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Monitoring YugabyteDB in Kubernetes With the Prometheus Operator and Grafana

Using the Prometheus Operator has become a common choice when it comes to running Prometheus in a Kubernetes cluster. It can manage Prometheus and Alertmanager for us with the help of CRDs in Kubernetes. The kube-prometheus-stack Helm chart (formerly known as prometheus-operator) comes with Grafana, node_exporter, and more out of the box.

In a previous blog post about Prometheus, we took a look at setting up Prometheus and Grafana using manifest files. We also explored a few of the metrics exposed by YugabyteDB. In this post, we will be setting up Prometheus and Grafana using the kube-prometheus-stack chart. And we will configure Prometheus to scrape YugabyteDB pods. At the end, we will take a look at the YugabyteDB Grafana dashboard that can be used to visualize all the collected metrics.

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Monitoring YugabyteDB in Kubernetes

monitoring YugabyteDB

Using the Prometheus Operator has become a common choice when it comes to running Prometheus in a Kubernetes cluster. It can manage Prometheus and Alertmanager for us with the help of CRDs in Kubernetes. The Kube-Prometheus-stack Helm chart (formerly known as Prometheus-operator) comes with Grafana, node_exporter, and more out of the box.

In a previous blog post about Prometheus, we took a look at setting up Prometheus and Grafana using manifest files. We also explored a few of the metrics exposed by YugabyteDB. In this post, we will be setting up Prometheus and Grafana using the Kube-Prometheus-stack chart. And we will configure Prometheus to scrape YugabyteDB pods. In the end, we will take a look at the YugabyteDB Grafana dashboard that can be used to visualize all the collected metrics.

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Exposing SonarQube Metrics to Grafana

There may be cases when we want to show measures of a metric of the Sonarqube into the Grafana dashboard. Here, we are going to one simple way to expose the Sonarqube metrics data to the Grafana dashboard using SpringBoot.

Following software are required for this tutorial:

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Grafana Analysis and Visualization with CA APM

Introduction

A Grafana is a multi-platform open-source analytical and visualization tool that consists of multiple individual panels arranged in a grid. It turns your time-series database (TSDB) data into beautiful graphs and visualizations. The panels interact with configured data sources, AWS CloudWatch, Prometheus, MySQL, InfluxDB, SQL Server, etc.

 Setup Grafana:

  • Refer to the instructions for your OS in the Installation section for instructions.
  • Open your web browser and go to http://localhost:3000/
  • On the login page, type admin for the username and password.

CA APM monitors the performance of applications and lets IT managers diagnose bottlenecks and other problems, it has capabilities to spot anomalies earlier, predict behavior, and enable automatic corrective actions.

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MicroProfile Metrics with Prometheus and Grafana [Video]

In this short video, Rudy de Busscher shows how to connect MicroProfile Metrics with Prometheus and Grafana to produce useful graphics and to help investigate your microservice architecture.

The goal of MicroProfile Metrics is to expose monitoring data from the implementation in a unified way. It also defines a Java API so that the developer can define and supply his own values.

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5 Tips for Better REST API Design

There is no doubt that "API" has become the de-facto standard for exchanging information between systems and also helps in better integration within components of a system. 

In this article, I will share some of the practices that I followed while working on multiple REST APIs design and implementation.

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How to Use Grafana Variables to Make More Interactive Dashboard Visualizations

The (All Too Common) Problem: Boring, Kind of Useful, Static Dashboards

Those of us that work with data often want to make useful dashboards that make it easier for ourselves and other people within our team and organization, to gain insight and make sense of the data we collect. 

A common problem I’ve run into (both when creating dashboards and using them as a stakeholder) is that many dashboards aren’t interactive enough for non-technical stakeholders to get their questions answered without asking engineers to write new code or change the underlying queries powering the dashboard. Or worse, stakeholders try to dig into the code and accidentally break things!

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Monitoring ClickHouse on Kubernetes With Prometheus and Grafana

The ClickHouse Kubernetes operator is great at spinning up data warehouse clusters on Kubernetes. Once they are up, though, how can you see what they are actually doing? It’s time for monitoring!  

In this article, we’ll explore how to configure two popular tools for building monitoring systems: Prometheus and Grafana. The ClickHouse Kubernetes operator includes scripts to set these up quickly and add a basic dashboard for clusters. 

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How to Monitor MySQL Deployments With Prometheus and Grafana at ScaleGrid

Monitoring your MySQL database performance in real-time helps you immediately identify problems and other factors that could be causing issues now or in the future. It's also a good way to determine which components of the database can be enhanced or optimized to increase your efficiency and performance. This is usually done through monitoring software and tools either built-in to the database management software or installed from third-party providers.

Prometheus is an open-source software application used for event monitoring and alerting. It can be used along with a visualization tool like Grafana to easily create and edit dashboards, query, visualize, alert on, and understand your metrics. ScaleGrid provides full admin access to your MySQL deployments — this makes it easier to integrate the existing MySQL ecosystem of tools with your ScaleGrid MySQL deployments on AWS or Azure. Prometheus works well for recording any purely numeric time series, and also offers support for multi-dimensional data collection and querying. Grafana can be used with it to build dashboards that help visualize this data in a way that is easy to interpret and utilize.

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Monitoring and Profiling Your Spring Boot Application

Monitor and profile your Spring Boot application!
You may also like: Monitoring Using Spring Boot 2.0, Prometheus, and Grafana (Part 1 — REST API)

Monitoring is very essential for modern applications, modern applications are highly distributed in nature and have different dependencies like database, service, caching and many more. It’s more of a like service mesh, tracing and monitoring these services are very essential to adhere to SLA (Service Level Agreement). SLA is an agreement between client and server, It accounts for reliability, responsiveness and other service-level metrics.

We always tend not to violate any SLAs, violating any part of the SLA can have many consequences. If a service fails to meet the terms defined in an SLA, it risks brand reputation damage and revenue losses. Worst of all, a company may lose a customer to a competitor due to its inability to meet a customer’s service-level requirements.

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Raspberry Pi IoT: Sensors, InfluxDB, MQTT, and Grafana

Learn how to build a dashboard based on Grafana that visualizes data acquired by sensors.

This Raspberry Pi IoT tutorial will build an IoT system that monitors sensors using InfluxDB, MQTT, and Grafana. In other words, we will build a dashboard based on Grafana that visualizes the data acquired by sensors.

You may also like: Playing With Docker, MQTT, Grafana, InfluxDB, Python, and Arduino

With this, InfluxDB stores the values read by sensors. All the systems exchange data using MQTT. The picture below better describes the whole Raspberry Pi IoT project.

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Observability and Beyond — Building Resilient Application Infrastructure

The ability to construct observable apps can't be overstated.

The Journey from Being Reactive to Being Proactive

Things were quite simple in the old days. Proactively monitoring applications or infrastructure was not the norm. If there was a failure, a user would pick up the phone to inform the help-desk that the app is broken.

Troubleshooting was all reactive and the only path to resolution was for someone to roll up their sleeves and go in and look at log files and manually fix errors by themselves.

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Creating Grafana Dashboards to Visualize Alluxio Metrics

Overview

Monitoring metrics is highly important to operate distributed systems in production. Alluxio collects metrics using the Codahale Metrics Library on I/O throughput, RPC throughput, and resource usage. Alluxio metrics are shown in its webUI but are also available through a REST endpoint or exportable to several third-party sinks in a time-series manner (see docs).

Grafana, a comprehensive metrics visualization software, ties into this process by pulling the metrics that systems like Alluxio collect through a sink and visualizes them in a more helpful fashion. This guide will cover how to set up Grafana and Graphite, a supported sink for Alluxio, which will put metrics in a time-series database, along with exploring some of the possibilities that the combination offers.

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Simplified Time-Series Analytics Using the time_bucket() Function

If you are working with time-series data, you need a way to be able to easily manipulate, query, and visualize that data. Often times, time-series databases contain a number of time-oriented functions that aren't found in traditional databases.

These functions are meant to provide two key benefits: improved ease of use for time-series analytics, and improved performance. In this case, I'm going to demonstrate with two TimescaleDB functions time_bucket()and time_bucket_gapfill(). 

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Components of Effective Software Monitoring: App Logs, Infrastructure Telemetry, Health-Check Reports

At Logicify, we are proud to be software monitoring geeks. We love to monitor both the apps we develop and the ones we use internally. Not because they are sloppy. Not because we don’t trust our code. But because we love to keep abreast of events, control performance and eliminate the risks of an error. Monitoring helps us be proactive and avert issues before real users are affected.

In our double-sided system of user behavior and app condition monitoring, we use Graylog as a single data storage for logs and other data about the web app, and Grafana, a powerful data visualization tool. Combined and wisely configured, these two tools give an objective picture of the app’s performance at all times. For comprehensive snapshots of system behavior and, what is more important for apps in production, for proactive moves to iron troubles out, we collect monitoring data from a multiple layers. App-specific metrics are complemented by other analytics to give a broader picture of system state and performance.