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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. This significantly increases event latency.

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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. Similarly, an increased throughput signifies an intensive workload on a server and a larger latency.

Metrics 130
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Build and operate multicloud FaaS with enhanced, intelligent end-to-end observability

Dynatrace

Observability is essential to ensure the reliability, security and quality of any software system. Higher latency and cold start issues due to the initialization time of the functions. Data visualization : how to present, explore and interpret observability data from serverless functions intuitively, clearly, and holistically?

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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Our previous blog post presented replay traffic testing — a crucial instrument in our toolkit that allows us to implement these transformations with precision and reliability. They enable us to further fine-tune and configure the system, ensuring the new changes are integrated smoothly and seamlessly.

Traffic 279
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Mastering MongoDB® Timeout Settings

Scalegrid

For example, if there’s some trouble connecting due to networking problems or excessive load requests from customers present on the server’s side, that might result in a resolution-related delay causing a subsequent timeout problem. Careful consideration must be given before making changes.

Java 130
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Zero Configuration Service Mesh with On-Demand Cluster Discovery

The Netflix TechBlog

Eureka and Ribbon presented a simple but powerful interface, which made adopting them easy. Second, we’ve moved from a Java-only environment to a Polyglot one: we now also support node.js , Python , and a variety of OSS and off the shelf software. This is the first in a series of posts on our journey to service mesh, so stay tuned.

Traffic 220
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The Netflix Cosmos Platform

The Netflix TechBlog

It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation. The subsystems all communicate with each other asynchronously via Timestone, a high-scale, low-latency priority queuing system. Warm capacity.