Mobile Observability Glossary

In the fast-paced world of mobile apps, ensuring a seamless user experience is paramount. Mobile observability provides the tools and insights necessary to understand how apps perform in real-world conditions, identify bottlenecks, and continuously improve. But diving into the world of mobile observability can feel overwhelming, especially with so many technical terms and concepts to grasp.

This Mobile Observability Glossary is your comprehensive guide to understanding the key terms that shape modern mobile app monitoring. Each term in this glossary is accompanied by:

• A clear definition: Breaking down complex jargon into simple, digestible language.
• Why it’s important: Highlighting how the concept contributes to user experience, reliability, and app success.
• How you apply it: Offering practical examples of how to implement the concept in your workflow or app strategy.

From foundational concepts like latency and telemetry to advanced tools like OpenTelemetry and mobile SLOs, this guide ensures you’re equipped to build and maintain high-performing mobile applications.

• Definition: The time period used to collect and analyze telemetry data, smoothing out delays or inconsistencies.
• Why it’s important: Ensures accurate metrics despite delayed data.
• How to apply it: Use longer aggregation windows for delayed mobile telemetry and shorter ones for real-time metrics.

• Definition: Errors on Android devices when an app’s main thread freezes, preventing user interaction. The OS prompts the user to terminate the app.
• Why it’s important: ANRs make apps unusable, creating poor experiences, user churn, and potential penalties in app stores.
• How to apply it: Monitor ANR rates, identify root causes, and optimize code to prevent freezes.

• Definition: The responsiveness and resource efficiency of a mobile app.
• Why it’s important: Performance impacts user satisfaction and retention.
• How to apply it: Define SLOs for key performance metrics like startup time or latency in user flows.

• Definition: The process of improving app efficiency and responsiveness.
• Why it’s important: Better performance drives user satisfaction and retention.
• How to apply it: Use monitoring data to identify bottlenecks, optimize code, and improve resource use.

• Definition: The file size of an app’s install package.
• Why it’s important: Impacts download rates and device storage usage.
• How to apply it: Monitor app size over time to minimize resource consumption and download delays.

• Definition: The process that launches a mobile app.
• Why it’s important: Slow startup can frustrate users and lead to app abandonment.
• How to apply it: Measure startup latency as an SLO and optimize code and dependencies to ensure a quick launch.

• Definition: Performance data provided by app stores, such as install rates and crash reports.
• Why it’s important: Offers insights into app performance and user feedback.
• How to apply it: Correlate app store data with internal telemetry for comprehensive observability.

• Definition: Negative consequences from app stores due to issues like ANRs and crashes.
• Why it’s important: Penalties reduce app visibility and user acquisition potential.
• How to apply it: Monitor stability metrics to prevent penalties.

• Definition: Trends in user interactions, such as session lengths and feature usage.
• Why it’s important: Helps identify user behavior and trends.
• How to apply it: Use telemetry to analyze usage patterns and adapt to user needs.

• Definition: A specific release of a mobile app.
• Why it’s important: Performance and issues can vary between versions.
• How to apply it: Track performance by version and prioritize maintenance for the most recent releases.

• Definition: Key-value pairs in telemetry that add context, such as region, device type, or customer.
• Why it’s important: Enhance data analysis by enabling filtering and grouping.
• How to apply it: Use attributes to analyze telemetry with richer context, like workload regions or message queues.

• Definition: An SLO metric measuring the success or failure rate of a process.
• Why it’s important: A critical metric for assessing app reliability.
• How to apply it: Define availability-based SLOs for essential flows like logins, specifying the expected success rates.

• Definition: Service Level Objective (SLO) for backend systems supporting mobile apps.
• Why it’s important: Correlates backend and mobile performance since many mobile flows depend on backend services.
• How to apply it: Establish related SLOs for mobile apps and backend systems to identify whether issues are client-side or server-side.

• Definition: Server-side systems that provide functionality and data to mobile apps.
• Why it’s important: Mobile apps rely heavily on backend services, so monitoring both ensures end-to-end performance.
• How to apply it: Correlate backend and mobile metrics to troubleshoot performance issues effectively.

• Definition: Monitoring of network requests and responses from the perspective of a mobile app.
• Why it’s important: Provides a complete view of the user’s network experience, which backend monitoring alone cannot.
• How to apply it: Track network performance metrics, including duration, failure rates, and user abandonment in critical flows.

• Definition: Telemetry data collected directly from mobile devices, including user actions and app performance.
• Why it’s important: Offers visibility into the actual user experience, beyond server-side insights.
• How to apply it: Use client-side telemetry to inform SLOs and improve user experience.

• Definition: Metadata describing relationships in telemetry data. Hard context links causally related events, while soft context adds descriptive metadata.
• Why it’s important: Hard context supports tracing, while soft context enriches data for deeper insights.
• How to apply it: Use hard context for distributed tracing and soft context for grouping and analyzing metrics or logs.

• Definition: The percentage of app sessions that don’t end in a crash.
• Why it’s important: Indicates app stability and user experience quality.
• How to apply it: Monitor crash-free rates and define SLOs to maintain stability targets.

• Definition: The time lag between when telemetry data is collected on a mobile device and when it is analyzed.
• Why it’s important: Ignoring delays can lead to incomplete or skewed data, especially for users experiencing issues.
• How to apply it: Use longer aggregation windows and design systems to handle delayed data effectively.

• Definition: Collecting a subset of telemetry data from mobile devices.
• Why it’s important: Reduces data volume and costs but may cause visibility gaps.
• How to apply it: Sample strategically, focusing on key user segments or business-critical data.

• Definition: The range of mobile devices with varying capabilities, operating systems, and performance characteristics.
• Why it’s important: Device variability can affect app performance and failure modes.
• How to apply it: Segment performance data by device type and prioritize fixes for high-impact device groups.

• Definition: Mobile hardware attributes like CPU, memory, and storage that impact app performance.
• Why it’s important: Resource limitations can significantly affect app performance.
• How to apply it: Monitor resource usage and optimize app behavior for low-resource devices.

• Definition: Tracking requests across services in a distributed system to understand flow and performance.
• Why it’s important: Identifies bottlenecks and failure points in user flows.
• How to apply it: Implement distributed tracing for key workflows like login or checkout.

• Definition: The allowable amount of failure or downtime within a specified period, based on the SLO.
• Why it’s important: Balances reliability with innovation by guiding resource allocation.
• How to apply it: Use error budgets to trigger corrective actions when exceeded, such as code freezes or incident responses.

• Definition: A documented process for addressing SLO violations.
• Why it’s important: Ensures a structured response to performance issues.
• How to apply it: Define clear protocols to prioritize and resolve issues when error budgets are exhausted.

• Definition: Aligning app metrics with the time events occurred for the user, rather than when they were processed by the server.
• Why it’s important: Provides accurate insights for SLO tracking, especially for delayed data.
• How to apply it: Use tools that support event-time mapping to analyze mobile performance effectively.

• Definition: Specific data points or events linked to metrics for detailed analysis.
• Why it’s important: Ties high-level metrics to traces for deeper investigation.
• How to apply it: Use exemplars to correlate high-latency metrics with specific traces for root cause analysis.

• Definition: Modes in which mobile apps operate—foreground (visible and interactive) or background (not visible, with limited resources).
• Why it’s important: Background states are critical for tasks like syncing but often overlooked in observability.
• How to apply it: Monitor and optimize app performance in both states for seamless user experiences.

• Definition: Data with many unique values, such as device models or user IDs.
• Why it’s important: Makes aggregating metrics for SLOs more complex.
• How to apply it: Create separate SLOs for different user segments, such as device types or regions.

• Definition: A metric measuring how long a process takes to complete.
• Why it’s important: Latency directly affects user experience; slow processes can cause frustration and abandonment.
• How to apply it: Set latency-based SLOs, such as “95% of product searches complete in ≤1 second.”

• Definition: Records of discrete events generated by an app, containing details like timestamps, error levels, and messages.
• Why It’s Important: Logs offer granular insights for debugging and troubleshooting issues.
• How You Apply It: Centralize mobile logs and analyze them to debug crashes or performance bottlenecks.

• Definition: Numerical data representing app performance and health, such as crash-free rates or API latency.
• Why it’s important: Provides high-level trends and health checks for applications.
• How to apply it: Monitor key performance metrics like app startup time and API response rates.

• Definition: Collaboration between DevOps and mobile teams to improve mobile app reliability.
• Why it’s important: Aligns mobile SLOs with overall system reliability to enhance performance.
• How to apply it: Foster collaboration between mobile and DevOps teams when creating and maintaining SLOs.

• Definition: Adding code to collect telemetry data from mobile apps.
• Why it’s important: Enables collection and analysis of telemetry from mobile devices.
• How to apply it: Instrument apps with OpenTelemetry libraries to gather detailed performance data.

• Definition: Variability in network speed and connectivity that impacts mobile app performance.
• Why it’s important: Network conditions directly affect user experience and app reliability.
• How to apply it: Monitor the performance of network requests and address issues caused by poor conditions.

• Definition: The practice of monitoring and gaining insights into the performance, stability, and user experience of mobile applications.
• Why it’s important: Provides insights into the end-user experience to improve app quality and business outcomes.
• How you would apply it: Adopt a mobile-first approach to observability, focusing on end-user experiences rather than just transactions. Use tools that capture mobile-specific telemetry, focus on user sessions, and handle delayed data.

• Definition: Software solutions designed to monitor and collect mobile app data.
• Why it’s important: Mobile-specific tools address the unique challenges of mobile telemetry, such as delayed data and high cardinality.
• How to apply it: Choose observability tools that handle mobile-specific data characteristics effectively.

• Definition: The ability of a mobile SDK to gracefully handle adverse conditions while continuing to report data.
• Why it’s important: Ensures telemetry collection under limited resources like low disk space or poor connectivity.
• How to apply it: Develop robust SDKs that can handle constraints such as limited network access or lack of storage.

• Definition: A target reliability level for mobile app components, focused on user impact.
• Why it’s important: Balances feature development with maintenance to ensure a high-quality user experience.
• How to apply it: Set specific targets like “99.9% of sessions start in less than 2 seconds” and monitor compliance to make data-driven decisions.

• Definition: A metric used to measure actual performance against an SLO.
• Why it’s important: Provides data to evaluate how well the service meets its reliability goals.
• How to apply it: For an SLO like “95% of product searches return results in ≤1 second,” the SLI tracks the percentage of searches meeting the target.

• Definition: Applying SRE principles to mobile app development and operations.
• Why it’s important: Ensures mobile apps are reliable and perform well under diverse conditions.
• How to apply it: Implement SRE practices like SLO definition and error budget management in the mobile development lifecycle.

• Definition: An open-source standard for collecting telemetry data across systems.
• Why it’s important: Enables consistent data collection and correlation across mobile and backend systems.
• How to apply it: Instrument mobile apps with OTel SDKs to capture traces, metrics, and logs.

• Definition: A component that receives, processes, and exports telemetry data.
• Why it’s important: Manages the flow of telemetry data and enables data transformation.
• How to apply it: Deploy collectors to filter, modify, and route telemetry from mobile apps to observability platforms.

• Definition: The standard format for transmitting telemetry data between OpenTelemetry components.
• Why it’s important: Ensures interoperability between different observability tools and systems.
• How to apply it: Use OTLP to transmit mobile telemetry data to backend observability tools.

• Definition: The 90th percentile, representing the experience of the slowest 10% of users.
• Why it’s important: Provides insight into the performance affecting a significant portion of users.
• How to apply it: Use P90 metrics to identify and address issues impacting user satisfaction and define SLOs.

• Definition: An SLO metric focused on whether a process completes accurately, without errors.
• Why it’s important: Ensures data integrity and delivers the expected outcomes to users.
• How to apply it: Define quality-based SLOs, such as “99% of homepage images load correctly.”

• Definition: Tracking the real-world user experience of a mobile application.
• Why it’s important: Provides end-to-end visibility into user interactions and app behavior.
• How to apply it: Implement RUM tools to collect session data and identify trends or issues.

• Definition: Constraints on system resources like memory, CPU, and storage in mobile devices.
• Why it’s important: Limited resources can degrade app performance and user experience.
• How to apply it: Monitor resource usage and optimize app efficiency for devices with lower capacities.

• Definition: Self-describing telemetry data standardized for easier analysis and correlation.
• Why it’s important: Simplifies data interpretation and reduces complexity.
• How to apply it: Use OpenTelemetry’s semantic conventions for consistent data collection.

• Definition: A numerical target for the reliability of user-impacting components in a system.
• Why it’s important: Guides engineering priorities between reliability and innovation.
• How to apply it: Define SLOs like “95% of logins complete in ≤1 second” to monitor and improve performance.

• Definition: A metric measuring performance against an SLO.
• Why it’s important: Provides real-time data for assessing system reliability.
• How to apply it: Define SLIs that reflect user experience, such as measuring the percentage of searches completed within a set time.

• Definition: A contract with customers guaranteeing specific performance levels.
• Why it’s important: Manages customer expectations and mitigates risks of non-compliance.
• How to apply it: Set measurable SLAs, ensure compliance, and avoid penalties by maintaining promised performance levels.

• Definition: The number of unique user sessions within a given time.
• Why it’s important: Provides insights into app usage and helps contextualize performance metrics.
• How to apply it: Use session counts to monitor user engagement and correlate them with performance trends.

• Definition: Combining multiple related sessions to provide a complete picture of user interactions.
• Why it’s important: Offers full context for understanding user journeys and troubleshooting issues.
• How to apply it: Use tools that stitch sessions across app states for continuous visibility into user flows.

• Definition: A unit of work within a distributed system, representing a single operation or step in a user flow.
• Why it’s important: Enables detailed performance monitoring and troubleshooting.
• How to apply it: Use spans to track and optimize critical workflows, such as user logins or checkouts.

• Definition: Data that describes a system’s behavior, including user actions, performance metrics, and device information.
• Why it’s important: Critical for understanding app performance and identifying areas for improvement.
• How to apply it: Collect and analyze telemetry data to inform SLOs and enhance user experience.

• Definition: End-to-end records of user-initiated requests as they flow through a system.
• Why it’s important: Provides visibility into bottlenecks and system-wide performance.
• How to apply it: Use distributed tracing tools like OpenTelemetry to optimize user flows.

• Definition: The integration of logs, metrics, and traces into a single, correlated dataset.
• Why it’s important: Offers a holistic view of system behavior and simplifies troubleshooting.
• How to apply it: Ensure telemetry sources share common identifiers to enable data correlation.

• Definition: The number of users experiencing specific issues or engaging with the app.
• Why it’s important: Helps prioritize fixes based on user impact.
• How to apply it: Combine user count with activity metrics to assess the severity of issues and set SLOs.

• Definition: The journeys users take to accomplish specific goals within an app.
• Why it’s important: Focuses observability on the most critical app interactions from a user perspective.
• How to apply it: Define SLOs for essential user flows like “add to cart” or “checkout.”

• Definition: Categorizing users based on variables like geography, device type, or app version.
• Why it’s important: Enables targeted improvements and prioritization.
• How to apply it: Set different SLOs for user segments, such as specific regions or app versions.

• Definition: A complete representation of user interactions during a single app session.
• Why it’s important: Provides context for troubleshooting and understanding user behavior.
• How to apply it: Monitor sessions to identify patterns and troubleshoot issues effectively.

• Definition: Dependence on a specific vendor’s tools or services that limits flexibility or migration to alternatives.
• Why it’s important: Can restrict innovation and increase long-term costs.
• How to apply it: Use open standards like OpenTelemetry to avoid vendor lock-in and maintain flexibility in your observability stack.

• Definition: The allocation of tasks or processes across devices, servers, or regions.
• Why it’s important: Balances resource utilization and ensures high availability.
• How to apply it: Monitor workload distribution to identify imbalances and optimize resource allocation.

• Definition: Metrics that measure the efficiency of processes, such as the ratio of successful transactions to attempts.
• Why it’s important: Indicates process effectiveness and highlights areas for improvement.
• How to apply it: Track yield metrics to identify inefficiencies and set SLOs to improve outcomes.

• Definition: A deployment process that ensures no service interruption for users.
• Why it’s important: Reduces user frustration and maintains service continuity during updates.
• How to apply it: Implement techniques like blue-green deployments or canary releases to achieve zero-downtime updates.