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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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How to Assess MySQL Performance

HammerDB

Predicting application performance is a difficult art, but an important one when choosing the target deployment environment. 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 storage, FIO is generally used.

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Azure Virtual Machines for SQL Server Usage

SQL Performance

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. Microsoft currently has eight main types of virtual machines designed for different types of workloads. GHz, 128MB of L3 cache, 128 PCIe 4.0 Azure VM Types and Series.

Azure 72
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PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

Scalegrid

In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting ! PgBouncer provides a virtual database that reports various useful statistics. Performance Testing. Throughput Benchmark. Part 2 – PgBouncer.

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HammerDB for Managers

HammerDB

HammerDB is a software application for database benchmarking. It enables the user to measure database performance and make comparative judgements about database hardware and software. Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. Derived Workloads.

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The Return of the Frame Pointers

Brendan Gregg

I'm sure this delivered large performance improvements and I wouldn't try arguing against it. Only in extreme circumstances does the cost (in processor time and I-cache footprint) translate to a tangible benefit - circumstances which usually resort to hand-coded assembly anyway. The actual overhead depends on your workload.

Java 145
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The Most Important MySQL Setting

Percona

To illustrate this, I ran the Sysbench-TPCC synthetic benchmark against two different GCP instances running a freshly installed Percona Server for MySQL version 8.0.31 on CentOS 7, both of them spec’d with four vCPUs but with the second one (server B) having a tad over twice as much memory than the reference one (server A).

Tuning 137