* Go multi threaded for each core as Shawn says. Try e.g. 2, 3 and 4 threads * Experiment with different batch sizes, e.g. try 500 and 2000 - depends on your docs what is optimal * Do NOT commit after each batch of 1000 docs. Instead, commit as seldom as your requirements allows, e.g. try commitWithin=60000 to commit every minute
Tip: Try to push Solr metrics to DataDog or some other service, where you can see a dashboard with stats on requests/sec, RAM, CPU, threads, GC etc which may answer your last question. Jan > 8. jun. 2022 kl. 14:06 skrev Shawn Heisey <apa...@elyograg.org>: > > On 6/8/2022 3:35 AM, Marius Grigaitis wrote: >> * 9 different cores. Each weighs around ~100 MB on disk and has >> approximately 90k documents inside each. >> * Updating is performed using update method in batches of 1000, around 9 >> processes in parallel (split by core) > > This means that indexing within each Solr core is single-threaded. The way > to increase indexing speed is to index in parallel with multiple threads or > processes per index. If you can increase the CPU power available on the Solr > server when you increase the number of processes/threads sending data to > Solr, that might help. > > Thanks, > Shawn >
