High-Throughput Route Tracking APIs
Legacy Architecture
Real-time truck coordinates API endpoints were failing at a 35% rate during transit peaks because the DB query architecture fetched coordinates in nested loops, choking connection pools.
Rebuilt State
Migrated coordinates telemetry logging to a TimescaleDB time-series database. Deployed a lightweight tracking microservice in Go to process coordinates packets asynchronously.
[ GPS Telemetry Hardware ]
|
| (Coordinates Packets)
v
[ Go Tracking Microservice ]
|
| (Bulk Insert Query)
v
[ TimescaleDB Partition Tables ]
|
+-------+-------+
| (Compression) | (Partition Pruning)
v v
[ Compressed Disk ] [ Historical Clean Logs ]
01. Telemetry Bottleneck
Logistics hardware sent position coordinates every 10 seconds. The relational DB couldn't handle bulk inserts while generating routes, collapsing the service during peak transit hours.
02. Time-Series Migration
We replaced the coordinates table with TimescaleDB partition tables. This allowed position records to be written sequentially in time blocks without global indexing latency.
03. Infrastructure Stability
P99 API response latencies dropped from 4.2 seconds to 120ms. The system has run stably at 0.02% error rate since deployment, processing 14 million telemetry points daily.
System Failure Report (Week 6-9 Analysis)
In Week 6, TimescaleDB partitions filled the primary storage disk during stress testing. We paused feature work to implement automated chunk compression policies and partition cleaning schedules.