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Percona Toolkit – A Quick Introduction to Summary

Percona

virtual = 2.2G Caches | 12.4G virtual = 2.2G Caches | 12.4G sudo cp -v /usr/lib/mysql/plugin/libfnv_udf.so /usr/lib sudo cp -v /usr/lib/mysql/plugin/libfnv1a_udf.so /usr/lib sudo cp -v /usr/lib/mysql/plugin/libmurmur_udf.so /usr/lib Then, those shared objects need to be loaded into into the server. Shared | 475.0M

Cache 100
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Bring Your Own Cloud (BYOC) vs. Dedicated Hosting at ScaleGrid

Scalegrid

Let’s walk through an example: Database: MySQL. A vast majority of the features are the same, outside of these advanced features available through the BYOC model: Virtual Private Clouds / Virtual Networks. This becomes really important for cache solutions like Redis™. Cloud Provider: AWS. Security Groups.

Cloud 242
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A MyRocks Use Case

Percona

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. I decided to use a virtual machine with two CPU cores, four GB of memory, and storage limited to a maximum of 1000 IOPs of 16KB.

Storage 56
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HammerDB v4.10 New Features: Purge and Write back for MariaDB TPROC-C

HammerDB

This is particularly important when running automated workloads back-back to generate a performance profile for a progressively increasing number of virtual users. Once the run has completed it will dynamically set the variables to run the purge and write back and restore your variables when complete.

C++ 62
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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 example, if you are buying the latest Amazon memory-optimized EC2 instance (R7iz), the AWS page ( [link] ) tells us the following: Up to 3.9 Size does matter!

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

For example, a higher number of data samples ingested per second or a monitoring database with a huge number of tables (1000+) can affect performance; similarly, the configuration for exporters or a custom metric resolution can also have an impact on the performance of a PMM server host. Virtual Memory utilization was averaging 48 GB of RAM.

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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.