HammerDB

DECEMBER 5, 2020

compared to previous releases is that the workload names have changed from TPC-C and TPC-H to TPROC-C and TPROC-H respectively and therefore a key question is how are the v4.0 The simple answer is nothing, the workloads are exactly the same workloads derived from the TPC-C and TPC-H specifications and HammerDB v4.0

C++ 40

C++ Benchmarking Virtualization Open Source 40

HammerDB

JANUARY 5, 2019

Some opinions claim that “Benchmarks are meaningless”, “benchmarks are irrelevant” or “benchmarks are nothing like your real applications” However for others “Benchmarks matter,” as they “account for the processing architecture and speed, memory, storage subsystems and the database engine.”

Benchmarking 40

Benchmarking Database Social Media Scalability 40

HammerDB

FEBRUARY 25, 2021

HammerDB is a load testing and benchmarking application for relational databases. However, it is crucial that the benchmarking application does not have inherent bottlenecks that artificially limits the scalability of the database. Basic Benchmarking Concepts. To benchmark a database we introduce the concept of a Virtual User.

Programming 67

Programming Benchmarking Virtualization C++ 67

HammerDB

APRIL 19, 2023

GHz 4th Generation Intel Xeon Scalable processors (code-named Sapphire Rapids) Up to 20% higher compute performance than z1d instances Up to 50 Gbps of networking speed Up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (EBS) We can also verify these capabilities by running some simple benchmarks on the different subsystems.

Performance 54

Performance Benchmarking Cache Storage 54

Scalegrid

JULY 13, 2020

of PostgreSQL users are currently in the process of migrating to the RDBMS, according to the 2019 PostgreSQL Trends Report , an astounding percentage considering this is the 4th most popular database in the world. Objective C. Oracle support for hardware and software packages is typically available at 22% of their licensing fees.

Open Source 244

Open Source Tuning C++ Database 244

The Morning Paper

MAY 23, 2019

Looking across a set of eight Java benchmarks, we find that only two of them are array dominated, the rest having between 40% to 75% of the heap footprint allocated to objects, the vast majority of which are small. Consider a B-Tree node from the B-tree Java benchmark: Uncompressed, it’s memory layout looks like (a) below. Evaluation.

Cache 61

Cache Benchmarking Hardware Java 61

Percona

MAY 1, 2023

Key areas include: Configuration parameter tuning : This tuning involves altering variables such as memory allocation, disk I/O settings, and concurrent connections based on specific hardware and requirements. Throughput: The throughput of an application is directly influenced by the speed at which the database can process and serve queries.

Tuning 52

Tuning Efficiency Database Performance 52

Brendan Gregg

SEPTEMBER 25, 2021

What about short-lived processes, like a service restarting in a loop? As a Xen guest, this profile was gathered using perf(1) and the kernel's software cpu-clock soft interrupts, not the hardware NMI. As (C) looked like a kernel rebuild, I started with (D) and (E). ## 5. These can be invisible to top(8).

Speed 126

Speed Java AWS Virtualization 126

Adrian Cockcroft

JUNE 21, 2022

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. We had specializations in hardware, operating systems, databases, graphics, etc. that a lot of people used.

Tuning 52

Tuning Benchmarking Engineering C++ 52

Brendan Gregg

SEPTEMBER 25, 2021

What about short-lived processes, like a service restarting in a loop? As a Xen guest, this profile was gathered using perf(1) and the kernel's software cpu-clock soft interrupts, not the hardware NMI. As (C) looked like a kernel rebuild, I started with (D) and (E). These can be invisible to top(8).

Speed 52

Speed Java AWS Virtualization 52

HammerDB

OCTOBER 12, 2018

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. As is also the case this limitation is at the database level (especially the storage engine) rather than the hardware level. order by c.

Best Practices 40

Best Practices Scalability Performance C++ 40

Brendan Gregg

SEPTEMBER 25, 2021

What about short-lived processes, like a service restarting in a loop? As a Xen guest, this profile was gathered using perf(1) and the kernel's software cpu-clock soft interrupts, not the hardware NMI. As (C) looked like a kernel rebuild, I started with (D) and (E). ## 6. These can be invisible to top(8).

Speed 40

Speed Java AWS Virtualization 40

Smashing Magazine

JANUARY 6, 2020

So, if we created an overview of all the things we have to keep in mind when improving performance — from the very start of the process until the final release of the website — what would that list look like? Also, as Patrick Meenan suggested, it’s worth to plan out a loading sequence and trade-offs during the design process.

Performance 145

Performance Cache Network Metrics 145

SQL Server According to Bob

JANUARY 11, 2020

DBCC Page Auditing DBCC page auditing is the process of executing common DBCC consistency checks as pages are read from disk. Character POS ASCII Value Formula Value A 1 65 67 B 2 66 68 Checksum 135 Now assume ​​ that ​​ the string was stored on disk but when read back it was “AC”.

Servers 40

Servers Cache Database Media 40

Smashing Magazine

JANUARY 11, 2021

Micro-optimizations are great for keeping performance on track, but it’s critical to have clearly defined targets in mind — measurable goals that would influence any decisions made throughout the process. Also, as Patrick Meenan suggested, it’s worth to plan out a loading sequence and trade-offs during the design process.

Performance 145

Performance Cache Media Metrics 145

High Scalability

NOVEMBER 25, 2019

Your Laptop has a filesystem used by hundreds of processes. It is limited by the disk space; it can’t expand storage elastically; it chokes if you run few I/O intensive processes or try collaborating with 100 other users. On prem data processing. Locks used for strong consistency across processes. Document store.

Architecture 52

Architecture Cache Azure Storage 52