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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. This architecture shift greatly reduced the processing latency and increased system resiliency. Reloaded was well-architected, providing good stability, scalability, and a reasonable level of flexibility.

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The Netflix Cosmos Platform

The Netflix TechBlog

It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation. The first generation of this system went live with the streaming launch in 2007. Delivery?—?A

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DevOps automation: From event-driven automation to answer-driven automation [with causal AI]

Dynatrace

In the world of DevOps and SRE, DevOps automation answers the undeniable need for efficiency and scalability. The evolution of DevOps automation Since the concept of DevOps emerged around 2007 and 2008 in response to pain points with Agile development, DevOps automation has been continuously evolving.

DevOps 209
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How To Measure the Working Set Size on Linux

Brendan Gregg

It is used for capacity planning and scalability analysis. My tool does this using /proc/PID/clear_refs and the Referenced value from /proc/PID/smaps, which were added in 2007 by David Rientjes (thanks). Your app may have populated 100 Gbytes of main memory, but only uses 50 Mbytes each second to do its job. Consider these overheads.

Cache 71
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How To Measure the Working Set Size on Linux

Brendan Gregg

It is used for capacity planning and scalability analysis. My tool does this using /proc/PID/clear_refs and the Referenced value from /proc/PID/smaps, which were added in 2007 by David Rientjes (thanks). Your app may have populated 100 Gbytes of main memory, but only uses 50 Mbytes each second to do its job. Consider these overheads.

Cache 40
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Transforming enterprise integration with reactive streams

O'Reilly Software

Build a more scalable, composable, and functional architecture for interconnecting systems and applications. This is mixing concerns and leads to code that becomes strongly coupled, monolithic, hard to write, hard to read, hard to evolve, hard to test, and hard to reuse. Welcome to a new world of data-driven systems.