#observability
11 posts.
How Much Telemetry Should We Keep? Balancing Collection Cost and Diagnostic Value
Examine metric, log, and trace costs from instrumentation to storage, then use aggregation, sampling, label budgets, and retention policies to preserve useful diagnostic evidence.
Where Is the Web Application Slow? A Guide to Narrowing the Problem
Map the path from browser through network, application, and database, then compare evidence from the same user action to narrow a performance problem and verify the fix.
What a Safe Strimzi Extension Must Preserve
Using the Pletor Kafka image as an example, learn how to add node-metrics-agent without breaking Strimzi's launcher, lifecycle, or JMX Exporter contracts.
Should Metrics Be Pulled or Pushed? Comparing Collection Models
Compare Prometheus-style scraping and OTLP export across convenience, scalability, failure handling, Kubernetes fit, and current hybrid architecture patterns.
AI Learned Code, but Software Learns in Operation
AI can absorb enormous written knowledge, but usable software improves through running instances, operational feedback, and production reality.
Trace Should Not Break When Kafka Is in the Middle
When Kafka sits inside an MSA flow, tracing does not continue automatically like synchronous HTTP calls. This post explains how to propagate trace context through Kafka headers and how to think about producer and consumer instrumentation.
JVM Metrics Alone Cannot Explain a Container
Introducing Pletor node-metrics-agent, an open source JVM agent that exposes host and container node metrics through JMX and works well with Prometheus JMX exporter.
Can Kafka Client Metrics Really Close the Observability Gap?
A practical look at Kafka client telemetry from KIP-714: how it works, which metrics it can collect, how to configure it, and where the operational limits are.
The Kafka Broker Slowed Down, but Kafka Was Not the Cause
A practical incident-style walkthrough of a Kafka broker throughput drop, follower replicas falling out of ISR, and the storage path contention below the VM.
Consumer Lag Is Not a Health Score: Thinking in Kafka Consuming Pressure
A practical way to read Kafka Consumer Lag together with producer rate and consumer group capacity instead of treating lag as an absolute health signal.
When Logging Becomes the Bottleneck: Keeping Heavy Appender Work Off the Request Path
A practical guide to Log4j2 Custom Appenders through hot-path protection, bounded queues, AsyncAppender, throttling, and operational trade-offs.