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

High Scalability

Application example: user profile cache, where profiles are constructed elsewhere (e.g., The latency table shows that 99th percentile latency for Yugabyte is quite high compared to others (lower is better). Workload C: Read only. This workload is 100% read. However, Very high Read latency for MonoDB makes it the last db to finish the task.

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Redis vs Memcached in 2024

Scalegrid

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. Redis is better suited for complex data models, and Memcached is better suited for high-throughput, string-based caching scenarios.

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Why Tcl is 700% faster than Python for database benchmarking

HammerDB

Python is a popular programming language, especially for beginners, and consequently we see it occurring in places where it just shouldn’t be used, such as database benchmarking. We use stored procedures because, as the introductory post shows, using single SQL statements turns our database benchmark into a network test).

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PostgreSQL: Pgpool-II Use Cases and Benefits

Percona

One of the key benefits of using PostgreSQL is its reliability, scalability, and performance. Query caching Pgpool-II can cache frequently used queries in memory, reducing the load on your PostgreSQL servers and improving response times. Two benchmarks from users can be found here: [1] [2] 4.

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HammerDB for Managers

HammerDB

HammerDB is a software application for database benchmarking. Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. The Transaction Processing Performance Council (TPC) was founded to bring standards to database benchmarking, and the history of the TPC can be found here.

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Static Analysis of Java Enterprise Applications: Frameworks and Caches, the Elephants in the Room

The Morning Paper

Static analysis of Java enterprise applications: frameworks and caches, the elephants in the room , Antoniadis et al., However, collecting a set of sizable, realistic benchmarks, showing that their analysis is feasible, and making progress in its precision are good ways to ensure further research in this high-value area.

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

But as companies grow and see more demand for their databases, we need to ensure that PMM also remains scalable so you don’t need to worry about its performance while tending to the rest of your environment. VictoriaMetrics maintains an in-memory cache for mapping active time series into internal series IDs.