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Ractor is a new concurrency mechanism in Ruby that unlocks great performance improvement possibilities. Its well defined sharing and messaging system makes it easier to reason about than threaded models. Raft is a distributed consensus algorithm that backs distributed data stores such as Etcd, Console, and soon, Kafka; it’s designed to be straightforward and easy to understand. In this session we’ll build a simple Raft implementation from scratch using Ractor. Ractor is one of the most exciting new features of Ruby, and modeling a distributed system is a great way to see what it can offer.
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The video titled "Learning Ractor with Raft," presented by Micah Gates at RubyConf 2021, explores the new Ractor concurrency mechanism in Ruby and how to implement the Raft distributed consensus algorithm using Ractor. Ractor is designed to improve parallel execution in Ruby without the thread safety issues prevalent in traditional threading models. The session combines practical coding with theoretical principles to exhibit the capabilities of Ractor while modeling a distributed system using the Raft protocol. Key Points Discussed: - **Introduction to Ractor**: Ractor, introduced in Ruby, facilitates concurrent execution while maintaining data integrity. Unlike threads, Ractors do not share state but can communicate via message passing, minimizing the risk of data races. - **Actor Model**: Ractor operates on an actor model where each Ractor has its own mailbox to receive messages, ensuring that only one way exists to access and manipulate data at any given time. - **Introduction to Raft**: Raft is explained as a consensus algorithm for managing a replicated log among servers. It ensures that a group of servers can agree on shared data even in the face of failures. - **Elections in Raft**: Only one leader can manage writes at a time. The process of electing a leader is outlined, emphasizing randomness to avoid conflicts when nodes attempt to elect themselves simultaneously. - **Log Replication**: The procedure of how data is added to the log and replicated across nodes is described, with emphasis on how nodes forward requests to the leader. - **Handling Leader Failures**: The significance of maintaining redundancy is highlighted, detailing how the system can recover by electing a new leader if the current one fails. - **Implementing Raft**: The session walks through coding a simple Raft cluster within a Ruby process using Reactors, demonstrating the election of a leader, data replication, and resilience against node failures. - **Caveats**: Gates cautions that Ractor is still experimental, noting potential changes in future Ruby versions and reminding that the provided Raft implementation is simplified and not exhaustive. In conclusion, Gates expresses enthusiasm for the potential of Ractor to enhance Ruby's concurrency capabilities while simplifying multi-threading challenges. He encourages viewers to explore Ractor while recognizing that many patterns and practices are still being developed. The video serves both as a theoretical overview of distributed systems and a practical coding guide for leveraging Ruby's Ractor with the Raft algorithm.
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