For example, query with partition will look like : QueryCursor<Cache.Entry<Integer, Integer>> qry = cache.query(new ScanQuery<Integer, Integer>().setPartition(part));
вт, 1 окт. 2019 г. в 15:25, Evgenii Zhuravlev <e.zhuravlev...@gmail.com>: > Hi, > > To parallelise everything properly, I would recommend starting an > affinityCallable per partition(1024 by default). Inside this compute job, > you can collect information for the certain partition only using > ScanQuery(or SQLQuery) > > > пт, 27 сент. 2019 г. в 18:09, Stas Girkin <stas.gir...@gmail.com>: > >> Hello everyone, >> >> I would like to use MapReduce over cache items representing events >> happened in a process to calculate certain statistics. Could you be so kind >> to help me how can I do that with apache ignite? >> >> I have tens of millions of processes that happened in the past. The >> processes look like a sequence of events [event1, event2, event3, ... >> eventN], where number of events per process could vary (50-100). Every >> event has certain sets of attributes like timestamp, event type, set of >> metrics. I put these data to a cache as process_id => [e1, e2, e3, e4, >> ...]. What I would like to get is to get a histogram how often event of a >> certain type happens in all the processes or processes that have certain >> condition. What I managed to do is to broadcast a callable that lands on >> ignite nodes and can access local cache items and counts what I want and >> returns it back to the caller in K chunks which I have to aggregate on the >> client. >> >> Ignite localIgnite = Ignition.localIgnite(); >> IgniteCache<String, MyProcess> localCache = >> localIgnite.cache("processes"); >> MyHistogram hist = new MyHistogram() >> for (Cache.Entry<String, MyProcess> e : localCache.localEntries()) { >> hist.process(e.getValue()); >> } >> return hist; >> >> The problem with the approach is it utilizes only a single core on the >> ignite node, while I have 64. How could I do something similar in more >> efficient manner? >> >> thank you in advance. >> >