This is the 7th and final part of our blog post series on how we upgraded our Elasticsearch cluster without downtime and with minimal user impact. In this post, we will focus on several of the benefits we have seen after the upgrade and provide more details on how our architecture looks today.
Welcome to this sixth part of our adventure of upgrading our Elasticsearch cluster. Until now, we have explained how we structured our work, improved our system to make this migration possible, how we took advantage of this opportunity to make otherwise hard changes, and made sure to keep the system performing well under load. All of these changes were the result of hard work and planning, but in the end, we all knew that one day we will be faced with the ultimate question: When can we turn the switch, and start using our new and shiny cluster? We don’t think that anybody would want to be in the place of the single person who would snap their fingers and make the decision to switch. We didn’t want it either, so we decided to let the data guide us.
This is part 5 in our series on how we upgraded our Elasticsearch cluster without any downtime and with minimal user impact.
Due to the large scope of this upgrade, it was clear from the beginning that this project was going to last for at least one year, if not more. This blog post describes how we reasoned about our development process and how we managed to support multiple Elasticsearch client libraries in our Java code bases for a long time.
This is part 4 in our series on how we upgraded our Elasticsearch cluster without any downtime and with minimal user impact.
In part 2 we explained that we decided to do a full reindexing of our entire dataset as part of this Elasticsearch upgrade project. This blog post explains some of the changes we made to our documents during that re-indexing.
This is part 3 in our series on how we upgraded our Elasticsearch cluster without any downtime and with minimal user impact.
As part of the Elasticsearch Upgrade project, we needed to investigate the search performance improvements between the old and the new versions. Running an older version of Elasticsearch has presented many performance issues over the years and we hoped that upgrading to a more recent version would help.
This blog post will describe how we tested the search performance of our new Elasticsearch cluster and the different optimizations we used to improve it. Specifically, we will focus on how we solved the major bottleneck for our use case: wildcards.