Indexing Code At Scale With Glean
- Simon Marlow Pepe Iborra tl;dr: “We’ll talk about why a system like Glean is important, explain the rationale for Glean’s design, and run through some of the ways we’re using Glean to supercharge our developer tooling at Meta.”featured in #578
A Software Architecture Reading List
- Dr Andrew Leigh tl;dr: “Dr Andrew Leigh reflects on 25 years of professional experience as a software architect and offers his insights and a reading list of recommendations.”featured in #578
The Science Of Routing Print Orders
tl;dr: “In this blog post, we'll explore how Canva’s Print team has built a configurable rules system for graph traversal. We'll describe how this system ensures deterministic results and produces the best route for our users and the environment.”featured in #574
Streamlining Financial Precision: Uber’s Advanced Settlement Accounting System
tl;dr: “We process about 1.2 billion settlements each month, handling around $130 billion in cash in transit annually from over 50 different PSPs. This process ensures that funds received from various payment methods are accurately accounted for and matched with the corresponding bank statements. Settlement accounting at Uber involves offsetting receivables booked during revenue accounting, verifying the cash deposited in the bank, and analyzing contracts with PSPs to determine fees, taxes, and other charges associated with transactions.”featured in #562
How Vercel Adopted Microfrontends
tl;dr: “By rethinking our architecture, we shifted to vertical microfrontends, leading to a simpler development experience and over a 40% improvement in preview build times and local development compilation. Streamlined dependencies by removing code for the other microfrontends also reduced page weight and boosted end-user performance, with gains in Core Web Vitals like Largest Contentful Paint (LCP) and Interaction to Next Paint (INP).”featured in #561
Introducing Netflix’s TimeSeries Data Abstraction Layer
tl;dr: Netflix developed the TimeSeries Abstraction — a versatile and scalable solution designed to efficiently store and query large volumes of temporal event data with low millisecond latencies, all in a cost-effective manner across various use cases. “In this post, we will delve into the architecture, design principles, and real-world applications of the TimeSeries Abstraction, demonstrating how it enhances our platform’s ability to manage temporal data at scale.”featured in #559
Genie: Uber’s Gen AI On-Call Copilot
tl;dr: “For building an on-call copilot, we chose between fine-tuning an LLM model or leveraging Retrieval-Augmented Generation (RAG). Fine-tuning requires curated data with high-quality, diverse examples for the LLM to learn from. It also requires compute resources to keep the model updated with new examples.”featured in #558
Making Uber’s ExperimentEvaluation Engine 100x Faster
tl;dr: “How we made efficiency improvements to Uber’s Experimentation platform to reduce the latencies of experiment evaluations by a factor of 100x (milliseconds to microseconds). We accomplished this by going from a remote evaluation architecture (client to server RPC requests) to a local evaluation architecture (client-side computation). Some of the terminology in this blog post (e.g., parameters, experiments, etc.) is referenced from our previous blog post on Uber Experimentation. To learn more, check out Supercharging A/B Testing at Uber.”featured in #556
Introducing Netflix’s Key-Value Data Abstraction Layer
tl;dr: “In this post, we dive deep into how Netflix’s KV abstraction works, the architectural principles guiding its design, the challenges we faced in scaling diverse use cases, and the technical innovations that have allowed us to achieve the performance and reliability required by Netflix’s global operations.”featured in #552
Should We Decompose Our Monolith?
- Will Larson tl;dr: “Even as popular sentiment has generally turned away from microservices, many engineering organizations have a bit of both, often the reminents of one or more earlier but incomplete migration efforts. This strategy looks at a theoretical organization stuck with a bit of both approaches, let’s call it Theoretical Compliance Company, which is looking to determine its path forward.”featured in #550