Amuse Case Study

Unique Datadog challenge

Problem Description:

Amuse is an e-commerce cannabis delivery business reinventing how consumers order and consume cannabis hosted in AWS. Amuse has aggressive SLO, which needs constant monitoring of the search latencies, one of the product’s core capabilities. They get an enormous volume of search requests to search the exhaustive list of products. Sometimes the search requests fail, and it becomes difficult to capture the customer’s real customer experience. Magento stack has a very minimal out-of-box instrumentation solution. Monitoring the life cycle of messages that flow through various services is challenging.

Solutions Highlights:

AVM worked with the Amuse team to build instrumentation for the Magento stack powered by the Datadog libraries and Datadog as the observability platform. AVM enabled Amuse to instrument the Magento stack and configured the Datadog agent with the suitable parameter set.

The engineering team at AVM ensured that the spans and traces of the request were complete. Missing Datadog spans and traces were identified during troubleshooting, leading to the instrumentation code changed to fix the Datadog agent issues, and enriching the request context for end-to-end Datadog monitoring.

The solution enabled the Amuse team to use Datadog as a single pane glass for their entire  e-commerce platform

Industry

E-commerce

Challenge

Identify the latency and performance bottlenecks in the AWS-hosted Magento application using Datadog tracing and span capability.

Services & Technologies

Datadog

Magento

AWS Lambda Functions

MySql

Cloudflare

Elastic Search

Cloudinary