Remove Metrics Remove Microservices Remove Software Engineering
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There Is Only One Key Difference Between Observability 1.0 and 2.0

Honeycomb

phenomenon We’ve all heard the slogan, “metrics, logs, and traces are the three pillars of observability.” You probably use some subset (or superset) of tools including APM, RUM, unstructured logs, structured logs, infra metrics, tracing tools, profiling tools, product analytics, marketing analytics, dashboards, SLO tools, and more.

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Building Shared State Microservices for Distributed Systems Using Kafka Streams

Confluent

At Imperva, we took advantage of Kafka Streams to build shared state microservices that serve as fault-tolerant, highly available single sources of truth about the state of objects in our system. At the core of each shared state microservice we built was a Kafka Streams instance with a rather simple processing topology.

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The Cost Crisis in Observability Tooling

Honeycomb

Observability has three pillars: metrics, logs, and traces.” But logs are expensive and everybody wants dashboards… so we buy a metrics tool. Software engineers want to instrument their applications… so we buy an APM tool. The front-end engineers point out that they need sessions and browser data… so we buy a RUM tool.

Tools 143
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What Is Observability? Key Components and Best Practices

Honeycomb

The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach : observability. Observability is not just a buzzword; it’s a fundamental shift in how we perceive and manage the health, performance, and behavior of software systems.

Metrics 126
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The Top 13 Sessions From 2018 Ops and Dev Conferences

OverOps

At Serverless Computing London keynote, Charity explained what we mean when we say observability, what it means to have an observable system, how it connects to old methods of monitoring or debugging, and why the modern serverless software engineer should give a flying crap. Metrics, logging, monitoring, and reliability.

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How Netflix microservices tackle dataset pub-sub

Netflix Tech

By Ammar Khaku Introduction In a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. Today these metrics are used by the Gutenberg team to monitor our publish-propagation SLI and to alert in the event of widespread issues. we’re hiring !

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Pursuing DevOps Momentum: Measuring what matters

CloudGeometry

The quality of collaboration in software development is measured by a direct line of sight into the customer experience. DevOps is a given in today’s software engineering world. Especially in a SaaS business, metrics aggregated over time matter most. Read more about this in my prior post. and that’s ok.

DevOps 130