Scalegrid

JANUARY 25, 2024

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. It can achieve impressive performance, handling up to 50 million operations per second.

Metrics 130

Metrics Monitoring Latency Cache 130

The Netflix TechBlog

JUNE 4, 2019

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. This avoids thrashing caches too much for B and evens out the pressure on the L3 caches of the machine.

Cache 251

Cache Latency Airlines Logistics 251

Scalegrid

DECEMBER 21, 2023

Identifying key Redis® metrics such as latency, CPU usage, and memory metrics is crucial for effective Redis monitoring. With these essential support systems in place, you can effectively monitor your databases with up-to-date data about their health and functioning status at all times.

Strategy 130

Strategy Monitoring Latency DevOps 130

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. By caching hot datasets, indexes, and ongoing changes, InnoDB can provide faster response times and utilize disk IO in a much more optimal way.

Performance 123

Performance Hardware Tuning Storage 123

Scalegrid

FEBRUARY 8, 2024

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. By implementing data replication strategies, distributed storage systems achieve greater.

Storage 130

Storage Systems Big Data Azure 130

The Morning Paper

MAY 12, 2019

An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems Gan et al., The paper examines the implications of microservices at the hardware, OS and networking stack, cluster management, and application framework levels, as well as the impact of tail latency.

Open Source 61

Open Source Hardware Benchmarking Systems 61

The Morning Paper

DECEMBER 10, 2019

. …software operating on persistent data structures requires "global" pointers that remain valid after a process terminates, while hardware requires that a diverse set of devices all have the same mappings they need for bulk transfers to and from memory, and that they be able to do so for a potentially heterogeneous memory system.

Hardware 52

Hardware Virtualization Operating System Programming 52