Tools Used for Observability in Distributed Systems

Microservices have emerged as a popular architectural style for building complex and scalable applications. By 2030, Fortune Business Insights estimates the global cloud microservices market will be worth $6.04 billion USD, exhibiting a CAGR of 21.6% from 2023 to 2030. Enterprises large and small have adopted microservices architecture to scale rapidly and support growth. Businesses like Netflix, Amazon, Netflix, Uber, and Slack are just some household names to have made this move. This article will focus on the importance of observability in microservices and the tools that can enhance their capabilities.

What is a microservice?

A microservice is a small, independent service that focuses on performing a specific business function. Unlike monolithic architectures, where all functionality is contained within a single codebase, microservices break down an application into smaller, loosely coupled components that can be developed, deployed, and scaled independently.

Each microservice is responsible for a single application function or feature, such as user authentication, order processing, or inventory management. These services communicate with each other through well-defined REST APIs, often using lightweight protocols like HTTP or messaging queues.

Observability vs. monitoring: What’s the difference?

Monitoring is a proactive process of collecting data about a system’s health, performance, and behavior over time. It involves setting up predefined metrics, thresholds, and alerts to detect deviations from expected behavior. Monitoring systems typically focus on quantitative data points such as CPU utilization, memory usage, network traffic, and error rates.

The primary goal of monitoring is to provide insights into a system’s current state and alert administrators or operators when predefined thresholds are exceeded or when anomalies are detected.

On the other hand, observability is a broader concept that encompasses the ability to understand, debug, and troubleshoot a system, particularly in dynamic and distributed environments. Unlike monitoring, which relies on predefined metrics and thresholds, observability emphasizes the richness and depth of data available for analysis. 

Observability is particularly crucial for distributed systems due to their inherent complexity and dynamic nature as it addresses essential contexts of microservice applications like complexity management of scattered applications and communication mesh monitoring between them. It also influences the visibility of distributed system interactions, resilience, fault tolerance of microservices and the chain of dependencies of services that may be impacted when even a single microservice failure occurs. 

5G network architecture – microservices are the ideal cloud-based architecture for 5G 

In the world of microservices, one of the important concepts is the architecture of 5G networks. While working in our 5G Lab, we came across some monitoring issues within the environment. To address these issues, we implemented an observability stack. You can view the results of our efforts in our presentation during the TelcoDays event.

Why does observability remain vital for microservices? 

The mutual connections and the chain of communication between microservices aim to deliver a service. Unfortunately, this is associated with a tremendous complexity of communication flow, and any incorrect state or prolonged request handling time by a single microservice affects the user’s experience utilizing the service. 

Standard monitoring tools may fail in this case because you won’t be able to identify correlations that impact the negative perception of the service by the user in the complex matrix of connections. Similarly, in terms of the development of the platform performance, correctly building tools to collect metrics becomes crucial to determining the state and future development of a solution.  As a result, it’s essential to build a platform that has insights into distributed systems from a central point of view. 

Three pillars of observability 

The three pillars of observability contribute to a comprehensive view and understanding of an IT system’s operation. Together, they enable real-time and retrospective monitoring, analysis, and diagnostics of systems.

1. Logs

Logs contain valuable information about system events, errors, and user interactions. By centralizing and analyzing logs, engineers can gain insights into a system’s behavior, identify patterns, and troubleshoot issues.

2. Metrics

Metrics are quantitative measurements that provide a detailed view of a system’s performance and resource utilization. They enable engineers to monitor key performance indicators (KPIs), set alerts, and analyze trends over time.

3. Traces

Traces capture the flow of requests through a distributed system, allowing engineers to understand the end-to-end journey of a request. Engineers can pinpoint bottlenecks and optimize system performance by visualizing the path and timing of requests.

Three pillars of observability

Tracking trace, logs, and metrics simultaneously, along with the way they may correlate, enables a more comprehensive view of application behavior, leads to faster issue diagnosis, and optimizes system performance and effectiveness.

Observability is a key factor in distributed systems, but it can be challenging to achieve due to a system’s complexity and distribution. Microservices, which involve many small services that are frequently deployed and scaled, generate many metrics, logs, and traces that need to be monitored. However, these challenges can be overcome with the right tools and an experienced team.

Data dispersal across various hosts or clusters can also hinder data collection and analysis. Moreover, using different technologies in microservices can lead to complications in managing monitoring and data analysis. As a result, ensuring full observability in microservices requires advanced tools and strategies that address these challenges and enable effective analysis and troubleshooting throughout the microservices environment. Such a need requires a toolkit that addresses the microservices world’s requirements. Let’s focus on that element.

Grafana observability stack 

Grafana Loki 

Grafana Loki is a tool that aggregates, explores, and monitors logs in microservices and containerized environments. 

Designed for efficient collection, processing, and the storage of large volumes of log data, Grafana Loki optimizes resource usage. Loki employs a label-based methodology for indexing and querying logs, enabling fast searching, and filtering based on various criteria such as log level, component, or time. 

With Grafana Loki, users can easily monitor, analyze, and diagnose issues in their applications, which improves the availability and performance of microservices systems. Loki also supports integration with other monitoring and analytics tools, enabling comprehensive log management in microservices architectures. 

Grafana Tempo: A log aggregation system

Grafana Tempo 

Grafana Tempo, an open-source, distributed tracing backend developed by Grafana Labs, is designed to provide high-scale, cost-effective tracing for cloud-native applications. Tempo is built on top of the CNCF OpenTelemetry project, so it can ingest traces from various instrumentation libraries and export them to long-term storage. 

Tracing example of 5G network’s components

One of Grafana Tempo’s key features is its horizontally scalable architecture, which enables it to handle large volumes of tracing data efficiently. It utilizes object storage systems like Amazon S3 or Google Cloud Storage for long-term storage, making it suitable for retaining traces over extended periods. 

Another notable aspect of Grafana Tempo is its simplicity and ease of deployment. It can be deployed as a single binary or Docker container, with minimal configuration required. This simplicity makes it particularly attractive for organizations looking to add distributed tracing capabilities to their applications without introducing significant operational overhead. 

Grafana Tempo aims to democratize distributed tracing by providing a scalable, cost-effective solution that is easy to deploy and use. 

Prometheus 

Prometheus is a powerful open-source monitoring solution that collects query metrics from systems, applications, and services. It operates using a pull-based model, regularly fetching metrics from configured targets. These metrics are stored in a time-series database, allowing real-time monitoring, analysis, and alerting. 

Prometheus can be integrated with various components to create a comprehensive monitoring ecosystem. For example, paired with Grafana for data visualization, it can provide rich dashboards for monitoring and analysis. Additionally, Prometheus integrates with Alertmanager to manage and dispatch alerts based on defined rules, ensuring timely notification of potential issues. 

One of Prometheus’ strengths is its support for service discovery mechanisms, enabling dynamic monitoring of services in environments like Kubernetes. It can automatically discover and monitor new instances as they come online or scale up/down, ensuring continuous monitoring coverage. 

Furthermore, Prometheus has a vibrant exporter ecosystem, so users can collect metrics from various third-party systems and applications. These exporters bridge the gap between Prometheus and systems that do not natively expose Prometheus metrics, thereby enabling comprehensive monitoring of an entire infrastructure. 

Overall, Prometheus offers a flexible and scalable monitoring solution suitable for both traditional and cloud-native environments. Its robust feature set, including real-time monitoring, powerful querying capabilities, and seamless integration options, makes it a popular choice for DevOps teams seeking to ensure the reliability and performance of their systems and applications. 

Grafana 

Grafana is a robust open-source platform designed for analytics and visualization, commonly used for monitoring and observing systems and applications. It provides a user-friendly interface for creating customizable dashboards and querying various data sources to gain insights into system performance. 

When integrated with Grafana Loki, Grafana serves as the front end for querying and visualizing the logs Loki collects. Users can create dashboards in Grafana to display log data, perform searches, and analyze log patterns. This integration enhances observability by providing a unified platform for both metrics and logs analysis with straightforward and intuitive search and filter engine. Similarly, Grafana Tempo integrates with Grafana to visualize distributed traces collected by Tempo.  

By connecting Grafana with Tempo, users can create dashboards to visualize trace data, understand service dependencies, and troubleshoot performance issues in distributed systems. 

When integrated with Prometheus, Grafana is a visualization and alerting frontend for Prometheus metrics. Users can configure Grafana to query Prometheus for metrics data and create dashboards to visualize various aspects of system performance. Grafana also integrates with Prometheus Alertmanager to manage and display alerts generated by Prometheus, providing a centralized interface for monitoring and alerting. 

Overall, Grafana, by serving as a central hub for visualizing and analyzing data collected by Grafana Loki, Grafana Tempo, and Prometheus, offers a wide-ranging solution for monitoring, logging, and tracing in modern distributed systems architectures. 

Right toolkit for microservices – key takeaways 

The Grafana Stack comprehensively addresses the pillars of observability through its diverse tools and integrations. Prometheus provides metrics collection and storage, Loki enables log collection and analysis, while Tempo handles distributed tracing. Integrating these elements with Grafana allows for comprehensive data analysis, enabling users to understand, monitor, and react to changes in their systems and applications in real-time. As a result, users can quickly identify issues, optimize performance, and ensure the stability of their solutions. 

This isn’t all – our next article will showcase examples of observability tool applications, including configurations, technologies, and practical examples regarding microservices. 

Embracing observability for distributed systems 

Distributed systems’ intricately interconnected environments and dynamic nature mean it’s increasingly important to monitor and analyze their behavior – which is why observability is vital. To make the most of observability solutions, it is crucial to have an experienced team. If you are interested in using observability tools in your organization, use this contact form to get in touch with us.