Gnatsd on Brave New Geek

Introducing Liftbridge: Lightweight, Fault-Tolerant Message Streams

Fri, 27 Jul 2018 17:42:49 -0500

Last week I open sourced Liftbridge, my latest project and contribution to the Cloud Native Computing Foundation ecosystem. Liftbridge is a system for lightweight, fault-tolerant (LIFT) message streams built on NATS and gRPC. Fundamentally, it extends NATS with a Kafka-like publish-subscribe log API that is highly available and horizontally scalable.

I’ve been working on Liftbridge for the past couple of months, but it’s something I’ve been thinking about for over a year. I sketched out the design for it last year and wrote about it in January. It was largely inspired while I was working on NATS Streaming, which I’m currently still the second top contributor to. My primary involvement with NATS Streaming was building out the early data replication and clustering solution for high availability, which has continued to evolve since I left the project. In many ways, Liftbridge is about applying a lot of the things I learned while working on NATS Streaming as well as my observations from being closely involved with the NATS community for some time. It’s also the product of scratching an itch I’ve had since these are the kinds of problems I enjoy working on, and I needed something to code.

Iris Decentralized Cloud Messaging

Tue, 22 Jul 2014 22:34:31 -0600

A couple weeks ago, I published a rather extensive analysis of numerous message queues, both brokered and brokerless. Brokerless messaging is really just another name for peer-to-peer communication. As we saw, the difference in message latency and throughput between peer-to-peer systems and brokered ones is several orders of magnitude. ZeroMQ and nanomsg are able to reliably transmit millions of messages per second at the expense of guaranteed delivery.

Peer-to-peer messaging is decentralized, scalable, and fast, but it brings with it an inherent complexity. There is a dichotomy between how brokerless messaging is conceptualized and how distributed systems are actually built. Distributed systems are composed of services like applications, databases, caches, etc. Services are composed of instances or nodes—individually addressable hosts, either physical or virtual. The key observation is that, conceptually, the unit of interaction lies at the service level, not the instance level. We don’t care about which database server we interact with, we just want to talk to a database server (or perhaps multiple). We’re concerned with logical groups of nodes.

Dissecting Message Queues

Mon, 07 Jul 2014 00:33:53 -0500

Disclaimer (10/29/20) – The benchmarks and performance analysis presented in this post should not be relied on. This post was written roughly six years ago, and at the time, was just the result of my exploration of various messaging systems. The benchmarks are not implemented in a meaningful way, which I discussed in a follow-up post. This post will remain for posterity and learning purposes, but I do not claim that this information is accurate or useful.