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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

This is guest post by Sachin Sinha who is passionate about data, analytics and machine learning at scale. We note that for MongoDB update latency is really very low (low is better) compared to other dbs, however the read latency is on the higher side. Again Yugabyte latency is quite high. Author & founder of BangDB.

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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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MySQL Key Performance Indicators (KPI) With PMM

Percona

This includes metrics such as query execution time, the number of queries executed per second, and the utilization of query cache and adaptive hash index. query cache: Disable (query_cache_size: 0, query_cache_type:OFF) innodb_adaptive_hash_index: Check adaptive hash index usage to determine its efficiency.

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The Surprising Effectiveness of Non-Overlapping, Sensitivity-Based Performance Models

John McCalpin

To show that I can criticize my own work as well, here I show that sustained memory bandwidth (using an approximation to the STREAM Benchmark ) is also inadequate as a single figure of metric. (It Here I assumed a particular analytical function for the amount of memory traffic as a function of cache size to scale the bandwidth time.

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AppFabric Caching: Retry Later

ScaleOut Software

Likewise, object access paths must be heavily multi-threaded and avoid lock contention to minimize access latency and maximize throughput. During load-balancing, the client gets the following exception when accessing the cache: ErrorCode<ERRCA0017>:SubStatus<ES0006>:There is a temporary failure. Please retry later.

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HCI Performance testing made easy (Part 3)

n0derunner

Creating a HCI benchmark to simulate multi-tennent workloads. A particular problem occurs when a reporting / analytical workload shares storage with a transactional workload. In such a case we have a Bandwidth heavy workload profile (reporting) sharing with a Latency Sensitive workload (transactional).

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The Performance Inequality Gap, 2021

Alex Russell

A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage. Using a global ASP as a benchmark can further mislead thanks to the distorting effect of ultra-high-end prices rising while shipment volumes stagnate. The Moto G4 , for example.