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 <[email protected]>: > 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. >
