The-Architecture-Handbook-for-Milvus-Vector-Database

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The-Architecture-Handbook-for-Milvus-Vector-Database

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<h1 align="center">
The Architecture Handbook for Milvus Vector Database, First Edition</h1>
<p align="center">This is the code repository for <a href ="the-architecture-handbook-for-milvus-vector-database-first-edition"> The Architecture Handbook for Milvus Vector Database, First Edition</a>, published by Packt.
</p>

<h2 align="center">
Design and implement high-performance vector search systems with Milvus
</h2>
<p align="center">
Yudong Cai, Jeremy Zhu, Xuan Yang, Bang Fu</p>

<p align="center">
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<details open>
  <summary><h2>About the book</summary>
<a href="https://www.packtpub.com/en-in/product/the-architecture-handbook-for-milvus-vector-database-9781835881712">
<img src="https://content.packt.com/B31502/cover_image_small.jpg" alt="The Architecture Handbook for Milvus Vector Database, First Editionn" height="256px" align="right">
</a>

The rapid adoption of LLMs demands efficient storage and lightning-fast retrieval of unstructured data. Designed as a vector database, Milvus has earned widespread recognition in the community and support from tech giants like Apple and NVIDIA. Yet, many developers only scratch the surface of what Milvus is truly capable of. Written by the contributors of the Milvus project, this handbook gives you an insider’s perspective on its design and how it handles large-scale, high-dimensional vector data.
Starting with the basics, you’ll learn about everything from service deployment and SDK usage to Milvus’ layered architecture and how its components interact. You’ll learn how the indexing, replication, compaction, and garbage collection systems work and how to apply them to real scenarios. Through practical demos and configuration exercises, you’ll learn how to monitor, scale, and secure Milvus in production and then advance to performance evaluation and scalability testing using tools like VectorDBBench. You'll also explore Milvus' integration with LangChain for use cases such as vector search and RAG-based chatbots.
By the end of this book, you’ll be able to analyze Milvus internals, fine-tune for performance, ensure system stability, and integrate it into next-generation AI solutions.
*Email sign-up and proof of purchase required
</details>
<details open>
  <summary><h2>Key Learnings</summary>
<ul>

<li>Deploy Milvus using Docker, Kubernetes, and Helm</li>

<li>Configure Milvus and monitor system health with Prometheus, Grafana, and Loki</li>

<li>Understand core components like Knowhere, indexes, time sync, compaction, and garbage collection</li>

<li>Design and optimize schema, queries, and data modification flows</li>

<li>Benchmark performance and simulate real-world failure recovery</li>

<li>Scale Milvus clusters to support large datasets and high-concurrency traffic</li>

<li>Apply security hardening, rate-limiting, and role-based access control</li>

<li>Build AI applications using Milvus with LangChain</li>

</ul>

</details>

<details open>
  <summary><h2>Chapters</summary>

| Chapters | Colab | Kaggle | Gradient | Studio Lab |
| :-------- | :-------- | :------- | :-------- | :-------- |
| **Chapter 1: Introduction to Milvus** | | | | |
| **Chapter 2: Deploying Milvus in Multiple Ways** | | | | |
| **Chapter 3: Interacting with Milvus** | | | | |
| **Chapter 4: Configuring the Milvus System** | | | | |
| **Chapter 5: Understanding the Milvus Data Model and Architecture** | | | | |
| <ul><li>ch5_movies.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_movies.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_movies.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_movies.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_movies.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| <ul><li>ch5_wiki.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_wiki.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_wiki.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_wiki.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch5/ch5_wiki.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| **Chapter 6: Data Modification and Maintenance in Milvus** | | | | |
| **Chapter 7: Reading Data in Milvus** | | | | |
| **Chapter 8: Compaction and Garbage Collection** | | | | |
| **Chapter 9: Exploring Milvus' Vector Engine** | | | | |
| **Chapter 10: How to Select a Vector Index** | | | | |
| **Chapter 11: Handling Complicated Search Requests** | | | | |
| **Chapter 12: Getting Started with Milvus Performance Benchmarking** | | | | |
| **Chapter 13: Stability and Reliability Evaluation for the Milvus Vector Database** | | | | |
| **Chapter 14: Scalability Evaluation for the Milvus Vector Database** | | | | |
| **Chapter 15: Getting Started with Milvus Performance Tuning** | | | | |
| **Chapter 16: Implementing Multi-Tenancy in Milvus** | | | | |
| <ul><li>comprehensive_multi_tenancy_example.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/comprehensive_multi_tenancy_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/comprehensive_multi_tenancy_example.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/comprehensive_multi_tenancy_example.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/comprehensive_multi_tenancy_example.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| <ul><li>database_level_multi_tenancy.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/database_level_multi_tenancy.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/database_level_multi_tenancy.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/database_level_multi_tenancy.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/database_level_multi_tenancy.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| <ul><li>partition_based_multi_tenancy.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_based_multi_tenancy.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_based_multi_tenancy.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_based_multi_tenancy.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_based_multi_tenancy.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| <ul><li>partition_key_multi_tenancy.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_key_multi_tenancy.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_key_multi_tenancy.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_key_multi_tenancy.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/partition_key_multi_tenancy.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| <ul><li>resource_group_multi_tenancy.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/resource_group_multi_tenancy.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/resource_group_multi_tenancy.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/resource_group_multi_tenancy.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch16/resource_group_multi_tenancy.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| **Chapter 17: How Milvus Works in AI** | | | | |
| <ul><li>chatbot.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/chatbot.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/chatbot.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/chatbot.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/chatbot.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |
| <ul><li>image_search.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/image_search.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/image_search.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a><br> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/image_search.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a><br> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/The-Architecture-Handbook-for-Milvus-Vector-Database/blob/main/ch17/image_search.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a><br> |

</details>

<details open>
  <summary><h2>Requirements for this book</summary>
## Technical Requirements

To get the most out of this book, you should meet the following prerequisites:

### Knowledge Requirements
- Basic understanding of **Python programming**
- Familiarity with **machine learning concepts**
- Basic understanding of **large language models (LLMs)**
- Experience working with **APIs** and AI development environments
- Foundational knowledge of **databases**
- Familiarity with **containerization tools** such as **Docker**
- Basic understanding of **orchestration tools** such as **Kubernetes**
- Experience with **distributed systems** (optional but beneficial)
- No prior in-depth knowledge of **vector databases** is required

### Software Requirements
- **Visual Studio Code (VS Code)** or any preferred code editor
- **Python 3.x**
- **Docker**
- **Kubernetes (K8s)**

### Supported Operating Systems
- **macOS**
- **Linux**

### Hardware Requirements
- A machine with at least **8 GB RAM** (recommended for running complex examples)
- Access to a **Python development environment**

### Additional Notes
- If using the digital version of this book, it is recommended to **type the code manually**
- This helps avoid errors caused by copying and pasting code

</details>

<details>
  <summary><h2>Get to know Authors</h2></summary>

_Yudong Cai_ Yudong Cai is a senior software engineer with over 20 years of experience in large-scale system development. As one of the founding members of the Milvus project, he helped build Milvus from the ground up and has been involved in the development and iteration of every version since its initial open-source release. His key contributions include delivering the first production-grade Range Search implementation, as well as the refactoring of the entire Milvus configuration system, alongside the design and implementation of numerous other critical features. He is also the original developer and key maintainer of Knowhere, Milvus' core vector computation engine, where he designed its architecture to support multiple hardware acceleration frameworks and a wide range of vector search algorithms.

_Jeremy Zhu_ Jeremy Zhu is a quality assurance engineer at Zilliz, focused on ensuring the robustness and high performance of the Milvus vector database. His core responsibilities include designing comprehensive test cases, developing automated system test pipelines for diverse scenarios, and executing rigorous stress, recovery, and performance testing. Jeremy possesses deep expertise in chaos engineering, distributed systems testing, and test automation frameworks, playing a key role in maintaining Milvus' high-quality standards.

_Xuan Yang_ Xuan Yang is a senior software engineer at Zilliz in China, passionate about designing high-performance, scalable distributed database systems. As a core Milvus contributor, she architected the DataNode module, implemented the compaction process, and led the L0 segment design. She is the primary maintainer of PyMilvus, the official Python SDK, and VectorDBBench, an open-source benchmarking framework for vector databases. She cares deeply about system stability and performance and is always eager to collaborate with the community to push the boundaries of large-scale AI and vector data infrastructure.

_Bang Fu_ Bang Fu is a senior software engineer at Zilliz. With extensive experience in both Go and Python, he has actively contributed to the development of several key features for Milvus, including permission verification, request interception, incremental synchronization, and serverless metering functionalities. He is also interested in AI technology and led the development of the GPTCache project, which focuses on caching LLM responses to improve speed and reduce costs. In addition, he has participated in the development of the DeepSearcher project.

</details>
<details>
  <summary><h2>Other Related Books</h2></summary>
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</details>

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