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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130
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Azure Virtual Machines for SQL Server Usage

SQL Performance

This removes the burden of purchasing and maintaining your hardware, storage and networking infrastructure, while still giving you a very familiar experience with Windows and SQL Server itself. One important choice you will still have to make is what type and size of Azure virtual machine you want to use for your existing SQL Server workload.

Azure 72
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View from Nutanix storage during Postgres DB benchmark

n0derunner

In this example from prior post. The Linux VM running postgres has two virtual disks – one taking transaction log writes. Since the DB is small (50% the size of the Linux RAM) – the database is mostly cached on the read side – so we only see writes going to the DB files.

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A MyRocks Use Case

Percona

I wrote this post on MyRocks because I believe it is the most interesting new MySQL storage engine to have appeared over the last few years. The use case is the TPC-C benchmark but executed not on a high-end server but on a lower-spec virtual machine that is I/O limited like for example, with AWS EBS volumes.

Storage 56
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AMD EPYC Processors in Azure Virtual Machines

SQL Performance

Back on December 5, 2017, Microsoft announced that they were using AMD EPYC 7551 processors in their storage-optimized Lv2-Series virtual machines. The pricing details for the East US region are here , as an example. The L3 cache size is 64MB. The L3 cache size is 64MB. lanes for I/O connectivity.

Azure 42
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How to Assess MySQL Performance

HammerDB

Regardless of whether the computing platform to be evaluated is on-prem, containerized, virtualized, or in the cloud, it is crucial to consider several essential factors. As database performance is heavily influenced by the performance of storage, network, memory, and processors, we must understand the upper limit of these key components.

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

PMM2 uses VictoriaMetrics (VM) as its metrics storage engine. Virtual Memory utilization was averaging 48 GB of RAM. VictoriaMetrics maintains an in-memory cache for mapping active time series into internal series IDs. Capacity planning and setup details We used a dedicated 32-core CPU and 64GB of RAM for our testing.