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The Power of Caching: Boosting API Performance and Scalability

DZone

Caching is the process of storing frequently accessed data or resources in a temporary storage location, such as memory or disk, to improve retrieval speed and reduce the need for repetitive processing.

Cache 246
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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

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. By monitoring metrics such as error rates, response times, and network latency, developers can identify trends and potential issues, so they don’t become critical.

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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. It helps expose memory leaks, deadlocks, caching issues, and other system issues.

Traffic 339
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Taskbar Latency and Kernel Calls

Randon ASCII

The fact that this shows up as CPU time suggests that the reads were all hitting in the system cache and the CPU time was the kernel overhead (note ntoskrnl.exe on the first sampled call stack) of grabbing data from the cache. Now that we suspect file I/O it’s necessary to go to Graph Explorer-> Storage-> File I/O.

Latency 79