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World Congress 2021 • Jun 30, 2021

Advanced Caching Patterns used by 2000 microservices

Natan Silnitsky

How do you handle 500 billion daily requests across 2,000 microservices without cascading failures? Discover advanced caching patterns using Kafka, DynamoDB, and Varnish.

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#1 about 5 min

The necessity of caching for microservice reliability

High-scale environments use caching to decouple services and mitigate cascading failures.

#2 about 4 min

Caching strategies overview and architectural considerations

Premature caching introduces unnecessary architecture complexity before latency or scale demands it.

#3 about 4 min

Designing a read-through static configuration cache

A read-through pattern backed by object storage shields static data configuration from third-party service outages.

#4 about 11 min

Distributing cache updates using DynamoDB and Kafka

Replicating key-value updates across data centers via a change data capture topic ensures strong consistency and fast cache warmups.

#5 about 6 min

Reducing rendering latency with HTTP reverse proxies

Putting an open-source HTTP cache in front of heavy rendering services significantly decreases CPU load and response times.

#6 about 3 min

Selecting the appropriate cache type for workload requirements

A decision tree logic helps identify whether object storage, distributed databases, or proxy solutions match specific workload requirements.

#7 about 3 min

Enhancing event flows with high-level Kafka consumers

Custom SDK abstractions simplify parallel consumption and error handling in distributed event-driven architectures.

#8 about 4 min

Addressing HTTP headers and custom invalidation strategies

Handling HTTP responses and implementing accurate targeted purging rely on consistent identifier mapping across rendering pipelines.

#9 about 3 min

Mitigating cache stampedes during concurrent database misses

Strategies like increased memory limits and cluster sharding help buffer the impact of simultaneous access requests for uncached items.

#10 about 3 min

Using sidecar deployments to share JVM infrastructure

Encapsulating functionality into sidecar containers allows different language stacks to share core infrastructure without duplicating code.

#11 about 4 min

Safely handling concurrent writes with software transactional memory

Synchronizing state safely across threads using software transactional memory isolates writes and stops duplicate cache population attempts.

Matching moments

1:58 min

Trade-offs between storage and network resources in caching architectures

Pierre-Luc Noel +1 · World Congress 2023

5:48 min

Balancing delivery latency with stream reliability and scale

Phil Cluff · LIVE

4:08 min

Navigating caching and downstream throttling in event-driven setups

Marco Plaul Marco Plaul +1 · World Congress 2023

2:21 min

Reducing reliance on external services with caching

Michael Eder +1 · LIVE

2:44 min

Introduction to web acceleration and caching paradigms

Thijs Feryn Thijs Feryn · World Congress 2025

1:11 min

Examining strategic data caching patterns via serverless capabilities

Sani Yusuf · LIVE

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