Dincubating_modules_core_src_main_java_org_apache_fluo_core_worker_finder_hash_ScanTask.java-23L85&d=DwIFaQ&c=Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=BRyQS2DPBtEfUvHT-JKBXPWABrSyihP6yaJcfE1BJFQ&e=
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_fluo_blob_rel_fluo-2D1.1.0-2Dincubating_modules_core_src_main_java_org_apache_fluo_core_worker_finder_hash_ScanTask.java-23L88&d=DwIFaQ&c=Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=ZxURCZE5k65I008z7o4UQGsm6o0mBtJnwV_N6Y668oM&e=
>
> Were you able to find the ScanTask debug messages in the worker logs?
> Below are the log messages int the code to give a sense of what to look for.
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_fluo_blob_rel_fluo-2D1.0.0-2Dincubating_modules_core_src_main_java_org_apache_fluo_core_worker_finder_hash_ScanTask.java-23L130&d=DwIFaQ&c=Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=C141kYyjygBL3kWZyUObU1-nu4ZjvMnu7xp_QbIGkCA&e=
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_fluo_blob_rel_fluo-2D1.1.0-2Dincubating_modules_core_src_main_java_org_apache_fluo_core_worker_finder_hash_ScanTask.java-23L146&d=DwIFaQ&c=Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=4Qy1-LbMEpJ7NZLqngU8ZOEOBv6nB0nXM8mjkWdpEL4&e=
>
> IIRC I think if notifications were found in a tablet during the last scan, then it will
always scan it during the next scan loop.  As notifications are not found in a tablet then
that tablets next scan time doubles up to fluo.implScanTask.maxSleep.
>
> So its possible that all notifications found are being processed quickly and then the
workers are scanning for more.  The debug messages would show this.
>
> There is also a minSleep time.  This property determines the minimum amount of time it
will sleep between scan loops, seems to default to 5 secs.  Could try increasing this.
>
> Looking at the props, it seems they prop names for min and max sleep changed between
1.0.0 and 1.1.0.
>
>
>>
>> Caleb A. Meier, Ph.D.
>> Senior Software Engineer ♦ Analyst
>> Parsons Corporation
>> 1911 N. Fort Myer Drive, Suite 800 ♦ Arlington, VA 22209
>> Office:  (703)797-3066
>> Caleb.Meier@Parsons.com ♦ www.parsons.com
>>
>> -----Original Message-----
>> From: Keith Turner [mailto:keith@deenlo.com]
>> Sent: Thursday, October 26, 2017 6:20 PM
>> To: fluo-dev <dev@fluo.apache.org>
>> Subject: Re: fluo accumulo table tablet servers not keeping up with
>> application
>>
>> On Thu, Oct 26, 2017 at 5:47 PM, Meier, Caleb <Caleb.Meier@parsons.com> wrote:
>>> Hey Keith,
>>>
>>> We'll rerun the benchmarks tomorrow and track the outstanding notifications.
 We'll also see if compacting at some point during ingest helps with the scan rate.  Have
you observed such high scan rates for such a small amount of data in any of your benchmarking?
 What would account for the huge disparity in results read vs. results returned?  It seems
like our scans are extremely inefficient for some reason.  Our tablet servers are becoming
overwhelmed even before data gets flushed to disk.
>>
>> Oh I never saw you attachment, may not be able to attach stuff on mailing list.
>>
>> Its possible that what you are seeing is the workers scanning for notifications.
 If you look in the workers logs do you see messages about scanning for notifications?  If
so what do they look like?
>>
>> In 1.0.0 each worker scans all tablets in random order.  When it scans it has an
iterator that uses hash+mod to select a subset of notifications.  The iterator also suppresses
deleted notifications.
>> So the selection and suppression by that iterator could explain the read vs returned.
 It does exponential back off on tablets where it does not find data.  Once a worker scans
all tablets and finds a list of notifications, it does not scan again until half of those
notifications are processed.
>>
>> In the beginning, would you have a lot of notifications?  If so I would expect a
lot of scanning and then it should slow down once the workers get a list of notifications
to process.
>>
>> In 1.1.0 the workers divide up the tablets (so workers no longer scan
>> all tablets, groups of workers share groups of tablets).   If the
>> table is splits after the workers start, it may take them a bit to execute the distributed
algorithm that divys tablets among workers.
>>
>> Anyway the debug messages about scanning for notifications in the workers should
provide some insight into this.
>>
>> If its not notification scanning, then it could be that the application is scanning
over a lots of data that was deleted or something like that.
>>
>>>
>>> Caleb A. Meier, Ph.D.
>>> Senior Software Engineer ♦ Analyst
>>> Parsons Corporation
>>> 1911 N. Fort Myer Drive, Suite 800 ♦ Arlington, VA 22209
>>> Office:  (703)797-3066
>>> Caleb.Meier@Parsons.com ♦ www.parsons.com
>>>
>>> -----Original Message-----
>>> From: Keith Turner [mailto:keith@deenlo.com]
>>> Sent: Thursday, October 26, 2017 5:36 PM
>>> To: fluo-dev <dev@fluo.apache.org>
>>> Subject: Re: fluo accumulo table tablet servers not keeping up with
>>> application
>>>
>>> On Thu, Oct 26, 2017 at 2:50 PM, Meier, Caleb <Caleb.Meier@parsons.com>
wrote:
>>>> Hey Keith,
>>>>
>>>> Thanks for the reply.  Regarding our benchmark, I've attached some screenshots
of our Accumulo UI that were taken while the benchmark was running.  Basically, our ingest
rate is pretty low (about 150 entries/s, but our scan rate is off the charts - approaching
6 million entries/s!).  Also, notice the disparity between reads and returned in the Scan
chart.  That disparity would suggest that we're possibly doing full table scans somewhere,
which is strange given that all of our scans are RowColumn constrained.  Perhaps we are building
our Scanner incorrectly.   In an effort to maximize the number of TabletServers, we split
the Fluo table into 5MB tablets.  Also, the data is not well balanced -- the tablet servers
do take turns being maxed out while others are idle.  We're considering possible sharding
strategies.
>>>>
>>>> Given that our TabletServers are getting saturated so quickly for such a
low ingest rate, it seems like we definitely need to cut down on the number of scans as a
first line of attack to see what that buys us.  Then we'll look into tuning Accumulo and Fluo.
 Does this seem like a reasonable approach to you?  Does the scan rate of our application
strike you as extremely high?  When you look at the Rya Observers, can you pay attention to
how we are building our scans to make sure that we're not inadvertently doing full table scans?
 Also, what exactly do you mean by "are the 6 lookups in the transaction done sequentially"?
>>>
>>> Regarding the scan rate there are few things I Am curious about.
>>>
>>> Fluo workers scan for notifications in addition to the scanning done
>>> by your apps.  I made some changes in 1.1.0 to reduce the amount of
>>> scanning needed to find notifications, but this should not make much
>>> of a difference on a small amount of nodes.  Details about this are
>>> in
>>> 1.1.0 release notes.  I am not sure what the best way is to determine how much
of the scanning you are seeing is app vs notification finding.  Can you run the fluo wait
command to see how many outstanding notifications there are?
>>>
>>> Transactions leave a paper trail behind and compactions clean this up (Fluo has
a garbage collection iterator).  This is why I asked what effect compacting the table had.
 Compactions will also clean up deleted notifications.
>>>
>>>
>>>>
>>>> Thanks,
>>>> Caleb
>>>>
>>>> Caleb A. Meier, Ph.D.
>>>> Senior Software Engineer ♦ Analyst
>>>> Parsons Corporation
>>>> 1911 N. Fort Myer Drive, Suite 800 ♦ Arlington, VA 22209
>>>> Office:  (703)797-3066
>>>> Caleb.Meier@Parsons.com ♦ www.parsons.com
>>>>
>>>> -----Original Message-----
>>>> From: Keith Turner [mailto:keith@deenlo.com]
>>>> Sent: Thursday, October 26, 2017 1:39 PM
>>>> To: fluo-dev <dev@fluo.apache.org>
>>>> Subject: Re: fluo accumulo table tablet servers not keeping up with
>>>> application
>>>>
>>>> Caleb
>>>>
>>>> What if any tuning have you done?  The following tune-able Accumulo parameters
impact performance.
>>>>
>>>>  * Write ahead log sync settings (this can have huge performance
>>>> implications)
>>>>  * Files per tablet
>>>>  * Tablet server cache sizes
>>>>  * Accumulo data block sizes
>>>>  * Tablet server client thread pool size
>>>>
>>>> For Fluo the following tune-able parameters are important.
>>>>
>>>>  * Commit memory (this determines how many transactions are held in
>>>> memory while committing)
>>>>  * Threads running transactions
>>>>
>>>> What does the load (CPU and memory) on the cluster look like?  I'm curious
how even it is?  For example is one tserver at 100% cpu while others are idle, this could
be caused by uneven data access patterns.
>>>>
>>>> Would it be possible for me to see or run the benchmark?  I am going to take
a look at the Rya observers, let me know if there is anything in particular I should look
at.
>>>>
>>>> Are the 6 lookups in the transaction done sequentially?
>>>>
>>>> Keith
>>>>
>>>> On Thu, Oct 26, 2017 at 11:34 AM, Meier, Caleb <Caleb.Meier@parsons.com>
wrote:
>>>>> Hello Fluo Devs,
>>>>>
>>>>> We have implemented an incremental query evaluation service for Apache
Rya that leverages Apache Fluo.  We’ve been doing some benchmarking and we’ve found that
the Accumulo Tablet servers for the Fluo table are falling behind pretty quickly for our application.
 We’ve tried splitting the Accumulo Table so that we have more Tablet Servers, but that
doesn’t really buy us too much.  Our application is fairly scan intensive—we have a metadata
framework in place that allows us to pass query results through the query tree, and each observer
needs to look up metadata to determine which observer to route its data to after processing.
 To give you some indication of our scan rates, our Join Observer does about 6 lookups, builds
a scanner to do one RowColumn restricted scan, and then does many writes.  So an obvious way
to alleviate the burden on the TableServer is to cut down on the number of scans.
>>>>>
>>>>> One approach that we are considering is to import all of our metadata
into memory.  Essentially, each Observer would need access to an in memory metadata cache.
 We’re considering using the Observer context, but this cache needs to be mutable because
a user needs to be able to register new queries.  Is it possible to update the context, or
would we need to restart the application to do that?  I guess other options would be to create
a static cache for each Observer that stores the metadata, or to store it in Zookeeper.  Have
any of you devs ever had create a solution to share state between Observers that doesn’t
rely on the Fluo table?
>>>>>
>>>>> In addition to cutting down on the scan rate, are there any other approaches
that you would consider?  I assume that the problem lies primarily with how we’ve implemented
our application, but I’m also wondering if there is anything we can do from a configuration
point of view to reduce the burden on the Tablet servers.  Would reducing the number of workers/worker
threads to cut down on the number of times a single observation is processed be helpful? 
It seems like this approach would cut out some redundant scans as well, but it might be more
of a second order optimization. In general, any insight that you might have on this problem
would be greatly appreciated.
>>>>>
>>>>> Sincerely,
>>>>> Caleb Meier
>>>>>
>>>>> Caleb A. Meier, Ph.D.
>>>>> Senior Software Engineer ♦ Analyst
>>>>> Parsons Corporation
>>>>> 1911 N. Fort Myer Drive, Suite 800 ♦ Arlington, VA 22209
>>>>> Office:  (703)797-3066
>>>>> Caleb.Meier@Parsons.com<mailto:Caleb.Meier@Parsons.com> ♦
>>>>> www.parsons.com<https://webportal.parsons.com/,DanaInfo=www.parsons.
>>>>> c
>>>>> om+>
>>>>>

Mime
View raw message