Broadcom DX APM

Real User Monitoring (RUM) captures and analyzes every transaction of every user of a website or application in real-time to visualize actual client-side performance. This enables teams to detect and resolve specific user-facing issues, such as slow page loads or JavaScript errors, that synthetic testing often misses.

Browser monitoring captures real-time data on user interactions and page load performance directly from the end-user's web browser. This visibility allows teams to diagnose frontend latency, JavaScript errors, and rendering issues that backend monitoring might miss.

Session replay provides a visual reproduction of user interactions within an application, allowing teams to see exactly what a user saw and did leading up to an error or performance issue. This context is crucial for reproducing bugs and understanding user behavior beyond raw logs.

JavaScript Error Detection captures and analyzes client-side exceptions occurring in users' browsers to prevent broken experiences. This capability allows engineering teams to identify, reproduce, and resolve frontend bugs that impact application stability and user conversion.

AJAX monitoring captures the performance and success rates of asynchronous network requests initiated by the browser, essential for diagnosing latency and errors in dynamic Single Page Applications.

Single Page App Support ensures that performance monitoring tools accurately track user interactions, route changes, and soft navigations within frameworks like React, Angular, or Vue without requiring full page reloads. This visibility is crucial for understanding the true end-user experience in modern, dynamic web applications.

Core Web Vitals monitoring tracks essential metrics like Largest Contentful Paint, Interaction to Next Paint, and Cumulative Layout Shift to assess real-world user experience. This feature helps engineering teams optimize page load performance and visual stability, directly impacting search engine rankings and user retention.

Page load optimization tracks and analyzes the speed at which web pages render for end-users, providing critical insights to improve user experience, SEO rankings, and conversion rates.

Geographic Performance monitoring tracks application latency, throughput, and error rates across different global regions, enabling teams to identify location-specific bottlenecks. This visibility ensures a consistent user experience regardless of where end-users are accessing the application.

Mobile app monitoring provides real-time visibility into the stability and performance of iOS and Android applications by tracking crashes, network latency, and user interactions. This ensures engineering teams can rapidly identify and resolve issues that degrade the end-user experience on mobile devices.

Device Performance Metrics track hardware-level health indicators—such as CPU usage, memory consumption, battery impact, and frame rates—on the end-user's device. This visibility enables engineering teams to isolate client-side resource constraints from network or backend issues to optimize the application experience.

Mobile crash reporting captures and analyzes application crashes on iOS and Android devices, providing stack traces and device context to help developers resolve stability issues quickly. This ensures a smooth user experience and minimizes churn caused by app failures.

Synthetic monitoring simulates user interactions to proactively detect performance issues and verify uptime before real customers are impacted. It is essential for ensuring consistent availability and functionality across global locations and device types.

Availability monitoring tracks whether applications and services are accessible to users, ensuring uptime and minimizing business impact during outages. It provides critical visibility into system health by continuously testing endpoints from various locations to detect failures immediately.

Uptime tracking monitors the availability of applications and services from various global locations to ensure they are accessible to end-users. It provides critical visibility into service interruptions, allowing teams to minimize downtime and maintain service level agreements (SLAs).

SLA Management enables teams to define, monitor, and report on Service Level Agreements (SLAs) and Service Level Objectives (SLOs) directly within the APM platform to ensure reliability targets align with business expectations.

Apdex Scores provide a standardized method for converting raw response times into a single user satisfaction metric, allowing teams to align performance goals with actual user experience rather than just technical latency figures.

Throughput metrics measure the rate of requests or transactions an application processes over time, providing critical visibility into system load and capacity. This data is essential for identifying bottlenecks, planning scaling events, and understanding overall traffic patterns.

Latency analysis measures the time delay between a user request and the system's response to identify bottlenecks that degrade user experience. This capability allows engineering teams to pinpoint slow transactions and optimize application performance to meet service level agreements.

Custom metrics enable teams to define and track specific application or business KPIs beyond standard infrastructure data, bridging the gap between technical performance and business outcomes.

User Journey Tracking monitors specific paths users take through an application, correlating technical performance metrics with critical business transactions to ensure key workflows function optimally.

API monitoring tracks the availability, performance, and functional correctness of application programming interfaces to ensure seamless communication between services. This capability is essential for proactively detecting latency issues and integration failures before they impact the end-user experience.

Endpoint Health monitoring tracks the availability, latency, and error rates of specific API endpoints or application routes to ensure service reliability. This granular visibility allows teams to identify failing transactions and optimize performance before users experience degradation.

HTTP Status Monitoring tracks response codes returned by web servers to ensure application availability and reliability, allowing engineering teams to instantly detect errors and diagnose uptime issues.

Distributed tracing tracks requests as they propagate through microservices and distributed systems, enabling teams to pinpoint latency bottlenecks and error sources across complex architectures.

Transaction tracing enables teams to visualize and analyze the complete path of a request across distributed services to pinpoint latency bottlenecks and error sources. This visibility is critical for diagnosing performance issues within complex microservices architectures.

Cross-application tracing enables the visualization and analysis of transaction paths as they traverse multiple services and infrastructure components. This capability is essential for identifying latency bottlenecks and pinpointing the root cause of errors in complex, distributed architectures.

Span Analysis enables the detailed inspection of individual units of work within a distributed trace, such as database queries or API calls, to pinpoint latency bottlenecks and error sources. By aggregating and visualizing span data, teams can optimize specific operations within complex microservices architectures.

Waterfall visualization provides a graphical representation of the sequence and duration of events in a transaction or page load, essential for pinpointing bottlenecks and understanding dependency chains.

Root Cause Analysis enables engineering teams to rapidly pinpoint the underlying source of performance bottlenecks or errors within complex distributed systems by correlating traces, logs, and metrics. This capability reduces mean time to resolution (MTTR) and minimizes the impact of downtime on end-user experience.

Service dependency mapping visualizes the complex web of interactions between application components, databases, and third-party APIs to reveal how data flows through a system. This visibility is essential for IT teams to instantly isolate the root cause of performance issues and understand the downstream impact of failures in distributed architectures.

Hotspot identification automatically detects and isolates specific lines of code, database queries, or resource constraints causing performance bottlenecks. This capability enables engineering teams to rapidly pinpoint the root cause of latency without manually sifting through logs or traces.

Topology maps provide a dynamic visual representation of application dependencies and infrastructure relationships, enabling teams to instantly visualize architecture and pinpoint the root cause of performance bottlenecks.

Code profiling analyzes application execution at the method or line level to identify specific functions consuming excessive CPU, memory, or time. This granular visibility enables engineering teams to optimize resource usage and eliminate performance bottlenecks efficiently.

Thread profiling captures and analyzes the execution state of application threads to identify CPU hotspots, deadlocks, and synchronization issues at the code level. This visibility is critical for optimizing resource utilization and resolving complex latency problems that standard metrics cannot explain.

CPU Usage Analysis tracks the processing power consumed by applications and infrastructure, enabling engineering teams to identify performance bottlenecks, optimize resource allocation, and prevent system degradation.

Method-level timing captures the execution duration of individual code functions to identify specific bottlenecks within application logic. This granular visibility allows engineering teams to optimize code performance precisely rather than guessing based on high-level transaction metrics.

Deadlock detection identifies scenarios where application threads or database processes become permanently blocked waiting for one another, allowing teams to resolve critical freezes and prevent system-wide outages.

Error tracking captures and groups application exceptions in real-time, providing engineering teams with the stack traces and context needed to diagnose and resolve code issues efficiently.

Stack trace visibility provides granular insight into the sequence of function calls leading to an error or latency spike, enabling developers to pinpoint the exact line of code responsible for application failures. This capability is critical for reducing mean time to resolution (MTTR) by eliminating guesswork during debugging.

Exception aggregation consolidates duplicate error occurrences into single, manageable issues to prevent alert fatigue. This ensures engineering teams can identify high-impact bugs and prioritize fixes based on frequency rather than raw log volume.

Memory leak detection identifies application code that fails to release memory, causing performance degradation or crashes over time. This capability is critical for maintaining application stability and preventing resource exhaustion in production environments.

Garbage collection metrics track memory reclamation processes within application runtimes to identify latency-inducing pauses and potential memory leaks. This visibility is essential for optimizing resource utilization and preventing application stalls caused by inefficient memory management.

Heap dump analysis enables the capture and inspection of application memory snapshots to identify memory leaks and optimize object allocation. This feature is essential for diagnosing complex memory-related crashes and ensuring stability in production environments.

JVM Metrics provide deep visibility into the Java Virtual Machine's internal health, tracking critical indicators like memory usage, garbage collection, and thread activity to diagnose bottlenecks and prevent crashes.

CLR Metrics provide deep visibility into the .NET Common Language Runtime environment, tracking critical data points like garbage collection, thread pool usage, and memory allocation. This data is essential for diagnosing performance bottlenecks, memory leaks, and concurrency issues within .NET applications.

Network Performance Monitoring tracks metrics like latency, throughput, and packet loss to identify connectivity issues affecting application stability. This capability allows teams to distinguish between code-level errors and infrastructure bottlenecks for faster troubleshooting.

ISP Performance monitoring tracks network connectivity metrics across different Internet Service Providers to identify if latency or downtime is caused by the network rather than the application code. This visibility is crucial for diagnosing regional outages and ensuring a consistent user experience globally.

TCP/IP metrics provide critical visibility into the network layer by tracking indicators like latency, packet loss, and retransmissions to diagnose connectivity issues. This allows teams to distinguish between application-level failures and underlying network infrastructure problems.

DNS Resolution Time measures the latency involved in translating domain names into IP addresses, a critical first step in the connection process that directly impacts end-user experience and page load speeds.

SSL/TLS Monitoring tracks certificate validity, expiration dates, and configuration health to prevent security warnings and service outages. This ensures encrypted connections remain trusted and compliant without manual oversight.

Database monitoring tracks the health, performance, and query execution speeds of database instances to prevent bottlenecks and ensure application responsiveness. It is essential for diagnosing slow transactions and optimizing the data layer within the application stack.

Slow Query Analysis identifies and aggregates database queries that exceed specific latency thresholds, allowing teams to pinpoint the root cause of application bottlenecks. By correlating execution times with specific transactions, it enables targeted optimization of database performance and overall system stability.

SQL Performance monitoring tracks database query execution times, throughput, and errors to identify slow queries and optimize application responsiveness. This capability is essential for diagnosing database-related bottlenecks that impact overall system stability and user experience.

NoSQL Monitoring tracks the health, performance, and resource utilization of non-relational databases like MongoDB, Cassandra, and DynamoDB to ensure data availability and low latency. This capability is critical for diagnosing slow queries, replication lag, and throughput bottlenecks in modern, scalable architectures.

Connection pool metrics track the health and utilization of database connections, such as active usage, idle threads, and acquisition wait times. This visibility is essential for diagnosing bottlenecks, preventing connection exhaustion, and optimizing application throughput.

MongoDB monitoring tracks the health, performance, and resource usage of MongoDB databases, allowing engineering teams to identify slow queries, optimize throughput, and ensure data availability.

Infrastructure monitoring tracks the health and performance of underlying servers, containers, and network resources to ensure system stability. It allows engineering teams to correlate hardware and OS-level metrics directly with application performance issues.

Host Health Metrics track the resource utilization of underlying physical or virtual servers, including CPU, memory, disk I/O, and network throughput. This visibility allows engineering teams to correlate application performance drops directly with infrastructure bottlenecks.

Virtual machine monitoring tracks the health, resource usage, and performance metrics of virtualized infrastructure instances to ensure underlying compute resources effectively support application workloads.

Agentless monitoring enables the collection of performance metrics and telemetry from infrastructure and applications without installing proprietary software agents. This approach reduces deployment friction and overhead, providing visibility into environments where installing agents is restricted or impractical.

Lightweight agents provide deep application visibility with minimal CPU and memory overhead, ensuring that the monitoring process itself does not degrade the performance of the production environment. This feature is critical for maintaining high-fidelity observability without negatively impacting user experience or infrastructure costs.

Hybrid Deployment allows organizations to monitor applications running across on-premises data centers and public cloud environments within a single unified platform. This ensures consistent visibility and seamless tracing of transactions regardless of the underlying infrastructure.

Container monitoring provides real-time visibility into the health, resource usage, and performance of containerized applications and orchestration environments like Kubernetes. This capability ensures that dynamic microservices remain stable and efficient by tracking metrics at the cluster, node, and pod levels.

Kubernetes monitoring provides real-time visibility into the health and performance of containerized applications and their underlying infrastructure, enabling teams to correlate metrics, logs, and traces across dynamic microservices environments.

Service Mesh Support provides visibility into the communication, latency, and health of microservices managed by infrastructure layers like Istio or Linkerd. This capability allows teams to monitor traffic flows and enforce security policies without requiring instrumentation within individual application code.

Microservices monitoring provides visibility into distributed architectures by tracking the health, dependencies, and performance of individual services and their interactions. This capability is essential for identifying bottlenecks and troubleshooting latency issues across complex, containerized environments.

Docker Integration enables the monitoring of containerized environments by tracking resource usage, health status, and performance metrics across Docker instances. This visibility allows teams to correlate infrastructure constraints with application bottlenecks in real-time.

Serverless monitoring provides visibility into the performance, cost, and health of functions-as-a-service (FaaS) workloads like AWS Lambda or Azure Functions. This capability is critical for debugging cold starts, optimizing execution time, and tracing distributed transactions across ephemeral infrastructure.

AWS Lambda Support provides deep visibility into serverless function performance by tracking execution times, cold starts, and error rates within a distributed architecture. This capability is essential for troubleshooting complex serverless environments and optimizing costs without managing underlying infrastructure.

Azure Functions support provides critical visibility into serverless applications running on Microsoft Azure, allowing teams to monitor execution times, cold starts, and failure rates. This capability is essential for troubleshooting distributed, event-driven architectures where traditional server monitoring is insufficient.

Cache monitoring tracks the health and efficiency of caching layers, such as Redis or Memcached, to optimize data retrieval speeds and reduce database load. It provides critical visibility into hit rates, latency, and eviction patterns necessary for maintaining high-performance applications.

Redis monitoring tracks critical metrics like memory usage, cache hit rates, and latency to ensure high-performance data caching and storage. It allows engineering teams to identify bottlenecks, optimize configuration, and prevent application slowdowns caused by cache failures.

Message queue monitoring tracks the health and performance of asynchronous messaging systems like Kafka, RabbitMQ, or SQS to prevent bottlenecks and data loss. It provides visibility into queue depth, consumer lag, and throughput, ensuring decoupled services communicate reliably.

Kafka Integration enables the monitoring of Apache Kafka clusters, topics, and consumer groups to track throughput, latency, and lag within event-driven architectures. This visibility is critical for diagnosing bottlenecks and ensuring the reliability of real-time data streaming pipelines.

RabbitMQ integration enables the monitoring of message broker performance, tracking critical metrics like queue depth, throughput, and latency to ensure stability in asynchronous architectures. This visibility helps engineering teams rapidly identify bottlenecks and consumer lag within distributed systems.

Middleware monitoring tracks the performance and health of intermediate software layers like message queues, web servers, and application runtimes to ensure smooth data flow between systems. This visibility helps engineering teams detect bottlenecks, queue backups, and configuration issues that impact overall application reliability.

Log management involves the centralized collection, aggregation, and analysis of application and infrastructure logs to enable rapid troubleshooting and root cause analysis. It allows engineering teams to correlate system events with performance metrics to maintain application reliability.

Log aggregation centralizes log data from distributed services, servers, and applications into a single searchable repository, enabling engineering teams to correlate events and troubleshoot issues faster.

Contextual logging correlates raw log data with traces, metrics, and request metadata to provide a unified view of application behavior. This integration allows developers to instantly pivot from performance anomalies to specific log lines, significantly reducing the time required to diagnose root causes.

Log-to-Trace Correlation connects application logs directly to distributed traces, allowing engineers to view the specific log entries generated during a transaction's execution. This context is critical for debugging complex microservices issues by pinpointing exactly what happened at the code level during a specific request.

Live Tail provides a real-time view of log data as it is ingested, allowing engineers to watch events unfold instantly. This feature is essential for debugging active incidents and monitoring deployments without the latency of standard indexing.

Structured logging captures log data in machine-readable formats like JSON, enabling developers to efficiently query, filter, and aggregate specific fields rather than parsing unstructured text. This capability is critical for rapid debugging and correlating events across distributed systems.

Anomaly detection automatically identifies deviations from historical performance baselines to surface potential issues without manual threshold configuration. This capability allows engineering teams to proactively address performance regressions and reliability incidents before they impact end users.

Dynamic baselining automatically calculates expected performance ranges based on historical data and seasonality, allowing teams to detect anomalies without manually configuring static thresholds. This reduces alert fatigue by distinguishing between normal traffic spikes and genuine performance degradation.

Predictive analytics utilizes historical performance data and machine learning algorithms to forecast potential system bottlenecks and anomalies before they impact end-users. This capability allows engineering teams to shift from reactive troubleshooting to proactive capacity planning and incident prevention.

Smart Alerting utilizes machine learning and dynamic baselining to detect anomalies and distinguish critical incidents from system noise, reducing alert fatigue for engineering teams. By correlating events and automating threshold adjustments, it ensures notifications are actionable and relevant.

Noise reduction capabilities filter out false positives and correlate related events, ensuring engineering teams focus on actionable insights rather than being overwhelmed by alert fatigue.

Automated remediation enables the system to autonomously trigger corrective actions, such as restarting services or scaling resources, when performance anomalies are detected. This capability significantly reduces downtime and mean time to resolution (MTTR) by handling routine incidents without human intervention.

Pattern recognition utilizes machine learning algorithms to automatically identify recurring trends, anomalies, and correlations within telemetry data, enabling teams to proactively address performance issues before they escalate.

An alerting system proactively notifies engineering teams when performance metrics deviate from established baselines or errors occur, ensuring rapid incident response and minimizing downtime.

Incident management enables engineering teams to detect, triage, and resolve application performance issues efficiently to minimize downtime. It centralizes alerting, on-call scheduling, and response workflows to ensure service level agreements (SLAs) are maintained.

Jira integration enables engineering teams to seamlessly create, track, and synchronize issue tickets directly from performance alerts and error logs. This capability streamlines incident response by bridging the gap between technical observability data and project management workflows.

PagerDuty Integration allows the APM platform to automatically trigger incidents and notify on-call teams when performance thresholds are breached. This ensures critical system issues are immediately routed to the right responders for rapid resolution.

Slack integration allows APM tools to push real-time alerts and performance metrics directly into team channels, facilitating faster incident response and collaborative troubleshooting.

Webhook support enables the APM platform to send real-time HTTP callbacks to external systems when specific events or alerts are triggered, facilitating automated incident response and seamless integration with third-party tools.

Custom dashboards allow engineering teams to visualize specific metrics, logs, and traces relevant to their unique application architecture. This flexibility ensures stakeholders can monitor critical KPIs and correlate data points without being restricted to generic, pre-built views.

Historical Data Analysis enables teams to retain and query performance metrics over extended periods to identify long-term trends, seasonality, and regression patterns. This capability is essential for accurate capacity planning, compliance auditing, and debugging intermittent issues that span weeks or months.

Real-time visualization provides live, streaming dashboards of application metrics and traces, allowing engineering teams to spot anomalies and react to incidents the instant they occur. This capability ensures performance monitoring reflects the immediate state of the system rather than delayed historical averages.

Heatmaps provide a visual aggregation of system performance data, enabling engineers to instantly identify outliers, latency patterns, and resource bottlenecks across complex infrastructure. This visualization is essential for detecting anomalies in high-volume environments that standard line charts often obscure.

PDF Reporting enables the export of performance metrics and dashboards into portable documents, facilitating offline sharing and compliance documentation. This feature ensures stakeholders receive consistent snapshots of system health without requiring direct access to the monitoring platform.

Scheduled reports allow teams to automatically generate and distribute performance summaries, uptime statistics, and error rate trends to stakeholders at predefined intervals. This ensures critical metrics are visible to management and engineering teams without requiring manual dashboard checks.

Auto-discovery automatically identifies and maps application services, infrastructure components, and dependencies as soon as an agent is installed, eliminating manual configuration to ensure real-time visibility into dynamic environments.

Capacity planning enables teams to forecast future resource requirements based on historical usage trends, ensuring infrastructure scales efficiently to meet demand without over-provisioning.

Tagging and Labeling allow users to attach metadata to telemetry data and infrastructure components, enabling precise filtering, aggregation, and correlation across complex distributed systems.

Data granularity defines the frequency and resolution at which performance metrics are collected and stored, determining the ability to detect transient spikes. High-fidelity data is essential for identifying micro-bursts and anomalies that are often hidden by averages in lower-resolution monitoring.

Data retention policies allow organizations to define how long performance data, logs, and traces are stored before being deleted or archived, which is critical for compliance, historical analysis, and cost management.

Role-Based Access Control (RBAC) enables organizations to define granular permissions for viewing performance data and modifying configurations based on user responsibilities. This ensures operational security by restricting sensitive telemetry and administrative actions to authorized personnel.

Single Sign-On (SSO) enables users to authenticate using centralized credentials from an existing identity provider, ensuring secure access control and simplifying user management. This capability is essential for maintaining security compliance and reducing administrative overhead by eliminating the need for separate platform-specific passwords.

Data masking automatically obfuscates sensitive information, such as PII or financial details, within application traces and logs to ensure security compliance. This capability protects user privacy while allowing teams to debug and monitor performance without exposing confidential data.

PII Protection safeguards sensitive user data by detecting and redacting personally identifiable information within application traces, logs, and metrics. This ensures compliance with privacy regulations like GDPR and HIPAA while maintaining necessary visibility into system performance.

GDPR Compliance Tools provide essential mechanisms within the APM platform to detect, mask, and manage personally identifiable information (PII) embedded in monitoring data. These features ensure organizations can adhere to data privacy regulations regarding data residency, retention, and the right to be forgotten without sacrificing observability.

Audit trails provide a chronological record of user activities and configuration changes within the APM platform, ensuring accountability and aiding in security compliance and troubleshooting.

Multi-tenancy enables a single APM deployment to serve multiple distinct teams or customers with strict data isolation and access controls. This architecture ensures that sensitive performance data remains segregated while efficiently sharing underlying infrastructure resources.

Cloud integration enables the APM platform to seamlessly ingest metrics, logs, and traces from public cloud providers like AWS, Azure, and GCP. This capability is essential for correlating application performance with the health of underlying infrastructure in hybrid or multi-cloud environments.

OpenTelemetry support enables the collection and export of telemetry data—metrics, logs, and traces—in a vendor-neutral format, allowing teams to instrument applications once and route data to any backend. This capability is critical for preventing vendor lock-in and standardizing observability practices across diverse technology stacks.

OpenTracing Support allows the APM platform to ingest and visualize distributed traces from the vendor-neutral OpenTracing API, enabling teams to instrument code once without vendor lock-in. This capability is essential for maintaining visibility across heterogeneous microservices architectures where proprietary agents may not be feasible.

Prometheus integration allows the APM platform to ingest, visualize, and alert on metrics collected by the open-source Prometheus monitoring system, unifying cloud-native observability data in a single view.

Grafana Integration enables the seamless export and visualization of APM metrics within Grafana dashboards, allowing engineering teams to unify observability data and customize reporting alongside other infrastructure sources.

CI/CD integration connects the APM platform with deployment pipelines to correlate code releases with performance impacts, enabling teams to pinpoint the root cause of regressions immediately. This capability is essential for maintaining stability in high-velocity engineering environments.

A Jenkins plugin integrates CI/CD workflows with the monitoring platform, allowing teams to correlate performance changes directly with specific deployments. This visibility is crucial for identifying the root cause of regressions immediately after code is pushed to production.

Deployment markers visualize code releases directly on performance charts, allowing engineering teams to instantly correlate changes in application health, latency, or error rates with specific software updates.

Version comparison enables engineering teams to analyze performance metrics across different application releases side-by-side to identify regressions. This capability is essential for validating the stability of new deployments and facilitating safe rollbacks.

Regression detection automatically identifies performance degradation or error rate increases introduced by new code deployments or configuration changes. This capability allows engineering teams to correlate specific releases with stability issues, ensuring rapid remediation or rollback before users are significantly impacted.

Configuration tracking monitors changes to application settings, infrastructure, and deployment manifests to correlate modifications with performance anomalies. This capability is crucial for rapid root cause analysis, as configuration errors are a frequent source of service disruptions.