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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

The second phase involves migrating the traffic over to the new systems in a manner that mitigates the risk of incidents while continually monitoring and confirming that we are meeting crucial metrics tracked at multiple levels. The batch job creates a high-level summary that captures some key comparison metrics.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources. To observe model drift and accuracy, companies can use holdout evaluation sets for comparison to model data.

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Kubernetes in the wild report 2023

Dynatrace

In comparison, on-premises clusters have more and larger nodes: on average, 9 nodes with 32 to 64 GB of memory. Of the organizations in the Kubernetes survey, 71% run databases and caches in Kubernetes, representing a +48% year-over-year increase. Kubernetes infrastructure models differ between cloud and on-premises.

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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

Caching them at the other end: How long should we cache files on a user’s device? In our specific examples above, the one-big-file pattern incurred 201ms of latency, whereas the many-files approach accumulated 4,362ms by comparison. Cache This is the easy one. And do any of our previous decisions dictate our options? ?

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

PMM2 uses VictoriaMetrics (VM) as its metrics storage engine. Please note that the focus of these tests was around standard metrics gathering and display, we’ll use a future blog post to benchmark some of the more intensive query analytics (QAN) performance numbers. Virtual Memory utilization was averaging 48 GB of RAM.

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The Performance Golden Rule Revisited

Tim Kadlec

As we increasingly focus our conversations of performance on how metrics that relate to the user experience, the divide between backend and frontend goes past murky and starts becoming problematic. It isn’t immediately obvious where backend performance ends and frontend performance starts. Nor should we be thinking about it that way.

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The Fastest Google Fonts

CSS Wizardry

It’s widely accepted that self-hosted fonts are the fastest option: same origin means reduced network negotiation, predictable URLs mean we can preload , self-hosted means we can set our own cache-control. For each test, I captured the following metrics: First Paint (FP): To what extent is the critical path affected? Because harry.is

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