Java is one of the most popular programming languages used by developers to build mission-critical applications. According to Oracle, there are over 21 billion active Java virtual machines (JVMs) running Java applications across industries like finance, healthcare, retail, and more.
With Java powering so many complex, large-scale applications, having visibility into the performance and health of these apps is crucial. This is where Java monitoring comes in.
In this comprehensive guide, we will cover:
- The importance of monitoring Java applications
- An overview of key Java metrics to monitor
- The top 12 Java monitoring tools available
- Comparative analysis and recommendations for choosing the right tool
- Tips for implementing Java monitoring
Let‘s get started!
Why Java Monitoring Matters
Java monitoring provides observability into the runtime performance, system resource utilization, and overall health of Java applications. It helps ensure critical enterprise Java apps meet demanding performance SLAs and deliver optimal user experiences.
Some key benefits of Java monitoring include:
- Faster issue detection and resolution – By monitoring JVM metrics, errors and anomalies can be caught early before causing application failure or performance degradation.
- Reduced troubleshooting time – Java monitoring tools provide context and visibility to rapidly diagnose root causes of problems.
- Optimized performance – By analyzing metrics like memory usage, GC activity, and threads, bottlenecks can be identified and tuning improved.
- Enhanced security – Unusual application activity may indicate attacks or breaches which monitoring can detect.
Key Java Metrics to Monitor
Some key JVM and application metrics to monitor include:
1. Memory Usage
Monitoring overall memory as well as detailed Java heap and permgen space usage is critical to detect leaks, overuse, and potential out of memory errors.
2. Garbage Collection
GC activity impacts application performance and responsiveness significantly. Tracking cycle time and frequency spots excessive GC.
3. Threads
Active threads consume resources and thread pool saturation can cause lag and errors. Optimal tuning requires watching thread metrics.
In addition to these JVM-level stats, tracking end user transaction performance, errors, and slow queries is also key.
Now let‘s explore some leading Java monitoring tools:
1. Datadog APM
Datadog is a full-stack monitoring platform with comprehensive Java application performance monitoring capabilities.
Some key features include:
- Pre-installed Java agent with zero configuration
- Distributed tracing across JVM services
- Granular metrics on memory, GC, threads, classes, and beyond
- Waterfall views displaying slow code execution paths
- Profiling production apps without restarts or traffic duplication
Datadog provides flexible custom dashboards to analyze metrics and traces using graphs and logs in one tool. It integrates seamlessly with 300+ technologies and offers monitoring-specific pricing.
Pros: Incredibly rich data and flexibility, distributed tracing, code-level visibility, great visualizations.
Cons: Can get pricey, requires vendor lock-in on some features.
2. New Relic
New Relic focuses heavily on application performance monitoring including comprehensive Java visibility.
Key Java monitoring features:
- JVM dashboard tracking memory, GC, and threads
- Java agent with zero-config installation
- End user experience metrics for web transactions
- Troubleshooting slow requests, queries, calls and code paths
- Visualizing dependencies and tracing distributed flows
It captures billions of data points per day and leverages applied intelligence to detect anomalies. Flexible alerting and collaboration tools help you react.
Pros: Rich data collection, great visualizations, code-level insights, intelligent correlations.
Cons: Can get expensive, steeper learning curve for configuration.
3. AppDynamics
AppDynamics offers strong application performance management for JVM-based applications from WebLogic and WebSphere to Tomcat, JBoss and custom apps.
Standout Java monitoring features include:
- Automated transaction detection
- Code-level diagnostics with exceptions, DB calls, etc
- Memory leak analysis
- Pricing based on AppAgents making it very affordable
The dashboard provides a single pane of glass for application performance across domains like database, network and user experience. Tight integration with cloud platforms simplifies monitoring cloud-native apps.
Pros: Code-level visibility, affordable pricing, user experience focus, advanced troubleshooting.
Cons: Steep learning curve, less flexibility than alternatives.
4. Dynatrace
Dynatrace takes an AI-powered approach to application performance monitoring and cuts through noise to identify precise problems instantly.
Key Java capabilities:
- Automatic discovery of services and topology
- PurePath tracing every transaction end-to-end
- Smartscape dashboard highlighting problems
- Granular visibility into JVM, host resources, database
It automatically baselines environments to find anomalies and leverages explainable AI to guide troubleshooting. Dynatrace uniquely offers topology mapping, infrastructure monitoring and logs in one solution.
Pros: Powerful AIOps, automatic discovery, great UX and visuals, full-stack visibility.
Cons: Complex licensing, limited custom alerting, expensive.
5. Elastic APM
The Elastic Stack is a popular open source logging and monitoring solution. Elastic APM adds detailed application performance monitoring.
For Java, Elastic APM provides:
- Distributed tracing showing entire transactions
- Error rates, latency and throughput by endpoint
- Hotspot visualization highlighting bottlenecks
- OpenTracing support
It seamlessly integrates with Beats for collecting metrics and logs which feed into the Kibana dashboard for analysis. The friendly UI and OSS offering make it popular despite limitations in advanced features.
Pros: Affordable, open source, user-friendly, integrated platform.
Cons: Less comprehensive than commercial products, steeper learning curve.
6. Instana
Instana emphasizes automated application monitoring with a focus on modern tech stacks.
For Java apps, Instana provides:
- Automatic discovery of all runtime environments
- Tracing showing each method with code context
- Host and container views correlated to apps
- Built-in anomaly detection powered by AI
The agent automatically profiles code and maps dependencies while the dashboard highlights problems. Instana automatically baselines metrics to spot anomalies and diagnose root cause among dynamic infrastructure.
Pros: Lightning fast deployment, infrastructure context, great anomaly detection.
Cons: Limited legacy Java version support, focus on containers and microservices.
7. SignalFx
Recently acquired by Splunk, SignalFx is a real-time cloud monitoring solution with Java APM tracing.
It gives comprehensive visibility including:
- Distributed tracing for JVM services
- Tagging metrics dimensions for filtering
- Profiling code execution down to method level
- Kubernetes and host correlation
Smart agents stream millions of data points per second to the cloud analytics platform. SignalFx automatically detects problems using advanced analytics and ML techniques.
Pros: Scalable architecture, powerful analytics, flexible metadata, great visibility.
Cons: Complex pricing and packaging, focus on infrastructure over code.
8. StackPulse
StackPulse takes a code-centric approach to application reliability with a focus on engineers.
For Java apps, key capabilities include:
- Errors and logs monitoring
- Live production debugging
- Flagging anomalies in traffic and errors
- Git context and deploy tracking
It structures data around code issues first with tools tailored for developers to troubleshoot and compare builds. StackPulse operates entirely on open source technologies, integrating with Grafana, Prometheus and more.
Pros: Code-oriented UX, great for developers, OSS stack, affordable.
Cons: Light on metrics analysis, minimal legacy Java support.
9. Scout APM
Scout focuses on developer productivity with their performance monitoring centered around tracing.
Key Scout Java visibility includes:
- Waterfall tracing visualizing request flow
- Call stack integration showing code execution
- Error monitoring with affected users
- Host correlation
Scout tracks web transactions end-to-end attributing time to backend calls. The focus remains on tracing rather than metrics. Simple pricing and immediate value appeal to lean teams.
Pros: Code-level insights, simple and affordable, great tracing capabilities.
Cons: Light on metrics, targeting smaller teams.
10. SolarWinds AppOptics
SolarWinds AppOptics delivers comprehensive application performance management supporting metrics, tracing, profiling and more.
For Java, it provides:
- Method-level metrics per class and package
- Host, JVM, and container correlation
- Database monitoring
- Anomaly detection for metrics
Flexible custom dashboards leverage data from across the robust SolarWinds monitoring portfolio. Distributed tracing shows comprehensive transaction flows.
Pros: Broad capabilities, method-level metrics, SolarWinds integration.
Cons: Complex pricing and licensing, expensive.
11. IOpipe
IOpipe provides serverless monitoring and observability for applications on AWS Lambda including Java runtimes.
It captures Lambda performance metrics like:
- Invocations, errors, durations, memory
- HTTP information
- Downstream HTTP services impact
- Log tailing
IOpipe analyzes metrics, traces, and logs to provide optimization recommendations tailored to serverless environments. Pricing scales directly with Lambda usage.
Pros: Specialized for serverless, tracks external impact, log analysis.
Cons: AWS Lambda only, no self-hosted option.
12. OverOps
OverOps focuses specifically on error handling and logging for critical applications built in Java, Scala and beyond.
Key features include:
- Aggregated errors and logs
- Error analytics
- Replay debugging production errors
- Alerts on new issues
The timeline ties code deployments to errors along with environment context. OverOps enhances existing logging rather than replacing it. Lightweight deployment appeals to developers.
Pros: Specialized for errors and logs, replay debugging, simple integration.
Cons: No performance metrics, limited dashboards.
Comparing Java Monitoring Tools
| Tool | Datadog | New Relic | AppDynamics | Dynatrace | Elastic |
|---|---|---|---|---|---|
| Approach | Metrics, traces, logs | APM | APM | APM | APM |
| Language Support | Java, Scala, Groovy | Java, Scala | Java, Scala, Clojure | Java, Scala, Groovy | Java, Scala, Kotlin |
| Code Visibility | Method-level | Method-level | Method-level | Method-level | Class-level |
| JVM Metrics | All key metrics | All key metrics | All key metrics | All key metrics | selective |
| Tracing | Distributed, profiler | Distributed | Distributed | Distributed, automatic | Distributed |
| Dashboards | Fully customizable | Customizable, rich visuals | Prebuilt | SmartscapeTM | Customizable |
| Host/Infra Correlation | Yes | Yes | Yes | Yes | Yes |
| Applied Intelligence | Algorithmic, ML | ML focus | Baseline analytics | AIOps, ML | statistical |
| Integrations | 300+ | 100+ | 90+ | 180+ | ELK, Beats |
Get a complete view across your entire stack with end-to-end tracing. Integrations provide seamless connectivity. Look for modern interface with rich visualizations that allow easy analysis. Leverage smart analytics to cut through the noise automatically. Ultimately focus on developer productivity.
Implementing Effective Java Monitoring
Follow these best practices when implementing Java monitoring:
- Instrument apps without changing any code via agents
- Start monitoring early in dev lifecycle
- Analyze trace sampling rates to balance data volumes
- Add custom instrumentation for business context
- Integrate monitoring with CI/CD pipeline
- Set baseline thresholds leveraging app intelligence
Prioritize end user experience by monitoring front end transactions. Distributed tracing connects the full infrastructure dataflow. Focus dashboards on critical metrics and alerts that require attention.
Carefully evaluate capabilities versus complexity and cost. Open source tools work great for smaller scale needs. Consider commercial platforms providing rich data, intelligent analysis and managed services for mission critical Java apps.
Conclusion
As Java continues to dominate enterprise application development, having deep visibility into the performance and reliability of JVM-based services is mandatory.
Modern application monitoring solutions give unprecedented code-level insights to optimize critical apps and deliver exceptional digital experiences. Robust Java support paired with powerful applied analytics makes it seamless.
The tools covered provide capabilities catering to organizations of all sizes and needs. Whether open source or commercial, on-prem or SaaS, get started with Java monitoring to take your app observability to the next level today.
