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Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…

The Netflix TechBlog

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform.

Latency 212
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Designing Instagram

High Scalability

Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. High Level Design. The streaming data store makes the system extensible to support other use-cases (e.g. System Components. Component Design. API Design. Problem Statement.

Design 334
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems.

Systems 226
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API Design Principles for Optimal Performance and Scalability

DZone

API performance optimization is the process of improving the speed, scalability, and reliability of APIs. It involves a combination of techniques and best practices aimed at reducing latency, improving user experience, and increasing the overall efficiency of the system. What Is API Performance Optimization?

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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. These essential data points heavily influence both stability and efficiency within the system.

Metrics 130
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Build automated self-healing systems with xMatters and Dynatrace (Part 2 of 3)

Dynatrace

Welcome back to the blog series in which we share how you can easily solve three common problem scenarios by using Dynatrace and xMatters Flow Designer. In Part 1 we explored how DevOps teams can prevent a process crash from taking down services across an organization in five easy steps. xMatters creates and updates Jira issues.

Systems 176
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

GenAI is prone to erratic behavior due to unforeseen data scenarios or underlying system issues. The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources.

Cache 205