Application observability in 2025: A modern guide for complex architectures

Modern applications, from e-commerce platforms to fintech solutions, are often built upon microservices, APIs, containers, and cloud-native infrastructure. This complexity presents significant challenges to understanding application behavior once deployed in production. Traditional logging or basic monitoring approaches frequently lack the depth needed to effectively diagnose and resolve issues.

Application observability emerges as a critical discipline to address this gap. It involves instrumenting applications to gain profound visibility into their internal states, empowering developers and operators to detect, debug, and diagnose problems in real time.

This guide provides a detailed technical exploration of application observability, covering its principles, implementation, tooling, and benefits.

Defining application observability

Application observability is the capability to measure, monitor, and comprehend the runtime behavior of your application through the telemetry it emits. This focus extends beyond infrastructure or network health, specifically targeting:

• Application performance
• Request paths and logic flow
• Code-level errors and exceptions
• Business logic visibility (e.g., user actions, transaction outcomes)

Application observability enables you to answer critical questions such as:

• What causes a spike in application latency?
• Why did this specific request fail?
• How does a particular feature perform across different user segments?

 

Application observability vs. monitoring

It's important to acknowledge that observability is not (just)monitoring. Monitoring is a part of observability. Here are some key differences between monitoring and observability:

Feature	Monitoring	Observability
Primary goal	Know if something is wrong.	Understand why something is wrong.
Data collected	Predefined metrics and logs.	Rich telemetry (logs, metrics, traces).
Question type	Answers predefined, known questions.	Answers novel, unknown questions.
Approach	Reactive (alerts based on known thresholds).	Proactive (explores system behavior and unknowns).

Core telemetry for application observability

Effective application observability relies on the collection of three primary telemetry types directly from the application during runtime:

1. Application logs

Purpose: To capture discrete events within the application lifecycle.

Typical content: Error messages, stack traces, custom log messages (e.g., "user login failed for ID: 1234").

Key practices:

• Employ structured logging (e.g., JSON format).
• Include contextual metadata: trace_id, user_id, request_id.
• Utilize distinct log levels: DEBUG, INFO, WARN, ERROR.

2. Application metrics

Purpose: To provide quantifiable insights into application performance and health.

Types: Counters (e.g., login count), Gauges (e.g., queue length), Histograms (e.g., request durations).

Example metrics: Request rate (req/s), Error rate (errors/s), Latency (e.g., 95th percentile response time), Custom business metrics (e.g., checkout success rate).

3. Distributed traces

Purpose: To capture the flow of requests across various services and internal components.

Benefits: Understand causality between services/functions, visualize latency and execution paths, identify bottlenecks or misbehaving components.

Implementation: Each request receives a unique trace_id. Spans represent individual operations (e.g., HTTP calls, DB queries). Traces are often visualized as waterfall graphs or flame charts.

 

What to instrument in your application

To achieve observability, strategic instrumentation of key application components is essential:

• HTTP handlers: Log inbound and outbound requests, capture status codes, durations, headers, and request/response size.
• Database queries: Measure query latency and frequency, track slow queries and error patterns.
• External dependencies: Observe outbound API calls, interaction with caching layers, and third-party integration performance.
• Message queues / Asynchronous jobs: Track job enqueuing times, processing durations, and failure occurrences.
• Business logic: Capture application-specific events such as user registrations, failed payments, and feature usage patterns.

Tooling & instrumentation libraries

A rich ecosystem of tools and libraries support application observability:

Category	Examples
Instrumentation/ Instrumentation Libraries	OpenTelemetry (OTel), Micrometer, StatsD, Applications Manager
Log aggregation	Loki, Fluent Bit, Elasticsearch, Splunk
Metrics collection	Applications Manager, Prometheus, StatsD / Telegraf, Grafana Cloud
Tracing platforms	Applications Manager, Jaeger, Zipkin

 

Sample observability stack

 A modern observability stack for a microservices application might include: 

 

Design patterns for application observability

Adopting specific architectural patterns enhances application observability:

• Centralized context propagation: Employ a single trace_id or correlation ID for all logs, traces, and metrics associated with a request. Ensure context propagates through all services, queues, and background jobs.
• Structured logging with semantic fields: Emit structured events (e.g., JSON) with well-defined fields (timestamp, level, user ID, error message, trace ID) instead of unstructured text logs.
• Auto-instrumentation: Utilize SDKs and libraries (like OpenTelemetry) that automatically capture telemetry for common frameworks and libraries (HTTP, gRPC, databases).
• High-cardinality label management: Exercise caution with unbounded label values in metrics (e.g., raw user IDs). Employ controlled vocabularies or sampling techniques to manage telemetry volume effectively.

 

Use cases & benefits of application observability

Application observability provides significant advantages across various operational aspects:

Use case	Application observability enables
Debugging	Trace the root cause of errors across complex service interactions.
Incident response	Alert on elevated error rates or degradation of specific features.
Performance optimization	Identify slow API endpoints, resource contention, and inefficient code execution paths.
Feature rollouts	Track the real-time impact of new feature deployments on application health and user behavior.
Compliance	Audit user and system actions for security and regulatory requirements.

 

Challenges and potential pitfalls of observability implementations

While the transformative benefits of application observability are undeniable, its successful adoption and ongoing maintenance present several potential challenges and pitfalls that organizations must proactively address. A lack of careful planning and execution can hinder the effectiveness of observability efforts and even introduce new complexities.

Performance overhead from over-instrumentation

The act of instrumenting an application – injecting code to emit telemetry data – inherently consumes resources. If not implemented judiciously, excessive instrumentation can lead to significant performance overhead, impacting application latency, CPU utilization, and memory consumption. This can paradoxically worsen the very performance issues observability aims to help resolve.

Mitigation strategies include carefully selecting key areas for instrumentation, employing efficient and low-overhead instrumentation libraries (like optimized OpenTelemetry implementations), and potentially using sampling techniques for high-frequency telemetry. Regular performance profiling of the instrumented application is also crucial to identify and address any introduced overhead.

Increased costs and complexity due to high telemetry volume

The comprehensive nature of observability, encompassing logs, metrics, and traces, can generate substantial volumes of data. This surge in telemetry directly translates to increased storage requirements, higher data ingestion costs for observability platforms, and greater complexity in data analysis and querying. Without effective data management strategies, organizations can quickly find their observability initiatives becoming cost-prohibitive and difficult to manage.

Solutions involve implementing intelligent sampling techniques (especially for traces), strategically aggregating metrics at appropriate intervals, employing efficient data compression and retention policies, and carefully selecting observability platforms with cost-effective scaling models.

Difficulty in identifying signals amidst log noise

In high-traffic applications, the sheer volume of generated logs can easily overwhelm teams, making it incredibly challenging to discern critical error messages, warnings, or relevant events from informational or debug logs. This "noise" effectively obscures the "signal," hindering effective troubleshooting and incident analysis.

Best practices include adopting structured logging with well-defined severity levels and semantic fields, implementing robust log filtering and searching capabilities within the chosen log aggregation platform, and establishing clear guidelines for log message formatting and content. Correlation of logs with traces and metrics is also vital to provide context and reduce the need to sift through vast amounts of unstructured data.

Impeded troubleshooting due to lack of telemetry correlation

One of the core tenets of observability is the ability to correlate disparate telemetry signals – logs, metrics, and traces – to understand the interconnectedness of events within a system. Without proper correlation mechanisms, these data streams exist in silos, making it exceptionally difficult to trace the end-to-end flow of a request, identify the root cause of issues that span multiple services or components, and gain a holistic understanding of system behavior.

Essential strategies involve ensuring consistent and pervasive context propagation (carrying trace IDs and span IDs across all services and processes), utilizing observability platforms that offer robust correlation features, and adopting unified data models that facilitate the linking of different telemetry types based on shared identifiers. Investing in tools that automatically correlate data and provide integrated views is crucial for efficient troubleshooting and comprehensive system understanding.

 

Best practices for application observability

To maximize the value and minimize the pitfalls of application observability, adhere to these key best practices:

• Adopt OpenTelemetry or a vendor neutral observability tool to avoid vendor lock-in

  By using a neutral standard, you prevent your telemetry data from being tied to a specific vendor's proprietary format and platform. OpenTelemetry's (OTel) vendor-neutral nature ensures portability and avoids vendor lock-in for your telemetry data. OTel provides a unified set of APIs, SDKs, and tools for generating, collecting, and exporting logs, metrics, and traces. While some initial setup is required, vendor-neutral tools often provide comprehensive documentation and broad community support, simplifying the adoption process and reducing the learning curve associated with proprietary solutions. This promotes consistency across your entire application landscape, regardless of the underlying technologies or observability backends you choose.

• Ensure consistent context propagation (Trace ID) throughout the application lifecycle

  Implement robust mechanisms to propagate context, particularly the trace ID and span IDs, across all services, processes, and asynchronous boundaries within your application ecosystem. This end-to-end context propagation is crucial for correlating telemetry data and understanding the complete journey of a request. Without it, tracing becomes fragmented and troubleshooting across distributed systems becomes significantly more challenging and time-consuming.

• Avoid logging sensitive or personally identifiable information (PII)

  Exercise extreme caution when configuring logging within your applications. Refrain from logging any data that could be considered sensitive or personally identifiable, such as user passwords, credit card details, or social security numbers. Such practices not only pose significant security and privacy risks but can also lead to compliance violations. Employ robust filtering and scrubbing techniques if there's any risk of sensitive data inadvertently being logged.

• Implement sampling for high-volume traces while retaining critical ones

  In high-traffic systems, generating traces for every single request can lead to an overwhelming volume of data and increased costs. Implement intelligent sampling strategies to capture a representative subset of traces. However, ensure that critical traces, such as those associated with errors, high-latency requests, or specific user actions, are always retained for thorough analysis and debugging. Adaptive sampling techniques that automatically adjust the sampling rate based on system behavior can also be beneficial.

• Regularly review and refine dashboards and alerts for accuracy and relevance

  Your observability dashboards and alerting rules are living artifacts that require periodic review and refinement. Ensure that your dashboards provide meaningful insights into the health and performance of your applications and that the alerts you have configured are accurate, actionable, and not overly noisy. Outdated or poorly configured dashboards can lead to missed issues, while excessive or irrelevant alerts can cause alert fatigue and reduce their effectiveness. Establish a regular cadence for reviewing and updating these critical components based on evolving application behavior and business needs.

 

Embracing observability for reliable systems

Application observability is not merely a desirable feature; it is a fundamental necessity for operating reliable, performant, and scalable modern systems. By strategically instrumenting applications to emit structured and context-rich telemetry, development and operations teams gain profound insights into application behavior. This deep understanding empowers them to proactively detect issues, troubleshoot efficiently, and resolve problems effectively. As application architectures continue to scale in complexity, investing in observability early will yield significant dividends in terms of system uptime, enhanced user experience, and improved developer productivity.

 

Application observability with ManageEngine Applications Manager

ManageEngine Applications Manager provides comprehensive application observability features, enabling IT and DevOps teams to gain deep insights into the performance and behavior of their applications. It goes beyond basic monitoring by offering tools to understand the "why" behind performance issues, aligning with the core principles of observability.

Here's how you can leverage Applications Manager for application observability:

Key observability capabilities in Applications Manager:

• Full-stack visibility: Applications Manager offers monitoring across the entire application stack, from the infrastructure layer (servers, containers, cloud resources) up to the application code and end-user experience. This holistic view is crucial for understanding dependencies and the impact of one layer on another.
• Code-level insight: For supported languages and application servers (Java, .NET, Python, Node.js, etc.), Applications Manager provides application performance monitoring (APM) with code-level visibility. This allows you to trace transactions, identify slow-performing methods and functions, and pinpoint the exact lines of code causing issues.
• Distributed transaction tracing: In modern microservices architectures, requests often span multiple services. Applications Manager's distributed tracing capabilities allow you to follow the path of a transaction across different components, visualizing latency and identifying bottlenecks in inter-service communication.
• Application service maps: These dynamic maps automatically discover and visualize the relationships and dependencies between various application components and services. This context helps in understanding the impact of failures and identifying potential root causes.
• Real user monitoring (RUM): Applications Manager captures and analyzes the performance of web applications from the end-user's perspective. It provides insights into frontend performance metrics like page load times, network latency, and browser rendering times, segmented by geography, browser, and device. This helps understand the actual user experience.
• Synthetic monitoring: You can simulate user interactions with critical application workflows (e.g., login, checkout) to proactively test performance and availability from various locations. This helps identify issues before they impact real users.
• Error analytics: Applications Manager analyzes application logs for errors, exceptions, and patterns that can provide valuable context for performance issues.
• Metrics collection: It gathers a wide range of application-specific metrics, including request rates, error rates, response times, resource utilization (CPU, memory, JVM heap, etc.), and custom business metrics.
• Alerting and anomaly detection: You can set static and dynamic thresholds for various metrics and receive intelligent alerts when deviations occur. AI-powered anomaly detection can identify unusual patterns that might indicate emerging issues.
• Dashboards and reporting: Applications Manager offers customizable dashboards to visualize key performance indicators (KPIs) and provides comprehensive reports for performance analysis, trend identification, and capacity planning.
• Container monitoring (Docker, Kubernetes, OpenShift): For applications running in containers, Applications Manager provides deep visibility into container performance, resource utilization, and the health of the orchestration platform. This includes monitoring nodes, pods, services, and other Kubernetes objects.

Leveraging Applications Manager for observability:

By utilizing these features, you can achieve a high degree of application observability with ManageEngine Applications Manager. This enables your teams to:

• Understand the "why": Go beyond simply seeing a problem and delve into the root cause of performance degradations and errors.
• Proactively identify issues: Detect anomalies and potential problems before they impact users or critical business processes.
• Optimize performance: Gain insights into bottlenecks and areas for improvement to enhance application efficiency and responsiveness.
• Improve mean time to resolution (MTTR): With rich contextual data and tracing capabilities, troubleshoot and resolve issues faster.
• Enhance user experience: Ensure optimal front-end performance and identify issues affecting end-user satisfaction.

In essence, ManageEngine Applications Manager provides a unified platform to collect, correlate, and analyze various telemetry data points, offering the comprehensive visibility required for effective application observability in today's complex IT environments.

Why choose Applications Manager?

With its intuitive interface, robust alerting capabilities, and flexible deployment options, Applications Manager empowers organizations to reduce downtime, enhance operational efficiency, and deliver superior user experiences. Whether you’re managing on-premise, cloud, or hybrid environments, Applications Manager simplifies the complexity of IT monitoring.

Elevate your application observability game with Applications Manager. Download now and experience the difference, or schedule a personalized demo for a guided tour.