<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Avro on Jonathan Franklin</title><link>https://jonnie.github.io/tags/avro/</link><description>Recent content in Avro on Jonathan Franklin</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 11 Sep 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://jonnie.github.io/tags/avro/index.xml" rel="self" type="application/rss+xml"/><item><title>Elasticsearch Product Search Index</title><link>https://jonnie.github.io/work/search-index-pipeline/</link><pubDate>Sat, 11 Sep 2021 00:00:00 +0000</pubDate><guid>https://jonnie.github.io/work/search-index-pipeline/</guid><description>&lt;h2 id="problem"&gt;Problem&lt;/h2&gt;&#10;&lt;p&gt;Pricesearcher&amp;rsquo;s search service used separate Elasticsearch product indexes for different countries. Index builds needed to carry product identity and cross-merchant links into searchable records, while accommodating market-specific merchant rules and schedules. The team also used Google Sheets to maintain smaller datasets that needed to be searchable.&lt;/p&gt;&#10;&lt;h2 id="architecture"&gt;Architecture&lt;/h2&gt;&#10;&lt;p&gt;A Makefile-driven pipeline generated Avro source data through Athena, downloaded it from S3 and used a PyPy builder to populate Elasticsearch. It supported the site&amp;rsquo;s &lt;code&gt;nitor&lt;/code&gt; index and a separate &lt;code&gt;nested&lt;/code&gt; index type. Separate configurable builders published selected Google Sheet tabs as Elasticsearch indexes.&lt;/p&gt;</description></item></channel></rss>