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Service level objective examples: 5 SLO examples for faster, more reliable apps

Dynatrace

Certain service-level objective examples can help organizations get started on measuring and delivering metrics that matter. Teams can build on these SLO examples to improve application performance and reliability. In this post, I’ll lay out five SLO examples that every DevOps and SRE team should consider. or 99.99% of the time.

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Write Optimized Spark Code for Big Data Applications

DZone

This can significantly reduce network overhead and improve performance. For example, to broadcast a lookup table named lookup_table : Broadcast variables can be used to efficiently distribute large read-only data structures, such as lookup tables, to worker nodes.

Big Data 173
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CSS and Network Performance

CSS Wizardry

In this post I want to look at how CSS can prove to be a substantial bottleneck on the network (both in itself and for other resources) and how we can mitigate it, thus shortening the Critical Path and reducing our time to Start Render. This is best illustrated with an example. Employ Critical CSS. it can often be a net loss.

Network 278
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Kubernetes Observability: Code Profiling With Flame Graphs

Percona

It shows which code paths are more busy on the CPU in given samples. An example of a flame graph can be found below: Each box is a function in the stack, and wider boxes mean more time the system was busy on CPU on these functions. An example is kubectl-flame ( [link] ). ✔ Launching profiler. ✔ Profiling.

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Improving JavaScript Bundle Performance With Code-Splitting

Smashing Magazine

Improving JavaScript Bundle Performance With Code-Splitting. Improving JavaScript Bundle Performance With Code-Splitting. This problem is more apparent on unreliable and slow networks and lower-end devices. Let’s take a look at a simple example. When a user lands on a homepage, for example, the entire app.min.js

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Best practices and key metrics for improving mobile app performance

Dynatrace

Mobile applications (apps) are an increasingly important channel for reaching customers, but the distributed nature of mobile app platforms and delivery networks can cause performance problems that leave users frustrated, or worse, turning to competitors. Load time and network latency metrics. Proactive monitoring.

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Fallacy #8: The network is homogeneous

Particular Software

Most of the code running on the planet, at least the code that mattered, was written in.NET or Java, and interoperability via web services was at least serviceable. As an example, let’s take nullability. Interoperability is painful. Around 2005 or 2006, it wasn’t so bad. Since then, things have gotten gradually worse.

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