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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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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

While Atlas is architected around compute & storage separation, and we could theoretically just scale the query layer to meet the increased query demand, every query, regardless of its type, has a data component that needs to be pushed down to the storage layer.

Storage 288
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Designing Instagram

High Scalability

Firstly, the synchronous process which is responsible for uploading image content on file storage, persisting the media metadata in graph data-storage, returning the confirmation message to the user and triggering the process to update the user activity. Fetching User Feed. Sample Queries supported by Graph Database. Optimization.

Design 334
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Best practices and key metrics for improving mobile app performance

Dynatrace

From the customer perspective, mobile devices have become the singular touchpoint between businesses and users, for example, the new storefront, office, and customer support line. This includes how quickly the application loads, how much load it is putting on the device, how much storage is being used, and how frequently it crashes.

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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. The upstream service calls the existing and new replacement services concurrently to minimize any latency increase on the production path. For example, if some fields in the responses are timestamps, those will differ.

Traffic 339
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Implementing AWS well-architected pillars with automated workflows

Dynatrace

For example, optimizing resource utilization for greater scale and lower cost and driving insights to increase adoption of cloud-native serverless services. Storing frequently accessed data in faster storage, usually in-memory caching, improves data retrieval speed and overall system performance. Beyond

AWS 240
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File systems unfit as distributed storage backends: lessons from ten years of Ceph evolution

The Morning Paper

File systems unfit as distributed storage backends: lessons from 10 years of Ceph evolution Aghayev et al., It’s also a fabulous example of recognising and challenging implicit assumptions. In this case, the assumption that a distributed storage backend should clearly be layered on top of a local file system. SOSP’19.

Storage 64