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Lessons from Elasticsearch in production
This talk is for people who are planning to use Elasticsearch in their next project.
Elasticsearch is one of the core pieces of infrastructure at Helpshift, a mobile-first CRM product.
We have had mistakes and lessons learned in production, and we would like to share that so that others have a smoother experience with Elasticsearch.
The customer-facing side of Helpshift product is a simple chat feature within the app using the Helpshift mobile SDK. The business-facing side is a complex agent dashboard that helps the agent in processing as many issues as quickly as possible. We will be focusing on this business-facing side, the designs we built on top of Elasticsearch and the problems we faced and how we went about solving them, a few of them are:
- Architecture 101 - Importance of separating master-only and data-only nodes, etc.
- How we index documents for each customer - the flaw in having one index per customer, and possible solutions
- Multilingual data - the importance of the phonetic plugin
- Complex views - the importance of understanding filters and how to combine them
- The importance of benchmarking - how we implemented using percolators for live notifications of new issues
- Restarts & Upgrades - the importance of disabling shard allocation and clustering
- Bulk Indexing - the importance of controlling replica count, etc.
- Runtime debugging - the importance of cat APIs, etc.
Preferably, audience has some basic understanding of what Elasticsearch does.
I work in the backend team at Helpshift.com, a customer service platform for mobile apps, our customers include Flipboard, Supercell, Flipkart, and several others.
I have previously worked at Automatic.com (the "UI for your car" company), Infibeam.com, Adobe and Yahoo!.
I have also written a couple of Creative Commons-licensed books - A Byte of Python and A Byte of Vim.