We are thrilled to announce that NATS Server 2.11 is finally here! We want to extend a heartfelt thank you to everyone who contributed through discussions, issue reporting, code contributions, and testing early versions. ❤️ A Long Journey to 2.11 The 2.10.x series was our longest yet, having currently reached 26 patch releases. Throughout this extended period, our ongoing commitment to testing and quality assurance delivered excellent results, making 2.10.26 the most stable and reliable release to date. Read more...

This is the first part of a two-part series explaining how Vitrifi leverages NATS.io to build its Workflow Automation Platform. Some Background for Perspective At Vitrifi , we embarked on building a Workflow Automation Platform after discovering that existing solutions couldn’t meet our specific requirements. Our vision was clear - we needed a platform that would: Be fully BPMN 2.0 compatible Provide an intuitive interface for visually designing and simulating workflows Maintain a minimal footprint for deployment flexibility Dynamically scale task workers based on workload Support multi-cloud deployments with high resilience and scalability Process thousands of workflow states per minute with real-time status updates When existing open-source workflow solutions fell short, we took action. Read more...

Scalable Alternative to Apache Kafka and Flink for Advanced Streaming While NORAD’s real-time tracking of Santa Claus’s sleigh across the globe is an annual tradition of the ultimate fleet manager, the NATS.io community is constantly challenging the boundaries of what is possible from a distributed scale perspective. Whether for creating interactive analytic dashboards for fleets of millions of sensors, to integrating plant floor data for predictive maintenance, or delivering latency sensitive AI workflows to edge devices, the topics discussed in this article are exactly the areas that distributed engineers and architects are trying to solve today. Read more...

In the competitive landscape of the manufacturing industry (e.g., automotive), achieving efficient and scalable operations across multiple factory locations is critical. This case study examines the implementation of a real-time analytics solution using NATS and i-flow to overcome common challenges such as data silos, real-time processing requirements, scalability, and communication inefficiencies in OT/IT environments. The primary design goal was to seamlessly scale real-time analytics across various factory locations. Challenge There are several critical challenges that typically hinder operational efficiency and scalability through real-time analytics: Read more...

When speaking about async patterns (messaging, event sourcing, etc.) with other developers, they often seem afraid of eventual consistency. However, it is often expressed in the form of: The frontend needs the answer directly The business process needs to be validated with perfect data My next page needs fresh data to display properly With a messaging system, you have to wait for the message to be processed and the state to be updated. Read more...

This is the first post in a new series where we explore patterns, practices, and samples of applications built using nothing but features available from NATS. We take a critical look at dependencies and frameworks and ask, “Do we really need the extra complexity?". If we’re already using NATS, then what would it be like to use nothing but our NATS infrastructure for application development? Introducing Natster Natster is an open source reference application that illustrates how to build a secure media sharing platform that leverages core NATS primitives for things we might otherwise get from third party applications, platforms, or libraries. Read more...

In the realm of IoT, real-time data processing presents specific challenges, including limited edge computing resources, network constraints, scalability considerations, and device diversity. Overcoming these obstacles requires the application of efficient edge computing techniques, robust security measures, standardized protocols, scalable management systems, and advanced data processing capabilities. By combining the strengths of NATS JetStream, RisingWave, and Superset, a powerful solution emerges for the development of reliable and scalable real-time IoT applications. Read more...

As people continue to leverage their NATS infrastructure by increasing the number of apps and services that rely on NATS, we tend to see some common patterns emerge. People end up with two different views of their infrastructure: the NATS connectivity map and the cluster-style resource views that come from their workload scheduler(s). With so many distributed applications, the infrastructure and connectivity is in place to support NATS and NATS-based applications, but is often underutilized. Read more...

Ever felt lost in the complexity of microservices and message queues? That’s precisely why FastStream was created. Initially, it was our solution to the challenges we faced with messaging queues in our own projects. But as it simplified our lives, we realized it could do the same for others. So, we decided to share it with the world. FastStream is a Python framework for building asynchronous services that interact with event streams like Apache Kafka, RabbitMQ, and NATS. Read more...