How to Model Time-Series Data for Game Telemetry

Telemetry in the wrong store is slow to query and expensive to keep. A time-series model fixes both. Here is how.

How to fix it

1. Use a time-series store

Store telemetry in a store designed for time-based data and queries.

2. Partition by time

Partition data by time window so queries scan only relevant ranges.

3. Downsample and expire

Keep fine-grained data briefly and downsampled aggregates longer.

Catching the ones you can't reproduce

The hardest version of this to fix is the one you can't reproduce — it only happens on a player's hardware, OS, driver, or save state, under conditions that simply aren't present on your machine. A report that says “it crashed” or “it froze” gives you nothing to act on, so the bug survives release after release while quietly costing you players.

Automatic error capture closes that gap. Each failure arrives with its full stack trace, the device and OS, the build number, and a breadcrumb trail of what the player did right before it broke, so even a failure you have never seen becomes a specific, reproducible issue. Fold identical failures into one signature ranked by how many players each hits, and your worklist sorts itself worst-first instead of arriving as a stream of vague complaints.

This is where a tool like Bugnet earns its place. Its SDK captures every backend error automatically with the full stack trace plus device, OS, memory, build, and game-state context, folds duplicates into one grouped issue with an occurrence count, and ties each to the build it first appeared on — so you fix the problem that hurts the most players first and confirm it is gone when its signature disappears from the next release.