Sorry, I did mean larger number of rows per partition. On 9 June 2016 at 10:12, John Thomas <jthom...@gmail.com> wrote:
> The example I gave was for when N=1, if we need to save more values I > planned to just add more columns. > > On Thu, Jun 9, 2016 at 12:51 AM, kurt Greaves <k...@instaclustr.com> > wrote: > >> I would say it's probably due to a significantly larger number of >> partitions when using the overwrite method - but really you should be >> seeing similar performance unless one of the schemas ends up generating a >> lot more disk IO. >> If you're planning to read the last N values for an event at the same >> time the widerow schema would be better, otherwise reading N events using >> the overwrite schema will result in you hitting N partitions. You really >> need to take into account how you're going to read the data when you design >> a schema, not only how many writes you can push through. >> >> On 8 June 2016 at 19:02, John Thomas <jthom...@gmail.com> wrote: >> >>> We have a use case where we are storing event data for a given system >>> and only want to retain the last N values. Storing extra values for some >>> time, as long as it isn’t too long, is fine but never less than N. We >>> can't use TTLs to delete the data because we can't be sure how frequently >>> events will arrive and could end up losing everything. Is there any built >>> in mechanism to accomplish this or a known pattern that we can follow? The >>> events will be read and written at a pretty high frequency so the solution >>> would have to be performant and not fragile under stress. >>> >>> >>> >>> We’ve played with a schema that just has N distinct columns with one >>> value in each but have found overwrites seem to perform much poorer than >>> wide rows. The use case we tested only required we store the most recent >>> value: >>> >>> >>> >>> CREATE TABLE eventyvalue_overwrite( >>> >>> system_name text, >>> >>> event_name text, >>> >>> event_time timestamp, >>> >>> event_value blob, >>> >>> PRIMARY KEY (system_name,event_name)) >>> >>> >>> >>> CREATE TABLE eventvalue_widerow ( >>> >>> system_name text, >>> >>> event_name text, >>> >>> event_time timestamp, >>> >>> event_value blob, >>> >>> PRIMARY KEY ((system_name, event_name), event_time)) >>> >>> WITH CLUSTERING ORDER BY (event_time DESC) >>> >>> >>> >>> We tested it against the DataStax AMI on EC2 with 6 nodes, replication >>> 3, write consistency 2, and default settings with a write only workload and >>> got 190K/s for wide row and 150K/s for overwrite. Thinking through the >>> write path it seems the performance should be pretty similar, with probably >>> smaller sstables for the overwrite schema, can anyone explain the big >>> difference? >>> >>> >>> >>> The wide row solution is more complex in that it requires a separate >>> clean up thread that will handle deleting the extra values. If that’s the >>> path we have to follow we’re thinking we’d add a bucket of some sort so >>> that we can delete an entire partition at a time after copying some values >>> forward, on the assumption that deleting the whole partition is much better >>> than deleting some slice of the partition. Is that true? Also, is there >>> any difference between setting a really short ttl and doing a delete? >>> >>> >>> >>> I know there are a lot of questions in there but we’ve been going back >>> and forth on this for a while and I’d really appreciate any help you could >>> give. >>> >>> >>> >>> Thanks, >>> >>> John >>> >> >> >> >> -- >> Kurt Greaves >> k...@instaclustr.com >> www.instaclustr.com >> > > -- Kurt Greaves k...@instaclustr.com www.instaclustr.com
