Efficient updates and deletes without performance penalties

When modifying or deleting existing data, hypercore avoids the inefficiencies of both asynchronous updates and in-place modifications. Instead of modifying compressed storage directly, affected batches are decompressed and staged in the interim rowstore chunk, where changes are applied immediately.

These modified batches remain in row storage until they are recompressed and reintegrated into the columnstore (which happens automatically via a background process). This approach ensures updates are immediately visible, but without the expensive overhead of decompressing and rewriting entire chunks. This approach avoids:

• The rigidity of immutable storage, which requires workarounds like versioning or copy-on-write strategies
• The delays of asynchronous updates, where modified data is only visible after batch processing
• The performance hit of in-place mutability, which makes compressed storage prohibitively slow for frequent updates
• The restrictions some databases have on not altering the segmentation or ordering keys

Query optimizations

Real-time analytics isn’t just about raw speed—it’s about executing queries efficiently, reducing unnecessary work, and maximizing performance. TimescaleDB optimizes every step of the query lifecycle to ensure that queries scan only what’s necessary, make use of data locality, and execute in parallel for sub-second response times over large datasets.

Skip unnecessary data

TimescaleDB minimizes the amount of data a query touches, reducing I/O and improving execution speed: