Holler is a messaging tech company that enriches conversations everywhere by creating and delivering useful, entertaining, expressive visual content to add texture and emotion to messaging environments. As the company has continued to grow, the engineering organization has scaled to meet the demand for its services. As a result, that scale has left Holler with a higher log volume than ever before.
Facilitating recommendations based on context, Holler’s messaging app keeps things relevant for website visitors. With major partners like Venmo, they have to make sure their iOS and Android-based SDKs are up and running.
As part of that growth journey, Holler has evolved its technology stack for observability, performance visibility, and monitoring. Although the engineering team preferred open source solutions, they faced challenges scaling their DIY ELK stack to keep pace.
Log Volume and Scaling
The raw scale of newly generated log data by Holler messages made it difficult to analyze. At the same time, troubleshooting production issues in a timely and cost-efficient manner got tougher..
As Daniel discusses in the Q&A on our blog, the observability journey evolved and now includes Logz.io as an alternative to ELK, which has helped to reduce some of the maintenance burden on the engineering team.
Additionally, Holler has worked closely with Logz.io to utilize tools reducing log volume. Using Drop Filters, Holler samples and filters data before indexing, breaking down the high volumes.
Read on below to learn how Holler’s log volume came down to an average of 200 GB per day from an excess of 1 TB per day and how Holler has built a more scalable and reliable logging pipeline that saves significant processing costs.
Daniel also discusses the growth path ahead, and Holler’s ability to analyze and monitor the performance of its services via Logz.io’s unified logs and metrics observability platform.