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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Effective management of memory stores with policies like LRU/LFU proactive monitoring of the replication process and advanced metrics such as cache hit ratio and persistence indicators are crucial for ensuring data integrity and optimizing Redis’s performance. All these contribute significantly towards ensuring smooth functioning.

Metrics 130
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Designing Instagram

High Scalability

The streaming data store makes the system extensible to support other use-cases (e.g. System Components. The system will comprise of several micro-services each performing a separate task. There are two major processes which gets executed when a user posts a photo on Instagram. Streaming Data Model. Optimization.

Design 334
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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. This process enables you to continuously evaluate software against predefined quality criteria and service level objectives (SLOs) in pre-production environments.

AWS 240
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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. Its goal is to assign running processes to time slices of the CPU in a “fair” way. Linux to the rescue?

Cache 251
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How to use Server Timing to get backend transparency from your CDN

Speed Curve

desc="Time to process request at origin" NOTE: This is not a new API. Caching the base page/HTML is common, and it should have a positive impact on backend times. The use of server-timing headers by content delivery networks closes a big gap. In the case of a cache miss, this should be zero.)

Servers 57
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Key Advantages of DBMS for Efficient Data Management

Scalegrid

If you’re considering a database management system, understanding these benefits is crucial. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.

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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. Fault-tolerance.

Big Data 154