Introduction What this talk is all about? We recently moved from MySQL to Elasticsearch where we got a direct 10x - 15x boost in our performance. We came up with unique use cases of heavy updates in Elasticsearch. That been challenging but yes currently Our Elaticsearch handling 200 million requests per day very efficiently. Our WRITE consist of the partial update, update with script conditions and of course simple indexing....
Below are some challenges & exceptions faced while setting up Elasticsearch. I just shared my experience and learning. Please correct me, If you guys feel somewhere i got wrong OR You can contribute if you have any experiences . Will keep update this gist. Every use case having different solutions. You can try accordingly.
I was working on elasticsearch and the requirement was to implement like query “%text%” ( like mysql %like% ). We could use wildcard, regex or query string but those are slow. Hence i took decision to use ngram token filter for like query. It was quickly implemented on local and works exactly i want. The problem To know the actual behavior, I implemented the same on staging server. I found some problem while we start indexing on staging....