¶ New Relic
¶ Operating Systems
¶ Introduction to New Relic: Observability as a Pillar of Modern Operating Environments
In the wide landscape of modern computing, the operating system has traditionally been understood as the central orchestrator of hardware, memory, processes, and storage. It stands as the substrate upon which applications run and through which users and programs interact with the underlying machinery. Yet as our systems have grown more complex—distributed across data centers, cloud platforms, containers, virtual machines, and networks—the traditional boundaries of operating systems have blurred. Today, the “system” is no longer a single machine or kernel. It is often an interconnected web of components whose behavior must be observed, understood, and managed holistically.
In this new world, tools like New Relic have become essential to the craft of operating systems, not because they replace the OS, but because they provide the visibility required to understand systems at scale. Observability has emerged as a foundational discipline—one that complements operating-system knowledge by allowing engineers to see how systems behave in real time, diagnose faults, ensure performance, and maintain the reliability necessary for modern digital experiences.
New Relic is one of the pioneering platforms in this domain. It sits at the intersection of operating-system behavior, application performance, user experience, and distributed infrastructure. While traditionally categorized as an observability and application monitoring platform, New Relic belongs in the broader study of operating systems because it surfaces exactly what operating systems manage: CPU scheduling, memory usage, disk I/O, system calls, network behavior, container orchestration, process health, and countless interactions within complex environments.
This introductory article is the beginning of a 100-article course devoted to New Relic as a conceptual and practical bridge between operating systems and modern observability. The goal here is not to dive into dashboards or metrics—that will come with time—but to understand why tools like New Relic have become indispensable, what philosophical and technical ideas underpin observability, and how New Relic reshapes the way engineers interact with systems.
¶ The Rise of Complexity: Why Observability Became Necessary
The earliest operating systems were self-contained. Engineers could understand their performance by inspecting logs, watching CPU graphs, and analyzing memory snapshots. A single machine contained the entire system, and the flow of execution was largely linear.
That world is gone.
Today’s operating environments are layered and dynamic:
- applications span dozens or hundreds of servers
- microservices communicate across ephemeral networks
- containers spin up and disappear within seconds
- user activity flows through multi-region infrastructures
- cloud platforms abstract hardware into virtual constructs
- operating systems themselves run inside orchestrated clusters
In such environments, the OS remains vital—but understanding what it is doing requires a broader set of tools. Observability platforms emerged not to replace system-level understanding but to illuminate it across distributed contexts. New Relic stepped into this domain early, recognizing that modern systems required more than logs or metrics alone. They needed unified insight: correlations, context, and real-time visibility across all layers of the stack.
Observability became the next frontier of operating-system awareness.
¶ New Relic’s Vision: Making Systems Understandable
At its core, New Relic exists to answer a deceptively simple question:
“What is my system doing, and why?”
Traditional monitoring answered only the “what”: CPU is high, memory is low, or traffic is rising. New Relic’s advancement was in addressing the “why”: correlations among services, relationships between transactions, hidden bottlenecks, cascading latency, degraded components, and anomalies in real time.
This conceptual shift marks the transition from monitoring to observability. Monitoring watches; observability explains.
The philosophy behind New Relic is built on several core beliefs:
¶ 1. Systems cannot be treated as black boxes.
Modern environments are too interconnected and ephemeral for blind assumptions. Engineers must observe systems through data rather than prediction or guesswork.
¶ 2. Metrics alone are not enough.
Logs, traces, and events must interact meaningfully. New Relic integrates these components into a coherent narrative of system behavior.
¶ 3. Insight should be accessible to all stakeholders.
Observability is not only for system administrators—it is for developers, SREs, operations teams, and decision-makers who require visibility into system health.
¶ 4. Performance and user experience are inseparable.
New Relic treats user interactions as first-class data sources, linking real-world experience to internal system behavior.
¶ 5. Observability must scale.
The platform is designed to handle vast, distributed architectures without losing coherence or precision.
These principles anchor New Relic’s approach and form the basis for understanding why it plays a role within the broader study of operating systems.
¶ The Convergence of Operating Systems and Observability
While operating systems manage local resources—memory, disks, processes—modern applications operate in environments where those resources are distributed or virtualized. Cloud platforms complicate the definition of "system." Containers encapsulate applications in lightweight isolation. Network behavior impacts performance as deeply as CPU scheduling.
To study operating systems today, one must study the mechanisms by which we observe them.
New Relic acts as an interpretive lens, making OS-level signals meaningful within broader contexts:
- CPU spikes connect to slow database calls
- memory pressure reveals inefficient caching
- disk I/O saturation correlates with specific workloads
- network latency maps to external APIs or microservices
- container restarts uncover flawed deployments
- anomalous logs reflect unexpected system behaviors
New Relic bridges the gap between traditional OS internals and distributed systems dynamics, offering a way to reason about infrastructure as a cohesive whole.
¶ The Evolution of New Relic as a Platform
When New Relic launched, its primary focus was application performance monitoring (APM). It gave developers insight into server-side transactions in a way that had previously required specialized expertise. As system architectures evolved, New Relic expanded its scope:
- infrastructure monitoring
- distributed tracing
- real-user monitoring (RUM)
- synthetic testing
- log analytics
- anomaly detection
- dashboards and visualizations
- cloud and container observability
- event correlation
- full-stack insights
This expansion transformed New Relic from a developer tool into an operating environment for observability—a platform that offers a unified perspective on everything from host-level metrics to browser performance to Kubernetes cluster activity.
New Relic’s evolution mirrors the evolution of computing itself: from monolithic applications on single servers to global, dynamic systems requiring continuous insight and correlation.
¶ A Cultural Shift: Observability as an Engineering Discipline
One of New Relic’s less obvious but most significant impacts is cultural. It changed how development teams think about system behavior.
In earlier eras, performance tuning was often reactive. Teams waited for issues to arise—outages, slowdowns, or user complaints—before investigating. Observability platforms like New Relic reoriented engineering practice toward proactive insight:
- teams diagnose issues before users feel them
- patterns of degradation are detected early
- deployments are validated through observable signals
- performance regressions become immediately visible
- architecture decisions rely on empirical data
- engineers collaborate with a shared view of system health
This cultural transformation matters deeply in the study of operating systems. It moves operational awareness from the periphery to the center of engineering practice.
¶ Why New Relic Matters Today
In an age defined by cloud-native architectures, container orchestration, hybrid infrastructure, and constant scaling, systems must be understood dynamically. Downtime is costly; slow performance erodes trust; distributed failures demand fast diagnosis.
New Relic matters because:
- it links system behavior to user experience
- it exposes hidden dependencies across services
- it reveals bottlenecks in real time
- it clarifies the relationships among logs, metrics, and traces
- it supports systems that span regions, providers, architectures
- it creates a unified language for discussing performance
- it enables engineers to test hypotheses about system behavior
- it reduces uncertainty in complex operating environments
In modern computing, insight is as important as infrastructure. New Relic provides that insight.
¶ Framing the Course Ahead: A Deep Exploration of Observability Systems
This 100-article course will examine New Relic through multiple lenses: technical, conceptual, architectural, operational, and philosophical. Though New Relic is a product, it embodies broader ideas about how systems should be understood. Studying it therefore provides both practical knowledge and theoretical insight.
Throughout the course, readers will explore:
- the conceptual foundations of observability
- how operating systems expose metrics to observability platforms
- how New Relic collects and interprets data
- distributed tracing as a lens on microservice ecosystems
- infrastructure monitoring and OS-level integration
- the role of agents, APIs, and instrumentation
- the interplay of logs, metrics, and events
- anomaly detection and data-driven diagnostics
- performance modeling and real-user monitoring
- visualization, dashboards, and communication of insights
- the evolution of distributed systems monitoring
- how cloud and container ecosystems reshape observability
The course will also explore the philosophical dimension of modern system operations—the idea that visibility is a fundamental requirement of reliability and that observability is now part of the everyday craft of engineering.
By the end of this course, readers will possess not only technical proficiency with New Relic but a renewed perspective on operating systems as living, observable entities shaped by dynamic interactions across many levels of the stack.
¶ Conclusion
New Relic represents more than a monitoring tool—it represents a shift in how we understand systems. It stands at the intersection of operating-system behavior, distributed architectures, application performance, and user experience. In a world where systems are no longer confined to a single machine or environment, observability becomes indispensable.
To study New Relic is to study the art and science of making complex systems understandable. It is to explore the signals, relationships, behaviors, and insights that allow engineers to maintain reliability in environments that are constantly changing. It is to appreciate the importance of correlation, context, and visibility in modern computing.
As this course unfolds, readers will gain deep insight into the operating foundations of observability and the role New Relic plays in shaping the stability and performance of contemporary systems. Ultimately, this journey is not just about learning a platform—it is about cultivating the mindset required to understand the systems that underpin today’s digital world.
¶ New Relic
I. Foundations of New Relic and OS Monitoring (1-20)
1. Introduction to New Relic: APM and Infrastructure Monitoring
2. Understanding the New Relic Platform and its Capabilities
3. Setting up Your New Relic Account and Agent
4. Installing and Configuring the New Relic Infrastructure Agent
5. Key Metrics for OS Performance Monitoring
6. Understanding CPU Utilization: Metrics and Interpretation
7. Memory Management in Operating Systems and New Relic Metrics
8. Disk I/O Performance: Monitoring and Troubleshooting
9. Network Performance Monitoring with New Relic
10. Process Monitoring: Identifying Resource Hogs
11. Log Management with New Relic: Centralized Log Analysis
12. Alerting and Notifications: Setting Up Thresholds and Triggers
13. Dashboards and Visualizations: Creating Custom Views
14. Introduction to New Relic Query Language (NRQL)
15. Understanding New Relic Events
16. New Relic Integrations with Cloud Providers (AWS, Azure, GCP)
17. New Relic Integrations with Container Orchestrators (Kubernetes, Docker)
18. New Relic for Serverless Monitoring
19. Getting Started with New Relic Documentation and Resources
20. Best Practices for New Relic Deployment and Configuration
II. CPU Monitoring and Analysis (21-35)
21. CPU Utilization Metrics: User, System, Idle, Iowait
22. CPU Saturation and Queue Length
23. Context Switching and its Impact on Performance
24. Identifying CPU Bottlenecks
25. Analyzing CPU Usage by Process
26. CPU Profiling with New Relic
27. CPU Scheduling and its Effects
28. Multi-Core CPU Performance Analysis
29. CPU Caching and its Impact
30. NUMA Architecture and Performance
31. Troubleshooting High CPU Utilization
32. Optimizing CPU Usage
33. CPU Steal Time in Virtualized Environments
34. CPU Throttling and its Detection
35. Advanced CPU Performance Analysis Techniques
III. Memory Monitoring and Management (36-50)
36. Memory Usage Metrics: Used, Free, Cached, Buffers
37. Virtual Memory and Swapping
38. Memory Leaks: Detection and Diagnosis
39. Memory Fragmentation
40. Out-of-Memory (OOM) Errors: Troubleshooting and Prevention
41. Memory Allocation and Deallocation
42. Garbage Collection and its Impact on Performance
43. Memory Mapping and Shared Memory
44. Memory Tuning and Optimization
45. Memory Pressure and its Detection
46. Memory Ballooning in Virtualized Environments
47. Memory Limits and Cgroups
48. Memory Analysis Tools and Techniques
49. Memory Forensics
50. Advanced Memory Management Concepts
IV. Disk I/O Monitoring and Performance (51-65)
51. Disk I/O Metrics: Read/Write Operations, Latency, Throughput
52. Disk Space Utilization and Fragmentation
53. RAID Levels and their Performance Implications
54. Disk Scheduling Algorithms
55. Identifying Disk I/O Bottlenecks
56. Analyzing Disk I/O by Process
57. File System Performance
58. Network File Systems (NFS) Performance
59. Storage Area Networks (SAN) Performance
60. Solid State Drives (SSDs) vs. Hard Disk Drives (HDDs)
61. Disk I/O Tuning and Optimization
62. Asynchronous I/O
63. Direct I/O
64. Disk I/O Queues and Saturation
65. Advanced Disk I/O Performance Analysis
V. Network Monitoring and Troubleshooting (66-80)
66. Network Interface Metrics: Traffic, Errors, Drops
67. Network Protocols and their Performance
68. Network Latency and Packet Loss
69. Network Bandwidth and Throughput
70. Network Congestion and its Detection
71. Network Security Monitoring
72. DNS Performance Monitoring
73. Load Balancing and its Monitoring
74. Firewall Performance Monitoring
75. Network Troubleshooting Tools and Techniques
76. Network Traffic Analysis
77. TCP/IP Performance Tuning
78. Network Virtualization and its Monitoring
79. Software-Defined Networking (SDN) Monitoring
80. Advanced Network Performance Analysis
VI. Process Monitoring and Management (81-90)
81. Process States and Lifecycle
82. Process Resource Consumption: CPU, Memory, Disk, Network
83. Process Dependencies and Interactions
84. Identifying Zombie Processes and Orphan Processes
85. Process Profiling and Tracing
86. Process Management Tools and Techniques
87. System Calls and their Monitoring
88. Container Monitoring with New Relic
89. Process Security and Isolation
90. Advanced Process Management Concepts
VII. Log Management and Analysis (91-95)
91. Log Collection and Aggregation
92. Log Parsing and Indexing
93. Log Search and Analysis
94. Log-Based Alerting and Notifications
95. Log Correlation and Event Correlation
VIII. Advanced New Relic and OS Topics (96-100)
96. Custom Instrumentation and Metrics
97. New Relic APIs and Integrations
98. New Relic for Distributed Tracing
99. Automating OS Monitoring with New Relic
100. Advanced New Relic Query Language (NRQL) Techniques