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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. It can achieve impressive performance, handling up to 50 million operations per second.

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Five Data-Loading Patterns To Improve Frontend Performance

Smashing Magazine

Every unnecessary bit of JavaScript code you bundle and serve will be more code the client has to load and process. On your first try, you can use it as a benchmark for optimizations later. How will you serve blazingly fast code, then? It is no wonder that the industry is getting more concerned with optimizations.

Cache 126
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The Speed of Time

Brendan Gregg

A Cassandra database cluster had switched to Ubuntu and noticed write latency increased by over 30%. The broken Java stacks turned out to be beneficial: They helped group together the os::javaTimeMillis() calls which otherwise might have have been scattered on top of different Java code paths, appearing as thin stacks everywhere.

Speed 126
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Redis vs Memcached in 2024

Scalegrid

Introduction Caching serves a dual purpose in web development – speeding up client requests and reducing server load. Benchmarking Cache Speed Memcached is optimized for high read and write loads, making it highly efficient for rapid data access in a basic key-value store.

Cache 130
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The Speed of Time

Brendan Gregg

A Cassandra database cluster had switched to Ubuntu and noticed write latency increased by over 30%. The broken Java stacks turned out to be beneficial: They helped group together the os::javaTimeMillis() calls which otherwise might have have been scattered on top of different Java code paths, appearing as thin stacks everywhere.

Speed 52
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The Speed of Time

Brendan Gregg

A Cassandra database cluster had switched to Ubuntu and noticed write latency increased by over 30%. The broken Java stacks turned out to be beneficial: They helped group together the os::javaTimeMillis() calls which otherwise might have have been scattered on top of different Java code paths, appearing as thin stacks everywhere.

Speed 40
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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 Building separate models for each of the benchmarks was required to get the correct asymptotic properties.