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LinkedIn’s Migration Journey to Serve Billions of Users by Nishant Lakshmikanth at QCon SF
Engineering Manager Nishant Lakshmikanth showcased LinkedIn's transformation at QCon SF 2025, detailing a shift from legacy batch-based systems to a real-time architecture. By decoupling recommendations and leveraging dynamic scoring techniques, LinkedIn achieved a 90% reduction in offline costs, enhanced session-level freshness, and improved member engagement while future-proofing its platform.
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How LinkedIn Built Enterprise Multi-Agent AI on Existing Messaging Infrastructure
LinkedIn extended its generative AI application platform to support multi-agent systems by repurposing its existing messaging infrastructure as an orchestration layer. This allowed the company to scale AI agents without building new coordination technology from scratch and achieve global availability while supporting complex multi-step workflows through agent coordination.
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LinkedIn Announces Northguard and Xinfra: Scaling beyond Kafka for Log Storage and Pub/Sub
LinkedIn today announced Northguard, a scalable log storage system that replaces Kafka, and Xinfra, a virtualized Pub/Sub layer. Northguard delivers sharded data & metadata, log striping, strong consistency, and self-balancing clusters at a larger scale than Kafka, while Xinfra enables seamless migration and unified access across Kafka and Northguard.
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AWS Batch Introduces Multi-Container Jobs for Large-Scale Simulations
Recently, AWS announced the support of multi-container jobs in AWS Batch through the management console. This new feature simplifies the process of running simulations, particularly for testing complex systems such as those used in autonomous vehicles and robotics.
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QCon London: gRPC Migration Automation at LinkedIn
At QCon London 2024, Karthik Ramgopal and Min Chen described how AI helped LinkedIn change the remote procedure calls (RPC) protocol for 50,000 production endpoints from Rest.li to Google's gRPC. A planned 2-3 year manual migration turned into an AI-supported migration lasting 2-3 quarters. It changed 20 million lines of code across 2000 services – without business interruption.
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How LinkedIn Uses Machine Learning to Address Content-Related Threats and Abuse
To help detect and remove content that violates their standard policies, LinkedIn has been using its AutoML framework, which trains classifiers and experiments with multiple model architectures in parallel, explain LinkedIn engineers Shubham Agarwal and Rishi Gupta.
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QCon San Francisco 2023 Day 2: Design for Resilience, Platform Engineering, Modern ML, JVM Trends
The 17th annual QCon San Francisco conference was held at the Hyatt Regency San Francisco in San Francisco, California. This five-day event, organized by C4Media, consists of three days of presentations and two days of workshops. Day Two, scheduled on October 3rd, 2023, included a keynote address by Neha Narkhede and presentations from four conference tracks and one sponsored track.
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LinkedIn's Open-Source "iris-message-processor" Achieves 86.6x Faster Escalation Management Speeds
LinkedIn developed a new open-source service called "iris-message-processor" to enhance the performance and reliability of its existing Iris escalation management system. "iris-message-processor" significantly improves processing speeds, being ~4.6x faster under average loads and ~86.6x faster under high loads than its predecessor.
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LinkedIn’s LIquid Graph Database: Scaling Real-Time Data Access for 930+ Million Members
LinkedIn recently published how LIquid, its graph database, automates the indexing and real-time access of all connections to members, schools, skills, companies, positions, jobs, events, etc. This knowledge graph, known as the Economic Graph, has 270 billion edges and growing, currently handling a workload of 2 million queries per second.
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LinkedIn Improves Development Productivity by 10x by Implementing a Messaging Client-Side SDK
LinkedIn recently published how it significantly improved development productivity by implementing a client-side Messenger SDK. Usage of the SDK reduces code maintenance costs across multiple apps by abstracting away thousands of lines of code into shared libraries. In one case, the new SDK saved 40+ developer weeks of effort when building a new LinkedIn experience.
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LinkedIn Open-Sourced Its Feature Store to Evangelize Productive Machine Learning
LinkedIn Engineering recently open-sourced its feature store Feathr, which helps engineers to develop machine Learning products by simplifying feature management and usage in production. It defines features, computes them for training and inference purposes, and makes them discoverable by other machine learning developers.
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Service Overload Detection and Remediation at LinkedIn
LinkedIn recently published how it handles overload detection and remediation in its microservices. Its solution, Hodor, provides an adaptive solution that works out of the box with no configuration. It is a platform-agnostic mechanism to run overload detectors and load shedders inside the monitored process that samples load and sheds traffic from within the application's processing chain.
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LinkedIn Migrates away from Lambda Architecture to Reduce Complexity
Software engineers from LinkedIn recently published how they migrated away from a Lambda architecture. The Lambda architecture implementation caused their solution to have high operational overhead and added complexity, leading to slow product iteration times. As a result, the engineers chose to migrate to a Lambda-less architecture, resulting in significant development velocity improvements.
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LinkedIn iOS Clipboard Copying Was Bug
The LinkedIn iOS app has been found to be reading the clipboard repeatedly during use, since iOS 14 (Beta) introduces a new feature indicating when the app interacts with the clipboard. Many other apps have been having similar issues. However, LinkedIn confirmed that the iOS behaviour was a bug, and has been fixed. Read on for what happened.
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Jagadish Venkatraman on LinkedIn's Journey to Samza 1.0
At the recent ApacheCon North America, Jagadish Venkatraman spoke about how LinkedIn developed Apache Samza 1.0 to handle stream processing at scale. He described LinkedIn's use cases involving trillions of events and petabytes of data, then highlighted the features added for the 1.0 release, including: stateful processing, high-level APIs, and a flexible deployment model.