README.md

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# Welcome to Milvus

## What is Milvus

Milvus is the world's fastest similarity search engine for massive feature vectors. Designed with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.

Milvus provides stable Python, Java and C++ APIs.

Keep up-to-date with newest releases and latest updates by reading Milvus [release notes](https://milvus.io/docs/en/releases/v0.5.0/).

- Heterogeneous computing

  Milvus is designed with heterogeneous computing architecture for the best performance and cost efficiency. 

- Multiple indexes

  Milvus supports a variety of indexing types that employs quantization, tree-based, and graph indexing techniques. 

- Intelligent resource management

  Milvus automatically adapts search computation and index building processes based on your datasets and available resources.

- Horizontal scalability

  Milvus supports online / offline expansion to scale both storage and computation resources with simple commands.

- High availability

  Milvus is integrated with Kubernetes framework so that all single point of failures could be avoided.

- High compatibility

  Milvus is compatible with almost all deep learning models and major programming languages such as Python, Java and C++, etc.

- Ease of use

  Milvus can be easily installed in a few steps and enables you to exclusively focus on feature vectors. 

- Visualized monitor

  You can track system performance on Prometheus-based GUI monitor dashboards.

## Architecture

![Milvus_arch](https://github.com/milvus-io/docs/blob/master/assets/milvus_arch.png)

## Get started

### Hardware Requirements

| Component | Recommended configuration           |
| --------- | ----------------------------------- |
| CPU       | Intel CPU Haswell or higher         |
| GPU       | NVIDIA Pascal series or higher      |
| Memory    | 8 GB or more (depends on data size) |
| Storage   | SATA 3.0 SSD or higher              |

### Install using docker

Use Docker to install Milvus is a breeze. See the [Milvus install guide](https://milvus.io/docs/en/userguide/install_milvus/) for details.

### Build from source

#### Software requirements

- Ubuntu 18.04 or higher
- CMake 3.14 or higher
- CUDA 10.0 or higher
- NVIDIA driver 418 or higher

#### Compilation

##### Step 1 Install dependencies

```shell
$ cd [Milvus sourcecode path]/core
./ubuntu_build_deps.sh
```

##### Step 2 Build

```shell
$ cd [Milvus sourcecode path]/core
$ ./build.sh -t Debug
or 
$ ./build.sh -t Release
```

When the build is completed, all the stuff that you need in order to run Milvus will be installed under `[Milvus root path]/core/milvus`.

#### Launch Milvus server

```shell
$ cd [Milvus root path]/core/milvus
```

Add `lib/` directory to `LD_LIBRARY_PATH`

```
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/milvus/lib
```

Then start Milvus server:

```
$ cd scripts
$ ./start_server.sh
```

To stop Milvus server, run:

```shell
$ ./stop_server.sh
```

To edit Milvus settings in `conf/server_config.yaml` and `conf/log_config.conf`, please read [Milvus Configuration](https://github.com/milvus-io/docs/blob/master/reference/milvus_config.md).

### Try your first Milvus program

#### Run Python example code

Make sure [Python 3.5](https://www.python.org/downloads/) or higher is already installed and in use.

Install Milvus Python SDK.

```shell
# Install Milvus Python SDK
$ pip install pymilvus==0.2.3
```

Create a new file `example.py`, and add [Python example code](https://github.com/milvus-io/pymilvus/blob/master/examples/AdvancedExample.py) to it.

Run the example code.

```shell
# Run Milvus Python example
$ python3 example.py
```

#### Run C++ example code

```shell
 # Run Milvus C++ example
 $ cd [Milvus root path]/core/milvus/bin
 $ ./sdk_simple
```

#### Run Java example code
Make sure Java 8 or higher is already installed.

Refer to [this link](https://github.com/milvus-io/milvus-sdk-java/tree/master/examples) for the example code.

## Contribution guidelines

Contributions are welcomed and greatly appreciated. If you want to contribute to Milvus, please read our [contribution guidelines](CONTRIBUTING.md). This project adheres to the [code of conduct](CODE_OF_CONDUCT.md) of Milvus. By participating, you are expected to uphold this code.

We use [GitHub issues](https://github.com/milvus-io/milvus/issues/new/choose) to track issues and bugs. For general questions and public discussions, please join our community.

## Join the Milvus community

To connect with other users and contributors, welcome to join our [slack channel](https://join.slack.com/t/milvusio/shared_invite/enQtNzY1OTQ0NDI3NjMzLWNmYmM1NmNjOTQ5MGI5NDhhYmRhMGU5M2NhNzhhMDMzY2MzNDdlYjM5ODQ5MmE3ODFlYzU3YjJkNmVlNDQ2ZTk). 

## Milvus Roadmap

Please read our [roadmap](https://milvus.io/docs/en/roadmap/) to learn about upcoming features.

## Resources

[Milvus official website](https://www.milvus.io)

[Milvus docs](https://www.milvus.io/docs/en/userguide/install_milvus/)

[Milvus bootcamp](https://github.com/milvus-io/bootcamp)

[Milvus blog](https://www.milvus.io/blog/)

[Milvus CSDN](https://zilliz.blog.csdn.net/)

[Milvus roadmap](https://milvus.io/docs/en/roadmap/)


## License

[Apache 2.0 license](LICENSE)