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? 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+> >>>
