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How to Assess MySQL Performance

HammerDB

Among the different components of modern software solutions, the database is one of the most critical. 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. Database: MySQL 8.0.31

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

Please note that the focus of these tests was around standard metrics gathering and display, we’ll use a future blog post to benchmark some of the more intensive query analytics (QAN) performance numbers. Virtual Memory utilization was averaging 48 GB of RAM. Virtual Memory utilization was averaging 48 GB of RAM.

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The Return of the Frame Pointers

Brendan Gregg

To explain this example in more detail: The profiler periodically interrupts software execution, and for those disconnected stacks it happens to be the execution of the kernel software ("vfs*", "ext*", etc.). It shouldn't be 10%, unless it's cache effects. These partial stacks get grouped together on the left.

Java 145
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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.

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Static Analysis of Java Enterprise Applications: Frameworks and Caches, the Elephants in the Room

The Morning Paper

Static analysis of Java enterprise applications: frameworks and caches, the elephants in the room , Antoniadis et al., Instead, analyses expect their users to provide customization for web applications, which is a significant burden, virtually never overcome in practice. PLDI’20. Introducing JackEE. JackEE in action.

Java 80
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Understanding MySQL Triggers: Exploring How Triggers Impact MySQL Memory Allocation

Percona

Testing and Benchmarking : Thoroughly test triggers in a staging environment to evaluate their impact on performance. Benchmark different trigger implementations to identify the most efficient option. These table cache instances could be accessed concurrently, allowing DML to use cached table descriptors without locking each other.

Cache 62
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AppFabric Caching: Retry Later

ScaleOut Software

We have spent a great deal of time at ScaleOut Software re-architecting our in-memory data grid (IMDG)’s code base to make best use of many cores and large memory. During load-balancing, the client gets the following exception when accessing the cache: ErrorCode<ERRCA0017>:SubStatus<ES0006>:There is a temporary failure.

Cache 40