In 2023, monitoring platforms will be critical to the work of modern DevOps teams. DevOps teams need reliable and flexible tools to effectively monitor and manage complex systems and provide real-time insight into system performance, availability and security.
Open source monitoring tools are becoming increasingly popular due to their cost-effectiveness, flexibility, and community support.
Pros and Cons of Open Source Monitoring Tools
Here are some advantages and disadvantages of open source monitoring tools compared to SaaS tools.
advantage
- Customization : Open source monitoring tools can provide greater customization and flexibility in monitoring configuration and integration with other tools.
- Cost-Effectiveness : Open source tools are often free or low-cost, making them an affordable solution for organizations with limited budgets.
- Transparency : The code of open source monitoring tools can be publicly reviewed and audited, providing greater transparency and accountability.
- Community support : Open source monitoring tools are often supported by a large community of developers who provide support and contribute to the development of the tool.
shortcoming
- Complexity: Open source tools typically require more technical expertise and effort to install, configure, and maintain than SaaS monitoring tools.
- Support: While community support is available, it may not be sufficient for organizations with complex or specialized monitoring needs.
- Security: Open source tools can be vulnerable to security vulnerabilities because they may lack the strong security features and updates provided by SaaS tools.
- Scalability: Compared to SaaS tools, open source monitoring tools may not be as scalable as they may require additional hardware and infrastructure to scale effectively.
Top 10 Open Source Monitoring Tools
We’ll cover the following open source monitoring tools that modern DevOps teams should pay attention to in 2023:
- Sensu Go
- SigNoz
- Elastic APM
- Jaeger
- Prometheus
- Grafana
- OpenTelemetry
- Zabbix
- Healthchecks.io
- Percona Monitoring and Management (PMM)
These tools provide a range of monitoring capabilities, including collecting and analyzing metrics, monitoring logs, tracking requests, and alerts. Each tool has its strengths and weaknesses, and the best choice for a specific DevOps team will depend on their unique needs and requirements.
Sensu Go
Sensu Go is an open source monitoring tool that allows you to monitor your infrastructure, including servers, containers, and cloud services. Sensu has 3 key points: simplicity, scalability, and multi-cloud monitoring.
Sensu Go uses a decentralized architecture, monitoring checks are performed on client nodes called agents, and the results are sent to a backend server for processing and storage. This architecture allows for a more flexible and scalable monitoring setup, where you can add or remove agents as needed and distribute the monitoring workload across your infrastructure.
Sensu provides monitoring-as-code functionality and automation that are critical to this dynamic environment, from fully automated deployment based on monitoring code templates (YAML configuration files), to flexible APIs that control all elements of the monitoring platform.
Sensu Go supports various types of monitoring checks, including Nagios-style checks, custom scripts, and plugins written in a variety of languages. You can also use Sensu Go to monitor containerized environments such as Kubernetes and Docker, as well as cloud services such as AWS and GCP.
advantage
- Developers can write their own monitoring items
- Simple configuration, good scalability and good performance
- message routing
- Compatible with Nagios plugins
- Written using go language
shortcoming
- UI is not very good
- Sensu Go has a learning curve, and users may need some time to become familiar with its features and configuration options.
SigNoz
SigNotz is an open source APM (application performance monitoring) tool that you can use to replace other tools such as Datadog and NewRelic. It can be very convenient when monitoring your application and troubleshooting issues.
In addition, SigNoz integrates OpenTelemetry and supports various languages and frameworks that implement it, such as Java, Ruby, Python, Elixir, etc. It supports various modern technologies and frameworks such as Kubernetes, Istio, Envoy, Kafka, gRPC, and more.
The main function
- Monitor application metrics such as latency, requests per second, and error rate.
- Monitor infrastructure metrics such as CPU utilization or memory usage.
- Track user requests across services.
- Set alerts on metrics.
- Find the root cause of the problem and pinpoint the clues that caused it.
- View detailed flame graphs for individual request traces.
Elastic APM
Elastic APM (Application Performance Monitoring) is part of the Elastic Stack and is a set of open source data analysis and visualization tools. Elastic APM is designed to provide developers and DevOps teams with real-time insights into the performance of their applications.
Elastic APM supports many programming languages and frameworks, including Java, Python, Ruby, Node.js, and more. It can monitor application performance metrics such as response time, throughput, error rate, and resource utilization. It also provides detailed transaction tracing, allowing developers to identify bottlenecks and performance issues in their code.
The main function
- Elastic APM also automatically collects unhandled errors and exceptions. Errors are grouped primarily based on stack traces, so you can identify new errors as they appear and keep an eye on the number of times a specific error occurred.
- Metrics are another important source of information when debugging production systems.
- The Elastic APM agent automatically fetches basic host-level metrics and agent-specific metrics, such as JVM metrics in the Java agent and Go runtime metrics in the Go agent, and many others.
Elastic APM Github repository →
Jaeger
Jaeger provides end-to-end distributed tracing, enabling users to track the flow of requests through complex systems and identify any performance bottlenecks or errors.
Jaeger supports various programming languages and frameworks, including Java, Python, Ruby, Go, etc. It can be integrated with popular web frameworks such as Spring Boot and Flask.
It can be used to monitor microservice-based distributed systems:
- Distributed context delivery
- Distributed transaction monitoring
- root cause analysis
- Service dependency analysis
- Performance/latency optimization
advantage
- Easy to install
- Easily configure a data source of your choice as a storage backend
- Open source
- Feature-rich UI
- CNCF project
What Jaeger lacks in maturity it makes up for in speed and flexibility, and its parallel architecture is novel and more decentralized. It also has higher performance and is easier to scale. Jaeger has better official language support than its older rivals, and you can also think of its support for CNCF as a badge of approval.
Cons
Jaeger's relative immaturity is a drawback. Jaeger's choice of Go as its primary language illustrates this point. Although Gophers are rapidly expanding their community, they are nowhere near as ubiquitous as Java. If you are new to Go, this may make your learning process longer.
Another area that's both a blessing and a curse for Jaeger is its more modern architecture. This architecture provides benefits in terms of performance, reliability, and scalability, but it is also far more complex and harder to maintain.
Prometheus
Prometheus is designed to monitor a wide range of metrics, including application performance metrics, server metrics, and network metrics. It uses a pull-based model to collect metrics from targets such as application servers, databases, and network devices. These metrics are then stored in a time-series database and can be visualized using the Prometheus Web UI or integrated with third-party tools like Grafana.
The main function
- multidimensional data model
- PromQL query language to query the collected indicator data.
- Collect data via HTTP protocol
- An alarm manager that handles alarms
- Basic visualization layer, but can be combined with Grafana to create rich visualizations.
shortcoming
Prometheus is a great metric monitoring tool, but nothing more. It is not a full-stack application monitoring tool like SigNoz:
- Prometheus only crawls metrics. To create a powerful monitoring framework, you need to track metrics, logs, and traces. For example, tools like SigNoz can capture both metrics and trace (log management in product roadmap).
- Prometheus is designed for stand-alone use. It cannot be scaled out.
Prometheus Github repository →
Grafana
Grafana provides a web-based user interface for creating and sharing custom dashboards that can be used to display and monitor key performance indicators (KPIs) and other metrics. Grafana supports a wide range of visualization options, including charts, graphs, gauges, and tables, and can be used to create custom alerts based on metric thresholds.
One of Grafana's main advantages is that it supports a wide range of data sources, including popular time series databases such as Prometheus, InfluxDB, and Graphite. It also supports log data sources such as Elasticsearch and cloud vendors such as AWS and Azure.
Grafana includes a powerful query editor that enables users to filter, aggregate and transform data in real time. The query editor supports various query languages, including PromQL (used by Prometheus), InfluxQL (used by InfluxDB), and Elasticsearch queries.
advantage
- Easily integrate Prometheus and Graphite data sources.
- Many plug-ins are available for almost any storage array or operating system.
- Free and open source. If you want more, you can get a Professional or Premium plan.
- Highly customizable software. Custom alerts, data sources, dashboards, notifications, and more.
- Grafana is the king of data visualization. It plots metrics from any data source.
- Work with other systems to send alerts and notifications.
shortcoming
- The highly customizable nature of Grafana makes it challenging and time-consuming to get started.
- No data is stored. If you also want to track historical data, you'll need a third-party storage solution.
- You need to be proficient in programming languages such as JSON and SQL to get the maximum benefit from Grafana.
OpenTelemetry
OpenTelemetry provides libraries for a variety of programming languages and frameworks, including Java, Python, Go, and .NET. These libraries allow developers to instrument their applications with minimal effort, making it easier to collect telemetry data such as traces, metrics, and logs.
OpenTelemetry uses a vendor-neutral data model that allows telemetry data to be collected from multiple sources and output to multiple destinations. This makes it easier to integrate with a wide range of observability tools and services.
advantage
- Reduce the performance overhead of generating and managing telemetry data for your application
- Provides libraries and proxies to automatically measure popular libraries and frameworks with minimal changes to your codebase.
- Provides OpenTelemetry Collector, which can receive, process and output data in multiple formats
- It is supported by technology giants such as Google and Microsoft and other large cloud computing vendors.
- Freely switch to new backend analysis tools by using relevant exporters
- Support for new frameworks and technologies
shortcoming
- The project has a lot of room for improving documentation and support
- It does not provide backend storage and visualization layer
Zabbix
Zabbix uses a client-server architecture, where the Zabbix server collects data from multiple agents installed on network devices, servers and applications. It can also collect data from other sources such as SNMP traps, JMX counters, and IPMI-enabled devices.
Zabbix supports a wide range of data collection methods, including simple checks such as ping, HTTP and SMTP checks, and more advanced checks such as SNMP, JMX and IPMI checks. It also supports custom checks that can be used to monitor the performance of custom applications and services.
advantage
- Rich features, a large number of possible integrations, out-of-the-box templates and multi-tenancy support, powerful API, support for most networks, servers, services, applications and IoT monitoring protocols. Almost anything can be monitored using standard protocols and custom scripts.
shortcoming
- The initial setup requires a lot of work and, in the long run, a lot of optimization. The documentation is not very clear for first-time users, especially common issues that arise during installation or post-installation management.
Healthchecks.io
Healthchecks.io is a service for monitoring cron jobs and similar periodic processes.
- Healthchecks.io listens for HTTP requests ("pings") from your cron jobs and scheduled tasks.
- As long as the PING arrives on time, it stays silent.
- When a ping does not arrive in time, it will issue an alert.
Healthchecks.io is not suitable for:
- Monitor website uptime by probing HTTP requests
- Collect application performance metrics
- Log summary
The main function
- Open source, can be deployed privately
- Simple, neat Dashboard
- Team & API access support
advantage
- The interface is extremely simple to set up, with clear implementation instructions.
- Within 5 minutes, you can get notified when your server fails to report and when the server comes back online.
- At the end of the month, you'll have an email report of your downtime.
shortcoming
- The service lacks advanced analytics and other advanced features.
- Those looking for such functionality may find it's not a good fit. However, I think the simplicity of this service is a benefit. Adding more features risks detracting from a great user experience.
Healthchecks.io Github repository →
Percona Monitoring and Management (PMM)
Percona Monitoring and Management (PMM) is an open source platform for managing and monitoring database performance. Percona Monitoring and Management can be used to monitor a wide range of open source database environments:
- Amazon RDS MySQL
- Amazon Aurora MySQL
- MySQL
- MongoDB
- Percona XtraDB Cluster
- PostgreSQL
- ProxySQL
The main function
- Monitor the health of your database infrastructure
- Explore database behavior patterns
- Manage and improve the performance of databases no matter where they are located
- Manage and improve the performance of databases no matter where they are located
- Access control/permissions
- Historical trend analysis
advantage
- Visibility into performance across cluster nodes.
- Easy to use with a good interface
- Very in-depth database metrics such as slow query logs, performance patterns, and more
shortcoming
- Alerting systems should be improved, such as alert templates.
- Large databases cannot be supported efficiently.
in conclusion
Today's complex technology environment requires flexible monitoring tools that are both powerful and cost-effective. Open source solutions, like those introduced above, offer a host of advantages, from transparency and customizability to cost-effectiveness and community support.
However, it's important to consider factors such as system complexity, technical expertise, scalability, and budget when choosing the right tool for your DevOps team. Keep an eye on the latest developments and updates to these tools to ensure your team has the best resources to maintain system performance, reliability, and security.
Choose wisely so your team has the information they need to make the best decisions and take effective action.