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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.

Metrics 130
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Dynatrace supports SnapStart for Lambda as an AWS launch partner

Dynatrace

The new Amazon capability enables customers to improve the startup latency of their functions from several seconds to as low as sub-second (up to 10 times faster) at P99 (the 99th latency percentile). This can cause latency outliers and may lead to a poor end-user experience for latency-sensitive applications.

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Best practices and key metrics for improving mobile app performance

Dynatrace

Examples of observability data include metrics, logs, and traces which provide visibility into the app’s behavior and performance at different levels of the stack, including the application code, infrastructure, and network. Load time and network latency metrics. Issue remediation. Performance optimization.

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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? Plotted on the same horizontal axis of 1.6s, the waterfalls speak for themselves: 201ms of cumulative latency; 109ms of cumulative download. 4,362ms of cumulative latency; 240ms of cumulative download. Cache This is the easy one.

Cache 291
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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

One of these solutions is Micrometer which provides 17+ pre-instrumented JVM-based frameworks for data collection and enables instrumentation code with a vendor-neutral API. This can be set up with a couple of lines of code in your Spring Boot project. You can find all the details and sample code in our documentation.

Metrics 206
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

It provides a good read on the availability and latency ranges under different production conditions. Adding forking logic and complexity to the device code can create dependencies on device application release cycles that generally run at a slower cadence than service release cycles, leading to bottlenecks in the migration.

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Seamlessly Swapping the API backend of the Netflix Android app

The Netflix TechBlog

On the Android team, while most of our time is spent working on the app, we are also responsible for maintaining this backend that our app communicates with, and its orchestration code. Image taken from a previously published blog post As you can see, our code was just a part (#2 in the diagram) of this monolithic service.

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