Scalegrid

JANUARY 25, 2024

Understanding Redis Performance Indicators Redis is designed to handle high traffic and low latency with its in-memory data store and efficient data structures. Evaluating factors like hit rate, which assesses cache efficiency level, or tracking key evictions from the cache are also essential elements during the Redis monitoring process.

Metrics 130

Metrics Monitoring Latency Cache 130

Percona

AUGUST 5, 2023

Various partition types, like RANGE, LIST, HASH, and KEY, are used for specific needs, from range-based data to custom criteria, to ensure efficient data handling and the optimization of queries. ensures that only relevant partitions are accessed, further enhancing query efficiency. Partition pruning, a feature in MySQL 5.7,

Strategy 40

Strategy Storage Database Best Practices 40

The Morning Paper

JUNE 27, 2019

The thrust of the argument is that there’s a chain of inter-linked assumptions / dependencies from the hardware all the way to the programming model, and any time you step outside of the mainstream it’s sufficiently hard to get acceptable performance that researchers are discouraged from doing so. Challenges compiling non-standard kernels.

Systems 87

Systems Tuning Programming Innovation 87

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

KeyCDN

SEPTEMBER 20, 2018

Let’s take a look at some of the best error monitoring tools and strategies for 2018. Error monitoring can get increasingly complicated as you deal with bugs reported by users and your production team, which is why having an efficient error tracking workflow from the beginning is so important. What is Error Tracking?

Strategy 67

Strategy Monitoring Website Google 67

Abhishek Tiwari

JULY 12, 2018

A data pipeline is a software which runs on hardware. The software is error-prone and hardware failures are inevitable. If tuned for performance, there is a good change reliability is compromised - and vice versa. This can be efficient at the beginning but counter-productive in a long run.

Latency 63

Latency Analytics Scalability Engineering 63

SQL Server According to Bob

JANUARY 11, 2020

Example 1: ​​ Hardware failure (CPU board) Battery backup on the caching controller maintained the data. Important Always consult with your hardware manufacturer for proper stable media strategies. For specific information on I/O tuning and balancing, you will find more details in the following document.

Servers 40

Servers Cache Media Hardware 40

O'Reilly

NOVEMBER 28, 2023

Even with cloud-based foundation models like GPT-4, which eliminate the need to develop your own model or provide your own infrastructure, fine-tuning a model for any particular use case is still a major undertaking. That pricing won’t be sustainable, particularly as hardware shortages drive up the cost of building infrastructure.

Open Source 108

Open Source Artificial Intelligence Google Infrastructure 108

O'Reilly

SEPTEMBER 19, 2023

Doubly so as hardware improved, eating away at the lower end of Hadoop-worthy work. Google goes a step further in offering compute instances with its specialized TPU hardware. You can download these models to use out of the box, or employ minimal compute resources to fine-tune them for your particular task.

Hardware 99

Hardware Storage Big Data Blockchain 99

SQL Server According to Bob

JANUARY 11, 2020

Restore backups on secondary systems ​​ frequently ​​ to make sure that your complete backup strategy is functioning correctly. Make sure that you ​​ understand any ​​ write-caching strategies ​​ that the utility ​​ uses.

Servers 40

Servers Cache Database Media 40

O'Reilly

OCTOBER 11, 2022

Andrew Ng , Christopher Ré , and others have pointed out that in the past decade, we’ve made a lot of progress with algorithms and hardware for running AI. Our current set of AI algorithms are good enough, as is our hardware; the hard problems are all about data. But the gain in efficiency would be relatively small.

Games 116

Games Healthcare Processing Retail 116

High Scalability

NOVEMBER 25, 2019

Version7 (Future): Move all compute to the public cloud, carve out more services for impact isolation, dynamic resource pooling to manage pets and cattle efficiently. What is your storage strategy? What is your object and content caching strategy? What is your client-side caching strategy? Do you use a reverse proxy?

Architecture 52

Architecture Cache Azure Storage 52