11 Observability Tools You Should Know

When organizations move toward the cloud, their systems also lean toward distributed architectures. One of the most common examples is the adoption of microservices. However, this also creates new challenges when it comes to observability.

You need to find the right tools to monitor, track and trace these systems by analyzing outputs through metrics, logs, and traces. It enables teams to quickly pinpoint the root cause of issues, fix them and optimize the application performance, giving them the confidence to deliver code faster.

So, this article looks at the features, limitations, and important selling points of eleven popular observability tools to help you select the best one for your project.

Helios

Helios is a developer-observability solution that provides actionable insight into the end-to-end application flow. It incorporates OpenTelemetry's context propagation framework and provides visibility across microservices, serverless functions, databases, and 3rd party APIs. You can check out their sandbox or use it for free by signing up here.

Key Features

• Provide a complete overview: Helios provides distributed tracing information in full context, showing how data flows through your entire application in any environment.
• Visualization: Enables users to collect and visualize trace data from multiple data sources to drill down and troubleshoot potential issues.
• Multi-language support: Supports multiple languages and frameworks, including Python, JavaScript, Node.js, Java, Ruby, .NET, Go, C++, and Collector.
• Share and reuse: You can easily collaborate with team members by sharing traces, tests, and triggers through Helios. In addition, Helios allows reusing requests, queries, and payloads with team members.
• Automatic test generation: Automatically generate tests based on trace data.
• Easy integrations: Integrates with your existing ecosystem, including logs, tests, error monitoring, and more.
• Workflow reproduction: Helios allows you to reproduce an exact workflow, including HTTP requests, Kafka and RabbitMQ messages, and Lambda invocations, in just a few clicks.

Popular Use Cases

• Distributed tracing
• Multi-language application trace integration
• Serverless application observability
• Test troubleshooting
• API call automation
• Bottleneck analysis

Prometheus

Prometheus is an open-source tool broadly used to enable observability in cloud-native environments. It can collect and store time-series data and provides visualization tools to analyze and visualize the data collected.

Key Features

• Data Collection: It can scrape metrics from various sources, including applications, services, and systems. It also supports many data formats supported out of the box, including logs, traces, and metrics.
• Data Storage: It stores the data collected in a time-series database, allowing efficient querying and aggregating of data over time.
• Alerting: Includes a built-in alerting system that can trigger alerts based on queries.
• Service Discovery: It can automatically detect and scrape metrics from services running in multiple environments, such as Kubernetes and other container orchestration systems.
• Grafana Integration: The tool has flexible integrations with Grafana, allowing it to create dashboards to display and analyze Prometheus metrics.

Limitations

• Limited root cause analysis capabilities: The tool is primarily designed for monitoring and alerting. Therefore, it does not provide built-in root cause analysis.
• Scaling: Although the tool can handle many metrics, it can become resource intensive since Prometheus stores all data in memory.
• Data modeling: Contains a key-value pair-based data model and does not support nested fields and joins.

Popular Use Cases

• Metrics collection and storage
• Alerting
• Service Discovery

Grafana

Grafana is an open-source tool predominantly used for data visualization and monitoring. It allows users to easily create and share interactive dashboards to visualize and analyze data from various sources.

Key Features

• Data visualization: Creates customizable and interactive dashboards to visualize metrics and logs from various data sources.
• Alerting: Allows users to set up alerts based on the state of their metrics to indicate potential issues.
• Anomaly detection: Allows users to set up anomaly detection to automatically detect and alert based on abnormal behavior in their metrics.
• Root cause analysis: Allows users to drill down into the metrics to analyze the root cause by providing detailed information with historical context.

Limitations

• Data storage: Its design does not support long-term storage and requires additional tools such as Prometheus or Elasticsearch to store metrics and logs.
• Data modeling: Grafana does not provide advanced data modeling capabilities. Hence, it is to model specific data types and perform complicated queries.
• Data aggregation: Grafana does not include built-in data aggregation capabilities.

Popular Use Cases

• Metrics visualization
• Alerting
• Anomaly detection

Elasticsearch, Logstash, and Kibana (ELK)

The ELK stack is a popular open-source solution that helps to manage logs and analyze data. It comprises three components: Elasticsearch, Logstash, and Kibana.

Elasticsearch is a distributed search and analytics engine that can handle large volumes of structured and unstructured data enabling users to store, index, and search large amounts of data.

Logstash is a data collection and processing pipeline that allows users to collect, process, and enrich data from numerous sources, such as log files.

Kibana is a data visualization and exploration tool that enables users to create interactive dashboards and visualizations based on the data within Elasticsearch.

Key Features

• Log management: ELK allows users to collect, process, store and analyze log data and metrics from multiple sources while providing a centralized console to search through the logs.
• Search and analysis: Allows users to search and analyze relevant log data crucial in resolving and drilling down the root cause of issues.
• Data visualization: Kibana allows users to create customizable dashboards which can visualize log data and metrics from multiple data sources.
• Anomaly detection: Kibana allows the creation of alerts for abnormal activity within the log data.
• Root cause analysis: ELK stack allows users to drill down into the log data to better understand the root causes by providing detailed logs and historical context.

Limitations

• Tracing: ELK does not natively support distributed tracing. Therefore, users may need to use additional tools such as Jaeger.
• Real-time monitoring: The design of ELK allows it to perform well as a log management and data analysis platform. But, there is a slight delay in the log reporting, and users will experience minor latencies.
• Complicated setup and maintenance: The platform involves a complex setup and maintenance process. Also, it requires specific knowledge to manage large amounts of data and numerous data sources.

Popular Use Cases

• Log management
• Data visualization
• Compliance and security

InfluxDB and Telegraf

InfluxDB and Telegraf are open-source tools that are popular for their time-series data storage and monitoring capabilities.

InfluxDB is a time-series database that stores and queries large amounts of time-series data using its SQL-like query language.

On the other hand, Telegraf is a well-known data collection agent that can collect and send metrics and events to a wide range of receivers, such as InfluxDB. It also supports many data sources.

Key Features

The combination of InfluxDB and Telegraf brings in many features that benefit applications' observability.

• Metrics collection and storage: Telegraf allows users to collect metrics from many sources and sends them to InfluxDB for storage and analysis.
• Data visualization: InfluxDB can be integrated with third-party visualization tools such as Grafana to create interactive dashboards.
• Scalability: InfluxDB's design allows it to handle large amounts of time-series data and scale horizontally.
• Multiple data source support: Telegraf supports over 200 input plugins to collect metrics.

Limitations

• Limited alerting capabilities: Both tools lack alerting capabilities and require a third-party integration to provide alerting.
• Limited root cause analysis: These tools lack native root cause analysis capabilities and require third-party integrations.

Popular Use Cases

• Metrics collection and storage
• Monitoring

Datadog

Datadog is a popular cloud-based monitoring and analytics platform. It is widely used to get insights into the health and performance of distributed systems to troubleshoot issues beforehand.

Key Features

• Multi-cloud support: Users can monitor applications running on multi-vendor cloud platforms such as AWS, Azure, GCP, etc.
• Service maps: Allows visualization of service dependencies, locations, services, and containers.
• Trace Analytics: Users can analyze traces while providing detailed information about application performance.
• Root cause analysis: Allows users to drill down into the metrics and traces to understand the root cause of the issues by providing detailed information with historical context.
• Anomaly detection: Can set up anomaly detection that can automatically detect and alert on abnormal behavior in metrics.

Limitations

• Cost: Datadog is a cloud-based paid service, and charges are known to increase with large-scale deployments.
• Limited log ingestion, retention, and indexing support: Datadog does not provide log analysis support by default. You have to purchase log ingestion and indexing support for that separately. Hence, most organizations decide only to keep a limited number of logs retained, which can cause issues in troubleshooting since you can't access the complete history of the issue.
• Lack of control over data storage: Datadog stores data on its own servers and doesn't allow users to store data locally or in their own data centers.

Popular Use Cases

• Observability pipelines
• Distributed tracing
• Container monitoring

New Relic

New Relic is a cloud-based monitoring and analytics platform that allows users to monitor applications and systems within a distributed environment. It uses the "New Relic Edge" service for distributed tracing and can observe 100% of an application's traces.

Key Features

• Application performance monitoring: Provides a comprehensive APM solution to monitor and troubleshoot application performance.
• Multi-cloud support: Supports monitoring applications on multiple cloud platforms such as AWS, Azure, GCP, and more.
• Trace analytics: Enables users to analyze traces while providing detailed information about system and application performance.
• Root cause analysis: Allows users to drill down into the metrics and traces to analyze the root cause of issues.
• Log management: Collect, process, and analyze log data from various sources, providing a holistic view of the logs.

Limitations

• Limited open-source integration: New Relic is a closed-source platform, and its integration with other open-source tools may be limited.
• Cost: New Relic can be costly compared to other solutions when working with large-scale deployments.

Popular Use Cases

• Application performance monitoring
• Multi-cloud monitoring
• Trace analytics

AppDynamics

AppDynamics is a monitoring and analytics platform that allows you to observe, visualize, and manage each component of your application. In addition, it provides root cause analysis to identify underlying issues that may impact the application's performance.

Key Features

• Data collection: Users can collect metrics and traces from numerous sources such as hosts, containers, cloud services, and applications.
• Anomaly detection: Enables users to set up anomaly detection, which can detect and alert on abnormal behavior.
• Trace Analytics: Users can analyze traces and provide detailed performance information.
• Application performance monitoring: Provides a comprehensive APM solution that allows users to monitor and troubleshoot the application's performance.

Limitations

• Limited open-source integration: The vendor maintains the tool. Therefore, there may be limited open-source integrations.
• Limited customization: Customization options are not flexible compared to other tools since the users can not customize the solution themselves.

Popular Use Cases

• Application performance monitoring
• Multi-cloud monitoring
• Business transaction management

Selecting the Best Observability Tool

Observability is an integral part of modern software development and operations. It helps organizations monitor the health and performance of their system and quickly solve problems before they become critical.

This article discussed the 11 best observability tools developers should know when working with distributed systems. As you can see, each tool has its features and limitations. Therefore, evaluating them against your requirements is important to find the right fit for your organization. The best observability tool for your organization will depend on your specific needs, such as your environments, tech stack, developer experience, user profiles, monitoring and troubleshooting requirements, and workflow.

I hope you have found this helpful. Thank you for reading!