Organizations are drowning in an ocean of metrics. Every click, interaction, and transaction generates quantitative data points, creating vast repositories of numbers that promise insights but often deliver confusion. The challenge isn’t just the volume of data – it’s the fundamental question of how to transform these raw metrics into meaningful qualitative understanding.
Author: William David Louth
Observability X – Queues, Scripts, and Currents
The Substrates API separates the concerns of task definition, scheduling, and execution, while simultaneously enabling robust patterns for composing asynchronous workflows. The capability to post scripts, coupled with control over execution order facilitates adaptable and maintainable event-driven systems.
Observability X – Resources, Scopes, and Closures
Resource management is crucial for scaling digital twin representations in service architectures. Proper resource cleanup ensures system stability during scaling up and down. Rigorous resource management prevents resource exhaustion and orphaned resources consuming capacity. The Substrates API’s approach to resource management ensures resources are acquired and released during scaling operations
Observability X – States and Slots
The State and Slot interfaces provide a type-safe approach to managing and transmitting state. The State interface offers an immutable way to collect and chain Slots, each with a name, type, and value. It enforces type safety through method overloading, supporting primitives, strings, names, and states. These interfaces create a foundation for constructing states and communicating changes type-safely and performantly for small sets.
Observability X – Subjects
How we model observables in our systems shapes how we understand, monitor, and reason about their behavior. In the proposed subject-based model, all changes are simply emissions from a subject, eliminating the artificial distinction between states and signals that plague many current observability systems.
Papers We Love: The Past, Present, and Future Will Be Simulated (2014)
We had Claude.ai compose a review in the style of Papers We Love for an article from 2014. The goal was to see how well it predicted the current state of Observability 10 years on.
Observability X – Circuits
A significant challenge with siloed-oriented telemetry toolkits, such as OpenTelemetry, is that each instrument employs a distinct data collection and transmission process. Each pillar, including tracing, metrics, and logging, possesses numerous configuration parameters to manage underlying resource usage, such as buffers and execution threads. There is no inherent mechanism to synchronize or share context among these systems, resulting in fragmented and potentially conflicting telemetry data.
Observability X – Channels
In observability, channels underpin streaming architectures for real-time monitoring and alerting, enabling dynamic system reactions and scalability. Understanding and leveraging channels creates resilient, scalable, and maintainable systems, which is essential in today’s interconnected world. Instruments provide a high-level, domain-specific interface on top of channels. Beneath the surface, they are essentially specialized wrappers around channel operations.
Observability X – Pipes & Pathways
Traditionally, observability data pipelining operates like a single assembly line, where workers halt, examine items, and consult manuals before proceeding. This approach is functional but slow due to inspections. Instead of thinking of observability as one long pipeline, think of it as a graph with many possible routes. Once we know where something needs to go, we can create a specific route for it, like how a GPS creates a particular path for your destination instead of having to check a map at every intersection.
Observability X – Sources
Observability has been reduced to the straightforward processing of data collected and its forwarding to a remote centralized endpoint. While frameworks such as OpenTelemetry have standardized this, they overlook a fundamental aspect of true observability: the dynamic nature of observation itself. A more effective and efficient observability approach establishes live, adaptable connections between information sources and observers, both locally and remotely, as well as online and offline.