Case Study: Buffer achieves faster experiment-driven growth and scalable event tracking with MongoDB

How Buffer uses MongoDB to power its Growth Platform

Buffer, a metric-driven social media company focused on increasing unique posts, faced a challenge scaling its event and experiment platform: their existing tools (Kissmetrics and internal systems) couldn’t flexibly capture activation data or support rapid iteration. They needed a datastore that would handle varied event metadata, high write throughput, and let developers iterate quickly without adding complexity.

Buffer adopted MongoDB to power an asynchronous event pipeline: app events are queued and stored in a dynamic-schema MongoDB store backed by SSD replica sets and simple multi-language clients. The change enabled fast experiment rollout and cohort aggregation for their growth dashboard, delivered reliable high-throughput metrics (preventing data loss during downtime), and simplified iteration—while also revealing operational lessons around disk I/O, indexing, and document sizing.

Buffer

Sunil Sadasivin

Chief Technology Officer