Case Studies on Precomputing.com

API Latency Case Study: A Million Requests Kept as Ready Answers Inside SQLite

Tue, 29 Sep 2026 00:00:00 +0000

A small web service that already keeps its data in SQLite wants the usual answers about its API: how many requests each endpoint gets, how long they take on average, and the p99. The simple way is a table with a row per request, scanned whenever someone looks. In the SQL demo the service keeps a compiled Precomputing policy in the same SQLite instead, and the answers stay current on every insert.

Market Data Case Study: Four Million Trades Turned Into Candles, Checked After a Pulled Plug

Mon, 28 Sep 2026 00:00:00 +0000

A stock chart is made of candles: for each minute, the first price, the highest, the lowest and the last, with the volume traded. A candle is a window summary, the same kind of summary Precomputing keeps for API latency, only in a field every investor knows. In the Engine demo a trading day of stock trades goes through the Engine, which keeps 1-second, 1-minute and 1-hour candles and a quote board ready in its SQLite file, and writes the file five times a second.

AI Usage Billing Case Study: A Month of Invoices That Match a Recount to the Billionth of a Dollar

Sun, 27 Sep 2026 00:00:00 +0000

An AI product that bills by the token has to count every request exactly once, even when the network does not cooperate. Clients retry, and a retry can arrive through a different gateway. A gateway that loses its link holds its reports and sends them hours later. A queue can stick at the end of the month, after the invoices are out. In the Meter demo the product’s gateways report every request to one meter, a compiled Precomputing policy inside SQLite, and the invoices are one SQL view over totals the meter keeps ready.

Web Shop Logs Case Study: A Dashboard Sent Upstream in 120 Times Fewer Bytes

Sat, 26 Sep 2026 00:00:00 +0000

A web shop sends its logs to a log platform so a dashboard can show requests, latency, errors, payments and revenue, and so someone can search the lines when things go wrong. The platform charges by the gigabyte ingested and by the million lines indexed, and many of those lines are never read. In the Logs demo a log reducer sits next to the shop’s services. It learns each kind of line as it arrives, keeps every line on site for 48 hours, and sends upstream only the answers the dashboard shows, the errors and anything new.