On Wed, Nov 8, 2017 at 4:49 PM, Meier, Caleb <caleb.me...@parsons.com> wrote: > Hey Keith, > > I finally got around to trying out some of your suggestions. The first thing > I tried was applying some variation of your RowHasher recipe, and we noticed > an immediate improvement. However, we are still running into issues with our > Tablet servers bogging down. Doing things like flushing and compacting > certainly buys us time, but there appears to be a diminishing margin of > return between each compaction/flush cycle. That is, after every compaction, > it seems like the outstanding notifications increase at a slightly faster > oscillatory rate than in the previous compaction cycle. I ran the experiment > you suggested -- I counted the number of deleted notifications and > outstanding notifications, ran a flush, counted again, ran a compaction, and > counted again. The numbers are as follows: > > Unprocessed: 679 > Deleted: 105,745 > > Flush > > Unprocessed: 352 > Deleted: 4395 > > Compaction > > Unprocessed: 81 > Deleted: 2663 > > Obviously flushing and compacting are extremely beneficial. Is there an > outstanding recipe to check for the number of deleted/unprocessed > notifications and then flush/compact based on some sort of threshold on those > notifications? Is there a way to configure the garbage collector to be more > active?
You could do two things lower the in memory map size for accumulo. This will cause Accumuo to buffer less and lead to more frequent flushes, which run the Fluo GC Iter. For compactions, you can adjust the compaction ratio lower which will cause more frequent compactions. Lowering the compaction ratio also results in less file per tablet, which is good for random seek performance. Could try setting it to 1.5, 1.75, or 2, lower is better until compactions are too frequent :) > > Finally, the piece of advice that you gave me which may end up providing the > biggest breakthrough is profiling the iterators on Accumulo using listscans. > It appears that most of the scanning (at least according to my random > sampling) is happening within one of our observers. I think that we can > cache most of those lookups, so I'm currently optimistic that we can put a > serious dent in the scan traffic that we're seeing. Thanks so much for your > help in debugging this issue. I'll let you know if caching solves our > problems. I have found using the watch command to eyeball something to be invaluable over the years. A technique I leaned from Eric Newton. What did you see in the list scans output that enabled you to pinpoint a particular observer? > > > 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.me...@parsons.com ♦ www.parsons.com > > -----Original Message----- > From: Keith Turner [mailto:ke...@deenlo.com] > Sent: Tuesday, October 31, 2017 5:14 PM > To: fluo-dev <dev@fluo.apache.org> > Subject: Re: fluo accumulo table tablet servers not keeping up with > application > > On Tue, Oct 31, 2017 at 2:22 PM, Meier, Caleb <caleb.me...@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://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_impl_FluoConfigurationImpl.java-23L30&d=DwIFaQ&c=Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7CrMHCgeo_4WXTD0qo8&m=k70bvUqcPprQoM9YUjVaq3OPArW9UM31cFg6zMbHi2E&s=9dKmFBU2QTqdxIGu9fsCH5NwaeiKKWfC13Ty_s0XJ7A&e= > >> >> 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.me...@parsons.com ♦ www.parsons.com >> >> -----Original Message----- >> From: Keith Turner [mailto:ke...@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.me...@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_or >> g_apache_fluo_core_worker_finder_hash_ScanTask.java-23L85&d=DwIFaQ&c=N >> wf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7 >> CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=BRy >> QS2DPBtEfUvHT-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_or >> g_apache_fluo_core_worker_finder_hash_ScanTask.java-23L88&d=DwIFaQ&c=N >> wf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ7 >> CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=ZxU >> RCZE5k65I008z7o4UQGsm6o0mBtJnwV_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_or >> g_apache_fluo_core_worker_finder_hash_ScanTask.java-23L130&d=DwIFaQ&c= >> Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ >> 7CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=C1 >> 41kYyjygBL3kWZyUObU1-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_or >> g_apache_fluo_core_worker_finder_hash_ScanTask.java-23L146&d=DwIFaQ&c= >> Nwf-pp4xtYRe0sCRVM8_LWH54joYF7EKmrYIdfxIq10&r=vuVdzYC2kksVZR5STiFwDpzJ >> 7CrMHCgeo_4WXTD0qo8&m=btY_WNg1O7SuwcHi1m2ksRp3ggzrI7nJlnC2B5cHgaU&s=4Q >> y1-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.me...@parsons.com ♦ www.parsons.com >>> >>> -----Original Message----- >>> From: Keith Turner [mailto:ke...@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.me...@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.me...@parsons.com ♦ www.parsons.com >>>> >>>> -----Original Message----- >>>> From: Keith Turner [mailto:ke...@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.me...@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.me...@parsons.com ♦ www.parsons.com >>>>> >>>>> -----Original Message----- >>>>> From: Keith Turner [mailto:ke...@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.me...@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.me...@parsons.com<mailto:caleb.me...@parsons.com> ♦ >>>>>> www.parsons.com<https://webportal.parsons.com/,DanaInfo=www.parsons. >>>>>> c >>>>>> om+> >>>>>>
