Percona

NOVEMBER 3, 2023

When it comes to Citus, successfully building out and scaling a PostgreSQL cluster across multiple nodes and even across data centers can feel, at times, to be an art form because there are so many ways of building it out. The following invocation generates almost 4GB of data.

Database 114

Database Benchmarking Latency C++ 114

Percona

MARCH 23, 2023

Hardware Memory The amount of RAM to be provisioned for database servers can vary greatly depending on the size of the database and the specific requirements of the company. Benchmark before you decide. have been released since then with some major changes. Transparent huge pages (THP) disabled.

Performance 144

Performance Hardware Tuning Storage 144

The Morning Paper

NOVEMBER 3, 2019

Google’s data center kernel is carefully performance tuned for their workloads. For the rest of us, if you really need that extra performance (maybe what you get out-of-the-box or with minimal tuning is good enough for your use case) then you can upgrade hardware and/or pay for a commercial license of a tuned distributed (RHEL).

Performance 108

Performance Benchmarking Tuning Hardware 108

The Morning Paper

JUNE 11, 2019

Beyond data and model parallelism for deep neural networks Jia et al., Traditional approaches to training exploit either data parallelism (dividing up the training samples), model parallelism (dividing up the model parameters), or expert-designed hybrids for particular situations. SysML’2019. Expanding the search space.

Network 81

Network Strategy Hardware Benchmarking 81

HammerDB

OCTOBER 23, 2023

HammerDB uses stored procedures to achieve maximum throughput when benchmarking your database. HammerDB has always used stored procedures as a design decision because the original benchmark was implemented as close as possible to the example workload in the TPC-C specification that uses stored procedures. On MySQL, we saw a 1.5X

Benchmarking 63

Benchmarking Database Network C++ 63

Percona

JUNE 22, 2023

To make data count and to ensure cloud computing is unabated, companies and organizations must have highly available databases. Fault tolerance aims for zero downtime and data loss. Data replication : Data is continually copied from one database to another to ensure that the system remains operational even if one database fails.

Availability 132

Availability Database Open Source Hardware 132

Scalegrid

JULY 13, 2020

Oracle requires more complex ongoing administration, as all database configurations must evolve in conjunction with the data schemas and custom code. Data encryption can be achieved with advanced security plugins like pgcrypto which are available for free. CitusDB – distributes data and queries horizontally across nodes.

Open Source 244

Open Source Tuning C++ Database 244

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. better cluster resource utilization.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Percona

MARCH 27, 2023

Disclaimer : This blog post is meant to show a less-known problem but is not meant to be a serious benchmark. The percentage in degradation will vary depending on many factors {hardware, workload, number of tables, configuration, etc.}. Setup The setup consists of creating 10K tables with sysbench and adding 20 FKs to 20 tables.

Cache 124

Cache Hardware Servers Benchmarking 124

Scalegrid

DECEMBER 6, 2023

In the digital age, data management has transformed from locally hosted servers to cloud solutions. These databases require significant time commitment along with necessary technical skills plus hardware & software costs, all of which are without dedicated team assistance.

Database 130

Database Cloud Hardware Storage 130

Adrian Cockcroft

SEPTEMBER 23, 2024

Most publications have simply reported the benchmark improvement claims, but if you stop to think about them, the numbers dont make sense based on a simplistic view of the technology changes. So first thing to understand is that the benchmark skips a generation and compares product that differs over about a two year interval.

Benchmarking 52

Benchmarking Energy Architecture Latency 52

Speed Curve

MARCH 7, 2022

Size matters especially to mobile users who have limited and/or metered data. It's important to mention that there have been changes to how the Archive collects data over the years. Having said that, looking at data over the past ten years, it's safe to make the observation that pages are definitely trending bigger.

Mobile 145

Mobile Google Benchmarking Metrics 145

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

Percona

JUNE 22, 2023

Database uptime and availability Monitoring database uptime and availability is crucial as it directly impacts the availability of critical data and the performance of applications or websites that rely on the MySQL database. Disk space usage Monitor the disk space usage of MySQL data files, log files, and temporary files.

Performance 133

Performance Monitoring Traffic Database 133

John McCalpin

APRIL 25, 2023

With that in mind, I decided to mine some historical data (and run a few new experiments) to see how single-thread/single-core memory bandwidth has evolved over the last 10-15 years. GHz, 1530 GB/s peak BW from 6 HBM stacks), I see single-thread sustained memory bandwidth of 304 GB/s on the ReadOnly benchmark used here. Stay tuned!

Benchmarking 97

Benchmarking Cache Latency Tuning 97

Alex Russell

APRIL 29, 2021

This post mines publicly available data on the pace of compatibility fixes and feature additions to assess the claim. As an engineer on a browser team, I'm privy to the blow-by-blow of various performance projects, benchmark fire drills, and the ways performance marketing (deeply) impacts engineering priorities. Converging Views.

Media 145

Media Games Education Engineering 145

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

Percona

AUGUST 22, 2023

We’ll also look at the differences, as it’s important to know what architecture(s) will help you best meet your unique requirements for maximizing data assets and achieving continuous uptime. Redundancy: The system stores multiple copies of data on separate devices. there cannot be high availability.

Availability 98

Availability Hardware Open Source Database 98

VoltDB

JULY 24, 2024

In this light, we are always seeking to prove Volt’s value against other data platforms for supporting applications that need to be fast without compromising on things like accuracy, consistency, or resiliency. As part of our new support for ARM processors , we recently ran benchmarks on both Intel C7 and ARM c7g on AWS.

AWS 52

AWS Benchmarking Hardware Latency 52

Brendan Gregg

MARCH 16, 2024

It fails because of a compiler optimization where the frame pointer register is used to store data instead of the frame pointer, but it's just a number so the profiler is unaware this happened and tries to match that address to a function symbol and fails (it is therefore an unknown symbol). You usually get an extra junk frame.

Java 137

Java Cache Google Hardware 137

The Morning Paper

MAY 23, 2019

Existing cache and main memory compression techniques compress data in small fixed-size blocks, typically cache lines. 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.

Cache 61

Cache Benchmarking Hardware Java 61

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. Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. Derived Workloads.

Benchmarking 63

Benchmarking Open Source C++ Cache 63

Percona

AUGUST 30, 2018

Because recognizing if the workload is read intensive or write intensive will impact your hardware choices, database configuration as well as what techniques you can apply for performance optimization and scalability. Let’s examine the TPC-C Benchmark from this point of view, or more specifically its implementation in Sysbench.

Benchmarking 66

Benchmarking Database Operating System Architecture 66

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. At hardware reset the boot code is granted maximally permissive architectural capabilities.

C++ 61

C++ Hardware Virtualization Benchmarking 61

HammerDB

FEBRUARY 25, 2021

HammerDB is a load testing and benchmarking application for relational databases. This helps to minimise locking allowing multiple sessions to access the same data at the same time. However, it is crucial that the benchmarking application does not have inherent bottlenecks that artificially limits the scalability of the database.

Programming 68

Programming Benchmarking Virtualization C++ 68

Abhishek Tiwari

DECEMBER 13, 2017

Can machine learning-based data structures i.e. learned data structures replace traditional data structures? The initial results show that learned data structures can outperform existing data structures. This has far-reaching implications how future data systems and algorithms will be designed. p = F(Key) ?

Artificial Intelligence 40

Artificial Intelligence Hardware Google Benchmarking 40

Percona

JANUARY 19, 2023

With disks being faster nowadays and CPU and memory resources being cheaper, we could easily say MySQL can handle TBs of data with good performance. For instance, in Percona Managed Services , we have many clients with TBs worth of data that are well performant. There are many compression tools and algorithms for data out there.

Open Source 111

Open Source Storage Database Big Data 111

The Morning Paper

JUNE 27, 2019

Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. This comes at a cost though; copying, rearranging, and materialising to memory two orders of magnitude more data than is strictly necessary.

Systems 87

Systems Programming Tuning Innovation 87

VoltDB

JUNE 1, 2017

Hardware Optimizers” want to get the maximum utilization out of hardware. These systems were designed to have a lifetime of half a decade or more, and rapidly changing hardware meant that the initial deployment had to be sized for 5-7 years out. Attendees could be broken down into several distinct groups.

Hardware 56

Hardware Latency Virtualization Cloud 56

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

Big Data 45

Big Data Cloud Performance Hardware 45

HammerDB

JULY 6, 2024

In a recent project comparing systems for MariaDB performance, a user had originally been using a tool called sysbench-tpcc to compare hardware platforms before migrating to HammerDB. This is a brief post to highlight the metrics to use to do the comparison using a separate hardware platform for illustration purposes. sum: 23997083.58

C++ 52

C++ Hardware Benchmarking Virtualization 52

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 55

Performance Benchmarking Cache Storage 55

Alex Russell

OCTOBER 22, 2017

This rework delays launch which, in turn, delays gathering data about the viability of a PWA strategy. Budgets are scaled to a benchmark network & device. Deciding what benchmark to use for a performance budget is crucial. Given the conflicted data we see across our other systems, this seems about right as a baseline.

Performance 74

Performance Network Benchmarking Mobile 74

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. Slow queries and data retrieval can lead to frustrating delays that impact the user experience. Nobody has time for that!

Tuning 52

Tuning Database Efficiency Performance 52

Percona

SEPTEMBER 1, 2023

This results in expedited query execution, reduced resource utilization, and more efficient exploitation of the available hardware resources. This not only enhances performance but also enables you to make more efficient use of your hardware resources, potentially resulting in cost savings on infrastructure.

Tuning 52

Tuning Database Performance Hardware 52

SQL Server According to Bob

SEPTEMBER 26, 2016

When we released Always On Availability Groups in SQL Server 2012 as a new and powerful way to achieve high availability, hardware environments included NUMA machines with low-end multi-core processors and SATA and SAN drives for storage (some SSDs). As we moved towards SQL Server 2014, the pace of hardware accelerated.

Availability 40

Availability Servers Hardware Benchmarking 40

KeyCDN

AUGUST 15, 2019

Before you begin tuning your website or application, you must first figure out which metrics matter most to your users and establish some achievable benchmarks. An interruption in data flow due to limited capacity is called a bottleneck. Test and collect data: Let your testing software do all the hard work, then assess the results.

Testing Tools 55

Testing Tools Best Practices Performance Testing Testing 55