InfoQ Homepage Microservices Content on InfoQ
-
Uber Builds GitFarm to Run Git Operations as a Service for Large-Scale Monorepos
Uber’s GitFarm provides Git operations as a centralized service, eliminating local repository clones across large scale monorepo workloads. The platform uses prewarmed checkouts, ephemeral sandboxes, repository synchronization, and gRPC streaming to reduce resource consumption and startup latency for automation services operating across thousands of repositories.
-
How Netflix Scaled its Real-Time Service Map
Netflix has described how it redesigned the streaming pipeline behind Service Topology, its real-time service dependencies map, to support production scale. The system uses three stages to separate intermediary resolution from enrichment and persistence, propagates backpressure to Kafka rather than dropping records, and uses server-sent events instead of gRPC for high-volume internal transfers.
-
Canva Shares S3 Based Architecture for Session Revocation across Hundreds of Millions of Sessions
Canva redesigned session revocation infrastructure to support 100M active sessions while reducing database lookups. The architecture uses Amazon S3 for durable revocation records and distributes compact, in-memory indexes to application gateways. Canva said the design improved deployment speed, reduced database infrastructure requirements, and cut the revocation cache memory footprint by 87.5%.
-
JioHotstar Explains the Distributed Engineering behind Personalized Ad Requests at Streaming Scale
JioHotstar explains the distributed architecture behind its real-time ad request workflow, covering ad decisioning, waterfall tiering, pacing algorithms, latency optimization, and service coordination required to select and deliver personalized advertisements during streaming playback at scale.
-
DoorDash Uses Envoy and Valkey for a 1.5M RPS Proxy Cache with 99.99999% Availability
DoorDash has developed Entity Cache, a transparent proxy caching platform built on Envoy and Valkey to reduce redundant service-to-service requests across its microservices architecture. Operating within DoorDash’s service mesh, the platform serves over 1.5M requests per second with 99.99999% availability through caching, event-driven invalidation, failure handling, and performance optimizations.
-
How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone
DoorDash details the architecture behind Ask DoorDash, its AI-powered conversational shopping assistant, combining LLMs, specialized AI agents, MCP-based tooling, and an intelligence layer with persistent consumer memory and live backend data. Early results show up to 24% higher checkout conversion, 17% larger baskets, and improved intent accuracy using memory-backed sessions.
-
Airbnb Shares Architecture behind Sitar-Agent Dynamic Configuration Sidecar for Kubernetes Services
Airbnb engineers detailed Sitar-agent, a Kubernetes sidecar for dynamic configuration delivery across tens of thousands of pods, processing updates several times per minute. The system was redesigned with Java, Amazon S3 snapshot bootstrapping, and a migration from Sparkey to SQLite to improve reliability, startup performance, and configuration availability at scale.
-
How Netflix Maps Thousands of Microservices in Real-Time
Netflix has shared details about Service Topology. This internal system creates and updates a live dependency graph for thousands of microservices. It helps engineers see how services connect and resolve issues more quickly. The system merges three separate data sources into a single, queryable graph. It updates almost in real-time as traffic patterns shift.
-
Shopify Reports 15X Faster Graphql Execution with Breadth First Engine
Shopify introduced GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution. The redesign improves large-scale GraphQL performance with up to 15x faster field execution, 6x lower GC overhead, and +4s P50 latency gains. It focuses on execution-layer efficiency and batched resolver processing for high-cardinality commerce queries.
-
Cloudflare Introduces Workflows V2 with Deterministic Execution and 50K Concurrent Workflows
Cloudflare introduces Workflows V2, a redesigned distributed workflow orchestration system with deterministic replayable execution, improved observability, and major scaling upgrades, including 50,000 concurrent instances and 2M queued workflows. It supports AI agents, data pipelines, and background processing with improved reliability across distributed systems.
-
Uforwarder: Uber’s Scalable Kafka Consumer Proxy for Efficient Event-Driven Microservices
Uber has open-sourced uForwarder, a push-based Kafka consumer proxy built to handle trillions of messages and multiple petabytes of data daily. The system introduces context-aware routing, head-of-line blocking mitigation, adaptive auto-rebalancing, and partition-level delay processing to improve scalability, workload isolation, and hardware efficiency in large-scale event-driven microservices.
-
Reducing Onboarding from 48 Hours to 4: inside Amazon Key’s Event-Driven Platform
Amazon Key modernized its event platform by adopting a centralized, event-driven architecture built on Amazon EventBridge. The redesign processes millions of daily events with millisecond latency, improves schema governance, automates cross-account routing, and reduces service onboarding time from 48 hours to four, while maintaining 99.99 percent reliability.
-
Uber and OpenAI Retool Rate Limiting Systems
Uber and OpenAI are replacing static rate limits with adaptive, infrastructure-level platforms. Uber’s Global Rate Limiter utilizes probabilistic shedding to manage 80M RPS, while OpenAI’s Access Engine implements a credit waterfall to prevent user interruptions. Both architectures utilize distributed enforcement and soft controls to maintain system stability and service continuity at scale.
-
Reddit Migrates Comment Backend from Python to Go Microservice to Halve Latency
Reddit has rebuilt its core backend, migrating Comments, Accounts, Posts, and Subreddits from a legacy Python monolith to Go microservices. The migration improves performance, halves critical write latency, and modernizes the platform for future scalability while preserving correctness across multiple datastores.
-
Monzo’s Real-Time Fraud Detection Architecture with BigQuery and Microservices
Monzo has redesigned its fraud prevention platform to detect scams in real time, handle growing payment volumes, and deploy new controls rapidly. Explore the bank’s modular control architecture, feature computation pipeline, and observability using BigQuery for accurate, low-latency fraud detection.