Segmenting and ordering data

To optimize query performance, TimescaleDB allows explicit control over how data is physically organized within columnar storage. By structuring data effectively, queries can minimize disk reads and execute more efficiently, using vectorized execution for parallel batch processing where possible.

• Group related data together to improve scan efficiency: organizing rows into logical segments ensures that queries filtering by a specific value only scan relevant data sections. For example, in the above, querying for a specific ID is particularly fast. (Implemented with <code>SEGMENTBY</code>.)
• Sort data within segments to accelerate range queries: defining a consistent order reduces the need for post-query sorting, making time-based queries and range scans more efficient. (Implemented with <code>ORDERBY</code>.)
• Reduce disk reads and maximize vectorized execution: a well-structured storage layout enables efficient batch processing (Single Instruction, Multiple Data, or SIMD vectorization) and parallel execution, optimizing query performance.

By combining segmentation and ordering, TimescaleDB ensures that columnar queries are not only fast but also resource-efficient, enabling high-performance real-time analytics.

Data mutability

Traditional databases force a trade-off between fast updates and efficient analytics. Fully immutable storage is impractical in real-world applications, where data needs to change. Asynchronous mutability—where updates only become visible after batch processing—introduces delays that break real-time workflows. In-place mutability, while theoretically ideal, is prohibitively slow in columnar storage, requiring costly decompression, segmentation, ordering, and recompression cycles.

Hypercore navigates these trade-offs with a hybrid approach that enables immediate updates without modifying compressed columnstore data in place. By staging changes in an interim rowstore chunk, hypercore allows updates and deletes to happen efficiently while preserving the analytical performance of columnar storage.