I am designing a search system based on ElasticSearch, after reading a lot I have seen that some systems such as logs use a policy of multiple indexes to save the same content, similar to mylogs-12-02-2020 and are creating an index by day, then to search, they perform the searches in all the indices that comply with the mylogs- * pattern, each of those indices has its primary shards and replicas.
My question would be regarding the performance of the searches, which would be more performant to look at an index of 5 million documents, with n shards or look for 50 indexes of 100,000 documents. Does anyone have any experience with the best practice to follow?
I am assuming that my system will have an approximate growth of 200,000 documents per day.
What is the best practice, separate in multiple indexes or have a single index with several primary shards in different nodes (so that they do not compete for the same resources when searching / indexing)?
When doing a search on mylogs-* elastic does it parallel to the indexes and within each index in its shards?
By default, an Elasticsearch index has 5 primary shards and 1 replica for each. But the problem is default configurations are not suitable for every use case.
Shard size is quite critical for search queries. If there would be too many shards that are assigned to an index, Lucene segments would be small which causes an increase in overhead. Lots of small shards would also reduce query throughput when multiple queries are made simultaneously. On the other hand, too large shards cause a decrease in search performance and longer recovery time from failure. Therefore, it is suggested by Elasticsearch that one shard’s size should be around 20 to 40 GB.
Keep in mind it is the shard that acts as a separate search engine in itself, not the index. indices are a type of data organization mechanism, allowing the user to partition data a certain way. that is all!
For further details read this article.