The problem might be in HDD not performing fast enough, and also suffering from random reads (IgniteCache::preloadPartition at least tries to read sequentially).
Also, do you have enough RAM to store all data? If not, you shouldn’t preload all the data, just the amount that fits into RAM. Anyway, I think that your best chance is to implement the same thing https://issues.apache.org/jira/browse/IGNITE-8873 does. E.g. you can try to backport the commit on top of 2.6. Stan From: Naveen Sent: 5 декабря 2018 г. 7:59 To: user@ignite.apache.org Subject: RE: Effective way to pre-load data around 10 TB Thanks Stan, this may take little longer time to implement, we are in hurry to build this functionality of preloading the data. Can someone correct me how to improve this pre-load process. This is how we are preloading. 1. Send an Async request for all the partitions with the below code, below loop will get repeated for all the caches we have for (int i = 0; i < affinity.partitions(); i++) { List<String> cacheList = Arrays.asList(cacheName); affinityRunAsync= compute.affinityRunAsync(cacheList, i, new DataPreloadTask(cacheList, i)); } 2. Inside DataPreloadTask which is running on the Ignite node. I just execute scan query for the given partition and iterate thru the cursor. not doing anything else. IgniteCache<Object, Object> igniteCache = localIgnite.cache(cacheName); try (QueryCursor<Cache.Entry<K, V>> cursor = igniteCache.query(new ScanQuery().setPartition(partitionNo))) { for (Cache.Entry<K, V> entry : cursor) { } } } However, this seems to be quite slow. Taking more than 3 hours to read one cache which has 400 M records. We have 30 such caches to load data, so not fining this so efficient. Can we improve this, we do have very powerful machines with 128 CPU, 2 TB RAM, HDD, our CPU utilization is also not so high when we are preloading the data. Changing thread pool size will have any impact this read ??? Thanks Naveen -- Sent from: http://apache-ignite-users.70518.x6.nabble.com/
