A Query Engine Built for Aggregates

Cube Store is a distributed columnar serving engine designed to answer analytical queries from pre-aggregated data efficiently, using aggregate awareness in Cube’s semantic layer to match queries with eligible rollups or fall back to source data when necessary. Built in Rust with Apache Arrow, DataFusion, Parquet, RocksDB, and object storage, it separates routing, metadata, distributed worker execution, refresh operations, and local data caching. Its performance approach relies on sorted indexes, partition pruning, Parquet statistics, local worker copies, partial and streaming aggregation, and incremental refreshes to reduce data reads, intermediate processing, and coordination overhead for recurring query patterns. In a study of 11 adapted TPC-H queries across source scales ranging from about 6 million to 6 billion line items, 43 reported query-and-scale combinations had median latencies of 173 to 620 milliseconds and p90 latencies of 291 to 879 milliseconds, although the results did not evaluate concurrent throughput, refresh costs, or the separate impact of individual optimizations.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.