Distributed Caching & Queueing in the Cloud
summary
Claims that sharding raises capacity from 5 GB per node to 15 GB by adding two nodes, replication uses a primary‑secondary master for writes and secondaries for reads to improve availability, and load balancing commonly uses round‑robin to avoid hot nodes. Uses Redis and Memcached for caching, Amazon ElastiCache and Google Cloud Memorystore as managed options. For queues, brokers (Apache Kafka, RabbitMQ, NATS) assign messages to partitions so multiple producers and consumers scale concurrently.
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chapters (11)
- 0:00Introduction to Distributed Caching and Queuing
- 0:10Understanding Caching
- 2:08Scaling with Distributed Caching
- 2:52Sharding in Distributed Caching
- 4:09Replication in Distributed Caching
- 5:37Load Balancing in Distributed Caching
- 7:34Popular Caching Systems
- 9:06Introduction to Queuing Systems
- 11:54Distributed Queuing Explained
- 13:24Popular Queuing Systems
- 15:10Conclusion and Contact Information
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