The New Stack Makers

Morgan McLean, co-founder of OpenTelemetry and senior director of product management at Splunk, has long tackled the challenges of observability in large-scale systems. In a conversation with Alex Williams onThe New Stack Makers, McLean reflected on his early frustrations debugging high-scale services and the need for better observability tools.OpenTelemetry, formed in 2019 from OpenTracing and OpenCensus, has since become a key part of modern observability strategies. As a Cloud Native Computing Foundation (CNCF) incubating project, it’s the second most active open source project after Kubernetes, with over 1,200 developers contributing monthly. McLean highlighted OpenTelemetry’s role in solving scaling challenges, particularly in Kubernetes environments, by standardizing distributed tracing, application metrics, and data extraction.Looking ahead, profiling is set to become the fourth major observability signal alongside logs, tracing, and metrics, with general availability expected in 2025. McLean emphasize

Observability is expensive because traditional tools weren’t designed for the complexity and scale of modern cloud-native systems, explains Christine Yen, CEO of Honeycomb.io. Logging tools, while flexible, were optimized for manual, human-scale data reading. This approach struggles with the massive scale of today’s software, making logging slow and resource-intensive. Monitoring tools, with their dashboards and metrics, prioritized speed over flexibility, which doesn’t align with the dynamic nature of containerized microservices. Similarly, traditional APM tools relied on “magical” setups tailored for consistent application environments like Rails, but they falter in modern polyglot infrastructures with diverse frameworks.Additionally, observability costs are rising due to evolving demands from DevOps, platform engineering, and site reliability engineering (SRE). Practices like service-level objectives (SLOs) emphasize end-user experience, pushing teams to track meaningful metrics. However, outdated observab

Generative AI is a data-driven story with significant infrastructure and operational implications, particularly around the rising demand for GPUs, which are better suited for AI workloads than CPUs. In an episode ofThe New Stack Makersrecorded at KubeCon + CloudNativeCon North America, Sudha Raghavan, SVP for Developer Platform at Oracle Cloud Infrastructure, discussed how AI’s rapid adoption has reshaped infrastructure needs.The release of ChatGPT triggered a surge in GPU demand, with organizations requiring GPUs for tasks ranging from testing workloads to training large language models across massive GPU clusters. These workloads run continuously at peak power, posing challenges such as high hardware failure rates and energy consumption.Oracle is addressing these issues by building GPU superclusters and enhancing Kubernetes functionality. Tools like Oracle’s Node Manager simplify interactions between Kubernetes and GPUs, providing tailored observability while maintaining Kubernetes’ user-friendly experience

The hardware industry is surging, driven by AI's demanding workloads, with Arm—a 35-year-old pioneer in processor IP—playing a pivotal role. In an episode ofThe New Stack Makersrecorded at KubeCon + CloudNativeCon North America, Pranay Bakre, principal solutions engineer at Arm, discussed how Arm is helping organizations migrate and run applications on its technology.Bakre highlighted Arm’s partnership with hyperscalers like AWS, Google, Microsoft, and Oracle, showcasing processors such as AWS Graviton and Google Axion, built on Arm’s power-efficient, cost-effective Neoverse IP. This design ethos has spurred wide adoption, with 90-95% of CNCF projects supporting native Arm binaries.Attendees at Arm’s booth frequently inquired about its plans to support AI workloads. Bakre noted the performance advantages of Arm-based infrastructure, delivering up to 60% workload improvements over legacy architectures. The episode also features a demo on migrating x86 applications to ARM64 in both cloud and containerized envir

Heroku has open-sourced its Twelve-Factor App methodology, initially created in 2011 to help developers build portable, resilient cloud applications. Heroku CTO Gail Frederick announced this shift at KubeCon + CloudNativeCon North America, explaining the move aims to involve the community in modernizing the framework. While the methodology inspired a generation of cloud developers, certain factors are now outdated, such as the focus on logs as event streams. Frederick highlighted the need for updates to address current practices like telemetry and metrics visualization, reflecting the rise of OpenTelemetry.The updated Twelve-Factor methodology will expand to accommodate modern cloud-native realities, such as deploying interconnected systems of apps with diverse backing services. Planned enhancements include supporting documents, reference architectures, and code examples illustrating the principles in action. Success will be measured by its applicability to use cases involving edge computing, IoT, serverless,

Falco, an open-source runtime observability and security tool, was created by Sysdig founder Loris Degioanni to collect real-time system events directly from the kernel. Leveraging eBPF technology for improved safety and performance, Falco gathers data like pod names and namespaces, correlating them with customizable rules. Unlike static analysis tools, it operates in real-time, monitoring events as they occur. In this episode of The New Stack Makers, TNS Editor-in-Chief, Heather Joslyn spoke with Thomas Labarussias, Senior Developer Advocate at Sysdig, Leonardo Grasso, Open Source Tech Lead Manager at Sysdig and Luca Guerra, Sr. Open Source Engineer at Sysdig to get the latest update on Falco. Graduating from the Cloud Native Computing Foundation (CNCF) in February 2023 after entering its sandbox six years prior, Falco’s maintainers have focused on technical maturity and broad usability. This includes simplifying installations across diverse environments, thanks in part to advancements from the Linux Foundat

Jetstack’s cert-manager, a leading open-source project in Kubernetes certificate management, began as a job interview challenge. Co-founder Matt Barker recalls asking a prospective engineer to automate Let’s Encrypt within Kubernetes. By Monday, the candidate had created kube-lego, which evolved into cert-manager, now downloaded over 500 million times monthly.Cert-manager’s journey to CNCF graduation, achieved in September, began with its donation to the foundation four years ago. Relaunched as cert-manager, the project grew under engineer James Munnelly, becoming the de facto standard for certificate lifecycle management. The thriving community and ecosystem around cert-manager highlighted its suitability for CNCF stewardship. However, maintainers, including Ashley Davis, noted challenges in navigating differing opinions within its vast user base.With graduation achieved, cert-manager’s roadmap includes sub-projects like trust-manager, addressing TLS trust bundle management and Istio integration. Barker aims

The tech industry faces a paradox: despite high demand for skills, many developers and engineers are unemployed. At KubeCon + CloudNativeCon North America in Salt Lake City, Utah, Andela and the Cloud Native Computing Foundation (CNCF) announced an initiative to train 20,000 technologists in cloud native computing over the next decade. oss O'neill, Senior Program Manager at Andela and Chris Aniszczyk, CNCF’s CTO, highlighted the lack of Kubernetes-certified professionals in regions like Africa and emphasized the need for global inclusivity to make cloud native technology ubiquitous.Andela, operating in over 135 countries and founded in Nigeria, views this program as a continuation of its mission to upskill African talent, aligning with its partnerships with tech giants like Google, AWS, and Nvidia. This initiative also addresses the increasing employer demand for Kubernetes and modern cloud skills, reflecting a broader skills mismatch in the tech workforce.Aniszczyk noted that companies urgently seek expertis

When open source projects shift to proprietary licensing, forks and new communities often emerge. Such was the case with MapLibre, born from Mapbox’s 2020 decision to make its map rendering engine proprietary. In conjunction with All Things Open 2024, Seth Fitzsimmons, a principal engineer at AWS and Tarus Balog, principal technical strategist for open source at AWS shared that this engine, popular for its WebGL-powered vector maps and dynamic customization features, was essential for organizations like BMW, The New York Times, and Instacart. However, Mapbox’s move disappointed its open-source user base by tying the upgraded Mapbox GL JS library to proprietary products.In response, three users forked the engine to create MapLibre, committing to modernizing and preserving its open-source ethos. Despite challenges—forking often struggles to sustain momentum—MapLibre has thrived, supported by contributors and corporate sponsors like AWS, Meta, and Microsoft. Notably, a community member transitioned the project f

At All Things Open in October, Anandhi Bumstead, AWS’s director of software engineering, highlighted OpenSearch's journey and the advantages of the Linux Foundation's stewardship. OpenSearch, an open source data ingestion and analytics engine, was transferred by Amazon Web Services (AWS) to the Linux Foundation in September 2024, seeking neutral governance and broader community collaboration. Originally forked from Elasticsearch after a licensing change in 2021, OpenSearch has evolved into a versatile platform likened to a “Swiss Army knife” for its broad use cases, including observability, log and security analytics, alert detection, and semantic and hybrid search, particularly in generative AI applications.Despite criticism over slower indexing speeds compared to Elasticsearch, significant performance improvements have been made. The latest release, OpenSearch 2.17, delivers 6.5x faster query performance and a 25% indexing improvement due to segment replication. Future efforts aim to enhance indexing, searc

Is Apache Spark too costly? Amazon Principal Engineer Patrick Ames tackled this question during an interview with The New Stack Makers, sharing insights into transitioning from Spark to Ray for managing large-scale data. Ames, described as a "go-to" engineer for exabyte-scale projects, emphasized a goal-driven approach to solving complex engineering problems, from simplifying daily chores to optimizing software solutions.Initially, Spark was chosen at Amazon for its simplicity and open-source flexibility, allowing efficient merging of data with minimal SQL code. The team leveraged Spark in a decoupled architecture over S3 storage, scaling it to handle thousands of jobs daily. However, as data volumes grew to hundreds of terabytes and beyond, Spark’s limitations became apparent. Long processing times and high costs prompted a search for alternatives.Enter Ray—a unified framework designed for scaling AI and Python applications. After experimentation, Ames and his team noted significant efficiency improvements,

In this New Stack Makers, Codiac aims to simplify app deployment on Kubernetes by offering a unified interface that minimizes complexity. Traditionally, Kubernetes is powerful but challenging for teams due to its intricate configurations and extensive manual coding. Co-founded by Ben Ghazi and Mark Freydl, Codiac provides engineers with infrastructure on demand, container management, and advanced software development life cycle (SDLC) tools, making Kubernetes more accessible.Codiac’s interface streamlines continuous integration and deployment (CI/CD), reducing deployment steps to a single line of code within CI/CD pipelines. Developers can easily deploy, manage containers, and configure applications without mastering Kubernetes' esoteric syntax. Codiac also offers features like "cabinets" to organize assets across multi-cloud environments and enables repeatable processes through snapshots, making cluster management smoother.For experienced engineers, Codiac alleviates the burden of manually managing YAML file

Valkey, an open-source fork of Redis launched in March, introduced its multithreaded Version 8.0 in September, now available through AWS ElastiCache. At All Things Open 2024 in Raleigh, AWS's Kyle Davis explains that Valkey was developed after Redis changed to a restrictive license, drawing contributors from companies like AWS, Google, Alibaba, and Oracle. Notably, some contributors emerged independently, including a significant contributor from Vietnam. Version 8.0 differentiates itself from Redis by leveraging multithreaded CPUs, addressing the efficiency of I/O operations in modern hardware. Additionally, data structure refinements were made to improve memory efficiency by up to 20%, particularly benefiting large-key databases.Looking ahead, Valkey plans two annual updates, with the next release expected in 2025. New modules are anticipated, including a JSON module for efficient data manipulation and a Bloom filter for probabilistic data presence checks. Version 9.0 may bring substantial changes to cluster

Deb Nicholson, executive director of the Python Software Foundation, attributes Python’s popularity to its minimal syntactical complexity, which appeals to beginners and seasoned developers alike. Python allows flexibility for those exploring coding without a specific focus, unlike purpose-built languages. Since her leadership began in 2022, Nicholson has overseen the foundation’s role in managing Python’s fiscal and operational needs, including the package index that hosts over half a million add-ons. This open ecosystem enables contributions from large corporations and individual developers while demanding vigilant security measures.Nicholson envisions Python's future advancements, particularly in improving multi-threading and expanding usage in mobile development. She acknowledges Python’s critical role in AI and data science but remains cautious about AI’s pervasive application, likening it to a temporary trend. On open source in the enterprise, Nicholson critiques companies profiting from open-source too

Platform engineering will be a key focus at KubeCon this year, with a special emphasis on AI platforms. Priyanka Sharma, executive director of the Linux Foundation, highlighted the convergence of platform engineering and AI during an interview on The New Stack Makers with Adobe’s Joseph Sandoval. KubeCon will feature talks from experts like Chen Goldberg of CoreWeave and Aparna Sinha of CapitalOne, showcasing how AI workloads will transform platform operations.Sandoval emphasized the growing maturity of platform engineering over the past two to three years, now centered on addressing user needs. He also discussed Adobe's collaboration on CNOE, an open-source initiative for internal developer platforms. The intersection of platform engineering, Kubernetes, cloud-native technologies, and AI raises questions about scaling infrastructure management with AI, potentially improving efficiency and reducing toil for roles like SRE and DevOps. Sharma noted that reference architectures, long requested by the CNCF commun

Rohit Choudhary, co-founder and CEO of Acceldata, placed an early bet on data observability, which has proven prescient. In a New Stack Makers podcast episode, Choudhary discussed three key insights that shaped his vision: First, the exponential growth of data in enterprises, further amplified by generative AI and large language models. Second, the rise of a multicloud and multitechnology environment, with a majority of companies adopting hybrid or multiple cloud strategies. Third, a shortage of engineering talent to manage increasingly complex data systems.As data becomes more essential across industries, challenges in data observability have intensified. Choudhary highlights the complexity of tracking where data is produced, used, and its compliance requirements, especially with the surge in unstructured data. He emphasized that data's operational role in business decisions, marketing, and operations heightens the need for better traceability. Moving forward, traceability and the ability to manage the growi

Rust has maintained its place among the top 15 programming languages and has been the most admired language for nine consecutive years. In a New Stack Makers podcast, Joel Marcey, director of technology at the Rust Foundation, discussed the language's growing importance, including initiatives to improve its security, performance, and adoption in various domains. While Rust is widely used in systems and backend programming, it’s also gaining traction in embedded systems, safety-critical applications, game development, and even the Linux kernel.Marcey highlighted Rust’s strengths as a safe and fast systems language, noting its use on the web through WebAssembly (Wasm), though adoption there is still early. He also addressed Rust vs. Go, explaining that Rust excels in performance-critical applications. Marcey discussed recent updates, such as Rust 1.81, and project goals for 2024, which include a new edition and async improvements.He also touched on government interest in Rust, including DARPA’s initiative to co

In a New Stack Makers episode, Ashley Williams, founder and CEO of axo, highlights how the software world depends on open-source code, which is largely maintained by unpaid volunteers. She likens this to a CVS relying on volunteer-run shipping companies, pointing out how unsettling that might be for customers. The conversation focuses on open-source maintainers’ reluctance to be seen as "suppliers" of software, an idea explored in a 2022 blog post by Thomas Depierre. Many maintainers reject the label, as there is no contractual obligation to support the software they provide. Williams critiques the industry's response to this, noting that instead of involving maintainers in software supply chain security, companies have relied on third-party vendors. However, these vendors have no relationship with the maintainers, leading to increased vulnerabilities. Williams advocates for better engagement with maintainers, especially at build time, to improve security. She also reflects on the growing pressures on maintai

In this New Stack Makers podcast, Xun Wang, CTO of Bloomreach, brings insights from his time at Nvidia, particularly lessons from its founder, Jensen Huang, to his current role in e-commerce personalization. Wang emphasizes structuring organizations to reflect the architecture of the products they build, applying a hands-on, detail-oriented approach that encourages deep understanding of engineering challenges. He credits Huang for teaching him the importance of focusing on fundamental architecture rather than relying on iterative testing alone. Wang highlights the impact of generative AI (GenAI) on Bloomreach, explaining how AI-driven search is essential to understanding human language and user intent. As GenAI reshapes application development, Wang stresses the need for engineers to adopt new skills in AI manipulation, while still maintaining traditional coding expertise. He advocates for continuous learning, acknowledging the challenge of staying updated in a rapidly evolving field. Wang, himself, reads ext

Code reviews can be highly beneficial but tricky to execute well due to the human factors involved, says Adrienne Braganza Tacke, author of *Looks Good to Me: Actionable Advice for Constructive Code Review.* In a recent conversation with *The New Stack*, Tacke identified three challenges teams must address for successful code reviews: ambiguity, subjectivity, and ego.Ambiguity arises when the goals or expectations for the code are unclear, leading to miscommunication and rework. Tacke emphasizes the need for clarity and explicit communication throughout the review process. Subjectivity, the second challenge, can derail reviews when personal preferences overshadow objective evaluation. Reviewers should justify their suggestions based on technical merit rather than opinion. Finally, ego can get in the way, with developers feeling attached to their code. Both reviewers and submitters must check their egos to foster a constructive dialogue.Tacke encourages programmers to first review their own work, as self-check