InfoQ Homepage Performance Content on InfoQ
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Building Resilient Serverless Systems
John Chapin explains how to use serverless technologies and an infrastructure-as-code approach to architect, build, and operate large-scale systems that are resilient to vendor failures.
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Conquering Microservices Complexity @Uber with Distributed Tracing
Yuri Shkuro talks about how Uber is using distributed tracing to make sense of a large number of microservices and the interaction among them.
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Multi-Tenancy in Kubernetes
Katharina Probst discusses both the mechanics and the implications of cluster sharing on cost, isolation, and operational efficiency, including use cases, even challenging ones.
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Variety: The Secret of Scale
Cat Swetel provides an approach for incurring variety where it makes sense within the coherence of a longer-term vision.
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Scaling Infrastructure Engineering at Slack
Julia Grace talks about Slack’s first infrastructure engineering organization, the architectural and organizational challenges, mistakes and war stories since August 2016 to today.
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Maximizing Performance with GraalVM
Thomas Wuerthinger discusses the best practices for Java code and compiler configurations to maximize performance with GraalVM and how to measure performance in a reliable manner.
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How to Scale Lead Time
Ilona Kędracka and Chris Matts share their experience of what is needed to scale lead time to an organization of five thousand people.
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Automatic Clustering at Snowflake
Prasanna Rajaperumal presents Snowflake’s clustering capabilities as well as their infrastructure to perform maintenance automatically. He covers real-world problems they run into and their solutions.
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A New Way to Profile Node.js
Matteo Collina presents a new and straightforward way to identify bottlenecks in Node.js and beyond.
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Scaling Erlang Cluster to 10,000 Nodes
Maxim Fedorov demonstrates an example of a live Erlang cluster being scaled from just a few nodes to 10,000 machines with no service interruption.
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Instrumentation, Observability & Monitoring of Machine Learning Models
Josh Wills discusses the monitoring and visibility needs of machine learning models in order to bridge gaps between ML practitioners and DevOps.
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Expect the Unexpected: How to Deal with Errors in Large Web Applications
Mats Bryntse demonstrates how to implement error monitoring in a web app and also shows how to reproduce errors without having to ask the user for a step-by-step description.
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