# StatsD at Shopify

DevFeed: [StatsD at Shopify](<https://devfeed.tech/articles/statsd-at-shopify-1262.md>)

Original publisher: [Read original article](<https://shopify.engineering/17488320-statsd-at-shopify>)

Author: Shopify Engineering

Published: 2011-07-28T19:04:00Z

Content type: article

Language: en

Sources: [Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering.md>), [Shopify Engineering - Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering-shopify-engineering.md>)

Topics: [telemetry](<https://devfeed.tech/topics/telemetry.md>), [web applications](<https://devfeed.tech/topics/web-applications.md>)

Tags: [cache](<https://devfeed.tech/tags/cache.md>), [data](<https://devfeed.tech/tags/data.md>), [git](<https://devfeed.tech/tags/git.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [performance](<https://devfeed.tech/tags/performance.md>), [production](<https://devfeed.tech/tags/production.md>), [shopify](<https://devfeed.tech/tags/shopify.md>)

## AI overview

Shopify describes using StatsD to collect custom metrics, monitor cache hit and miss trends, diagnose a caching regression, and test assumptions with production data.

## Source excerpt

Here at Shopify, we like data. One of the many tools in our data toolbox is StatsD. We've been using StatsD in production at Shopify for many months now, consistently sending multiple events to our StatsD instance on every request. What is StatsD good for? In my experience, there are two things that StatsD really excels at. First, getting a high level overview of some custom piece of data. We use NewRelic to tell us about the performance of our apps.