BTW, I did notice this Jira for setting a client timeout for cqlsh, so maybe this is the culprit for that user:
CASSANDRA-7516 - Configurable client timeout for cqlsh https://issues.apache.org/jira/browse/CASSANDRA-7516 Or, should they actually be using the --request-timeout command line option for cqlsh? -- Jack Krupansky On Tue, Apr 19, 2016 at 4:56 PM, Jack Krupansky <jack.krupan...@gmail.com> wrote: > Sylvain & Tyler, this Jira is for a user reporting a timeout for SELECT > COUNT(*) using 3.3: > https://issues.apache.org/jira/browse/CASSANDRA-11566 > > I'll let one of you guys follow up on that. I mean, I thought it was > timing out die to the amount of data, but you guys are saying that paging > should make that not a problem. Or is there a timeout in cqlsh simply > because the operation is slow - as opposed to the server reporting an > internal timeout? > > Thanks. > > > > -- Jack Krupansky > > On Tue, Apr 19, 2016 at 12:45 PM, Tyler Hobbs <ty...@datastax.com> wrote: > >> >> On Tue, Apr 19, 2016 at 11:32 AM, Jack Krupansky < >> jack.krupan...@gmail.com> wrote: >> >>> >>> Are the queries sent from the coordinator to other nodes sequencing >>> through partitions in token order and that's what allows the coordinator to >>> dedupe with just a single page at a time? IOW, if a target node responds >>> with a row from token t, then by definition there will be no further rows >>> returned from that node with a token less than t? >>> >> >> That's correct. The internal paging for aggregation queries is exactly >> the same as the normal "client facing" paging. >> >> >>> >>> And if I understand all of this so far, this means that for 3.x COUNT >>> (and other aggregate functions) are "safe but may be slow" (paraphrasing >>> Sylvain.) Is this for 3.0 and later or some other 3.x (or even some 2.x)? >>> >> >> I think count(*) started using paging internally in 2.1, but I'm having >> trouble finding the jira ticket. It could have been 2.0. >> >> The new aggregation functions in 2.2 utilize the same code path. >> >> >>> >>> There remains the question of recommended usage for COUNT. I think my >>> two proposed guidelines remain valid (ignoring the old timeout issue), with >>> the only remaining question about how large a row count is advisable for >>> "decent" request latency. 1,000? 10,000? Granted, it depends on the >>> specific data and hardware, but I'm thinking that the guidance should be >>> that you should only use COUNT(*) for no more than "low thousands" of rows >>> unless you are willing to accept it both being very slow and very >>> disruptive to normal cluster health. IOW, it's more like a batch analytics >>> operation than a real-time operation. An occasional administrative query to >>> measure table size should be okay, but common use for OLTP should be >>> restricted to relatively narrow slices or row counts... I think. Feedback >>> welcome. >>> >>> The upcoming support for 2GB partitions will be interesting, but the >>> same guidance should cover, I think. Maybe the numeric upper bound might be >>> a bit higher since only a single partition is involved, but if processing >>> many thousands of rows will remain time consuming, it sounds like that >>> should be treated more as a batch-style OLAP operation rather than a >>> real-time OLTP operation... I think. >>> >> >> I think this is decent guidance. I'll also clarify that aggregation >> functions should only be used on single partitions if you expect to get a >> response back with reasonable latency. Full table scans are still >> expensive, even when they're wrapped in an aggregation function. >> >> If count(*) is too slow, the standard alternatives are: >> - counters >> - a static count that's periodically refreshed by a batch/background >> process >> - LWT increments on an int column >> - an external datastore like redis >> >> Obviously, each of these has a different set of tradeoffs. >> >> -- >> Tyler Hobbs >> DataStax <http://datastax.com/> >> > >
