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BlazeRules – YAML rule engine for streaming data, 3M records/SEC

BlazeRules – YAML rule engine for streaming data, 3M records/SEC

by jspuri·Jul 19, 2026·3 points·0 comments
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AI Analysis

●●●BangerWizardryZero to One

Vectorized YAML rule execution hits 3M records/sec, outperforming row-based engines like DuckDB for streams.

Strengths
  • •Reprojects row-based JSON into columnar buffers for SIMD masked evaluation.
  • •Hot reloads compile and swap rule sets without interrupting active data streams.
  • •Native ONNX integration allows ML model scoring directly within the rule pipeline.
Weaknesses
  • •Niche focus on streaming decisions may limit adoption compared to general SQL engines.
  • •Requires data to be batched for vectorization, adding latency for single-event processing.
Category
Data
Target Audience

Backend engineers building high-throughput event filtering or fraud detection systems

Similar To

DuckDB · Apache Flink · eBPF

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.

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