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

The Netflix TechBlog

SEPTEMBER 24, 2021

Lastly, the packager kicks in, adding a system layer to the asset, making it ready to be consumed by the clients. Figure 1: A Simplified Video Processing Pipeline With this architecture, chunk encoding is very efficient and processed in distributed cloud computing instances. For write operations, those challenges do not apply.

Cloud 237

Cloud Media Storage Cache 237

Scalegrid

FEBRUARY 8, 2024

By implementing data replication strategies, distributed storage systems achieve greater. Durability Availability Fault tolerance These combined outcomes help minimize latency experienced by clients spread across different geographical regions. This strategy reduces the volume needed during retrieval operations.

Storage 130

Storage Systems Big Data Azure 130

Adrian Cockcroft

JANUARY 20, 2023

on Myths and Legends of High Performance Computing  — it’s a somewhat light-hearted look at some of the same issues by the leader of the team that built the Fugaku system I mention below. Jack Dongarra talked about the scores, and pointed out the low efficiency on some important workloads.

Architecture 52

Architecture Latency Benchmarking AWS 52

ScaleOut Software

MAY 15, 2014

For example, the IMDG must be able to efficiently create millions of objects in each server to make use of its huge storage capacity. Likewise, object access paths must be heavily multi-threaded and avoid lock contention to minimize access latency and maximize throughput. Please retry later.

Cache 40

Cache Servers Network Design 40