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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Redis® is an in-memory database that provides blazingly fast performance. This makes it a compelling alternative to disk-based databases when performance is a concern. Redis returns a big list of database metrics when you run the info command on the Redis shell. This blog post lists the important database metrics to monitor.

Metrics 130
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Mastering MongoDB® Timeout Settings

Scalegrid

How the MongoDB timeout is set up can significantly affect your application’s performance, no matter if you are an experienced MongoDB user or just starting with NoSQL databases. Typical applications are interacting with different database servers based on the business logic.

Java 130
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Best Practices for a Seamless MongoDB Upgrade

Percona

MongoDB is a dynamic database system continually evolving to deliver optimized performance, robust security, and limitless scalability. Our new eBook, “ From Planning to Performance: MongoDB Upgrade Best Practices ,” guides you through the entire process to ensure your database’s long-term success. In MongoDB 6.x:

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Towards a Reliable Device Management Platform

The Netflix TechBlog

A timeline of the transition from Spring KafkaListener to Alpakka-Kafka is presented here for a better understanding of the motivations for the transition. By the following morning, alerts were received regarding high memory consumption and GC latencies, to the point where the service was unresponsive to HTTP requests.

Latency 213
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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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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case. divide the input video into small chunks 2.

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Optimizing data warehouse storage

The Netflix TechBlog

This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture. These principles reduce resource usage by being more efficient and effective while lowering the end-to-end latency in data processing. Transparency to end-users.

Storage 203