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DevOps and Security Glossary Terms

Glossary Terms

Application performance monitoring (APM) - definition & overview

In this article
What is APM?
What is cloud APM?
Why is APM monitoring necessary?
APM: three categories of data
APM: how to measure application performance?
APM with Sumo Logic
What is APM?
What is cloud APM?
Why is APM monitoring necessary?
APM: three categories of data
APM: how to measure application performance?
APM with Sumo Logic

What is APM?

APM tools capture data, and aggregate and analyze the data to detect patterns and present actionable insights in a human-readable format.

What is cloud APM?

Cloud application performance monitoring, or cloud APM, is a set of software and tools used to monitor and measure the resources used to support environments built in the hybrid cloud, public cloud, or private cloud. Cloud APM monitoring tools ensure these systems have optimal performance by improving incident response time and management.

Key takeaways

  • APM is a capability that enables development teams to monitor and manage the performance of their software in a live environment.
  • APM tools drive value by capturing data from IT infrastructure, aggregating it into a single database, analyzing the data to detect patterns and trends, and presenting actionable insights in a human-readable format.
  • Cloud application performance monitoring (or cloud APM) is a set of software and tools used to monitor and measure the resources used to support environments built in the hybrid cloud, public cloud or private cloud.

Why is APM monitoring necessary?

Modern application performance management has become increasingly complicated for software developers and DevSecOps teams due in part to new software architectures, like IaaS (infrastructure as a service), PaaS (platform as a service) and CaaS (container as a service), the widespread use of microservices and functions; and new software development practices, like Agile and DevOps. Many IT organizations are also increasing the number of applications they deploy in hybrid cloud environments, which increases administrative overhead and makes it more difficult to manage applications without an APM cloud monitoring tool effectively.

With application performance monitoring, software development teams can track various metrics for applications deployed in the cloud. APM monitoring enables development teams to monitor and manage the performance of their software in the live environment.

APM has been described as the translation of IT metrics into business meaning. An application monitoring tool drives value by capturing data from IT infrastructure, aggregating it into a single database, analyzing the data to detect patterns and trends and presenting actionable insights in a human-readable format.
However, a shift in APM is underway. For decades, the domains of security services and IT operations have worked in separate silos within most organizations. This has been the case even when the teams use the same telemetry, most commonly seen with log files (e.g., SIEM).

As more and more applications become public-facing and business-critical, there is a growing overlap and similarity of the IT operations and security operations toolchains, cohorts and strategies. More collaboration between security and ITOps is practically inevitable.

APM: three categories of data

APM capabilities are very versatile and can be used to measure many different types of data. There are three categories of data that your IT organization should differentiate between when configuring your APM capability:

APM metrics

Metrics offers a wealth of information about application performance. A metric is a quantified measure that conveys the status of a specific process. Metrics are frequently generated by a variety of applications and operating systems and can easily be correlated across different elements of the IT infrastructure. Metrics can be compared to a known baseline to yield information about the status of a system or a process. Changes in metrics can often be viewed as symptoms of an underlying problem.

Examples of APM metrics are:

  • Error rates
  • CPU usage and storage
  • Garbage collection
  • Customer experience
  • Uptime
  • Response time and request rates
  • Number of instances

APM traces

A trace is the complete processing of a request. The trace itself illustrates the entire journey of a request as it moves through all of the services and components of the network. A trace is made of segments, operations that take place within an individual service or network component. A trace contains hundreds of data points that can be used to diagnose errors, identify and isolate network issues and detect security threats. Traces help security analysts or artificial intelligence applications track inter-dependencies between network objects and see how things are connected within the IT infrastructure.

Application logging

A log file is a computer-generated data file automatically created by an application or operating system. Each application's log file contains information about events and user behavior that took place on the application. An application may create several log files for recording different events—one for application logs, one for security logs, one for system logs, one for directory service logs, etc. Logs are useful for conducting root cause analysis and determining why a metric changed or where an event originated.

APM: how to measure application performance?

Application performance monitoring and observability

The primary distinction between observability and traditional APM is that observability -centric solutions support an exploratory, analytics-driven workflow that may bear more resemblance to business intelligence (BI) than IT operations. The continued growth in mobile, cloud-native applications and workload migrations from the traditional data center to cloud architectures continues fuels the APM and observability market.

When it comes to application performance monitoring, developing the capabilities is as important as understanding what specific things you should be tracking and how those measurements help you diagnose and resolve user issues. There are four general categories of KPIs that you should be tracking with your APM tool:

Your APM tool should measure system performance

An application is only as good as its underlying infrastructure. Monitor system performance for metrics including:

Load - understand how many users are accessing the servers, how many requests your servers are dealing with and whether you are overloaded

Resource usage - track usage of IT infrastructure across time and determine when it's time to increase your capacity

Input/output - gain visibility into the movement of data throughout your IT infrastructure and identify bottlenecks that could negatively impact system performance

Your APM monitoring should measure application performance

Application metrics can reveal critical data that reflects how users experience the application and whether they return. Monitor application performance to evaluate:

Latency - Strongly correlated with user satisfaction and positive user experience, latency measures the time that it takes for a user to complete a transaction on the application

Service uptime - Application downtime translates directly into lost revenue. APM solutions can provide real-time insight into application availability, enabling a rapid response to unplanned service interruptions

Throughput - Measures the data transfer rate into and out of your application. This can be correlated with user activity or measured against a baseline to verify that the application functions correctly.

Your APM should measure system and security events

Monitoring events means capturing log files from the IT infrastructure and analyzing them to diagnose events on the system. An effective APM tool should capture events such as:

System errors/failures - Any number of conditions can cause system errors or failures. Monitoring system events can help to initiate a rapid response that discovers and corrects underlying issues before customers are negatively impacted.

System changes - IT infrastructure changes that support your application can affect data transfer rates and latency, leading to user dissatisfaction and other issues. System changes should be monitored and evaluated to quantify their impact on the user experience.

Code deploys - If a code deploy contains an unknown issue, it may immediately trigger errors in the application. The ability to track new commits and correlate them to application errors and events can streamline restoring the application after a faulty code deployment.

Your APM platform should measure application events

Application event monitoring is achieved by capturing log data from the application itself. These logs contain a range of useful data that can be used to assess and improve application performance:

User actions - Your application performance monitoring capability should capture event logs reflecting user actions and user and entity behavior analytics (UEBA). Track the behavior of users in the application that can help you identify opportunities to improve user experience and funnel users toward preferred or target activities.

Real-time user monitoring - Your cloud application performance monitoring should have some functionality or integration with real-time user monitoring. This allows you to see the true digital experience and refer back to recordings of actual behavior in applications.

User transactions - Trace the pathways that users take when navigating your application to identify and remove bottlenecks or failure points

Success/failure - Track user conversion successes and failures to determine when a serious issue could affect your bottom line.

Compare different application monitoring tools.

APM with Sumo Logic

Sumo Logic’s customers leverage its unified platform to address multiple use cases to manage the reliability and security of cloud-native apps, monitoring and managing application performance across your entire hybrid cloud environment.

Explore how Sumo Logic makes it easy to capture and aggregate event logs and other data from your applications and IT infrastructure and turn it into actionable insights with the help of artificial intelligence and pattern recognition algorithms.


How does APM integrate with other IT management and monitoring tools?

  • Event correlation platforms or IT service management (ITSM) systems to correlate performance metrics with system events, alerts and incidents. This integration helps identify the root causes of performance issues by analyzing correlated data from multiple sources.
  • Alerting and notification systems like email, SMS or collaboration tools like Slack or Microsoft Teams. This allows IT teams to receive real-time alerts and notifications about performance degradation or anomalies, enabling prompt response and issue resolution.
  • IT operations analytics (ITOA) platforms to analyze and visualize performance data alongside other operational data sources such as logs, metrics and events. This integration provides a comprehensive view of IT infrastructure health and enables advanced analytics for trend analysis and predictive insights.
  • Log management and analysis platforms to correlate performance metrics with log data. This integration helps identify performance issues caused by application errors, infrastructure issues, or security events logged in system logs.
  • Infrastructure monitoring solutions to monitor underlying infrastructure components such as servers, networks, databases and cloud services. This integration provides visibility into the impact of infrastructure performance on application performance and vice versa.
  • Configuration management databases (CMDBs) or configuration management tools to track changes in application configurations and their impact on performance. This integration helps ensure that changes are properly monitored and validated before deployment.
  • DevOps tool and CI/CD pipelines to automate performance testing, monitoring and feedback loops throughout the software development lifecycle. This integration enables proactive performance monitoring and optimization during application development and deployment phases.
  • Cloud management platforms to monitor performance metrics and resource utilization across cloud environments. This integration provides visibility into the performance of cloud-based applications and helps optimize cloud resource allocation for cost efficiency and performance.
  • Service desk and ticketing systems to automatically create and manage tickets for performance-related incidents. This integration streamlines incident management processes and ensures performance issues are promptly addressed and resolved according to predefined SLAs.

How does application performance monitoring support application performance management?

APM plays a critical role in supporting application performance management by providing comprehensive application insights into the performance of enterprise applications and their underlying infrastructure. Here's how APM supports APM:

Real-time monitoring: APM continuously monitors various aspects of application performance in real-time, including response times, throughput, error rates and resource utilization. This real-time monitoring provides immediate visibility into the health and performance of applications, allowing IT teams to promptly detect and respond to performance issues.

End-to-end visibility: APM offers end-to-end visibility into the entire application stack, including front-end user interfaces, application servers, databases, third-party services and network components. This holistic view enables IT teams to understand the dependencies and interactions between different software application components, identify a performance bottleneck or a performance problem and prioritize optimization efforts effectively.

Transaction tracing: APM tools trace individual transactions as they traverse through various application stack layers, capturing detailed information about the execution path, latency and performance characteristics of each transaction. This transaction tracing capability helps IT teams diagnose a performance issue, identify slow or inefficient code paths and optimize application performance accordingly.

Root cause analysis: APM facilitates root cause analysis by correlating performance data with system events, logs and infrastructure metrics. By analyzing correlated data from multiple sources, an APM tool helps IT teams pinpoint the underlying factors contributing to a performance problem, such as code defects, configuration errors or infrastructure failures.

Alerting and notification: An APM solution provides alerting and notification mechanisms to alert IT teams about real-time performance degradation, anomalies or threshold breaches. By proactively notifying IT staff about critical issues, an APM tool enables prompt response and remediation to minimize the impact on end users.

Performance optimization: APM supports app performance optimization efforts by providing insights into performance trends, patterns and opportunities for improvement. APM tools help IT teams optimize application code, improve resource utilization, and enhance scalability and reliability by analyzing historical performance data and identifying optimization opportunities.

Capacity planning: APM assists capacity planning by forecasting future performance requirements based on historical data and usage patterns. By analyzing performance trends and predicting future demand, APM tools help IT teams optimize resource allocation, scale infrastructure proactively and avoid performance bottlenecks during peak usage.

Continuous improvement: APM facilitates continuous application performance improvement by providing feedback loops for development, testing, deployment, and operations teams. By monitoring performance metrics throughout the software development lifecycle, APM tools help identify opportunities for optimization, validate performance improvements, and ensure that applications meet performance objectives over time.

How is application observability different than application performance monitoring?

Application observability and application performance monitoring (APM) are related but distinct concepts in application monitoring and management. Here's how they differ:

Scope and focus:

  • APM primarily focuses on monitoring and optimizing the performance of applications and their underlying infrastructure.
  • Application observability encompasses a broader set of capabilities to gain insights into applications' internal workings and interactions with the surrounding environment. Observability focuses on understanding application behavior, dependencies, and performance in real-world scenarios by collecting and analyzing diverse data sources, including logs, traces, metrics, events, and metadata.

Data collection and analysis:

  • APM typically collects structured performance data from applications and infrastructure components, such as metrics from application servers, databases and network devices.
  • Application observability platforms collect a wide range of telemetry data, including logs, distributed traces, events and metadata, from various sources across the application stack. These data sources are analyzed holistically to provide insights into application behavior, dependencies and performance across different layers and components.

Granularity and detail:

  • APM tools often focus on providing granular insights into specific performance metrics and transactions within applications, enabling detailed analysis of performance issues at the transaction level.
  • Observability platforms offer a more holistic view of application behavior and performance, providing insights into the entire lifecycle of requests and transactions as they traverse through distributed systems and microservices architectures. Observability emphasizes understanding the context and relationships between different components and services to effectively diagnose complex issues and troubleshoot distributed systems.

Tooling and methodologies:

  • An APM solution typically uses agent-based instrumentation and predefined monitoring metrics to track application performance. They often rely on predefined dashboards, alerting thresholds, and correlation rules to proactively monitor and manage application performance.
  • Observability platforms leverage advanced telemetry collection techniques, such as distributed tracing, structured logging and event-driven monitoring, to capture rich, contextual data about application behavior. Observability emphasizes flexibility, extensibility, and open standards and APIs to enable custom analysis, visualization and application performance troubleshooting.

In summary, while application performance monitoring tools focus on monitoring and optimizing application performance, application observability offers a broader approach to understanding application behavior, dependencies and performance across complex and distributed environments.

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