April 2023 Summaries

New Relic has introduced updated APM agents that automatically collect and forward application logs, eliminating the need for manual configuration and third-party log forwarders. This integration allows developers to troubleshoot performance issues more efficiently by providing logs in the context of APM metrics, traces, and events, all within an enhanced user interface. The update supports a variety of programming languages including Java, .NET, Ruby, Python, Node.js, Go, and PHP, and it provides features for managing security and compliance, such as the ability to opt-out of log forwarding and control data ingestion to prevent the disclosure of sensitive information. These improvements aim to streamline application performance monitoring, providing a holistic view of application health and performance while ensuring data privacy and compliance.

New Relic Flex is an application-agnostic tool for collecting metric data from various services without a suitable agent, allowing for an agentless approach to gather telemetry data from external sources exposed only through an API layer. Australia Post partnered with New Relic to minimize instability in their systems caused by erratic weather and manage demand during these periods. The company set up a simple configuration file using the New Relic Flex binary or infrastructure agent, which targets current weather and records city as a custom attribute. The configuration was then expanded to include hourly weather, precipitation, rain, and showers data, and finally sent to New Relic for analysis and decision-making purposes, providing valuable insights into external factors impacting their applications.

Australia Post collaborated with New Relic to address system instability during erratic weather and manage demand more efficiently by collecting telemetry data through New Relic Flex, an all-in-one tool that gathers data from various services without the need for traditional agents. Using the New Relic infrastructure agent or the standalone Flex binary, users can configure and run HTTP/HTTPS requests, execute shell commands, parse files, and extract database data, which is then sent to New Relic for analysis. The article provides a detailed guide on setting up New Relic Flex, including configuration with a weather API, and demonstrates how Australia Post utilized precipitation data to forecast parcel volumes and traffic, adapting their operations accordingly. New Relic Flex is highlighted as a versatile tool for custom telemetry instrumentation, enabling developers, system administrators, and business owners to make data-driven decisions by integrating external factors into their applications. The article encourages exploring the Flex repository on GitHub for further configurations and emphasizes the endless possibilities for gathering and analyzing telemetry data.

Building observability into your DevOps practices is a journey that requires preparation and a solid foundation. New Relic's learning paths are designed to help you level up quickly, with modular, bite-sized content that meets clear objectives for specific courses or use cases. The Observability Foundations learning path provides an onboarding experience for those new to software or platforms, while the Observability Practitioner learning path is geared towards developers who want to build and implement observability practices into their business. With a free account, you can access 100 GB/month of free data ingest, one free full-platform user, unlimited free basic users, and access to 500+ pre-built quickstart integrations. The New Relic University Learn site offers additional features such as tags, credentials, and FAQs to help you get started and make the most from your learning journey.

New Relic offers a structured educational approach to integrating observability into DevOps practices through its Learning Paths, which are tailored to different roles and expertise levels. The Observability Foundations learning path is designed for beginners to gain a basic understanding of New Relic's platform and modern observability practices, covering essentials, platform navigation, and data monitoring. For developers looking to implement New Relic and observability practices, the Observability Practitioner path goes beyond the basics, teaching installation, application instrumentation, telemetry data capture, and creating custom dashboards with NRQL. The courses are crafted to fit into busy schedules with modular content, and completion results in earning badges. Additional resources on the New Relic University Learn site include a video tour and features like tags for easy course navigation, alongside access to a free account offering substantial data ingestion and integration capabilities. The blog emphasizes that the views expressed are those of the author and not officially endorsed by New Relic.

At plentymarkets, a leading e-commerce enterprise resource planning system provider, real-time monitoring of services is crucial to ensure high website performance and customer satisfaction. The company previously relied on over ten separate monitoring solutions, which became overwhelming and time-consuming to manage. Implementing New Relic has improved alert standards, application performance monitoring, and real-time troubleshooting for customer issues. With New Relic, the company can now provide a seamless single sign-on experience, tailor alerts for each team member, and collect all relevant data through a custom dashboard, resulting in reduced alert fatigue and faster mean time to resolution. This shift has enabled plentymarkets to increase employee happiness across the organization by optimizing working conditions and making work more efficient.

Plentymarkets, an e-commerce ERP platform serving over 55,000 users, has transitioned to using New Relic for enhanced real-time monitoring, aiming to improve customer experience and operational efficiency. Previously reliant on over ten separate monitoring tools, the company faced challenges with alert overload and inefficiencies due to a limited number of engineers understanding these tools. By adopting New Relic, plentymarkets streamlined its monitoring processes, reduced false alarms, and empowered more team members to handle system issues, leading to improved application performance monitoring and faster customer issue resolution. This shift also allowed for better cost optimization and fostered a collaborative environment where all team members have equal access to monitoring data, thus enhancing the overall user experience and employee satisfaction.

New Relic has introduced Data Plus, a new option within its observability platform designed to enhance data capabilities for Dev and Ops teams by offering features such as extended data retention, advanced logs obfuscation, and increased query limits. Priced at a promotional rate of $0.55 per gigabyte, Data Plus offers a 65% discount compared to purchasing each capability separately. It supports compliance with FedRAMP Moderate and HIPAA, allows for historical and streaming data exports, and provides flexibility with multi-cloud provider options, including integration with Microsoft Azure. The offering aims to simplify billing with straightforward per-GB pricing, eliminating complex billing structures and overage premiums, while bolstering the observability practices of data-first engineering organizations with industry-leading security, performance, and governance capabilities.

A distributed tracing tool is a powerful diagnostic and performance monitoring solution for hybrid and microservices-based environments, providing real-time visibility into application performance, dependencies, and interactions between components. It consolidates records of events across components, enables rapid issue resolution, optimized resource utilization, effective debugging, and improved user experience. Distributed tracing offers substantial benefits, including enhanced visibility, faster issue resolution, optimized resource utilization, effective debugging, performance monitoring, and trend analysis. It provides a holistic view of the application's performance, identifying bottlenecks and dependencies. The W3C Trace Context standard improves interoperability issues by providing a universally agreed-upon format for exchange of trace context propagation data, ensuring that traces can be correlated and propagated across different tools and environments. Head-based sampling decides to randomly select traces at the beginning of the trace, while tail-based sampling makes decisions after all spans have arrived. Choosing the right distributed tracing tool is crucial, considering factors such as transparency in performance claims, straightforward pricing, actionable insights, comprehensive language support, scalability, real-time visualization, alignment with team needs, streamlined troubleshooting, and continuous learning and optimization.

Distributed tracing is a critical tool for diagnosing performance issues in hybrid and microservices-based environments, offering real-time insights into the health and performance of applications by consolidating records of events across distributed systems. It works by assigning a unique identifier to a request, propagating it across different services, and recording information to create a trace that can be visualized and analyzed. This tool enhances visibility, speeds up issue resolution, and optimizes resource utilization, providing a holistic view of application behavior and interactions. With the ability to trace the end-to-end journey of requests, distributed tracing complements logging by offering transaction-level insights, which help in identifying performance bottlenecks and dependencies. The tool uses spans and transactions to structure traces, and the W3C Trace Context standard ensures interoperability across different platforms and services. Trace sampling, whether head-based or tail-based, helps manage data volume while maintaining performance insights. When choosing a distributed tracing tool, considerations include transparency, pricing, language support, scalability, and alignment with team needs, all aimed at transforming raw data into actionable insights for continuous system optimization.