How Basepair is using Moesif: Improving API performance

automatic API monitoring and analytics

Improving API performance
How Basepair improves API performance with segmentation and heatmaps.

Overview

Basepair is a next generation DNA sequencing (NGS) data analytics SaaS trusted by scientists at Harvard Medical School, Rutgers, Boston Children's Hospital, and others. They make NGS data analysis as simple as booking a flight online. Basepair provides a RESTful interface to add new samples, run analysis, download results, among other tasks. Being focused on the scientific medical community, they also provide a Python CLI tool with higher level methods for common activities.

Problem

Basepair was facing performance issues with the upload API for their service. Most queries were really quick, but there was an occasional query which was very slow. Basepair was looking for a solution that could track both the performance and also errors of the API. They were already using New Relic for infrastructure monitoring, but it only provided aggregate metrics for servers. Basepair's API was built on the popular Django framework which enabled them to quick set up with Moesif in a few minutes with Moesif's Django SDK. They set up three apps/environments in Moesif called dev, test, and prod. Basepair also integrated Moesif's update_user() API to ensure API calls are attributed to each customer profile including email and name.

Results

They found the query level detail very helpful, where they can see the specific response time of a single query for a single customer. With Moesif Basepair's customer success can really tell which users are suffering from slow speeds and which ones are having great experiences with their API and CLI tools. With Moesif, Basepair's development team was able to improve the performance of their APIs drastically for their customers. Basepair found the geo heatmap views helpful in getting a overview of where API performance issues are. They have also found Moesif segmentation valuable for first filtering for slow response types, then grouping by REST route to see which end points are most frequently slow.


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