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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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How Bloom Filters Work in MyRocks

Percona

A bloom filter is a space-efficient way of storing information about a list of keys. If all the bits are “1”, the value may be present. Tuning In terms of tuning, two parameters can be tuned, the size of the bitmap and the number of bits set by every value.

Storage 125
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Improving our video encodes for legacy devices

The Netflix TechBlog

and thus fall back to less efficient encode families. Since then, we have applied innovations such as shot-based encoding and newer codecs to deploy more efficient encode families. Further tuning of pre-defined encoding parameters. One such encode family that has wide decoder support amongst legacy devices is our H.264/AVC

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Meet Hydrogen: A React Framework For Dynamic, Contextual And Personalized E-Commerce

Smashing Magazine

On top of this foundation, we add layers of caching, prerendering and edge delivery optimizations — not the other way around. Hydrogen fuels dynamic commerce by uniting React Server Components, streaming server-side rendering, and smart caching controls. Stay tuned for more in 2022! Large preview ). Large preview ).

Cache 135
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The case for a learned sorting algorithm

The Morning Paper

Radix Sort is carefully designed to make effective use of the L2 cache and sequential memory accesses, whereas Learned Sort is making random accesses all over the destination array. How can learned sort be adapted to make it cache-efficient? Sympathy for the machine. For the evaluation set-up, this meant $f$ was around 1,000.

Cache 137
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ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

The Netflix TechBlog

In this talk, Kinjal used the example of the LinkedIn Feed, to demonstrate how they use bandit algorithms to solve for the optimal parameter selection problem efficiently. He concluded by stressing the efficiency their teams had achieved by doing online parameter exploration instead of the much slower human-in-the-loop manual explorations.

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ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

The Netflix TechBlog

In this talk, Kinjal used the example of the LinkedIn Feed, to demonstrate how they use bandit algorithms to solve for the optimal parameter selection problem efficiently. He concluded by stressing the efficiency their teams had achieved by doing online parameter exploration instead of the much slower human-in-the-loop manual explorations.