Distributed Caching & Queueing in the Cloud

Supercharge Your Application with Distributed Caching & Queueing 🚀

Want to optimize your application’s performance without a complex microservices setup? Learn how distributed caching and queueing can help you build more resilient, scalable, and high-performing backend systems.

🔹 Caching 101 – Why caching works, which tools to use, and how to avoid cache invalidation pitfalls.
🔹 Queueing Essentials – How to decouple requests, prevent system overload, and manage background processing efficiently.
🔹 Cloud-Friendly Scaling – Practical strategies for using managed caching & queueing solutions on AWS, Azure, and GCP.

Transcript

1. Distributed Caching & Queueing in the Cloud Wisdom Matthew Senior Backend Engineer @ Nala
2. What is a Cache?
3. Direct Database Access
4. Database Bottleneck
5. Leveraging Caching for Scale
6. What is Distributed Caching? 🗂 Spreads cache data across multiple nodes instead of storing it on a single machine. 🛡 Ensures scalability, fault tolerance, and high performance by preventing cache overload.
7. How Distributed Caching Works
8. Distributed Caching Strategies 🔀 Sharding 🔄 Replication ⚖ Load Balancing 🔗 Consistent Hashing
9. Sharding
10. Replication
11. Load Balancing
12. Popular Caching Systems Redis: An open-source in-memory data store offering advanced data structures, replication, sharding, and persistence; widely adopted for high-performance caching. Memcached: A simple, high-speed key-value store known for its ease of use and fast lookups, ideal for stateless caching scenarios.
13. Distributed Caching on the Cloud Amazon ElastiCache Google Cloud Memorystore Azure Cache for Redis Redis Cloud Self-Hosted
14. What is a Queue?
15. How a Queue Works
16. What is Distributed Queueing? 🔢 Spread across multiple queue partitions, allowing high-volume asynchronous processing. ⚡Enables multiple producers and consumers to operate concurrently, ensuring scalability and resilience under heavy load.
17. How Distributed Queueing Works
18. Popular Queueing Systems Apache Kafka: A high-throughput, distributed streaming platform designed for real-time event streaming, widely adopted for its scalability and fault tolerance. RabbitMQ: A versatile and mature message broker that supports complex routing patterns and multiple protocols, making it popular in many enterprise scenarios. NATS: A lightweight, high-performance messaging system designed for cloud-native applications, offering simplicity and low latency.
19. Distributed Queueing on the Cloud Amazon SQS Google Cloud Pub/Sub Azure Service Bus Self-Hosted
20. Thank You! @wisdommatt @wisdommatt @wisdommatthew11