Dynatrace

AUGUST 12, 2020

As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. With Azure Functions, engineers don’t have to worry about provisioning and maintaining underlying hardware; they simply upload their code, and it’s up and running seconds later. So stay tuned! Dynatrace news.

Serverless 236

Serverless Azure Monitoring Lambda 236

SQL Performance

JANUARY 23, 2020

Tom Davidson, Opening Microsoft's Performance-Tuning Toolbox SQL Server Pro Magazine, December 2003. Waits and Queues has been used as a SQL Server performance tuning methodology since Tom Davidson published the above article as well as the well-known SQL Server 2005 Waits and Queues whitepaper in 2006. The Top Queries That Weren't.

Tuning 115

Tuning Servers Monitoring Hardware 115

Dynatrace

AUGUST 12, 2020

As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. With Azure Functions, engineers don’t have to worry about provisioning and maintaining underlying hardware; they simply upload their code, and it’s up and running seconds later. So stay tuned! Dynatrace news.

Serverless 142

Serverless Azure Monitoring Lambda 142

Brendan Gregg

SEPTEMBER 25, 2021

As a Xen guest, this profile was gathered using perf(1) and the kernel's software cpu-clock soft interrupts, not the hardware NMI. Software-based clocksources could fix those issues and provide accurate monotonically-increasing time. Theory (A) is most likely based on the frame widths in the flame graph.

Speed 126

Speed Java AWS Virtualization 126

Percona

APRIL 17, 2023

The main objective of this post is to share my experience over the past years tuning MongoDB and centralize the diverse sources that I crossed in this journey in a unique place. systemctl stop tuned $ systemctl disable tuned Dirty ratio The dirty_ratio is the percentage of total system memory that can hold dirty pages.

Best Practices 52

Best Practices Design Tuning Database 52

HammerDB

JANUARY 5, 2019

As the chart shows because we know that both HammerDB and the implementation of the TPC-C workload scales then we can determine that with this particular database engine both the software and hardware scales as well. In all cases this is random data so presents no security challenges. can you be sure that it scales?

Benchmarking 40

Benchmarking Database Social Media Scalability 40

HammerDB

OCTOBER 18, 2019

cpupower frequency-info analyzing CPU 0: driver: intel_pstate CPUs which run at the same hardware frequency: 0 CPUs which need to have their frequency coordinated by software: 0 maximum transition latency: Cannot determine or is not supported. hardware limits: 1000 MHz - 4.00 hardware limits: 1000 MHz - 4.00

Benchmarking 63

Benchmarking Performance Hardware C++ 63

O'Reilly Software

APRIL 4, 2018

Fast forward to the present day and we find ourselves in a world where the number of connected devices is constantly increasing. Here are five considerations every software architect and developer needs to take into account when setting the architectural foundations for a fast data platform. Determine requirements first. At least once?

Architecture 80

Architecture Scalability Google Operating System 80

Brendan Gregg

SEPTEMBER 25, 2021

As a Xen guest, this profile was gathered using perf(1) and the kernel's software cpu-clock soft interrupts, not the hardware NMI. Software-based clocksources could fix those issues and provide accurate monotonically-increasing time. Theory (A) is most likely based on the frame widths in the flame graph.

Speed 40

Speed Java AWS Virtualization 40

HammerDB

OCTOBER 12, 2018

As is also the case this limitation is at the database level (especially the storage engine) rather than the hardware level. For this reason sysbench presents a vastly more simplified workload than HammerDB. . driver: intel_pstate CPUs which run at the same hardware frequency: 0 . hardware limits: 1000 MHz - 3.80

Best Practices 40

Best Practices Scalability Performance C++ 40

SQL Server According to Bob

JANUARY 11, 2020

​​ Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information presented after the date of publication. This White Paper is for informational purposes only.

Servers 40

Servers Cache Media Hardware 40

Brendan Gregg

SEPTEMBER 5, 2017

Linux has been adding tracing technologies over the years: kprobes (kernel dynamic tracing), uprobes (user-level dynamic tracing), tracepoints (static tracing), and perf_events (profiling and hardware counters). They use third-party software like Docker or Kubernetes to simplify their administration. People rarely create them manually.

Virtualization 76

Virtualization AWS Engineering Hardware 76

John McCalpin

JANUARY 22, 2018

Dr. Damon McDougall gave a short presentation on this study at the IXPUG 2018 Fall Conference ( pdf ) — I originally wrote these notes to help organize my thoughts as we were preparing the IXPUG presentation, and later decided that the extra details contained here are interesting enough for me to post it.

Latency 40

Latency Hardware Code Testing 40

SQL Server According to Bob

JANUARY 11, 2020

The suspension takes place when ​​ no user requests are ​​ present ​​ and wakes ​​ when a ​​ new ​​ request arrives. This White Paper is for informational purposes only.

Servers 40

Servers Cache Database Media 40

Smashing Magazine

AUGUST 22, 2021

As such, tuning congestion logic is usually only done by a select few developers, and evolution is slow. In reality, these are actually TLS features that were already present in some form in TLS 1.2 Many network interface controllers (NICs) even have built-in hardware-offload features for TCP.

Performance 107

Performance Network Latency Servers 107

All Things Distributed

NOVEMBER 30, 2016

Effectively applying AI involves extensive manual effort to develop and tune many different types of machine learning and deep learning algorithms (e.g. automatic speech recognition, natural language understanding, image classification), collect and clean the training data, and train and tune the machine learning models.

AWS 165

AWS Lambda Artificial Intelligence Mobile 165