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Redis vs Memcached in 2024

Scalegrid

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. Redis is better suited for complex data models, and Memcached is better suited for high-throughput, string-based caching scenarios.

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Geek Reading - Week of June 5, 2013

DZone

A handy list of RSS readers with feature comparisons ( Hacker News). Using MongoDB as a cache store ( Architects Zone – Architectural Design Patterns & Best Practices). Email Reveals Google App Engine Search API About Ready For Preview Release, Charges Planned For Storage, Operations ( TechCrunch). Hacker News).

Java 244
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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

From chunk encoding to assembly and packaging, the result of each previous processing step must be uploaded to cloud storage and then downloaded by the next processing step. Since not all projects are terabytes projects, allocating the largest cloud storage to all packager instances is not an efficient use of cloud resources.

Cloud 237
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Given the scale of the data being generated using replay traffic, we record the responses from the two sides to a cost-effective cold storage facility using technology like Apache Iceberg. Comparison After normalizing, we diff the responses on the two sides and check whether we have matching or mismatching responses.

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Kubernetes in the wild report 2023

Dynatrace

In comparison, on-premises clusters have more and larger nodes: on average, 9 nodes with 32 to 64 GB of memory. Of the organizations in the Kubernetes survey, 71% run databases and caches in Kubernetes, representing a +48% year-over-year increase. Kubernetes infrastructure models differ between cloud and on-premises.

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Use Distributed Caching to Accelerate Online Web Sites

ScaleOut Software

The Solution: Distributed Caching. The solution to this challenge is to use scalable, memory-based data storage for fast-changing data so that web sites can keep up with exploding workloads. It’s not enough simply to lash together a set of servers hosting a collection of in-memory caches.

Cache 52
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Use Distributed Caching to Accelerate Online Web Sites

ScaleOut Software

The Solution: Distributed Caching. The solution to this challenge is to use scalable, memory-based data storage for fast-changing data so that web sites can keep up with exploding workloads. It’s not enough simply to lash together a set of servers hosting a collection of in-memory caches.

Cache 52