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/> >
