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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Effective management of memory stores with policies like LRU/LFU proactive monitoring of the replication process and advanced metrics such as cache hit ratio and persistence indicators are crucial for ensuring data integrity and optimizing Redis’s performance. Cache Hit Ratio The cache hit ratio represents the efficiency of cache usage.

Metrics 130
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Use Distributed Caching to Accelerate Online Web Sites

ScaleOut Software

The Solution: Distributed Caching. A widely used technology called distributed caching meets this need by storing frequently accessed data in memory on a server farm instead of within a database. It’s not enough simply to lash together a set of servers hosting a collection of in-memory caches.

Cache 52
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Use Distributed Caching to Accelerate Online Web Sites

ScaleOut Software

The Solution: Distributed Caching. A widely used technology called distributed caching meets this need by storing frequently accessed data in memory on a server farm instead of within a database. It’s not enough simply to lash together a set of servers hosting a collection of in-memory caches.

Cache 52
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What is a Distributed Storage System

Scalegrid

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. They maintain fault tolerance and redundancy by replicating this information throughout various nodes in the system.

Storage 130
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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

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
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An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems

The Morning Paper

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.

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Redis® Monitoring Strategies for 2024

Scalegrid

This includes latency, which is a major determinant in evaluating the reliability and performance of your Redis® instance, CPU usage to assess how much time it spends on tasks, operations such as reading/writing data from disk or network I/O, and memory utilization (also known as memory metrics).

Strategy 130