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Azure Virtual Machines for SQL Server Usage

SQL Performance

One initial, easy step to moving your SQL Server on-premises workloads to the cloud is using Azure VMs to run your SQL Server workloads in an infrastructure as a service (IaaS) scenario. You will still have to maintain your operating system, SQL Server and databases just like you would in an on-premises scenario.

Azure 72
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Why Tcl is 700% faster than Python for database benchmarking

HammerDB

Python is a popular programming language, especially for beginners, and consequently we see it occurring in places where it just shouldn’t be used, such as database benchmarking. We use stored procedures because, as the introductory post shows, using single SQL statements turns our database benchmark into a network test).

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The Most Important MySQL Setting

Percona

If we were to select the most important MySQL setting, if we were given a freshly installed MySQL or Percona Server for MySQL and could only tune a single MySQL variable, which one would it be? Sysbench ran on a third server, which I’ll refer to as the application server (APP).

Tuning 137
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PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

Scalegrid

However, these must be set up outside the PostgreSQL system, while PgBouncer can offload this to the PostgreSQL server. PgBouncer provides a virtual database that reports various useful statistics. All of the PostgreSQL benchmark tests were run under the following conditions: Initialized pgbench using a scale factor of 100.

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HammerDB for Managers

HammerDB

HammerDB is a software application for database benchmarking. Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. The Transaction Processing Performance Council (TPC) was founded to bring standards to database benchmarking, and the history of the TPC can be found here.

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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. For the network, we can use Iperf to assess the network bandwidth between the client and the database server to ensure it will be enough to meet our peak requirement.

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

Planning for resources of a PMM Server host instance can be tricky because the numbers can change depending on the DB instances being monitored by PMM. Virtual Memory utilization was averaging 48 GB of RAM. Virtual Memory utilization was averaging 48 GB of RAM.