The compute-storage separation model has become increasingly popular in cloud OLAP systems because it allows for independent scaling of resources and often supports features like storage-based billing and compute-based billing separately.
The opposite of shared-nothing architecture, Compute and Storage Separation:
- Compute and storage layers are decoupledStorage is centralized and accessible by all compute nodesCompute resources can scale independently from storageEnables more flexible resource allocation and cost optimizationExamples: Snowflake, Google BigQuery, Amazon Redshift Spectrum, ClickHouse Cloud
# Never as fast as OLAP speed
Yury Izrailevsky and Jordan Tigani were saying that the speed will never be as fast as OLAP or DWH when compute and storage is decoupled ^812871
It’s interesting what StarRocks is doing with reading Iceberg and converting to its native format—same approach as StarTree with Pinot and ClickHouse. But again, it will never be as fast, and there will always be trade-offs.
# Further Reading
Origin: shared-nothing architecture
References:
Created 2025-05-20
