HammerDB

FEBRUARY 25, 2021

HammerDB is a load testing and benchmarking application for relational databases. On high-performance multi-core systems all the supported databases can return performance in the many millions of transactions per minute. This is why the choice of programming language is so important from the outset. Basic Benchmarking Concepts.

Programming 67

Programming Benchmarking Virtualization C++ 67

The Morning Paper

MAY 12, 2019

An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems Gan et al., A typical architecture diagram for one of these services looks like this: Suitably armed with a set of benchmark microservices applications, the investigation can begin! Hardware implications.

Open Source 61

Open Source Hardware Benchmarking Systems 61

Scalegrid

JANUARY 25, 2024

Key metrics like throughput, request latency, and memory utilization are essential for assessing Redis health, with tools like the MONITOR command and Redis-benchmark for latency and throughput analysis and MEMORY USAGE/STATS commands for evaluating memory. It depends upon your application workload and its business logic.

Metrics 130

Metrics Monitoring Latency Cache 130

Dynatrace

NOVEMBER 22, 2022

How can IT teams deliver system availability under peak loads that will satisfy customers? Five-nines availability: The ultimate benchmark of system availability. Site reliability engineering teams often measure system availability in percentages in the pursuit of 100% uptime. But is five nines availability attainable?

Infrastructure 176

Infrastructure Availability Systems Retail 176

HammerDB

JUNE 27, 2022

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. HammerDB has graphical and command line interfaces for the Windows and Linux operating systems. Why HammerDB was developed.

Benchmarking 62

Benchmarking Open Source C++ Cache 62

The Morning Paper

MAY 23, 2019

These techniques work well for scientific programs that are dominated by arrays. However, they are ineffective on object-based programs because objects do not fall neatly into fixed-size blocks and have a more irregular layout. Consider a B-Tree node from the B-tree Java benchmark: Uncompressed, it’s memory layout looks like (a) below.

Cache 61

Cache Benchmarking Hardware Java 61

The Morning Paper

MAY 27, 2019

Last week we saw the benefits of rethinking memory and pointer models at the hardware level when it came to object storage and compression ( Zippads ). The protections are hardware implemented and cannot be forged in software. And this all has to work for whole-system executions, not just the C-language portion of user processes.

C++ 61

C++ Hardware Virtualization Benchmarking 61