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HammerDB: Using MySQL 5.7 vs 8.0 to understand performance profiles

HammerDB

One of the most important concepts in analysing database performance is that of understanding scalability. Plotting these data points enables us to understand the scalability of the database software being tested on that system. Then we can run an interactive workload for a single Virtual User as follows for MySQL 8.0.25./hammerdbcli

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HammerDB MySQL and MariaDB Best Practice for Performance and Scalability

HammerDB

This post complements the previous best practice guides this time with the focus on MySQL and MariaDB and achieving top levels of performance with the HammerDB MySQL TPC-C test. SELECT DISTINCT c from sbtest where id between ? order by c. SELECT c from sbtest where id=? Copyright (C) 2003-2018 Steve Shaw.

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Why Tcl is 700% faster than Python for database benchmarking

HammerDB

So instead we are going to take a cut down version of the HammerDB TPROC-C driver script and do the same in Python and use the HammerDB infrastructure to measure performance. (We use stored procedures because, as the introductory post shows, using single SQL statements turns our database benchmark into a network test). usr/local/bin/tclsh8.6

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HammerDB Concepts and Architecture

HammerDB

This article Threads Done Right… With Tcl gives an excellent overview of these capabilities and it should be clear that to build a scalable benchmarking tool this thread performance and scalability is key. These bin and lib directories are not included with the source code. You can download and compile TCL/TK 8.6

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What Adrian Did Next?—?Part 2?—?Sun Microsystems

Adrian Cockcroft

I became the Sun UK local specialist in performance and hardware, and as Sun transitioned from a desktop workstation company to sell high end multiprocessor servers I was helping customers find and fix scalability problems. I also applied Six Sigma to capacity planning and presented this at a conference in 2003.

Tuning 52
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SQL Server I/O Basics Chapter #2

SQL Server According to Bob

Note: ​​ Pages that are nev er modified ( never​​ dirtied) remain in the clean state until they are dis c arded at which time the checksum is validated for the constant page. ​​ The following table outlines the virtual protection states. ​​ When the modification latch is acquired.

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