Re: java.lang.OutOfMemoryError: Java heap space
The short-term fix is probably to try increasing heap space (in cassandra-env.sh). 8GB in the most standard but more may help in some circumstances. That said, your logs are pointing to a number of other issues which won’t be helping and probably need to be fixed for long-term stability: - swap enabled ( Cassandra server running in degraded mode. Is swap disabled? : false, Address space adequate? : true, nofile limit adequate? : true, nproc limit adequate? : true) - low disk space ( Only 36948 MB free across all data volumes. Consider adding more capacity to your cluster or removing obsolete snapshots) - large partitions ( Writing large partition feed/messages:MANAGER:0 (175811867 bytes)) Cheers Ben On Sat, 11 Jun 2016 at 01:11 Tobin Landricombewrote: > Hi, > > I've been googling various parts of this all day but none of the > suggestions seem to fit. > > I have 2 nodes, one of which is a seed. I'm trying to add a third node > but, after a few minutes in the UJ state, the node dies with the above > error (http://pastebin.com/iRvYfuAu). > > Here are the warnings from the logs: http://pastebin.com/vYLvsHrv > > I've googled them but nothing seems appropriate. > > Debug log part 1: http://pastebin.com/b8ZSYtqV > Debug log part 2: http://pastebin.com/1Bbb7Vf8 > > Thanks for any suggestions, > Tobin > > -- Ben Slater Chief Product Officer, Instaclustr +61 437 929 798
java.lang.OutOfMemoryError: Java heap space
Hi, I've been googling various parts of this all day but none of the suggestions seem to fit. I have 2 nodes, one of which is a seed. I'm trying to add a third node but, after a few minutes in the UJ state, the node dies with the above error (http://pastebin.com/iRvYfuAu). Here are the warnings from the logs: http://pastebin.com/vYLvsHrv I've googled them but nothing seems appropriate. Debug log part 1: http://pastebin.com/b8ZSYtqV Debug log part 2: http://pastebin.com/1Bbb7Vf8 Thanks for any suggestions, Tobin smime.p7s Description: S/MIME cryptographic signature
Re: Interesting use case
woops was obviously tired, what I said clearly doesn't make sense. On 10 June 2016 at 14:52, kurt Greaveswrote: > Sorry, I did mean larger number of rows per partition. > > On 9 June 2016 at 10:12, John Thomas 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 >> 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 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 > -- Kurt Greaves k...@instaclustr.com www.instaclustr.com
Re: Interesting use case
Sorry, I did mean larger number of rows per partition. On 9 June 2016 at 10:12, John Thomaswrote: > 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 > 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 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