# Sail
[](https://github.com/lakehq/sail/actions)
[](https://app.codecov.io/gh/lakehq/sail)
[](https://pypi.org/project/pysail/)
[](https://pypi.org/project/pysail/)
[](https://www.launchpass.com/lakesail-community/free)
The mission of Sail is to unify stream processing, batch processing, and compute-intensive (AI) workloads.
Currently, Sail features a drop-in replacement for Spark SQL and the Spark DataFrame API in both single-host and distributed settings.
**✨Using Sail? [Tell us your story](https://lakesail.com/share-story/) and get free merch!✨**
## Installation
Sail is available as a Python package on PyPI. You can install it along with PySpark in your Python environment.
```bash
pip install pysail
pip install "pyspark[connect]"
```
Alternatively, you can install the lightweight client package `pyspark-client` since Spark 4.0.
The `pyspark-connect` package, which is equivalent to `pyspark[connect]`, is also available since Spark 4.0.
The [Installation](https://docs.lakesail.com/sail/latest/introduction/installation/) guide contains more information about installing Sail from source for better performance for your hardware architecture.
## Getting Started
### Starting the Sail Server
**Option 1: Command Line Interface** You can start the local Sail server using the `sail` command.
```bash
sail spark server --port 50051
```
**Option 2: Python API** You can start the local Sail server using the Python API.
```python
from pysail.spark import SparkConnectServer
server = SparkConnectServer(port=50051)
server.start(background=False)
```
**Option 3: Kubernetes** You can deploy Sail on Kubernetes and run Sail in cluster mode for distributed processing.
Please refer to the [Kubernetes Deployment Guide](https://docs.lakesail.com/sail/latest/guide/deployment/kubernetes.html) for instructions on building the Docker image and writing the Kubernetes manifest YAML file.
```bash
kubectl apply -f sail.yaml
kubectl -n sail port-forward service/sail-spark-server 50051:50051
```
### Connecting to the Sail Server
Once you have a running Sail server, you can connect to it in PySpark.
No changes are needed in your PySpark code!
```python
from pyspark.sql import SparkSession
spark = SparkSession.builder.remote("sc://localhost:50051").getOrCreate()
spark.sql("SELECT 1 + 1").show()
```
Please refer to the [Getting Started](https://docs.lakesail.com/sail/latest/introduction/getting-started/) guide for further details.
## Documentation
The documentation of the latest Sail version can be found [here](https://docs.lakesail.com/sail/latest/).
## Further Reading
- [Supercharge Spark: Quadruple Speed, Cut Costs by 94%](https://lakesail.com/blog/supercharge-spark/) - This post presents detailed **benchmark results** comparing Sail with Spark.
- [Sail 0.2 and the Future of Distributed Processing](https://lakesail.com/blog/the-future-of-distributed-processing/) - This post discusses the Sail **distributed processing architecture**.
## Contributing
Contributions are more than welcome!
Please submit [GitHub issues](https://github.com/lakehq/sail/issues) for bug reports and feature requests.
You are also welcome to ask questions in [GitHub discussions](https://github.com/lakehq/sail/discussions).
Feel free to create a [pull request](https://github.com/lakehq/sail/pulls) if you would like to make a code change.
You can refer to the [development guide](https://docs.lakesail.com/sail/main/development/) to get started.
## Support
LakeSail offers flexible enterprise support options for Sail. Please [contact us](https://lakesail.com/support/) to learn more.
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