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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

The idea CFS operates by very frequently (every few microseconds) applying a set of heuristics which encapsulate a general concept of best practices around CPU hardware use. The second placement looks better as each CPU is given its own L1/L2 caches, and we make better use of the two L3 caches available.

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.

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Using Parallel Query with Amazon Aurora for MySQL

Percona

On multi-core machines – which is the majority of the hardware nowadays – and in the cloud, we have multiple cores available for use. I’m using the “Airlines On-Time Performance” database from [link] (You can find the scripts I used here: [link] ). With faster disks (i.e. AWS Aurora (based on MySQL 5.6) MySQL on ec2.

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