Why Sail?

When Spark was invented over 15 years ago, it was revolutionary. It redefined distributed data processing and became the backbone of data infrastructure for companies across every major industry.

For over a decade, it has powered everything from ETL to machine learning pipelines at scale. But as real-time demands increase, cloud costs rise, and AI workloads evolve, Spark’s architecture is showing its age.

Due to its JVM foundation, Spark struggles with latency, scalability, and operational complexity. This results in higher cloud expenses, slower product cycles, and increased operational overhead.

Our open-source framework, Sail, built natively in Rust, eliminates these problems entirely.

  • Rust-native engine with memory-safety
  • Spark Connect compatibility
  • Lightning-fast Python UDFs
  • Stateless and lightweight workers
  • Columnar format and zero-copy data transfer
  • 2-8x faster execution
  • Spark
  • Compute
  • Garbage Collection
  • Compute
  • Garbage Collection
  • ...
  • Sail
  • Compute

Runtime

Predictable Execution Times

Built in Rust, Sail adopts deterministic memory management. Compute operations are not interleaved with garbage collection pauses, resulting in more consistent task completion times with far fewer tail latency spikes.
Sail ensures low memory management overhead and predictable execution times, which reduces risk, complexity, and costs for teams delivering time-sensitive workloads.

Spark

2 min

Sail

15 sec

Same workload—8x faster execution.

Execution Speed

Native Performance with Columnar Format

Sail leverages the Apache Arrow in-memory format and the Apache DataFusion query engine. The columnar in-memory format allows SIMD instructions to process multiple data records in a single CPU cycle, yielding higher throughput per core. In contrast, JVM-based and row-based solutions add layers between the code and the metal, process data records in loops, and limit the performance that can be extracted from the hardware.
Sail consistently delivers 2x to 8x faster execution times, translating to shorter time-to-insight and lower resource usage.
  • Spark

  • Java Process
  • Serialization
  • Python Process
  • Serialization
  • Java Process
  • Sail
  • Rust Thread
  • Memory Buffer
  • Python Thread
  • Memory Buffer
  • Rust Thread

Data Flow

Zero-Copy Data Transfer & No Serialization

The Sail process embeds a Python interpreter to execute Python UDFs (User-Defined Functions). No data serialization or copying occurs between built-in operations and your custom Python code. Sail workers in a cluster exchange data using the Arrow format with no data serialization between query execution stages.
Python UDFs are highly performant in Sail. Join and aggregation operations in Sail also come with low data shuffling overhead.
  • Spark

    Sail

  • Heavy
    Containers
    Light
  • Slow
    Scaling Up
    Fast
  • High
    Setup Effort
    Low
  • High
    Cloud Costs
    Low

Cloud Efficiency

Lightweight Workers that Scale Instantly

The Sail process starts within seconds and consumes only a few dozen megabytes of memory when idle. In cloud environments where elasticity is essential, Sail reduces the need for capacity planning and manual tuning compared to JVM-based solutions with resource-intensive executors.
Sail empowers businesses to achieve dramatically lower cloud infrastructure costs and a smoother experience, especially in containerized environments.
  • Spark

    Sail

  • Possible
    Invalid Memory Access
    None
  • Possible
    Null Pointer Exceptions
    None
  • Possible
    Race Conditions
    None
  • Moderate
    Operation Confidence
    High

Safety & Reliability

Memory Management & Concurrency You Can Trust

Sail benefits from Rust’s unique approach to memory management. The rules enforced at compile time eliminate whole categories of memory and concurrency bugs. Sail’s internals have unparalleled robustness compared to JVM-based solutions.
Sail reduces production risk, debugging time, and operational costs by offering a solid engine for your data needs.
Spark
Sail
Source ...
  • SQL
  • DataFrame APIs
Sink ...

Compatibility

Migration Made Easy

Your Spark session acts as a gRPC client that communicates with the Sail server via the Spark Connect protocol. With Sail, there’s no need to rewrite your Spark applications. You can immediately deploy Sail in shadow mode for your production pipelines or migrate your workloads incrementally.
Sail removes barriers for teams to modernize their data stacks. Switching to Sail can be a straightforward business decision.

Modern Infrastructure.
No Rewrite Needed.

Spark served its purpose. But today’s data demands real-time performance, cloud-native architecture, and AI readiness. Sail replaces the complexity, latency, and cost of Spark with a modern, faster, and safer solution—without rewriting your code.

If you’re ready to eliminate technical debt and future-proof your infrastructure, let us build your migration plan.

Join the LakeSail Community

Get support, contribute code, and help shape the future of high-performance data and AI workloads.