<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Flask on Jonathan Franklin</title><link>https://jonnie.github.io/tags/flask/</link><description>Recent content in Flask on Jonathan Franklin</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 16 Jul 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://jonnie.github.io/tags/flask/index.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Source Product Search API</title><link>https://jonnie.github.io/work/product-search-api/</link><pubDate>Fri, 16 Jul 2021 00:00:00 +0000</pubDate><guid>https://jonnie.github.io/work/product-search-api/</guid><description>&lt;h2 id="problem"&gt;Problem&lt;/h2&gt;&#10;&lt;p&gt;Pricesearcher&amp;rsquo;s website, browser extension and external integrations needed product data from both the company&amp;rsquo;s Elasticsearch index and third-party providers. Combining those sources meant dealing with different response formats, provider latency and client ranking requirements. The API also needed clearer boundaries between the interfaces used by different clients.&lt;/p&gt;&#10;&lt;h2 id="architecture"&gt;Architecture&lt;/h2&gt;&#10;&lt;p&gt;The Python/Flask API combined the internal Elasticsearch index with Amazon, Yahoo and Kelkoo. Third-party requests ran asynchronously and provider failures were handled separately. Redis with RediSearch supported filtering, sorting and facets over cached results. Web, browser-extension, external-client and core APIs ran as separate deployments with their own OpenAPI specifications.&lt;/p&gt;</description></item></channel></rss>