BlazeRules – YAML rule engine for streaming data, 5M records/SEC

Name: BlazeRules – YAML rule engine for streaming data, 5M records/SEC
Availability: InStock
Author: jspuri

by jspuri·Jul 25, 2026·1 point·0 comments

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AI Analysis

●●●BangerWizardryBig Brain

Vectorized YAML rule engine hitting 5M records/sec before data hits your warehouse.

Strengths

•Reprojects rows to columns for SIMD masked evaluation instead of pointer chasing.
•Hot reload swaps rule sets between batches without interrupting the active engine.
•Native ONNX integration adds model scores as decision signals within the same pipeline.

Weaknesses

•YAML for complex logic can become unmaintainable compared to typed DSLs or code.
•Niche audience: only matters if you need sub-millisecond decisions at massive scale.

Post Description

https://blazerules.dev

I initially wanted to make a sub-millisecond log parser in C++ but that blew into a embeddable decision engine, that can run YAML defined rules on incoming data. The rules are executed in a vectorized format on incoming data by reprojecting into a columnar format first, if it's not already. Depending on the payload size and rules complexity, the performance goes from 200K records/s to more than million records/sec, in terms of througput this would be around 200 MiB/s to 3 GiB/s on average.

Rules can be sql expressions too, or onnx models (numeric), window ops and quite a few more operations are supported.

It's comparable to DuckDB but for streaming data and on the fly decisions