September 2024 Summaries

New Relic has introduced a new Cardinality Management UI to help users diagnose and solve metric cardinality issues, allowing them to view account usage over time, get a metric-level breakdown, and prune high cardinality attributes to free up cardinality budget. The feature aims to prevent visual data gaps in time series charts and provide more control over cardinality. With the new Cardinality Management UI, users can easily manage their cardinality, understand which metrics are driving the most usage, and dive deeper into a metric's attributes to make informed decisions about data management.

The OpenTelemetry Collector is a crucial tool for utilizing OpenTelemetry to its full potential and getting the most out of application and system data. It's an executable file that can collect and modify telemetry, then send it to one or more backends, including New Relic, providing a highly configurable and extensible way to handle observability data within modern cloud-native environments. The Collector is modular, with customizable data pipelines for traces, logs, and metrics, allowing users to add components that enhance, filter, and batch their data. It can receive data in different formats from various sources and over different protocols, and export data in different supported formats, including New Relic's OpenTelemetry data natively. Users can define multiple exporters if needed, and use connectors to send data between pipelines. The Collector has multiple deployment options, including a single instance or as part of a gateway or agent deployment. It also has two official distributions: core and contrib, with the core distro being lightweight and efficient, and the contrib distro providing additional features but at varying levels of maturity. Users can monitor the Collector's health and performance using internal telemetry, including basic metrics exposed via Prometheus and logs emitted to stderr. To get started, users can use the New Relic fork of the OpenTelemetry demo app or join the Collector Special Interest Group on CNCF's Slack instance for additional support and resources.

ChatGPT o1 is a significant advancement in AI that brings its own set of challenges, including higher computational costs and slower processing times. To unlock its full potential, organizations need robust AI monitoring solutions like New Relic AI Monitoring to ensure optimal performance, maintain regulatory compliance, and harness the model's advanced capabilities for early detection of issues, efficient root cause analysis, and continuous performance monitoring. The model's enhanced transparency, fortified data security, reinforcement learning, and chain-of-thought processing enable it to provide a more robust and reliable performance across various applications, addressing core challenges faced by previous AI models. However, the introduction of ChatGPT o1 also brings new complexities, such as limited available resources on best practices for integration and trial-and-error testing for refined monitoring protocols. Robust monitoring is essential to navigate these complexities and ensure responsible AI deployment, aligning technological prowess with ethical and operational standards.

ChatGPT o1 represents a significant leap in artificial intelligence, offering advanced features such as enhanced transparency and data security, as well as improved problem-solving through reinforcement learning and chain-of-thought processing. Despite its potential, it poses challenges like higher computational costs and slower processing times, necessitating robust AI monitoring solutions like New Relic AI Monitoring to ensure optimal performance and regulatory compliance. The model's ability to analyze data critically enhances observability, enabling early detection of issues and proactive resolutions, which are crucial for maintaining system reliability and efficiency. However, balancing AI's innovative capabilities with responsible deployment is essential, as is the need for adaptive monitoring strategies to manage performance unpredictability, cost management, and data privacy. As AI technology continues to evolve, embracing comprehensive monitoring infrastructure becomes indispensable for aligning technological advances with ethical standards and preparing for future breakthroughs.

OpenTelemetry is recognized as a key open standard for instrumentation in the software observability domain, with the OpenTelemetry Collector being a vital component for maximizing its use in cloud-native environments. The Collector is an executable that collects, processes, and exports telemetry data to various backends, offering modular and customizable data pipelines for traces, logs, and metrics. It comprises several components, such as receivers, processors, exporters, and connectors, enabling flexible data handling. The Collector can be deployed in different architectures, such as a gateway or agent, and comes in two official distributions: core and contrib. Monitoring the Collector's health and performance is essential, with key metrics including resource utilization and processing latency. Users can experiment with the Collector through New Relic's OpenTelemetry demo app and engage with the community for further learning and contribution opportunities.

New Relic introduces a new cardinality management UI aimed at addressing metric cardinality issues, which can lead to visible data gaps when daily limits are exceeded. The platform, known for its generous metric cardinality limits, still enforces reasonable restrictions to maintain system performance. The updated UI allows users to easily monitor account usage over time, understand metrics driving high cardinality, and manage attributes by pruning those with high cardinality to free up resources. This management interface simplifies previously complex tasks, enabling users to diagnose and resolve cardinality issues with a few clicks, enhancing control over data insights. While high cardinality allows for detailed analysis, it can challenge data volume and query performance, so New Relic's approach ensures optimized system functionality while retaining raw data for queries even when rollups are halted.

This post focuses on data pipeline observability using platforms like New Relic to improve observability practices in modern digital businesses where data is crucial for decision-making. The author shares a framework and practical tips for implementing data pipeline observability, including knowing goals, understanding data, starting small, looking for quick wins, and leveraging platforms like New Relic for enterprise success. By following this approach, organizations can ensure accurate data capture, automate data quality checks, and improve overall pipeline performance.

Data pipeline observability is crucial for modern digital businesses, especially those using consumption-based models like New Relic, to ensure accurate billing and data quality. The article discusses the importance of setting specific goals, such as monitoring service level agreements and detecting anomalies, and emphasizes understanding the data's structure and volume for effective observability. It highlights starting with small, high-impact areas and leveraging tools like New Relic to implement observability practices. The use of custom metrics and events in New Relic allows for real-time monitoring and can provide quick wins by identifying macro-level deviations and aiding in capacity planning. Additionally, the article suggests using simple techniques, such as comparing sample data outputs to expected values, to verify pipeline functionality. Overall, the piece outlines a framework for improving data pipeline observability, offering practical tips and underscoring the role platforms like New Relic play in facilitating these practices.

The Oracle database architecture has undergone significant changes with the release of version 12c, moving from single-tenant databases to multitenant databases. This shift has resulted in changes to the New Relic database monitoring integration to ensure compatibility and provide necessary metrics for both application teams and database administrators. The updates have been made to accommodate the introduction of container databases like Exadata Cloud at Customer (ExaCC) by Oracle, enabling monitoring on host integration to work seamlessly with these databases. The New Relic Oracle Database integration has been redesigned to support both single and multitenant databases, allowing for flexible configuration settings that cater to different user needs, including setting the "SYS_METRICS_SOURCE" flag to connect to either `gv$con_sysmetric` or `gv$sysmetric`. To set up the integration, administrators must provision a database user with specific privileges, deploy the OHI integration, and configure settings according to the required data scope. Once complete, users can log in to the New Relic platform to view instrumented Oracle databases and access performance metrics through curated views or Data Explorer.

Since the release of Oracle Database version 12c, the architecture has transitioned from single-tenant to multitenant databases, introducing pluggable containers (PDBs) for application-specific data and root containers (CDBs) for system-wide performance data. This architectural shift led to adaptations in New Relic's database monitoring integration, ensuring compatibility with both single and multitenant Oracle databases by configuring the “SYS_METRICS_SOURCE” flag to access appropriate metrics from either PDBs or CDBs, thus enhancing observability for application teams and database administrators. Furthermore, changes in Oracle's database structure, such as the migration of system metrics to gv$con_sysmetric, required updates to New Relic’s on-host integration, which now requires specific setup steps, including user provisioning and configuration adjustments, to enable seamless access to performance metrics on the New Relic platform.

The New Relic fork of the OpenTelemetry Astronomy Shop Demo application has been updated with new features and improvements, including feature flags that simulate various scenarios, such as errors on specific calls, memory leaks, and high CPU load. The demo app can be deployed using Helm charts, and users can view the web store and load generator UIs once the app is deployed. The New Relic platform provides tools to troubleshoot issues with OpenTelemetry data, including the workloads and errors inbox feature. Additionally, a new Helm chart allows users to monitor their Kubernetes cluster with the OpenTelemetry Collector, providing enhanced monitoring capabilities for Kubernetes applications. The demo app also includes a roadmap for future development and maintenance, and users are encouraged to provide feedback and suggestions through the repository.

This blog post details the process of deploying the New Relic fork of the OpenTelemetry Community demo application in Kubernetes, emphasizing the use of Helm charts to facilitate monitoring through the OpenTelemetry Collector. It explains modifications made to the demo app, such as incorporating environment variables specific to New Relic and offering demo scenarios via feature flags managed by a service called flagd. The post guides users through enabling feature flags to simulate errors and how to navigate data using New Relic's platform, demonstrating how errors impact service metrics. Furthermore, it introduces the use of the nr-k8s-otel-collector Helm chart for monitoring Kubernetes clusters, which is part of a preview program and offers insights into ongoing and planned developments for the fork. The author, Reese Lee, also provides additional resources for learning more about OpenTelemetry and New Relic's integration, while clarifying that the views expressed are her own and not necessarily those of New Relic.

SAP systems are typically managed independently due to their strategic importance and complexity, but existing monitoring tools have presented challenges such as slow resolution to system or process issues, which can be attributed to manual processes, multiple logins, data silos, and continuous changes. To address these challenges, SAP customers are evolving from traditional monitoring to intelligent observability, which integrates large volumes of data with artificial intelligence for additional insights. This approach enables proactive mitigation of system disruptions, visual assessment of system-wide health at a glance, and faster troubleshooting through end-to-end observability. New Relic's all-in-one observability platform is designed to help SAP customers excel by providing out-of-the-box dashboards and correlating infrastructure health, application performance, and digital customer experience in a single location.

New Relic monitoring for SAP solutions provides end-to-end visibility and traceability across entire business processes, enabling organizations to quickly identify root causes of performance problems and bottlenecks. With New Relic's intelligent observability capabilities, businesses can optimize Fiori application response times and experiences, eliminate manual troubleshooting efforts, and improve operational efficiency by reducing Mean Time To Detect (MTTD), Mean Time To Acknowledge (MTTA), and Mean Time To Resolve (MTTR) metrics. By adopting this approach, organizations can expedite resolutions, ensure better business process uptime, and minimize negative financial impacts resulting from SAP infrastructure issues.

SAP systems, due to their strategic importance and complexity, are typically managed by specialized SAP Basis administrators, yet current monitoring tools pose challenges such as slow issue resolution due to manual processes, multiple logins, data silos, and frequent system changes. The document suggests that evolving from traditional monitoring to intelligent observability, which combines monitoring with artificial intelligence to process large data volumes, can enhance system management by providing a centralized view of infrastructure health and application performance, thereby speeding up troubleshooting. New Relic's solutions, in conjunction with SAP's RISE cloud service, allow organizations to optimize system performance by providing comprehensive performance data and enabling easy visualization of system health through out-of-the-box dashboards. This approach helps organizations proactively manage system disruptions, correlate alerts across different systems, and maintain visibility across their entire ecosystem, whether on-premises or in the cloud, without the need for extensive manual interventions.

Organizations using SAP for critical business processes face challenges such as IDoc errors, RFC communication failures, and batch job delays, which can disrupt operations and negatively impact finances. Manual troubleshooting typically slows resolution times, especially when business and IT teams are not aligned. New Relic offers a solution through its monitoring for SAP, enabling quick identification of performance issues and bottlenecks by providing end-to-end traceability from the SAP Fiori frontend to the backend systems. This approach enhances operational efficiency by reducing key metrics such as Mean Time to Detect (MTTD), Mean Time to Acknowledge (MTTA), and Mean Time to Resolve (MTTR) through AI-powered alerts and centralized dashboards. By transitioning from traditional monitoring to intelligent observability, New Relic helps organizations optimize SAP business processes, providing less system overhead and faster root cause identification.

The guide explores how to achieve "observability as code" for AI applications using Pulumi and New Relic. It starts by introducing the challenges of monitoring complex AI applications and then presents a solution that combines Pulumi's infrastructure-as-code platform with New Relic's intelligent observability platform. The approach enables teams to define AI and large language model (LLM) monitoring instrumentation along with cloud resources programmatically, secure API keys and cloud account credentials, and automatically deploy New Relic instrumentation alongside AI applications and infrastructure. Benefits include consistent monitoring across environments, version-controlled observability configuration, easier detection of performance issues, deeper insights into AI model behavior and resource usage. The guide also introduces Pulumi's products and services, including Pulumi Cloud, Pulumi Environments, Secrets, and Configuration (ESC), and demonstrates how to use Pulumi Copilot to generate Python code for infrastructure definitions. It provides a step-by-step process for deploying the chat application to AWS using Pulumi, configuring New Relic agents with AI, managing secrets with Pulumi ESC, generating infrastructure code with Pulumi Copilot, and deploying the application with Pulumi. The guide concludes by exploring New Relic's AI LLM dashboards, including AI Response metrics, AI Model comparison, and OpenAI custom dashboards.

This guide helps businesses monitor and optimize the performance of their chatbots using New Relic AI monitoring. The process involves setting up a chatbot application, integrating New Relic for real-time monitoring, tracking key performance metrics, identifying and resolving performance issues, monitoring token usage and optimization, and adopting techniques to lower token consumption and save on operational costs. By following these steps, businesses can ensure their chatbots deliver a smooth and efficient user experience while maintaining high levels of service quality as user demands grow.

Integrating New Relic's observability platform with Pulumi's infrastructure-as-code enables a comprehensive "observability as code" approach for AI applications, allowing teams to programmatically define monitoring instrumentation, securely manage credentials, and automatically deploy New Relic tools alongside AI infrastructures. This method provides consistent monitoring across environments, facilitates version-controlled observability configurations, and enhances detection of performance issues and insights into AI model behavior. Pulumi offers tools like infrastructure as code (IaC), cloud services, and Environments, Secrets, and Configuration (ESC) for secure management of sensitive data, while Pulumi Copilot aids in generating Python code for infrastructure setup. The guide demonstrates how to implement AI and LLM monitoring in an AI chat application using these tools, with deployment on AWS and monitoring via New Relic dashboards, capturing detailed telemetry data to ensure compliance, quality, and cost control.

As chatbots become integral to business operations by providing real-time assistance and automating customer service, ensuring their optimal performance is critical to avoid frustrating users and losing business. Utilizing an observability tool like New Relic AI monitoring can help track key performance metrics such as response time, token usage, and error rates. The guide provides a step-by-step tutorial on setting up a demo chatbot application, "Relicstraurants," using Flask and OpenAI's GPT-4o model. It explains how to integrate New Relic for real-time monitoring, diagnose and resolve performance issues like high response times and errors, and optimize token usage to reduce latency and operational costs. Emphasizing the importance of safeguarding sensitive data, the guide suggests disabling certain features or applying filters in New Relic to prevent data exposure. By integrating these monitoring tools, businesses can ensure their chatbots deliver efficient and reliable user experiences.

As modern IT environments become increasingly complex, maintaining system performance and reliability has become more challenging than ever. Observability offers a more comprehensive approach to understanding system behavior and improving its performance by gaining actionable insights from telemetry data such as metrics, events, logs, and traces (MELT). AI is emerging as a key enabler of observability, enhancing system monitoring, predicting potential issues, and optimizing performance. Intelligent observability allows teams to understand and proactively manage their complex IT environment, providing a detailed view of the system's health and performance. However, AI-driven systems introduce additional layers of complexity that must be addressed through observability practices. Tools like New Relic play a key role in addressing these challenges by providing advanced observability features that help detect and respond to issues such as model drift and data pipeline inefficiencies. AI significantly enhances the ability to detect anomalies, predicting potential issues, and optimizing performance. Predictive analytics for preventive monitoring can forecast potential system failures or performance bottlenecks before they occur. Root cause analysis is enhanced by employing AI-driven data correlation techniques that automatically analyze and correlate data from multiple sources, helping to surface the most likely root causes. Alerting correlation and noise reduction help reduce alert fatigue by grouping individual alerts into a single incident, reflecting the broader issue rather than treating each symptom as an isolated problem. New Relic's AI features empower teams to resolve issues faster and proactively manage their systems, including AI monitoring, New Relic AI, and MLOps. These advanced capabilities transform observability practices, allowing organizations to effectively manage the complexities of today’s IT environments.

Modern IT environments have become increasingly complex, necessitating advanced observability techniques to maintain system performance and reliability. Traditional monitoring tools often fall short in AI-driven systems, where observability involves not only collecting telemetry data such as metrics, events, logs, and traces (MELT) but also understanding system behaviors and performance characteristics specific to AI components. AI has become a crucial enabler in this domain, enhancing system monitoring through automated anomaly detection, predictive analytics, and root cause analysis. Tools like New Relic have integrated AI-driven capabilities to address challenges such as model drift and data pipeline inefficiencies, thereby transforming observability practices. Intelligent observability, powered by AI, allows for faster detection and resolution of issues, reducing mean time to detection and resolution (MTTD and MTTR), and providing deeper insights into system health and performance. This evolution in observability is essential for managing the complexities of modern IT infrastructures, particularly those that incorporate AI and distributed systems.

RISE With SAP is an offering from SAP that aims to accelerate digital transformation and bring AI power to customers by migrating them to cloud ERP. However, after embarking on a digital transformation journey, many customers struggle with monitoring their new digital estate due to the complexity of microservices architectures. To address this, RISE With SAP introduces intelligent observability, which creates an autonomous digital nervous system that can monitor and anticipate disruptions across multiple systems, including SAP and non-SAP systems. The New Relic unified telemetry data platform is foundational to this approach, allowing for consolidation of multiple monitoring tools and data into a centralized data store powered by AI-driven predictive insights and autonomous root cause analyses. This enables complete insights into the entire application technology stack, facilitating collaboration, predictable performance, and elimination of interruptions before the first alert.

RISE With SAP facilitates digital transformation by migrating customers from legacy ERP systems to AI-powered cloud ERP, but transforming how companies monitor their digital estate is crucial to avoid operational issues. Traditional SAP monitoring is insufficient for the complexity of modern microservices architectures, necessitating a proactive, data-driven approach to create an autonomous digital nervous system for comprehensive observability. New Relic's unified telemetry data platform consolidates data from various sources, including SAP systems, multiclouds, and microservices, enabling complete insights and intelligent observability. This approach eliminates interruptions, enhances collaboration, and improves performance predictability by consolidating data silos, providing faster root cause analysis, and enabling out-of-the-box insights. New Relic's agentless architecture offers ease of integration with SAP systems, and many large SAP customers have adopted New Relic for enhanced observability, leveraging its solutions to achieve unified visibility and end-to-end observability across their digital estate.

Amazon CloudWatch Internet Monitor is a service that provides visibility into how internet issues impact the performance and availability of applications hosted on Amazon Web Service (AWS). New Relic is a comprehensive observability platform that unifies monitoring, logging, and tracing across the entire technology stack. The integration between these two services simplifies managing modern applications and infrastructure, driving improved business outcomes by delivering real-time insights into internet performance and availability. New Relic's powerful dashboards and alerting systems enable teams to quickly identify and resolve issues, reducing downtime and improving operational efficiency. The integration offers various telemetry and data points, including health events, performance and availability scores, bytes transferred, round-trip time, and alerts that can be integrated with tools such as Slack or PagerDuty. To begin exploring the integration, visit New Relic's docs, quickstarts, or sign up for a free account to learn more about Amazon CloudWatch Internet Monitor and how to instrument it with New Relic.

Amazon CloudWatch Internet Monitor, a service offering insights into internet performance and availability for applications on AWS, has integrated with New Relic, a comprehensive observability platform, to provide a unified solution for monitoring application and infrastructure telemetry. This integration aims to simplify managing modern applications by offering real-time insights into internet performance, enabling businesses to quickly identify and resolve issues affecting user experience. Users can report metric data via Amazon CloudWatch Metric Streams or an older polling method, while logs can be ingested using Log ingestion Lambda, with the option to set alerts for performance and availability scores. The collaboration between Amazon CloudWatch Internet Monitor and New Relic enhances operational efficiency by tracking metrics like IP prefixes, latency, and geographical data, ensuring reduced downtime and improved business outcomes.