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We recently launched Related Posts across WordPress.com, so its time to pop the hood and take a look at what ended up in our engine. There’s a lot of good information spread across the web on how to use Elasticsearch, but I haven’t seen too many detailed discussions of what it looks like to scale an ES cluster for a large application. Being an open source company means we get to talk about these d
In part 1 I gave an overview of our cluster configuration. In this part we’ll dig into: How our data is partitioned into indices to scale over time Optimizing bulk indexing Scaling real time indexing How we manage indexing failures and downtime. The details of our document mappings are mostly irrelevant for our indexing scaling discussion, so we’ll skip them until part 3. Data Partitioning Since W
Recently I’ve been working on how to build Elasticsearch indices for WordPress blogs in a way that will work across multiple languages. Elasticsearch has a lot of built in support for different languages, but there are a number of configuration options to wade through and there are a few plugins that improve on the built in support. Below I’ll lay out the analyzers I am currently using. Some cavea
Update: Also check out my series on scaling Elasticsearch. I’ve been working with Elasticsearch off and on for over a year, but recently I attended Elasticsearch.com’s training class (well worth the time and money) and discovered a few significant things that I was doing just plain wrong. Before using Elasticsearch I used Lucene directly, and so a few of the errors I made were due to not understan
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