- Industry
- Drayage logistics
- Engagement
- Contract through Resultstack, lead developer
- When
- 2026
- Stack
- PostgreSQL
- TypeScript
- Node.js
- Next.js
- Python
A logistics data platform that answers in seconds
A drayage logistics operator needed dispatch and billing data to reach people inside a working shift. There was no warehouse to start from, the pipeline cycle took 11 to 16 minutes, and the Python dispatch engine at the centre of the work carried accumulated defects.
- Built the warehouse and its pipeline from scratch, in layers, so raw data, cleaned data and reporting tables each have one job.
- Timed every stage of the cycle and cut it in rounds; at the 50-second stage, 30 seconds of each cycle was repeated metadata checks rather than real work, and a cached refresh log removed that overhead in code.
- Read the query plans for the container lookup, which is searched on nearly every keystroke, and added a trigram index for the long patterns people actually type.
- Restructured the dispatch engine in a single commit, from 22 modules to 60, taking its type errors from 36 to 0.
- Measured the repository with a code-health tool before and after, on the same settings, so the improvement is a reading and not an opinion.
The platform got faster through diagnosis and code, on the same server.
Play both cycles
Both cycles on one clock
Before the fixes the cycle took 11 to 16 minutes. After the last round, 6.8 to 17.3 seconds, under 20 s. On one time axis the new cycle is a sliver at the start of the old one.
Both bars share one linear time axis from 0 to 16 minutes. Solid bars run to the fastest recorded cycle and the striped part to the slowest. The replay is compressed 120 times: 1 second on screen stands for 2 minutes of pipeline time.