52:02
Moving Past the Scaling Myth
Michael Feathers examines the notion of scale variant structuring and what systems design could look like without the assumption that structural reorganization at different scales is not necessary.
59:25
Achieving High Load in Advertising Technology
Peter Milne talks about the technologies used and how they are implemented to deal with the high load demanded by digital marketing. He tells some of the war stories and how problems were solved.
51:11
Scaling Instagram Infrastructure
Lisa Guo overviews Instagram's infrastructure, its history, multi-data center support, tuning uwsgi parameters for scaling, performance monitoring and diagnosis, and Django/Python upgrade.
49:46
Low Latency Trading Architecture at LMAX Exchange
Sam Adams overviews the architecture LMAX Exchange uses to deliver over $2 trillion a year through their platform, and shares their experience building a high-availability stateful system.
47:56
In-Memory Caching: Curb Tail Latency with Pelikan
Yao Yue introduces Pelikan - a framework to implement distributed caches such as Memcached and Redis. She discusses the system aspects that are important to the performance of such services.
47:20
High Performance Managed Languages
Martin Thompson explores how their managed runtimes can equal, and even better in some cases, the performance of native languages.
50:33
Continuous Performance Testing
Mark Price talks about techniques for making performance testing a first-class citizen in a Continuous Delivery pipeline.
40:53
Performance Testing in Java
Ix-chel Ruiz and Andres Almiray talk about the tools, like JMeter and JMH, and some techniques that should make engaging in performance testing a rewarding experience.
21:07
Machine Learning at Scale
Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
49:40
Causal Consistency for Large Neo4j Clusters
Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
40:00
Deep Learning at Scale
Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.
34:33
Reactive & Asynchronous - Adventures with APIs in Financial Trading
Michael Barker discusses several low-latency APIs used for financial trading, what makes them fast and how they compare to HTTP/REST/JSON/XML APIs.
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