O'Reilly

SEPTEMBER 19, 2023

It progressed from “raw compute and storage” to “reimplementing key services in push-button fashion” to “becoming the backbone of AI work”—all under the umbrella of “renting time and storage on someone else’s computers.” Cloud computing? ” scenarios at industrial scale.

Hardware 97

Hardware Storage Big Data Blockchain 97

Percona

JANUARY 19, 2023

It can help us to save costs on storage and backup times. While MySQL can handle large data sets, it is always recommended to keep only the used data in the databases, as this will make data access more efficient, and also will help to save costs on storage and backups. It supports native sharding being transparent for the application.

Open Source 85

Open Source Storage Database Big Data 85

Percona

NOVEMBER 3, 2023

PostgreSQL Cluster One coordinator node citus-coord-01 Three worker nodes citus1 citus2 citus3 Hardware AWS Instance Ubuntu Server 20.04, SSD volume type 64-bit (x86) c5.xlarge A future blog will continue my exploration into Citus by scaling out pgbench into other architectures.

Database 102

Database Benchmarking Latency C++ 102

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

Percona

AUGUST 29, 2023

Among its many capabilities, a Citus cluster can: Create distributed tables that are sharded across a cluster of PostgreSQL nodes to combine their CPU, memory, storage, and I/O capacity. Columnar storage of tables can compress data, speeding up scans and supporting fast projections, both on regular and distributed tables.

C++ 77

C++ Database Storage Testing 77

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 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. In other words, whether the workload is dominated by reads or writes. Why should you care?

Benchmarking 46

Benchmarking Database Operating System Architecture 46

Abhishek Tiwari

JULY 12, 2018

These nodes and edges require a good amount of compute and storage which is typically distributed across a large number servers either running in the cloud or your own data center. A data pipeline is a software which runs on hardware. The software is error-prone and hardware failures are inevitable.

Latency 63

Latency Analytics Scalability Engineering 63