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feat: enhance benchmark functionality with dataset discovery, validation, and performance monitoring
- Add --describe command for datasets and engines with columnar display - Implement real-time performance summaries (QPS, P50/P95 latency) - Add comprehensive dataset validation system with GitHub Actions - Complete dataset metadata with vector_count and description fields - Improve download reliability with proper HTTP headers - Standardize precision formatting (0.01 increments up to 0.97, then 0.0025) - Enhanced Docker configurations for better Redis testing defaults - Add validation documentation and automated CI/CD checks This maintains backward compatibility while significantly improving usability, data quality, and performance insights for vector database benchmarking.
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‎.dockerignore

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# Results and data
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results/
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datasets/
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# Include datasets.json and random-100 dataset for basic functionality
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datasets/*
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!datasets/datasets.json
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!datasets/random-100/
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*.h5
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*.hdf5
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*.json.gz
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# Docker files themselves
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Dockerfile*
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.dockerignore
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docker-compose*.yml
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docker-*.sh

‎.github/workflows/validate-datasets.yml

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name: Validate Datasets
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on:
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push:
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paths:
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- 'datasets/datasets.json'
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- 'run.py'
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- 'benchmark/dataset.py'
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- '.github/workflows/validate-datasets.yml'
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pull_request:
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paths:
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- 'datasets/datasets.json'
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- 'run.py'
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- 'benchmark/dataset.py'
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- '.github/workflows/validate-datasets.yml'
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jobs:
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validate-datasets:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Validate datasets.json
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run: |
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echo "Running dataset validation..."
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python validate_datasets.py

‎DOCKER_SETUP.md

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# Docker Setup and Publishing Guide
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This guide explains how to set up Docker publishing for the `vector-db-benchmark` project to Docker Hub repository `redis-performance/vector-db-benchmark`.
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This guide explains how to set up Docker publishing for the `vector-db-benchmark` project to Docker Hub repository `filipe958/vector-db-benchmark`.
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## 🔐 Required GitHub Secrets
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Once secrets are configured, Docker images will be automatically published:
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### Default Branch Commits (update-redisearch)
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- **Trigger**: Every push to `update-redisearch` branch
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- **Tags**: `latest`, `update-redisearch-{sha}`, `update-redisearch-{timestamp}`
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### Default Branch Commits (update.redisearch)
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- **Trigger**: Every push to `update.redisearch` branch
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- **Tags**: `latest`, `update.redisearch-{sha}`, `update.redisearch-{timestamp}`
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- **Platforms**: `linux/amd64`, `linux/arm64`
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### Releases
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### Example Tags for Release v1.2.3
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```
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redis-performance/vector-db-benchmark:v1.2.3
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redis-performance/vector-db-benchmark:1.2.3
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redis-performance/vector-db-benchmark:1.2
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redis-performance/vector-db-benchmark:1
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redis-performance/vector-db-benchmark:latest
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filipe958/vector-db-benchmark:v1.2.3
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filipe958/vector-db-benchmark:1.2.3
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filipe958/vector-db-benchmark:1.2
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filipe958/vector-db-benchmark:1
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filipe958/vector-db-benchmark:latest
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```
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## 🛠️ Manual Building and Publishing
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### Pull and Run
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```bash
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# Latest version
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docker pull redis-performance/vector-db-benchmark:latest
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docker run --rm redis-performance/vector-db-benchmark:latest run.py --help
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docker pull filipe958/vector-db-benchmark:latest
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docker run --rm filipe958/vector-db-benchmark:latest run.py --help
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# Specific version
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docker pull redis-performance/vector-db-benchmark:v1.2.3
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docker run --rm redis-performance/vector-db-benchmark:v1.2.3 run.py --help
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docker pull filipe958/vector-db-benchmark:v1.2.3
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docker run --rm filipe958/vector-db-benchmark:v1.2.3 run.py --help
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```
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### Example Usage
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```bash
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# Basic Redis benchmark
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docker run --rm --network=host redis-performance/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-m-16-ef-64
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docker run --rm --network=host filipe958/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-default-simple
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# With custom Redis host
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docker run --rm redis-performance/vector-db-benchmark:latest \
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run.py --host redis-server --engines redis --dataset random-100 --experiment redis-m-16-ef-64
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docker run --rm filipe958/vector-db-benchmark:latest \
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run.py --host redis-server --engines redis --dataset random-100 --experiment redis-default-simple
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# With results output (mount current directory)
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docker run --rm -v $(pwd)/results:/app/results --network=host \
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redis-performance/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-m-16-ef-64
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# Using docker-compose for full setup
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docker-compose up redis
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docker-compose run --rm vector-db-benchmark run.py --host redis --engines redis --experiment redis-m-16-ef-64
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filipe958/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-default-simple
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# Using with Redis container
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docker run -d --name redis-test -p 6379:6379 redis:8.2-rc1-bookworm
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docker run --rm --network=host filipe958/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --experiment redis-default-simple
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docker stop redis-test && docker rm redis-test
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```
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## 🔍 Monitoring and Troubleshooting
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- Prevents merging PRs with broken Docker builds
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### Docker Hub
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- View images at: https://hub.docker.com/r/redis-performance/vector-db-benchmark
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- View images at: https://hub.docker.com/r/filipe958/vector-db-benchmark
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- Check image sizes and platforms
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- Review vulnerability scan results
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‎README.md

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| Dataset | Dimensions | Train size | Test size | Neighbors | Distance |
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| ----------------------------------------------------------------------------------------------------------- | ---------: | ---------: | --------: | --------: | --------- |
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| [LAION-1M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 1,000,000 | 10,000 | 100 | Angular |
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| [LAION-10M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 10,000,000 | 10,000 | 100 | Angular |
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| [LAION-20M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 20,000,000 | 10,000 | 100 | Angular |
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| [LAION-40M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 40,000,000 | 10,000 | 100 | Angular |
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| [LAION-100M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 100,000,000 | 10,000 | 100 | Angular |
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| [LAION-200M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 200,000,000 | 10,000 | 100 | Angular |
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| [LAION-400M: from LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 400,000,000 | 10,000 | 100 | Angular |
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| **LAION Image Embeddings (512D)** | | | | | |
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| [LAION-1M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 1,000,000 | 10,000 | 100 | Cosine |
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| [LAION-10M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 10,000,000 | 10,000 | 100 | Cosine |
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| [LAION-20M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 20,000,000 | 10,000 | 100 | Cosine |
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| [LAION-40M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 40,000,000 | 10,000 | 100 | Cosine |
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| [LAION-100M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 100,000,000 | 10,000 | 100 | Cosine |
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| [LAION-200M: subset of LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 200,000,000 | 10,000 | 100 | Cosine |
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| [LAION-400M: from LAION 400M English (image embedings)](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 400,000,000 | 10,000 | 100 | Cosine |
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| **LAION Image Embeddings (768D)** | | | | | |
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| [LAION-1M: 768D image embeddings](https://laion.ai/blog/laion-400-open-dataset/) | 768 | 1,000,000 | 10,000 | 100 | Cosine |
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| [LAION-1B: 768D image embeddings](https://laion.ai/blog/laion-400-open-dataset/) | 768 | 1,000,000,000| 10,000 | 100 | Cosine |
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| **Standard Benchmarks** | | | | | |
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| [GloVe-25: Word vectors](http://ann-benchmarks.com) | 25 | 1,183,514 | 10,000 | 100 | Cosine |
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| [GloVe-100: Word vectors](http://ann-benchmarks.com) | 100 | 1,183,514 | 10,000 | 100 | Cosine |
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| [Deep Image-96: CNN image features](http://ann-benchmarks.com) | 96 | 9,990,000 | 10,000 | 100 | Cosine |
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| [GIST-960: Image descriptors](http://ann-benchmarks.com) | 960 | 1,000,000 | 1,000 | 100 | L2 |
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| **Text and Knowledge Embeddings** | | | | | |
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| [DBpedia OpenAI-1M: Knowledge embeddings](https://www.dbpedia.org/) | 1,536 | 1,000,000 | 10,000 | 100 | Cosine |
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| [LAION Small CLIP: Small CLIP embeddings](https://laion.ai/blog/laion-400-open-dataset/) | 512 | 100,000 | 1,000 | 100 | Cosine |
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| **Yandex Datasets** | | | | | |
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| [Yandex T2I: Text-to-image embeddings](https://research.yandex.com/) | 200 | 1,000,000 | 100,000 | 100 | Dot |
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| **Random and Synthetic** | | | | | |
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| Random-100: Small synthetic dataset | 100 | 100 | 9 | 9 | Cosine |
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| Random-100-Euclidean: Small synthetic dataset | 100 | 100 | 9 | 9 | L2 |
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| **Filtered Search Datasets** | | | | | |
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| H&M-2048: Fashion product embeddings (with filters) | 2,048 | 105,542 | 2,000 | 100 | Cosine |
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| H&M-2048: Fashion product embeddings (no filters) | 2,048 | 105,542 | 2,000 | 100 | Cosine |
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| ArXiv-384: Academic paper embeddings (with filters) | 384 | 2,205,995 | 10,000 | 100 | Cosine |
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| ArXiv-384: Academic paper embeddings (no filters) | 384 | 2,205,995 | 10,000 | 100 | Cosine |
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| Random Match Keyword-100: Synthetic keyword matching (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Match Keyword-100: Synthetic keyword matching (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Match Int-100: Synthetic integer matching (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Match Int-100: Synthetic integer matching (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Range-100: Synthetic range queries (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Range-100: Synthetic range queries (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Geo Radius-100: Synthetic geo queries (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Geo Radius-100: Synthetic geo queries (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Match Keyword-2048: Large synthetic keyword matching (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Match Keyword-2048: Large synthetic keyword matching (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Match Int-2048: Large synthetic integer matching (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Match Int-2048: Large synthetic integer matching (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Range-2048: Large synthetic range queries (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Range-2048: Large synthetic range queries (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Geo Radius-2048: Large synthetic geo queries (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Geo Radius-2048: Large synthetic geo queries (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
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| Random Match Keyword Small Vocab-256: Small vocabulary keyword matching (with filters) | 256 | 1,000,000 | 10,000 | 100 | Cosine |
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| Random Match Keyword Small Vocab-256: Small vocabulary keyword matching (no filters) | 256 | 1,000,000 | 10,000 | 100 | Cosine |
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## 🐳 Docker Usage
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```bash
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# Pull the latest image
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docker pull redis-performance/vector-db-benchmark:latest
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docker pull filipe958/vector-db-benchmark:latest
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# Run with help
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docker run --rm redis-performance/vector-db-benchmark:latest run.py --help
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docker run --rm filipe958/vector-db-benchmark:latest run.py --help
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# Basic Redis benchmark with local Redis
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docker run --rm --network=host redis-performance/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-m-16-ef-64
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docker run --rm --network=host filipe958/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-default-simple
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# With results output (mount current directory)
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docker run --rm -v $(pwd)/results:/app/results --network=host \
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redis-performance/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-m-16-ef-64
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filipe958/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-default-simple
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```
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### Using Docker Compose
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### Using with Redis
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For a complete setup with Redis included:
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```bash
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# Start Redis
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docker-compose up redis
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# Start Redis container
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docker run -d --name redis-test -p 6379:6379 redis:8.2-rc1-bookworm
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# Run benchmark against Redis
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docker-compose run --rm vector-db-benchmark run.py --host redis --engines redis --dataset random-100 --experiment redis-m-16-ef-64
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docker run --rm --network=host filipe958/vector-db-benchmark:latest \
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run.py --host localhost --engines redis --dataset random-100 --experiment redis-default-simple
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# Or use the convenience script
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./docker-run.sh -H redis -e redis -d random-100 -x redis-m-16-ef-64
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./docker-run.sh -H localhost -e redis -d random-100 -x redis-default-simple
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# Clean up Redis container when done
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docker stop redis-test && docker rm redis-test
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```
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### Available Docker Images
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- **Latest**: `redis-performance/vector-db-benchmark:latest`
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- **Specific versions**: `redis-performance/vector-db-benchmark:v1.0.0`
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- **Development builds**: `redis-performance/vector-db-benchmark:update-redisearch-{sha}`
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- **Latest**: `filipe958/vector-db-benchmark:latest`
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For detailed Docker setup and publishing information, see [DOCKER_SETUP.md](DOCKER_SETUP.md).
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