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Kubernetes for Big Data Workloads

Abhishek Tiwari

faster access to external storage and data locality (I/O, bandwidth). A recent performance benchmark completed by Intel and BlueData using the BigBench benchmarking kit has shown that the performance ratios for container-based Hadoop workloads on BlueData EPIC are equal to and in some cases, better than bare-metal Hadoop [7].

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Why MySQL Could Be Slow With Large Tables

Percona

It can help us to save costs on storage and backup times. While MySQL can handle large data sets, it is always recommended to keep only the used data in the databases, as this will make data access more efficient, and also will help to save costs on storage and backups. 1 mysql mysql 704M Dec 30 02:28 employees.ibd -rw-r --.

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AMD EPYC 7002 Series Processors and SQL Server

SQL Performance

AMD is definitely pushing the idea that a one-socket AMD Ryzen 7002 based server is a superior replacement for nearly any two-socket Intel-based server. It will also use less power than a two-socket Intel server, with a lower hardware cost, and potentially lower licensing costs (for things like VMware). Single-Socket Database Servers.

Servers 52
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SQL Server I/O Basics Chapter #1

SQL Server According to Bob

Stable Media Stable media is often confused with physical storage. SQL Server defines stable media as storage that can survive system restart or common failure. Stable media is commonly physical disk storage, but other devices and certain caching facilities qualify as well.

Servers 40
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The Performance Inequality Gap, 2021

Alex Russell

A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage. Hardware Past As Performance Prologue. Using a global ASP as a benchmark can further mislead thanks to the distorting effect of ultra-high-end prices rising while shipment volumes stagnate.

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Can You Afford It?: Real-world Web Performance Budgets

Alex Russell

Budgets are scaled to a benchmark network & device. Deciding what benchmark to use for a performance budget is crucial. Simulated packet loss and variable latency, however, can make benchmarking extremely difficult and slow. They use “do what it takes” language to describe the efforts to get and stay fast.

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Egnyte Architecture: Lessons learned in building and scaling a multi petabyte content platform

High Scalability

It is limited by the disk space; it can’t expand storage elastically; it chokes if you run few I/O intensive processes or try collaborating with 100 other users. Over time, costs for S3 and GCS became reasonable and with Egnyte’s storage plugin architecture, our customers can now bring in any storage backend of their choice.