β‘ Madara: Starknet Sequencer π π
Welcome to Madara, a blazing fast
Built on the robust Substrate framework and fast, thanks to Rust
Dive into the world of Madara and join our passionate community of contributors! Together, we're pushing the boundaries of what's possible within the Starknet ecosystem.
π£ Peripheral repositories
- Madara Explorer by the great LambdaClass team π«Ά: A block explorer for Madara.
- Madara Infra:
A collection of scripts and tools to deploy and manage Madara
on different environments (e.g. AWS, docker, ansible, etc.).
It also contains
the Starknet Stack
demo
docker-composefile. - Madara Kit Application:
A simple application that demonstrates how to use Madara. Deployed on
https://app.madara.zone. - Madara Docsite:
The source code of the Madara documentation website. Deployed on
https://docs.madara.zone.
π Features
- Starknet sequencer
πΊ - Built on Substrate
π - Rust-based for safety and performance
ποΈ - Custom FRAME pallets for Starknet functionality
π§ - Comprehensive documentation
π - Active development and community support
π€
π Documentation
Get started with our comprehensive documentation, which covers everything from project structure and architecture to benchmarking and running Madara:
ποΈ Build & Run
Want to dive straight in? Check out our Getting Started Guide for instructions on how to build and run Madara on your local machine.
Benchmarking
Benchmarking is an essential process in our project development lifecycle, as it helps us to track the performance evolution of Madara over time. It provides us with valuable insights into how well Madara handles transaction throughput, and whether any recent changes have impacted performance.
You can follow the evolution of Madara's performance by visiting our Benchmark Page.
However, it's important to understand that the absolute numbers presented on this page should not be taken as the reference or target numbers for a production environment. The benchmarks are run on a self-hosted GitHub runner, which may not represent the most powerful machine configurations in real-world production scenarios.
Therefore, these numbers primarily serve as a tool to track the relative performance changes over time. They allow us to quickly identify and address any performance regressions, and continuously optimize the system's performance.
In other words, while the absolute throughput numbers may not be reflective of a production environment, the relative changes and trends over time are what we focus on. This way, we can ensure that Madara is always improving, and that we maintain a high standard of performance as the project evolves.
One can use flamegraph-rs to generate flamegraphs and look for the performance bottlenecks of the system by running the following :
flamegraph --root --open -- ./target/release/madara --dev --pool-limit=100000 --pool-kbytes=500000 --rpc-methods=unsafe --rpc-cors=all --in-peers=0 --out-peers=1 --no-telemetryIn parallel to that, run npm run test within the benchmarking folder.
Once you stop the node, the flamegraph will open in your browser.
π Connect to the dev webapp
Once your Madara node is up and running, you can connect to our Dev Frontend App to interact with your chain. Connect here!
π€ Contribute
We're always looking for passionate developers to join our community and contribute to Madara. Check out our contributing guide for more information on how to get started.
π License
This project is licensed under the MIT license.
See LICENSE for more information.
Happy coding!
Contributors β¨
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!