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Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook

The Morning Paper

Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook , Cao et al., Or in the case of key-value stores, what you benchmark. So if you want to design a system that will offer good real-world performance, it’s really useful to have benchmarks that accurately represent real-world workloads.

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Five Data-Loading Patterns To Improve Frontend Performance

Smashing Magazine

On your first try, you can use it as a benchmark for optimizations later. Developing software is becoming easier as frameworks like React, Vue, or Angular become the go-to solution for creating even the simplest applications. Because of this, Jamstack apps usually load faster, but it’s harder for developers to make dynamic content.

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20X Faster Backup Preparation With Percona XtraBackup 8.0.33-28!

Percona

After the “data dictionary” (DD) engine and DD cache are initialized on a server, the Storage Engines can ask for a table definition. Initializing a DD engine and the cache adds complexity and other server dependencies. Essentially LRU cache is disabled by loading the tables as non-evictable. ibd > t1.sdi

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New (Old) Paper.

n0derunner

A 2007 paper, that still has lots to say on the subject of benchmarking storage and filesystems. Primarily aimed at researchers and developers, but is relevant to anyone about to embark on a benchmarking effort. A Nine year study of filesystem and storage benchmarking Download. The post New (Old) Paper.

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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

It is primarily the responsibility of the database administrator or developer to tune PostgreSQL according to their system’s workload. Hardware optimization : You need to ensure that the CPU, memory, and storage components meet the performance requirements of the database workload. Why is PostgreSQL performance tuning important?

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Redis vs Memcached in 2024

Scalegrid

Key Takeaways Redis offers complex data structures and additional features for versatile data handling, while Memcached excels in simplicity with a fast, multi-threaded architecture for basic caching needs. Redis is better suited for complex data models, and Memcached is better suited for high-throughput, string-based caching scenarios.

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Towards multiverse databases

The Morning Paper

If we do that naively though, we’re going to end up with a lot of universes to store and maintain and the storage requirements alone will be prohibitive. The amount of computation required on a base update can be reduced by sharing computation and cached data between universes.