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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

When you’re running in the cloud your containers are in a shared space; in particular they share the CPU’s memory hierarchy of the host instance. However, the key insight here is that these caches are partially shared among the CPUs, which means that perfect performance isolation of co-hosted containers is not possible.

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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Additionally, DBMS is critical in reservation systems, where it stores and manages records like ticket bookings, schedules, seat allocation, and other pertinent transaction data for airlines, hotels, and railways. By implementing data abstraction techniques, these challenges can be addressed more effectively.

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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. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

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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. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

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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. Aurora Parallel Query response time (for queries which can not use indexes) can be 5x-10x better compared to the non-parallel fully cached operations. This query is 100% cached.

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