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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key metrics like throughput, request latency, and memory utilization are essential for assessing Redis health, with tools like the MONITOR command and Redis-benchmark for latency and throughput analysis and MEMORY USAGE/STATS commands for evaluating memory. All these contribute significantly towards ensuring smooth functioning.

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MySQL Key Performance Indicators (KPI) With PMM

Percona

We will also discuss related configuration variables to consider that can impact these KPIs, helping you gain a comprehensive understanding of your MySQL server’s performance and efficiency. Query performance Query performance is a key performance indicator (KPI) in MySQL, as it measures the efficiency and speed of query execution.

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2022 in review: New dashboards, Core Web Vitals enhancements, third-party tracking & more!

Speed Curve

we also: Launched a series of new and improved dashboards to help you identify and fix your performance issues more quickly and efficiently (More on those below.). Your current competitive benchmarks status. Evaluate CDN performance by exploring the impact of time-of-day traffic patterns. Expanded Industry Speed Benchmarks.

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Netflix at AWS re:Invent 2019

The Netflix TechBlog

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. In this talk, we share how Netflix deploys systems to meet its demands, Ceph’s design for high availability, and results from our benchmarking.

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Netflix at AWS re:Invent 2019

The Netflix TechBlog

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. In this talk, we share how Netflix deploys systems to meet its demands, Ceph’s design for high availability, and results from our benchmarking.

AWS 100
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

We earned the trust of our engineers by developing empathy for their operational burden and by focusing on providing efficient tracer library integrations in runtime environments. However, having a scalable stream processing platform doesn’t help much if you can’t store data in a cost efficient manner. Storage: don’t break the bank!

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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Looking across a set of eight Java benchmarks, we find that only two of them are array dominated, the rest having between 40% to 75% of the heap footprint allocated to objects, the vast majority of which are small. Consider a B-Tree node from the B-tree Java benchmark: Uncompressed, it’s memory layout looks like (a) below. Evaluation.

Cache 61