Scaling
We track 37 posts about Scaling from 31 engineering blogs. Most active: Badoo, Airbnb, Grab. Latest post: Oct 6, 2026.
Companies writing about Scaling
Recent posts
Scaling and Operating a Large dbt Project on Databricks: IFCO's Data Team on Performance, Visibility, and Debugging (opens on the source site)
AbstractIFCO runs one of the world's largest reusable packaging pools with hundreds of millions of crates and pallets...
Scaling and benchmarking a critical message bus using a new indexing strategy (opens on the source site)
The following is part of a series of posts about 2026 summer intern projects—for more, see “What the interns have wrought, special jumbo 2026 edition”
From 4 Months to 2 Years: Scaling Systems and Stepping into Seniority at Bazaarvoice (opens on the source site)
A follow-on from the smash hit that was “My First 4 Months at Bazaarvoice.” While my first post focused on onboarding and adapting to a global organization, this post shifts focus to developing at scale, navigating major organizational shifts, and my journey to becoming a Senior Engineer. What’s Been Going On? (The High-Level View) To […]
Scaling Beyond Borders: The 2026 Communication Blueprint for Chinese Brands (opens on the source site)
Twilio ·
Chinese enterprises expanding overseas face fragmented communication channels, strict compliance regimes, and data residency mandates across 180+ markets. A programmable communication platform (CPaaS) eliminates the need to negotiate carrier-by-carrier in every country.
Scaling GitOps: The "pull" architecture for global fleets (opens on the source site)
Red Hat ·
Validating new architectures at scale is a critical step in enterprise software deployment. We conducted a performance and scale test to validate that the new argocd-agent pull architecture scales to robust enterprise requirements. The post Scaling GitOps: The "pull" architecture for global fleets appeared first on Red Hat Developer.
Scaling localnet user-defined networks (CUDNs) in Red Hat OpenShift to 3,500 networks (opens on the source site)
Red Hat ·
When migrating from legacy virtualization platforms to Red Hat OpenShift, networking architecture is often one of the biggest hurdles. Most legacy virtualization platform environments rely on flat VLAN-based tenant networks rather than overlay networks. Asking customers to re-architect their network topology to adopt Red Hat OpenShift Virtualization creates unnecessary friction. The post Scaling localnet user-defined networks (CUDNs) in Red Hat OpenShift to 3,500 networks appeared first on Red Hat Developer.
Logarithmic auto-scaling for Laravel Horizon (opens on the source site)
A thoughtful write-up on adding logarithmic auto-scaling to Laravel Horizon. It shows how log-based weighting keeps huge queue spikes from starving smaller realtime queues while still giving large backlogs enough workers. Read more
Scaling your money safely with AI (opens on the source site)
Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience.
Scaling Conditional Learned Retrieval for Pinterest Home Feed (opens on the source site)
Devin Kreuzer | Sr. Machine Learning Engineer; Yichi Wang | Machine Learning Engineer I; Sujan Reddy Ale | Machine Learning Engineer I; Zelun Wang | Sr. Machine Learning Engineer; Hongtao Lin | Sr. Machine Learning Engineer; Piyush Maheshwari | Staff Machine Learning EngineerPinterest home feed candidate generation is a large-scale User-to-Pin retrieval problem. A common approach is a two-tower model: a user tower encodes the user, an item tower encodes candidate Pins, and approximate nearest neighbor search retrieves Pins close to the user embedding. But Pinterest users often have multiple…
Why Scaling AI Compute Performance Requires a New Power Architecture (opens on the source site)
Nvidia ·
Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just wattage. It’s how power gets from the grid to the GPU. In traditional power delivery, electricity travels from the grid as an alternating current (AC) […]
From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking (opens on the source site)
Facebook ·
Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking appeared first on Engineering at Meta.
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption (opens on the source site)
Grab ·
Introduction: The evolution of Grab’s Data Lake At Grab’s scale, managing petabytes of data across billions of S3 objects demands more than a storage layer. It demands a robust architectural primitive that supports the high-concurrency needs of a modern “Lakehouse.” Our goal is full storage-compute separation, leveraging S3 as an elastic foundation for both near-real-time metrics and large-scale batch transformations. For years, the vast majority of our tables were Hive Parquet, managed through the Hive Metastore with a directory-based layout. This model served us well, but as data volume…
Scaling AI in CPG: How Adaptive Teams Can Unlock Consumer Goods Growth (opens on the source site)
Toptal ·
AI initiatives across the consumer goods industry often stall in pilot mode, constrained not by technology but by legacy operating models. Learn how successful CPG companies scale artificial intelligence through adaptive teams, outcome alignment, and smarter execution.
Scaling out Distroless adoption With AI (opens on the source site)
Grab ·
Distroless adoption at Grab Grab is migrating from heavy base images to Distroless images to reduce security risks. By limiting each container to the application and its runtime dependencies, we shed non-essential binaries and associated Common Vulnerabilities and Exposures (CVEs). This migration is more than a compliance mandate; it is a strategic security decision to build a more resilient environment. Why Distroless requires rigorous testing Distroless adoption risk: Runtime failure Shifting to Distroless images introduces a critical technical risk: Runtime Failure. A service might build…
Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world (opens on the source site)
Airbnb ·
How Airbnb’s data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into Homes, Experiences, and Services.By: Patrick Lam, Namrata Lamba, Jamie StoberWith the May 2025 Summer Release, Airbnb redesigned its app, relaunched Experiences, and debuted Services, pushing us beyond our traditional Homes focus. For the data teams, this meant rapidly evolving a decade-old infrastructure to integrate two brand-new product pillars. Our data engineers and analytics engineers rose to the challenge by building a consistent and flexible…
Coding Is No Longer the Constraint: Scaling Developer Experience to Teams and Agents at Spotify (opens on the source site)
Spotify ·
At Code with Claude, Spotify’s chief architect shared how we make both teams and AI agents more effective. The post Coding Is No Longer the Constraint: Scaling Developer Experience to Teams and Agents at Spotify appeared first on Spotify Engineering.
Scaling Airbnb’s identity graph with a unified knowledge graph infrastructure (opens on the source site)
Airbnb ·
How Airbnb shifts from PaaS to an internal knowledge graph infrastructure at scale.By: Lucen Zhao, Shukun Yang, Ashish JainKnowledge graphs offer a natural and powerful way to represent relationships between entities. Many real-world systems are fundamentally about connections.Airbnb’s identity graph captures relationships between users in a graph database. The identity graph serves aggregated insights that enable user identity resolution and relationship understanding. These capabilities support a wide range of Trust and Safety use cases, from detecting suspicious activities to identifying…
Scaling Personalized Marketing for Multi-Tenant Commerce Platforms (opens on the source site)
TL;DRBackground: Marketing Across Marketplace and StorefrontInstacart operates across two distinct commerce experiences:Instacart Marketplace, our first-party consumer marketplaceStorefront Pro, our white-label e-commerce platform for retailersFor years, our marketing automation infrastructure was built primarily to support Marketplace use cases. That model worked well in a first-party environment, where the product experience, customer relationship, and brand were all centrally managed by Instacart.Storefront Pro introduced a very different set of requirements. As the platform scaled to more…
Scaling Localization with AI at Lyft (opens on the source site)
Lyft ·
Written by Stefan ZierFor years, Lyft’s localization infrastructure relied exclusively on human translation. While this model usually ensured excellent quality, it was bound by multi-day turnarounds and costs that scaled linearly with every new language. For the few languages Lyft initially supported (Spanish, Portuguese, and French), these limits were acceptable.However, Lyft’s expansion goals quickly outpaced what traditional workflows could support. Lyft’s recent Québec launch required compliance with Bill 96 (legislation mandating French-first user experiences) which demanded faster…
From Always-On to On-Demand: Scaling Kafka Sinks with KEDA (opens on the source site)
Trivago ·
Introduction / Context Kafka sits at the heart of how we move data between systems at trivago. Many teams publish changes to Kafka, and downstream services consume those changes to keep user-facing features up to date—things like accommodation reviews, highlights, and other derived attributes. To make that possible at low latency, we run a fleet of Kafka consumers we call sinks. Each sink takes events from Kafka, applies the necessary business logic (filtering, normalization, policy checks, etc.), and writes the result into a service-local database as a materialized view. This pattern works…
From Bash to Bliss: Scaling Vespa Operations with Temporal (opens on the source site)
Vinted ·
Growing platform - Growing maintenance Keeping the Lights On (KTLO) is an essential, yet often taxing, part of a platform engineer’s role. It represents the routine operational work required to keep the business running and the platform stable. For our team, this primarily involves maintenance on our search engine, Vespa - ranging from version upgrades and service restarts to draining traffic from nodes for hardware replacements.
How The New York Times is scaling Unit Test Coverage using AI Tools (opens on the source site)
How AI tools are helping our software engineers write better tests at scaleIllustration by Nick LittleBy Eric Chima and Leonardo QuixadáAt The New York Times, we’re all excited to build fresh new experiences that delight our users. Our product managers are driven to find new ways to get our work in front of our audience and build reader engagement. Our engineers are motivated to solve unique technical challenges. And just when you think all that work is on track, breaking news strikes and all of our plans change at once.With all that going on, who could blame us if our test coverage couldn’t…
Scaling HNSWs (opens on the source site)
Antirez ·
I’m taking a few weeks of pause on my HNSWs developments (now working on some other data structure, news soon). At this point, the new type I added to Redis is stable and complete enough, it’s the perfect moment to reason about what I learned about HNSWs, and turn it into a blog post. That kind of brain dump that was so common pre-AI era, and now has become, maybe, a bit more rare. Well, after almost one year of thinking and implementing HNSWs and vector similarity stuff, it is time for some writing. However this is not going to be an intro on HNSWs: too many are present already. This is the…
Scaling HAProxy on AKS for Billions of Transactions with Dynamic Autoscaling and Token Management (opens on the source site)
Haptik ·
At our scale, we needed to handle billions of transactions efficiently - routing across multiple environments and internal data centers - while maintaining reliability, security, and dynamic control. To achieve this, we chose HAProxy, a proven, high-performance load balancer known for its lightweight footprint, flexibility, and ability to handle massive concurrency with minimal overhead. This blog walks you through how we deployed HAProxy in high-availability (HA) mode on Azure Kubernetes Service (AKS) -complete with autoscaling powered by KEDA, seamless integration with Azure Application…
Scaling Subscriptions at The New York Times with Real-Time Causal Machine Learning (opens on the source site)
How real-time algorithms and causal ML transformed our digital subscription funnel from static paywalls to dynamic, millisecond decision-makingIllustration by Mathieu LabrecqueThe New York Times became a subscription-first news and lifestyle service with the launch of its paywall in 2011. Since then, our subscription strategy has evolved substantially. Initially, users could access a limited number of free articles per month before they encountered the paywall. In 2019, we began personalizing this number using a Machine Learning (ML) model — The Dynamic Meter. In the past few years, we have…
Smart Auto-Scaling for Amazon EKS: Meet Karpenter (opens on the source site)
VNGRS ·
If you're running workloads on Kubernetes, you've probably faced the constant balancing act between over-provisioning (wasting money) and under-provisioning (hurting performance). This is where Karpenter comes in — an intelligent, high-performance autoscaler purpose-built for Kubernetes.What is Karpenter?Karpenter is an open-source autoscaler supported by Amazon Web Services (AWS). While it's optimized for Amazon EKS (Elastic Kubernetes Service), it's also compatible with other cloud providers and even on-prem environments.What Does Karpenter Do?Monitors Pod Requests: It watches for…
Scaling recommendations service at OLX (opens on the source site)
OLX ·
Optimizing FastAPI at Scale: Lessons from OLX’s Recommendation PlatformPhoto by Rosy KoIn distributed systems, there is a motto that says ‘you are as slow as your slowest tasks’. In Python, thanks to the notorious Global Interpreter Lock (GIL), this issue is amplified: ‘your slowest task will make every other task slower’. In this article, I’ll walk you through the optimizations we made to scale a FastAPI service that now handles tens of thousands of requests per second, achieving a p99 latency under 10ms.IntroductionOLX is a global online marketplace that enables users to buy and sell goods…
Preserving text size when scaling SVGs (opens on the source site)
SVGs support non-scaling strokes using the vector-effect attribute, which we can even use to draw non-scaling rectangles and circles. For example, in graphs and charts, text can become too small or too large, so it would be really nice to make it non-scaling. But unfortunately, there is no native solution - text will always scale together with the SVG. We can manually define different font sizes on different breakpoints, but text is still going to be scaled within a single breakpoint. If we need truly non-scalable text, we'll have to use JavaScript. Luckily, not a lot of it - ten lines will…
Scaling Large Language Models for e-Commerce: The Development of a Llama-Based Customized LLM (opens on the source site)
Ebay ·
Third-party LLMs like Llama 3.1 allow us to adapt powerful tools for the e-commerce domain with a mix of eBay and general data to enable our magical AI experiences.
Scaling Technology with Architectural Principles (opens on the source site)
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This page is generated automatically from the engineering blogs we follow. Every post links to its source, where it was published. See all sources.