Time Travel restores confidence during incidents and accelerates investigations. Instead of restoring a large backup to a side cluster, you query the database as it looked at a precise second in the past. Vela delivers this capability at the storage layer so queries remain fast and operational overhead stays low. Learn more about the approach in the How Vela Works guide and see performance context in our benchmarks.
Applications and analysts run the same SQL against a historical view. The result reflects the exact state of tables at a requested timestamp. This approach removes guesswork and avoids approximate reconstructions based on logs.
Teams label important moments such as pre‑migration checkpoints, quarter‑end snapshots, or pre‑release gates. Each bookmark stores metadata and a stable reference you can audit later.
Engineers create an isolated branch from a past moment, apply a fix, and validate the result without touching production. The process takes seconds and avoids full copies.
Vela stores changes as new blocks while keeping previous versions compact. Queries at a timestamp read a consistent set of blocks without replaying WAL into a temporary database. The system maintains high throughput even as history grows.
Readers use snapshot isolation, so concurrent writes proceed normally. You do not pause production or throttle ingest to explore the past. Observability shows how far back you can read and how many bookmarks exist.
Clients connect with a standard DSN and include a timestamp or bookmark reference. Dashboards and scripts reuse the same connection model they already use for live traffic.
Because the feature lives in the storage layer, you avoid snapshot sprawl and long restores. You keep costs predictable while gaining powerful recovery options.
A script removes too many rows. You run the failing query against the database as it existed one minute earlier, confirm the correct dataset, and restore the records into a branch for reconciliation.
Auditors request proof that a price table had specific values on a date. You reference a bookmark created at month end and produce exact results with an explanation the auditor can verify.
Product and data teams compare cohorts across months without copying production into a separate warehouse first. They query a consistent historical view and iterate quickly.
Create a policy to generate a bookmark before each release. Add a bookmark on migration start and end. When an incident occurs, create a branch from the relevant bookmark and validate the fix before merging. The pattern becomes muscle memory after a few cycles.
Read more in the docs. Try the interactive demo . Then estimate impact with the calculator. With Time Travel, you debug faster, pass audits with less friction, and recover without downtime.