storage
DuckDB uses an efficient columnar storage format optimized for analytical queries.
DuckDB uses an efficient columnar storage format optimized for analytical queries. Unlike traditional row-based storage systems, columnar storage groups data by column rather than by row, allowing for faster data retrieval and compression when dealing with large datasets. This approach is particularly beneficial for OLAP (Online Analytical Processing) workloads, where queries often involve aggregations and scans of specific columns rather than entire rows. DuckDB's storage engine supports both in-memory and persistent disk-based storage, allowing users to work with datasets that exceed available RAM. The storage format also includes metadata and indexing structures to further enhance query performance. When data is persisted to disk, DuckDB uses a custom file format that maintains the columnar structure and includes features like checkpointing for durability and crash recovery.
Related terms
Columnar storage is a data layout that groups values from the same column together on disk, rather than storing complete rows contiguously, which speeds up analytical scans and compression.
Parquet →Apache Parquet is a columnar storage file format designed for efficient data processing and analytics.
Cloud Storage →Cloud storage refers to a model of data storage where digital information is kept on remote servers accessed through the internet, rather than on local hard…
storage layer →The storage layer refers to the component of a data system responsible for persistently storing and managing data.
Object storage →Object storage is a data storage architecture that manages data as discrete objects — each with data, metadata, and a unique identifier — in a flat namespace, accessed over HTTP APIs rather than a traditional file system hierarchy.
Row-oriented storage →Row-oriented storage is a data layout that stores each record's fields contiguously on disk, so a whole row can be read or written in a single operation.