Analytics built with Rust and ClickHouse.

Most web analytics platforms are built on general-purpose infrastructure: a scripting language doing ingestion, a row store doing queries, and layers of caching to make it look fast. That architecture works until your event volume grows — then the caches grow, the costs grow, and the queries slow down.

Tradly Memory is built differently. The ingestion path runs on Rust, chosen for memory safety, concurrency, and low overhead. The storage engine is ClickHouse, a columnar database designed for exactly this workload: high write throughput and fast aggregations over behavior data. The result is a stack that stays fast and affordable as your signal volume scales.

Memory safety without a garbage collector

Rust's ownership model catches memory bugs at compile time and avoids a garbage collector's pause overhead. For an ingestion path that runs continuously on high-volume traffic, that means fewer surprises in production and more predictable latency.

Concurrency without the boilerplate

Event intake is inherently parallel. Rust's model makes safe, high-throughput concurrent services practical — so the pipeline scales with your traffic without a rewrite.

Low footprint

Rust services are small and efficient. The infrastructure cost stays proportionate to the value, instead of ballooning as the event stream grows.

Columnar storage for fast aggregations

Behavior analytics asks column-shaped questions — "how many visitors viewed pricing this week?" ClickHouse stores each column separately, so it scans only the fields a query needs instead of whole rows.

High write throughput

Analytics pipelines append far more than they update. ClickHouse is engineered for exactly that: high-volume inserts that keep up with a busy site's event stream.

Compression that keeps cost down

Columnar layout compresses well, and compression is where the storage bill is won or lost. ClickHouse keeps a growing behavior history affordable.