Case Study: Chartmetric achieves real-time music analytics at lower cost with ClickHouse

How Chartmetric is scaling music analytics with ClickHouse

Chartmetric, a music analytics platform, needed a faster and more scalable way to handle billions of rows of streaming, playlist, and engagement data as its usage grew. After running into limits with Postgres, Snowflake, and Elasticsearch, the company turned to ClickHouse Cloud to support real-time analytics, API access, and large-scale time-series workloads.

ClickHouse helped Chartmetric move key workloads into a multi-warehouse ClickHouse setup and optimize them with projections, WHERE + IN filters, batching, and the VersionedCollapsingMergeTree engine. The result was major performance and cost gains: queries that once timed out now run in under 3 seconds, some top-artist queries dropped from 20 seconds in Snowflake to about 1.5 seconds in ClickHouse, memory use fell by over 99% in some cases, and storage was reduced by 10 TB on RDS, while the playlist cache scaled to 5.5 billion rows and 15 million new records per day.

Chartmetric

Umang Sharaf

Lead Data Engineer