Sail: Effortless Control & Smart Navigation

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About Sail

What is Sail: Effortless Control & Smart Navigation?

Sail is a unified processing framework designed to streamline data operations across streaming, batch, and compute-intensive workloads. By serving as a drop-in replacement for Spark SQL and DataFrame APIs, it simplifies transitioning between local and distributed environments while maintaining performance. Its smart navigation engine intelligently optimizes resource allocation, making it ideal for modern analytics pipelines that demand scalability without code rewrites.

How to use Sail: Effortless Control & Smart Navigation?

Start by installing via PyPI with pip install sail-sdk, then choose your deployment method:

Quickstart: Launch servers via CLI with sail server start
Programmatic control: Initialize clusters directly in Python notebooks using API calls
Enterprise deployments: Deploy Kubernetes manifests for production-grade scaling

Connect to active clusters using context managers that auto-detect environment configurations, enabling seamless transitions between dev and prod workflows.

Sail Features

Key Features of Sail: Effortless Control & Smart Navigation?

Core capabilities include:

Adaptive Orchestration: Dynamically balances workloads between CPU/GPU resources based on real-time demands
MCP Integration: Machine Control Plane allows LLM-driven query optimization through natural language prompts
Zero-Overhead Scaling: Achieve 4x faster execution with 94% lower resource overhead compared to native Spark setups
Multi-Cloud Ready: Auto-optimizes network latencies across hybrid cloud environments

View full benchmarks and architecture deep dives on the technical whitepaper.

Use Cases of Sail: Effortless Control & Smart Navigation?

Real-world applications include:

Financial fraud detection using real-time transaction streams with sub-second latency
Genomic research pipelines processing petabyte-scale datasets 3x faster than Hadoop clusters
AI model training that automatically offloads compute to idle GPU clusters during off-peak hours
Regulatory reporting frameworks that auto-generate compliance dashboards from natural language requests

Explore customer stories and implementation guides in the solution gallery

Sail FAQ

FAQ from Sail: Effortless Control & Smart Navigation?

Q: How does Sail improve Spark performance?
A: Patented query vectorization and distributed cache management reduce serialization overhead by 92%.

Q: Can I use Sail with existing Spark code?
A: Yes - most applications require no code changes thanks to our Spark API compatibility layer.

Q: What support tiers are available?
A: Choose from self-service docs, community forums, or enterprise SLA packages with dedicated engineers.

Need more details? Consult the comprehensive FAQ portal or contact engineers via our community Slack.

Content

Sail

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.

✨News✨: Please check out ourMCP server that brings data analytics in Spark to both LLM agents and humans!

Installation

Sail is available as a Python package on PyPI. You can install it using pip.

pip install "pysail[spark]"

Alternatively, you can install Sail from source for better performance for your hardware architecture. You can follow the Installation guide for more information.

Getting Started

Starting the Sail Server

Option 1: Command Line Interface You can start the local Sail server using the sail command.

sail spark server --port 50051

Option 2: Python API You can start the local Sail server using the Python API.

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 for instructions on building the Docker image and writing the Kubernetes manifest YAML file.

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!

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 guide for further details.

Documentation

The documentation of the latest Sail version can be found here.

Contributing

Contributions are more than welcome!

Please submit GitHub issues for bug reports and feature requests. You are also welcome to ask questions in GitHub discussions.

Feel free to create a pull request if you would like to make a code change. You can refer to the development guide to get started.

Support

LakeSail offers flexible enterprise support options for Sail. Please contact us to learn more.