Why Coinbase and Pinterest Chose StarRocks: Lakehouse-Native Design and Fast Joins at Terabyte Scale

Key Takeaways

• Sub-second joins on terabyte-scale data.
• Native lakehouse support eliminates data movement.
• Cost-based optimizer outperforms ClickHouse on join-heavy workloads.
• Simplified architecture reduces engineering overhead.
• Community size remains smaller than ClickHouse.

Summary

StarRocks is attracting heavyweight users such as Coinbase, Pinterest and Fresha because it delivers sub‑second query latency on terabyte‑scale analytics while reading directly from lakehouse storage. The platform’s shared‑nothing architecture, colocated joins, caching layer and a cost‑based optimizer let it outperform Snowflake, ClickHouse and other OLAP engines on join‑heavy workloads. Companies switched to cut operational costs and eliminate the latency of pre‑aggregating data in Spark or Flink pipelines. StarRocks also supports real‑time ingestion and materialized views, simplifying data pipelines for customer‑facing analytics.

Pulse Analysis

Enterprises are increasingly pressured to provide instant analytics to power dashboards, AI agents, and customer‑facing applications. Traditional data warehouses like Snowflake excel at scalability but often introduce minute‑level latency, especially when data resides in object stores such as S3. The need to pre‑aggregate or denormalize data in Spark or Flink pipelines adds engineering complexity and cost, prompting a search for solutions that can query lakehouse formats directly while maintaining sub‑second response times.

StarRocks addresses this gap with a hybrid shared‑nothing/shared‑data architecture that stores data locally on backend nodes for hot workloads and streams cold partitions from object storage when needed. Its hallmark is the colocated join mechanism, which aligns bucket keys, bucket counts, and replica placement across tables so joins execute locally without costly network shuffles. Coupled with a sophisticated cost‑based optimizer, vectorized execution engine, and intelligent materialized views, StarRocks can accelerate TPC‑H and join‑intensive queries far beyond ClickHouse or Druid. The system also caches frequently accessed S3 chunks on backend nodes, turning cold storage into near‑hot performance for repeat queries.

The business impact is evident: Coinbase reduced ODS latency from minutes to sub‑seconds, cutting Snowflake and Databricks spend while supporting billions of daily crypto transactions. Fresha achieved a four‑second baseline for complex dashboards, simplifying its data pipeline and lowering operational overhead. While the community around StarRocks is still maturing compared with ClickHouse, its ability to blend real‑time ingestion, lakehouse compatibility, and fast multi‑table joins positions it as a compelling alternative for finance, e‑commerce, and other data‑intensive sectors. Companies adopting StarRocks can expect faster insight delivery, lower cloud costs, and a more streamlined analytics stack.