2019 Cache Servers Virtualization 
Scalegrid
OCTOBER 2, 2019
October 2, 2019 – ScaleGrid, a rapidly growing leader in the Database-as-a-Service (DBaaS) space, has just launched their new fully managed Hosting on Azure for Redis™ service. The demand for Redis™ is skyrocketing across dozens of use cases, particularly for cache, queues, geospatial data, and high speed transactions.
Brendan Gregg
FEBRUARY 28, 2023
My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory.
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Brendan Gregg
FEBRUARY 28, 2023
My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory.
Scalegrid
OCTOBER 2, 2019
October 2, 2019 – ScaleGrid, a rapidly growing leader in the Database-as-a-Service (DBaaS) space, has just launched their new fully managed Redis on Azure service. The demand for Redis is skyrocketing across dozens of use cases, particularly for cache, queues, geospatial data, and high speed transactions. PALO ALTO, Calif.,
High Scalability
OCTOBER 3, 2019
This Redis management solution allows startups up to enterprise-level organizations automate their Redis operations on Microsoft Azure dedicated cloud servers, alongside their other open source database deployments, including MongoDB , MySQL and PostgreSQL.
SQL Server According to Bob
DECEMBER 18, 2018
SQL Server relies on Forced-Unit-Access (Fua) I/O subsystem capabilities to provide data durability, detailed in the following documents: SQL Server 2000 I/O Basic and SQL Server I/O Basics, Chapter 2. Be sure to deploy SQL Server 2017 CU6 or newer for best data durability and performance results. “. 4 Socket, TPCC.
HammerDB
JUNE 27, 2022
An important concept was to simulate database users called Virtual Users in parallel (rather than concurrently) to accurately simulate a real database workload with multiple users running from separate systems. Cached vs Scaled Workloads. The workload also outputted the data from the Virtual Users by simulating individual terminals.
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