> You are, of course, free to use batches in your application I'm not looking to justify the use of batches, I'm looking for the path forward that will give us the Best Results™ both near and long term, for some definition of Best (which would be a balance of client throughput and cluster pressure). If individual writes are best for us, that's what I want to do. If batches are best for us, that's what I want to do.
I'm just struggling that I'm not able to reproduce your advice experimentally, and it's not just a few percent difference, it's 5x to 8x difference. It's really difficult for me to adopt advice blindly when it differs from my own observations by such a substantial amount. That means something is wrong either with my observations or with the advice, and I would really like to know which. I'm not trying to be argumentative or push for a particular approach, I'm trying to resolve an inconsistency. RE your questions: I'm sorry this turns into a wall of text, simple questions about parallelism and distributed systems rarely can be adequately answered in just a few words. I'm trying to be open and transparent about my testing approach because I want to find out where the disconnect is here. At the same time I'm trying to bridge the knowledge gap since I'm working with parallelism toolset with which you're not familiar, and that could obviously have a substantial impact on the results. Hopefully someone else in the community familiar with Scala will notice this and provide feedback that I'm not making a fundamental mistake. 1) My original runs were in EC2 being driven by a different server than the Cassandra cluster, but in the same AZ as one of the Cassandra servers (typical 3-AZ setup for Cassandra). All four instances (3x C*, 1x test driver) were i2.2xl, so have gigabit network between them. 2) The system was under some moderate other load, this is our test cluster that takes a steady stream of simulated data to provide other developers with something to work against. That load is quite constant and doesn't work these servers particularly hard - only a few thousand records per second typically. Load averages between 1 and 3 most of the time. Unfortunately I'm not successful getting cassandra-stress talking to this cluster because of ssl configuration (it doesn't seem to actually pay attention to -ts and -tspw command line flags). I can find out if our ops guys would be ok with turning off ssl for a while, but as that would break our other applications using the same cluster, and may block our other engineers as a result. So it has farther reaching implications than just being something I can happily turn on or off at whim. I'm curious how you would expect the performance of my stress tool to differ when the cluster was being overworked - could you explain what you anticipate the change in results to look like? I.e. would single-writes remain about constant for performance while batches would degrade in performance? 3) Well I specifically attempt to control for this by testing three different concurrency models, these were named by me "parallel," "scatter," and "traverse" (just aliases to make it easier to control the driver). You can see the code between the different approaches here - they are pretty similar to each other, but probably involve some knowledge of how concurrency works in Scala to really appreciate the differences: https://gist.github.com/MightyE/1c98912fca104f6138fc/a7db68e72f99ac1215fcfb096d69391ee285c080#file-testsuite-L181-L203 I know you're not a Scala guy, so I'll explain roughly what they do, but the point is that I'm trying hard to control for just having chosen a bad concurrency model: scatter -> Take all of the Statements and call executeAsync() on them as *fast* the Session will let me. This is the Unintelligent Brute Force approach, and it's definitely not how I would model a typical production application as it doesn't attempt to respond to system pressure at all, and it's trying to gobble up as many resources as it can. Use the Scala Futures system to combine the the set of async calls into a single Future that completes when all the futures returned from executeAsync() have completed. traverse -> Give all of the Statements to the Scala Futures system and tell it to call executeAsync() on them all at the rate that it thinks is appropriate. This would be much closer to my recommendation on how to model a production application, because in a real application, there's more than a single class of work to be done, and the Futures system schedules both this work and other work intelligently and configurably. It gives us a single awaitable Future that completes when it has finished all of its work and all of the async calls have been completed. You guys are using Netty for your native protocol, and Netty offers true event driven concurrency which gets along famously well with Scala's Futures system. parallel -> Use a Scala Parallel collection to perform parallel synchronous execution. This is very much like a typical Java multithreaded approach. There is a thread pool (defaults to 32 threads), and we fill up that pool with workers, each of which will take one element from the work queue, work on it until it completes, before taking another one from the queue. The whole collection completes when the last worker finishes its last task, and the parallel operation blocks until then. So: 3a) This is the approach used by the "scatter" strategy. I'm sure it puts a lot of pressure on the cluster (it's designed to put as much pressure as the client driver will let it), but I didn't see any GC in the logs for either batch or single statements. 3b) I await the result of an entire run, not of individual calls. In the earliest tests I posted here, that was the completion of 50,000 records for each run (so either 50,000 simultaneous statement.execAsync calls, or 500 batch.execAsync calls based on a batch size of 100). The cluster may have a chance to calm down between runs while some straggler finishes up, but inside of a run both the "scatter" and "traverse" approaches should be keeping it pretty busy (the parallel approach only allows for 32 executors at a time by default, so I'm pretty sure that I'd need a whole lot more workers before I was able to keep a cluster busy). 4) > you're still only using 3 servers. It's true, I don't have access to a larger test cluster. I'm reluctant to try to purposefully stress out our customer facing cluster. If you have access to a larger test cluster, I'd be *very happy* to bundle up a standalone executable of this test suite so that you can try it out there for yourself, and I'd love to see such results. For what it's worth, our testing on our larger (~50 nodes) production cluster which still uses Thrift MutationBatches we found that batch sizes in the neighborhood of 1500 business objects (about 10-15 mutations each) gave us the best throughput. Thrift is not as asynchronous as the native protocol, so I would expect larger batches to offer better performance there than over the native protocol as each round trip blocks a connection in the pool from doing any other work. > The horror of using batches increases linearly as you add servers I'm curious if you could explain this statement in more detail, you've said something like it a couple of times, but I'm still not understanding. You talked about coordination overhead, but practical experience shows this is not likely to produce a linear degradation as cluster size increases, as mostly the cost you described which was related to cluster size was associated with coordination overhead. Writes need to be coordinated no matter what in a typical setup, and this overhead should not be worse than token-naive writing. Token awareness is awesome and we should try to benefit from, but it's hard to understand how token naïveté degrades linearly, especially for writes, and the practical lower bound for performance as cluster size approaches infinity should be the one where all operations need to be coordinated. In fact even the DataStax driver is not perfect in its token awareness. If you're not using prepared statements you automatically fall back to the TokenAwarePolicy's child policy, which would have to be a token naive policy (try new com.datastax.driver.core.SimpleStatement("some CQL", someValues*).getRoutingKey() - you'll get back null, which triggers TokenAwarePolicy to defer to its child policy). I even ran a test yesterday which grouped statements to have a common replica and ran batches where all statements in the batch possessed the same replica (RF=1, completely eliminating coordination overhead), the results were not statistically different from token naive batches. You also talked about possible GC pressure, but that wouldn't be related to cluster size, only to batch size. 5) I'm summing time time between the first call to .executeAsync, and the time when the last Future completes within a given test run. On Mon, Dec 15, 2014 at 7:56 PM, Jonathan Haddad <j...@jonhaddad.com> wrote: > You are, of course, free to use batches in your application. Keep in mind > however, that both my and Ryan's advice is coming from debugging issues in > production. I don't know why your Scala script is performing better on > batches than async. It could be: > > 1) network. are you running the test script on your laptop and connecting > to cluster over WAN? If so, I would not be shocked if batch was faster > since your latency is going to be crazy high. > > 2) is the system under any other load? I'd love to see the results of the > tests while cassandra stress was running. This is a step closer to > production where you have to worry about such things > > 3) The logic for doing async queries may be incorrect. > a) Are you just throwing all the queries at once against the cluster? If > so, I'd love to see what's happening with GC. Typically in a real workload > you'd be > b) Are you keeping the servers busy? If you're calling wait() on a group > of futures, you're now blocking requests from being submitted and limiting > the throughput. > > 4) you're still only using 3 servers. The horror of using batches > increases linearly as you add servers. > > 5) What exactly are you summing in the end? The total real time taken, or > an aggregation of the async query times? If it's the async query times > that's going to be pretty misleading (and incorrect). Again, my Scala is > terrible so I could be reading it wrong. > > Sorry I don't have more time to debug the script. Any of the above ideas > apply? > > Jon > > On Mon Dec 15 2014 at 1:11:43 PM Eric Stevens <migh...@gmail.com> wrote: > >> > Unfortunately my Scala isn't the best so I'm going to have to take a >> little bit to wade through the code. >> >> I think the important thing to take from this code is that: >> >> 1) execution order is randomized for each run, and new data is randomly >> generated for each run to eliminate biases. >> 2) we write to five different key layouts in an attempt to eliminate bias >> from some poorly chosen scheme, we test both clustering and non-clustering >> approaches >> 3) We can fork *just* on batch-vs-single strategy (see >> https://gist.github.com/MightyE/1c98912fca104f6138fc/ >> a7db68e72f99ac1215fcfb096d69391ee285c080#file-testsuite-L167-L180 ) >> thanks to the DS driver having a common executable ancestor between them >> (an extremely nice feature) >> 4) We test three different parallelism strategies to eliminate bias from >> a poorly chosen concurrency model (see https://gist.github.com/ >> MightyE/1c98912fca104f6138fc/a7db68e72f99ac1215fcfb096d6939 >> 1ee285c080#file-testsuite-L181-L203 ) >> 5) The code path is identical wherever possible between strategies. >> 6) Principally this just sets up an Iterable of Statement (sometimes >> members are batches, sometimes members are single statements), and times >> how long they take to execute and complete with different concurrency >> models. >> >> *RE: Cassandra-Stress* >> > It may be useful to run cassandra-stress (it doesn't seem to have a >> mode for batches) to get a baseline on non-batches. I'm curious to know if >> you get different numbers than the scala profiler. >> >> We always use SSL for everything, and I've struggled to get >> cassandra-stress to talk to our SSL cluster. Just so I don't keep spinning >> my wheels on a temporary effort, I used CCM to stand up a 2.0.11 cluster >> locally, and ran both tools against here. I'm dubious about what you can >> infer from such a test because it's not apples to apples (they write >> different data). >> >> Nevertheless, here is the output of "ccm stress" against my local machine >> - I inserted 113,825 records in 62 seconds, and used this data size to >> drive my tool: >> >> Created keyspaces. Sleeping 3s for propagation. >> total interval_op_rate interval_key_rate latency 95th 99.9th >> elapsed_time >> 11271 1127 1127 8.9 144.7 401.1 10 >> 27998 1672 1672 9.5 140.5 399.4 20 >> 42189 1419 1419, 9.3 148.0 494.5 31 >> 59335 1714 1714 9.3 147.0 493.2 41 >> 84957 2562 2562 6.1 137.1 493.3 51 >> 113825 2886 2886 5.1 131.5 493.3 62 >> >> >> After a ccm clear && ccm start , here's my tool this same local cluster >> (note that I'm actually writing a total of 5x the records because I write >> the same data to each of 5 tables). My little local cluster just about >> brought down my machine under this test (especially the second one). >> >> ==== Execution Results for 1 runs of 113825 records ============= >> 1 runs of 113,825 records (3 protos, 5 agents, ~15 per bucket) as single >> statements >> Total Run Time >> traverse test2 ((aid, bckt), end) = >> 25,488,179,000 >> traverse test4 ((aid, bckt), proto, end) no explicit ordering = >> 25,497,183,000 >> traverse test5 ((aid, bckt, end)) = >> 25,529,444,000 >> traverse test3 ((aid, bckt), end, proto) reverse order = >> 31,495,348,000 >> traverse test1 ((aid, bckt), proto, end) reverse order = >> 33,686,013,000 >> >> ==== Execution Results for 1 runs of 113825 records ============= >> 1 runs of 113,825 records (3 protos, 5 agents, ~15 per bucket) in batches >> of 10 >> Total Run Time >> traverse test3 ((aid, bckt), end, proto) reverse order = >> 11,030,788,000 >> traverse test1 ((aid, bckt), proto, end) reverse order = >> 13,345,962,000 >> traverse test2 ((aid, bckt), end) = >> 15,110,208,000 >> traverse test4 ((aid, bckt), proto, end) no explicit ordering = >> 16,398,982,000 >> traverse test5 ((aid, bckt, end)) = >> 22,166,119,000 >> >> For giggles I added token aware batching (grouping statements within a >> single batch by meta.getReplicas(statement.getKeyspace, >> statement.getRoutingKey).iterator().next - see https://gist.github.com/ >> MightyE/1c98912fca104f6138fc#file-testsuite-L176-L189 ), here's that >> run; comparable results with before, and easily inside one sigma of >> non-token-aware batching, so not a statistically significant difference. >> >> ==== Execution Results for 1 runs of 113825 records ============= >> 1 runs of 113,825 records (3 protos, 5 agents, ~15 per bucket) in batches >> of 10 >> Total Run Time >> traverse test2 ((aid, bckt), end) = >> 11,429,008,000 >> traverse test1 ((aid, bckt), proto, end) reverse order = >> 12,593,034,000 >> traverse test4 ((aid, bckt), proto, end) no explicit ordering = >> 13,111,244,000 >> traverse test3 ((aid, bckt), end, proto) reverse order = >> 25,163,064,000 >> traverse test5 ((aid, bckt, end)) = >> 30,233,744,000 >> >> >> >> On Sat, Dec 13, 2014 at 11:07 AM, Jonathan Haddad <j...@jonhaddad.com> >> wrote: >> >>> >>> >>> On Sat Dec 13 2014 at 10:00:16 AM Eric Stevens <migh...@gmail.com> >>> wrote: >>> >>>> Isn't the net effect of coordination overhead incurred by batches >>>> basically the same as the overhead incurred by RoundRobin or other >>>> non-token-aware request routing? As the cluster size increases, each node >>>> would coordinate the same percentage of writes in batches under token >>>> awareness as they would under a more naive single statement routing >>>> strategy. If write volume per time unit is the same in both approaches, >>>> each node ends up coordinating the majority of writes under either strategy >>>> as the cluster grows. >>>> >>> >>> If you're not token aware, there's extra coordinator overhead, yes. If >>> you are token aware, not the case. I'm operating under the assumption that >>> you'd want to be token aware, since I don't see a point in not doing so :) >>> >>> Unfortunately my Scala isn't the best so I'm going to have to take a >>> little bit to wade through the code. >>> >>> It may be useful to run cassandra-stress (it doesn't seem to have a mode >>> for batches) to get a baseline on non-batches. I'm curious to know if you >>> get different numbers than the scala profiler. >>> >>> >>> >>>> >>>> GC pressure in the cluster is a concern of course, as you observe. But >>>> delta performance is *substantial* from what I can see. As in the >>>> case where you're bumping up against retries, this will cause you to fall >>>> over much more rapidly as you approach your tipping point, but in a healthy >>>> cluster, it's the same write volume, just a longer tenancy in eden. If >>>> reasonable sized batches are causing survivors, you're not far off from >>>> falling over anyway. >>>> >>>> On Sat, Dec 13, 2014 at 10:04 AM, Jonathan Haddad <j...@jonhaddad.com> >>>> wrote: >>>> >>>>> One thing to keep in mind is the overhead of a batch goes up as the >>>>> number of servers increases. Talking to 3 is going to have a much >>>>> different performance profile than talking to 20. Keep in mind that the >>>>> coordinator is going to be talking to every server in the cluster with a >>>>> big batch. The amount of local writes will decrease as it owns a smaller >>>>> portion of the ring. All you've done is add an extra network hop between >>>>> your client and where the data should actually be. You also start to have >>>>> an impact on GC in a very negative way. >>>>> >>>>> Your point is valid about topology changes, but that's a relatively >>>>> rare occurrence, and the driver is notified pretty quickly, so I wouldn't >>>>> optimize for that case. >>>>> >>>>> Can you post your test code in a gist or something? I can't really >>>>> talk about your benchmark without seeing it and you're basing your stance >>>>> on the premise that it is correct, which it may not be. >>>>> >>>>> >>>>> >>>>> On Sat Dec 13 2014 at 8:45:21 AM Eric Stevens <migh...@gmail.com> >>>>> wrote: >>>>> >>>>>> You can seen what the partition key strategies are for each of the >>>>>> tables, test5 shows the least improvement. The set (aid, end) should be >>>>>> unique, and bckt is derived from end. Some of these layouts result in >>>>>> clustering on the same partition keys, that's actually tunable with the >>>>>> "~15 per bucket" reported (exact number of entries per bucket will vary >>>>>> but >>>>>> should have a mean of 15 in that run - it's an input parameter to my >>>>>> tests). "test5" obviously ends up being exclusively unique partitions >>>>>> for >>>>>> each record. >>>>>> >>>>>> Your points about: >>>>>> 1) Failed batches having a higher cost than failed single statements >>>>>> 2) In my test, every node was a replica for all data. >>>>>> >>>>>> These are both very good points. >>>>>> >>>>>> For #1, since the worst case scenario is nearly twice fast in batches >>>>>> as its single statement equivalent, in terms of impact on the client, >>>>>> you'd >>>>>> have to be retrying half your batches before you broke even there (but of >>>>>> course those retries are not free to the cluster, so you probably make >>>>>> the >>>>>> performance tipping point approach a lot faster). This alone may be >>>>>> cause >>>>>> to justify avoiding batches, or at least severely limiting their size >>>>>> (hey, >>>>>> that's what this discussion is about!). >>>>>> >>>>>> For #2, that's certainly a good point, for this test cluster, I >>>>>> should at least re-run with RF=1 so that proxying times start to matter. >>>>>> If you're not using a token aware client or not using a token aware >>>>>> policy >>>>>> for whatever reason, this should even out though, no? Each node will end >>>>>> up coordinating 1/(nodecount-rf+1) mutations, regardless of whether they >>>>>> are batched or single statements. The DS driver is very careful to >>>>>> caution >>>>>> that the topology map it maintains makes no guarantees on freshness, so >>>>>> you >>>>>> may see a significant performance penalty in your client when the >>>>>> topology >>>>>> changes if you're depending on token aware routing as part of your >>>>>> performance requirements. >>>>>> >>>>>> >>>>>> I'm curious what your thoughts are on grouping statements by primary >>>>>> replica according to the routing policy, and executing unlogged batches >>>>>> that way (so that for token aware routing, all statements are executed >>>>>> on a >>>>>> replica, for others it'd make no difference). Retries are still more >>>>>> expensive, but token aware proxying avoidance is still had. It's pretty >>>>>> easy to do in Scala: >>>>>> >>>>>> def groupByFirstReplica(statements: Iterable[Statement])(implicit >>>>>> session: Session): Map[Host, Seq[Statement]] = { >>>>>> val meta = session.getCluster.getMetadata >>>>>> statements.groupBy { st => >>>>>> meta.getReplicas(st.getKeyspace, st.getRoutingKey).iterator(). >>>>>> next >>>>>> } >>>>>> } >>>>>> val result = >>>>>> Future.traverse(groupByFirstReplica(statements).values).map(st >>>>>> => newBatch(st).executeAsync()) >>>>>> >>>>>> >>>>>> Let me get together my test code, it depends on some existing >>>>>> utilities we use elsewhere, such as implicit conversions between Google >>>>>> and >>>>>> Scala native futures. I'll try to put this together in a format that's >>>>>> runnable for you in a Scala REPL console without having to resolve our >>>>>> internal dependencies. This may not be today though. >>>>>> >>>>>> Also, @Ryan, I don't think that shuffling would make a difference for >>>>>> my above tests since as Jon observed, all my nodes were already replicas >>>>>> there. >>>>>> >>>>>> >>>>>> On Sat, Dec 13, 2014 at 7:37 AM, Ryan Svihla <rsvi...@datastax.com> >>>>>> wrote: >>>>>> >>>>>>> Also..what happens when you turn on shuffle with token aware? >>>>>>> http://www.datastax.com/drivers/java/2.1/com/datastax/ >>>>>>> driver/core/policies/TokenAwarePolicy.html >>>>>>> >>>>>>> On Sat, Dec 13, 2014 at 8:21 AM, Jonathan Haddad <j...@jonhaddad.com> >>>>>>> wrote: >>>>>>>> >>>>>>>> To add to Ryan's (extremely valid!) point, your test works because >>>>>>>> the coordinator is always a replica. Try again using 20 (or 50) nodes. >>>>>>>> Batching works great at RF=N=3 because it always gets to write to >>>>>>>> local and >>>>>>>> talk to exactly 2 other servers on every request. Consider what >>>>>>>> happens >>>>>>>> when the coordinator needs to talk to 100 servers. It's unnecessary >>>>>>>> overhead on the server side. >>>>>>>> >>>>>>>> To save network overhead, Cassandra 2.1 added support for response >>>>>>>> grouping (see http://www.datastax.com/dev/blog/cassandra-2-1-now- >>>>>>>> over-50-faster) which massively helps performance. It provides >>>>>>>> the benefit of batches but without the coordinator overhead. >>>>>>>> >>>>>>>> Can you post your benchmark code? >>>>>>>> >>>>>>>> On Sat Dec 13 2014 at 6:10:36 AM Jonathan Haddad <j...@jonhaddad.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> There are cases where it can. For instance, if you batch multiple >>>>>>>>> mutations to the same partition (and talk to a replica for that >>>>>>>>> partition) >>>>>>>>> they can reduce network overhead because they're effectively a single >>>>>>>>> mutation in the eye of the cluster. However, if you're not doing >>>>>>>>> that (and >>>>>>>>> most people aren't!) you end up putting additional pressure on the >>>>>>>>> coordinator because now it has to talk to several other servers. If >>>>>>>>> you >>>>>>>>> have 100 servers, and perform a mutation on 100 partitions, you could >>>>>>>>> have >>>>>>>>> a coordinator that's >>>>>>>>> >>>>>>>>> 1) talking to every machine in the cluster and >>>>>>>>> b) waiting on a response from a significant portion of them >>>>>>>>> >>>>>>>>> before it can respond success or fail. Any delay, from GC to a >>>>>>>>> bad disk, can affect the performance of the entire batch. >>>>>>>>> >>>>>>>>> >>>>>>>>> On Sat Dec 13 2014 at 4:17:33 AM Jack Krupansky < >>>>>>>>> j...@basetechnology.com> wrote: >>>>>>>>> >>>>>>>>>> Jonathan and Ryan, >>>>>>>>>> >>>>>>>>>> Jonathan says “It is absolutely not going to help you if you're >>>>>>>>>> trying to lump queries together to reduce network & server overhead >>>>>>>>>> - in >>>>>>>>>> fact it'll do the opposite”, but I would note that the CQL3 spec >>>>>>>>>> says “ >>>>>>>>>> The BATCH statement ... serves several purposes: 1. It saves >>>>>>>>>> network round-trips between the client and the server (and sometimes >>>>>>>>>> between the server coordinator and the replicas) when batching >>>>>>>>>> multiple >>>>>>>>>> updates.” Is the spec inaccurate? I mean, it seems in conflict with >>>>>>>>>> your >>>>>>>>>> statement. >>>>>>>>>> >>>>>>>>>> See: >>>>>>>>>> https://cassandra.apache.org/doc/cql3/CQL.html >>>>>>>>>> >>>>>>>>>> I see the spec as gospel – if it’s not accurate, let’s propose a >>>>>>>>>> change to make it accurate. >>>>>>>>>> >>>>>>>>>> The DataStax CQL doc is more nuanced: “Batching multiple >>>>>>>>>> statements can save network exchanges between the client/server and >>>>>>>>>> server >>>>>>>>>> coordinator/replicas. However, because of the distributed nature of >>>>>>>>>> Cassandra, spread requests across nearby nodes as much as possible to >>>>>>>>>> optimize performance. Using batches to optimize performance is >>>>>>>>>> usually not >>>>>>>>>> successful, as described in Using and misusing batches section. For >>>>>>>>>> information about the fastest way to load data, see "Cassandra: Batch >>>>>>>>>> loading without the Batch keyword."” >>>>>>>>>> >>>>>>>>>> Maybe what we really need is a “client/driver-side batch”, which >>>>>>>>>> is simply a way to collect “batches” of operations in the >>>>>>>>>> client/driver and >>>>>>>>>> then let the driver determine what degree of batching and >>>>>>>>>> asynchronous >>>>>>>>>> operation is appropriate. >>>>>>>>>> >>>>>>>>>> It might also be nice to have an inquiry for the cluster as to >>>>>>>>>> what batch size is most optimal for the cluster, like number of >>>>>>>>>> mutations >>>>>>>>>> in a batch and number of simultaneous connections, and to have that >>>>>>>>>> be >>>>>>>>>> dynamic based on overall cluster load. >>>>>>>>>> >>>>>>>>>> I would also note that the example in the spec has multiple >>>>>>>>>> inserts with different partition key values, which flies in the face >>>>>>>>>> of the >>>>>>>>>> admonition to to refrain from using server-side distribution of >>>>>>>>>> requests. >>>>>>>>>> >>>>>>>>>> At a minimum the CQL spec should make a more clear statement of >>>>>>>>>> intent and non-intent for BATCH. >>>>>>>>>> >>>>>>>>>> -- Jack Krupansky >>>>>>>>>> >>>>>>>>>> *From:* Jonathan Haddad <j...@jonhaddad.com> >>>>>>>>>> *Sent:* Friday, December 12, 2014 12:58 PM >>>>>>>>>> *To:* user@cassandra.apache.org ; Ryan Svihla >>>>>>>>>> <rsvi...@datastax.com> >>>>>>>>>> *Subject:* Re: batch_size_warn_threshold_in_kb >>>>>>>>>> >>>>>>>>>> The really important thing to really take away from Ryan's >>>>>>>>>> original post is that batches are not there for performance. The >>>>>>>>>> only case >>>>>>>>>> I consider batches to be useful for is when you absolutely need to >>>>>>>>>> know >>>>>>>>>> that several tables all get a mutation (via logged batches). The >>>>>>>>>> use case >>>>>>>>>> for this is when you've got multiple tables that are serving as >>>>>>>>>> different >>>>>>>>>> views for data. It is absolutely not going to help you if you're >>>>>>>>>> trying to >>>>>>>>>> lump queries together to reduce network & server overhead - in fact >>>>>>>>>> it'll >>>>>>>>>> do the opposite. If you're trying to do that, instead perform many >>>>>>>>>> async >>>>>>>>>> queries. The overhead of batches in cassandra is significant and >>>>>>>>>> you're >>>>>>>>>> going to hit a lot of problems if you use them excessively (timeouts >>>>>>>>>> / >>>>>>>>>> failures). >>>>>>>>>> >>>>>>>>>> tl;dr: you probably don't want batch, you most likely want many >>>>>>>>>> async calls >>>>>>>>>> >>>>>>>>>> On Thu Dec 11 2014 at 11:15:00 PM Mohammed Guller < >>>>>>>>>> moham...@glassbeam.com> wrote: >>>>>>>>>> >>>>>>>>>>> Ryan, >>>>>>>>>>> >>>>>>>>>>> Thanks for the quick response. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> I did see that jira before posting my question on this list. >>>>>>>>>>> However, I didn’t see any information about why 5kb+ data will cause >>>>>>>>>>> instability. 5kb or even 50kb seems too small. For example, if each >>>>>>>>>>> mutation is 1000+ bytes, then with just 5 mutations, you will hit >>>>>>>>>>> that >>>>>>>>>>> threshold. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> In addition, Patrick is saying that he does not recommend more >>>>>>>>>>> than 100 mutations per batch. So why not warn users just on the # of >>>>>>>>>>> mutations in a batch? >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Mohammed >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> *From:* Ryan Svihla [mailto:rsvi...@datastax.com] >>>>>>>>>>> *Sent:* Thursday, December 11, 2014 12:56 PM >>>>>>>>>>> *To:* user@cassandra.apache.org >>>>>>>>>>> *Subject:* Re: batch_size_warn_threshold_in_kb >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Nothing magic, just put in there based on experience. You can >>>>>>>>>>> find the story behind the original recommendation here >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> https://issues.apache.org/jira/browse/CASSANDRA-6487 >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Key reasoning for the desire comes from Patrick McFadden: >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> "Yes that was in bytes. Just in my own experience, I don't >>>>>>>>>>> recommend more than ~100 mutations per batch. Doing some quick math >>>>>>>>>>> I came >>>>>>>>>>> up with 5k as 100 x 50 byte mutations. >>>>>>>>>>> >>>>>>>>>>> Totally up for debate." >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> It's totally changeable, however, it's there in no small part >>>>>>>>>>> because so many people confuse the BATCH keyword as a performance >>>>>>>>>>> optimization, this helps flag those cases of misuse. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> On Thu, Dec 11, 2014 at 2:43 PM, Mohammed Guller < >>>>>>>>>>> moham...@glassbeam.com> wrote: >>>>>>>>>>> >>>>>>>>>>> Hi – >>>>>>>>>>> >>>>>>>>>>> The cassandra.yaml file has property called >>>>>>>>>>> *batch_size_warn_threshold_in_kb. >>>>>>>>>>> * >>>>>>>>>>> >>>>>>>>>>> The default size is 5kb and according to the comments in the >>>>>>>>>>> yaml file, it is used to log WARN on any batch size exceeding this >>>>>>>>>>> value in >>>>>>>>>>> kilobytes. It says caution should be taken on increasing the size >>>>>>>>>>> of this >>>>>>>>>>> threshold as it can lead to node instability. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Does anybody know the significance of this magic number 5kb? Why >>>>>>>>>>> would a higher number (say 10kb) lead to node instability? >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Mohammed >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> >>>>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>>>>>>>>> >>>>>>>>>>> Ryan Svihla >>>>>>>>>>> >>>>>>>>>>> Solution Architect >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> [image: twitter.png] <https://twitter.com/foundev>[image: >>>>>>>>>>> linkedin.png] >>>>>>>>>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> DataStax is the fastest, most scalable distributed database >>>>>>>>>>> technology, delivering Apache Cassandra to the world’s most >>>>>>>>>>> innovative >>>>>>>>>>> enterprises. Datastax is built to be agile, always-on, and >>>>>>>>>>> predictably >>>>>>>>>>> scalable to any size. With more than 500 customers in 45 countries, >>>>>>>>>>> DataStax >>>>>>>>>>> is the database technology and transactional backbone of choice for >>>>>>>>>>> the >>>>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, >>>>>>>>>>> and eBay. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>> >>>>>>> -- >>>>>>> >>>>>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>>>>> >>>>>>> Ryan Svihla >>>>>>> >>>>>>> Solution Architect >>>>>>> >>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image: >>>>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>>>>> >>>>>>> DataStax is the fastest, most scalable distributed database >>>>>>> technology, delivering Apache Cassandra to the world’s most innovative >>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably >>>>>>> scalable to any size. With more than 500 customers in 45 countries, >>>>>>> DataStax >>>>>>> is the database technology and transactional backbone of choice for the >>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and >>>>>>> eBay. >>>>>>> >>>>>>> >>>>>> >>>> >>
