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BLMgithub merged 1 commit into from
May 19, 2026
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docs: update broken link paths #77

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3 changes: 3 additions & 0 deletions README.md
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Expand Up @@ -64,6 +64,8 @@ By leveraging the Polars Rust engine (Lazy API), the system achieves near-optima
| 40M Snapshot (8GB / 4 vCPU) |
| :---: |
| ![engine-performance-8gb](assets/screenshots/engine-performance-8gb-4cpu.png) |
> Benchmark data: [`40m_stats_log.csv`](assets/benchmarks/polars/)
> Dataset : [`Dataset Information`](data/)

| Metric | Data |
|:---|:---|
Expand All @@ -72,6 +74,7 @@ By leveraging the Polars Rust engine (Lazy API), the system achieves near-optima
| Efficiency (Processing) | ~307k Rows / Second |
| Total Runtime (Wall-Clock) | 130 Seconds |


* **Maximized Memory Density:** The **Primitive Integer Pipeline** allows a ~5.34GB analytical model to process within the 8GB RAM limit by shrinking join-key overhead by ~16x.
* **Near-Linear Performance Scaling:** The engine saturates available vCPUs, yielding high throughput during streaming execution.
* **Zero-Idle Economics:** 100% serverless execution ensures zero billable time during idle periods.
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2 changes: 1 addition & 1 deletion assets/benchmarks/polars/README.md
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# Measurement Methodology

This section details the methodology used to capture the memory metrics in the [`GCP Stress-Test Metrics (Scaling Efficiency)`](../../../README.md#gcp-stress-test-metrics-scaling-efficiency)
This section details the methodology used to capture the memory metrics in the [`GCP Stress-Test Metrics (Scaling Efficiency)`](../../../README.md###gcp-stress-test-metrics-scaling-efficiency)

The telemetry logger below was added to the orchestrator for a specific benchmarking run.

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2 changes: 1 addition & 1 deletion data/README.md
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This directory serves as the local state provider for the pipeline when executing in a non-cloud environment. It mimics the structure of the Google Cloud Storage (GCS) buckets.

## Synthetic Dataset
To replicate the high-volume environment described in the [GCP Stress-Test Metrics (Scaling Efficiency)](/README.md#gcp-stress-test-metrics-scaling-efficiency) section, you can download the 40M-row synthetic dataset here: [**Kaggle Dataset Link**](https://www.kaggle.com/datasets/melvidabryan/e-commerce-synthetic-dataset)
To replicate the high-volume environment described in the [GCP Stress-Test Metrics (Scaling Efficiency)](/README.md###GCP-Stress-Test-Metrics) section, you can download the 40M-row synthetic dataset here: [**Kaggle Dataset Link**](https://www.kaggle.com/datasets/melvidabryan/e-commerce-synthetic-dataset)

> *Note: This upload contains the **Contracted Version** of the dataset. The original "Raw" state, totaling approximately ~26GB of unrefined CSVs was omitted to prioritize transfer efficiency.*

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