At the moment, we're able to bulk index data at a rate faster than we actually need. Indexing is not as important to use as being able to quickly search for data. Once we start reaching ~30 million documents indexed, we start to see performance decreasing in ours search queries. What are the best techniques for sacrificing indexing time in order to improve search performance?
A bit more info: - We have the resources to improve our hardware (memory, CPU, etc) but we'd like to maximize the improvements that can be made programmatically or using properties before going for hardware increases. - Our searches make very heavy uses of faceting and aggregations. - When we run the optimize query, we see *significant* improvements in our search times (between 50% and 80% improvements), but as documented, this is usually a pretty expensive operation. Is there a way to sacrifice indexing time in order to have Elasticsearch index the data more efficiently? (I guess sort of mimicking the optimization behavior at index time) -- You received this message because you are subscribed to the Google Groups "elasticsearch" group. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/0e134001-9a55-40c5-a8fc-4c1485a3e6fc%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
