Asserts - Automation for Prometheus and OpenTelemetry stack (Page 1)

How to control costs of your Observability stack

Observability is mandatory but really expensive. Commercial tools provide convenience at a price. Open source tools are now mature enough to challenge those commercial offerings and provide considerable savings.

Prometheus

Prometheus Exporters and Libraries

Expanding Prometheus coverage of your applications is easy. Exporters can be added with just minor configuration changes. Adding client libraries for code coverage is just a few lines.

Prometheus

Prometheus - Getting data in is easy, getting value out is tough

With the minimal effort of a day, a Prometheus server can be up, running, and collecting data. However, as many soon find out this is but the first step of a very long and never-ending journey.

SLOs

Keep Your Applications SAAFE with Asserts

Asserts is built around the best practices laid out in the SRE Handbook [https://sre.google/sre-book/table-of-contents/] and uses SAAFE for its assertion categories extending the Golden Signals [https://sre.google/sre-book/monitoring-distributed-systems/#xref_monitoring_golden-signals] described in the handbook. Traditionally Golden Signals have used the Rate, Errors and

Tracing

Tracing is on Trial

Tracing is just structured logging at DEBUG level. Would you run logging at DEBUG in production? No!

Prometheus

An Introduction to Prometheus

Over the years it has become the de facto standard for time series metrics across the CNCF Landscape, with many projects providing a Prometheus metrics endpoint as standard.

Root Cause Analysis

Speed up RCA by Going SLO

Once you stop fretting over each individual service, you should change your monitoring strategy accordingly. Your primary consideration becomes making sure your application is processing requests in a prompt and error free manner.

Prometheus

Flexible Entity Model for Prometheus Metrics

Metrics and Entities When a monitoring tool collects performance metrics, it usually records the measurement as well as its metadata information. For example, a container CPU usage metric also reports the container name, the node name, instance type, and perhaps the application service to which the container belongs. This context

Prometheus

Monitor React Applications with Prometheus

Prometheus was originally designed for monitoring backend services, so using it for observing React applications has not been a common practice. In this blog post, we will take a look at how we can use Prometheus to monitor React applications there by using a single monitoring tool across your application

Serverless Monitoring with Prometheus and Asserts

AWS Lambda AWS Lambda allows you to write functions and run them as a scalable service. It supports popular languages and runtimes like JavaScript/NodeJS, Python, Go and Java. It allows an event-driven model wherein Lambda functions are invoked in response to events, e.g., requests to an API Gateway,

Monitoring Springboot Applications with Prometheus and Asserts

Introduction Springboot [https://spring.io/projects/spring-boot] is a popular Java framework for building modern applications. With the help of the actuator [https://docs.spring.io/spring-boot/docs/current/reference/html/actuator.html] module and the micrometer [https://micrometer.io/] library, we can configure a Springboot application to expose performance

Why My P99 < Average

We use both average and quantiles to measure latencies, but monitoring tools often have limitations with percentiles, which sometimes produce counterintuitive results. While investigating a latency problem, we noticed that Prometheus -- the system we use for monitoring -- reported the P99 latency for our app was less than the

Service Level Objectives and symptom-based alerting, part 2

In the previous post [https://blog.asserts.ai/blog/monitoring-with-service-level-objectives/] in this series, I explained why alerts should be based on the user-visible symptoms of problems rather than on problem causes. A quick recap: it is extremely hard to reliably alert on all the possible failure modes of a complex

Service Level Objectives and symptom-based alerting, part 1

If you follow application monitoring and the numerous monitoring tools vying for developer attention, you've probably noticed that everyone seems to be talking about service level objectives (or SLOs) all of a sudden. There are plenty of resources on the web defining what SLOs are – if you haven't seen them,

Golden Signals - Latency

Observability of modern cloud-native applications should provide deep visibility into an application’s performance, call attention to problems brewing through high fidelity alerts and provide an intuitive troubleshooting experience that shouldn’t require expert domain knowledge. With the advent of open-source approaches either based on OpenTelemetry [https://github.com/open-telemetry]

Observability Knowledge Graph

Compared to the old term “monitoring”, the new buzzword “observability [https://en.wikipedia.org/wiki/Observability]” is not merely a marketing slogan. It does convey a new set of challenges with the cloud-native paradigm shift. This blog provides an engineer’s perspective on these challenges and why Asserts thinks Knowledge

Announcing Asserts

We’re excited to announce Asserts -- a metric intelligence and observability platform. By scanning your app's metrics in any Prometheus compatible time-series database (TSDB), Asserts intelligently discovers a real-time map of your app and infrastructure architecture, builds dashboards, tracks service level objectives (SLOs), and runs automated checks (Assertions) to

The Benefits of Prometheus Counters

Introduction Prometheus has four metric types [https://prometheus.io/docs/concepts/metric_types/], among which the two basic ones are counter and gauge. A gauge is intuitive as it matches our expectation for a metric, i.e., it represents a numeric value that measures something like temperature, memory usage, etc.

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