Caching Strategies

5 minutes 5 Questions

Caching strategies determine how data is managed and maintained across an application deployment. There are various caching strategies like Lazy Loading and Write-through. Lazy Loading populate data into cache only when requested, which minimizes the initial data loading. It improves the performance by reducing I/O and CPU usage. However, it may create cache stampede due to cache expiring or cache becoming empty. Write-through ensures data is written to cache and primary data store simultaneously. It provides consistency but can cause latency issues during data write operations.

Complete Guide on Amazon Elasticache Caching Strategies

What is it:
Amazon Elasticache caching strategies is a feature of AWS which allows users to easily set up, manage and scale distributed in-memory data store or cache environment in the cloud. It helps improve the performance of web applications by retrieving information from in-memory caches instead of relying on slower disk-based databases.

Why it is Important:
Caching strategies are crucial as they enhance application performance, reduce database load, and enable quick data access.

How it Works:
Data that's frequently accessed is stored in a cache. When an application needs this data, it first checks the cache. If the data is there (a cache hit), the application uses it. If not (a cache miss), the data is retrieved from the database, put into the cache, and then used.

Answering Questions in Exams:
You may encounter questions in the exam that test your understanding of use-cases for different caching strategies, such as Lazy Loading and Write-Through. Understanding the difference between these strategies and when to use each can help you answer these questions correctly.

Exam Tips:
Remember that Lazy Loading retrieves data as needed, while Write-Through caches data as it is written into your database. Understanding the scenarios where each caching strategy is beneficial will maximize your chances of successfully answering these questions in the exam.

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Question 1

An application displays user avatars in a web page. The avatars are retrieved from S3 storage, but the requests are causing excessively high latency during peak times. What would be the best caching strategy to reduce latency?

Introduce CloudFront for caching avatars close to users Use AWS Lambda to cache avatars in local memory Increase the number of S3 Buckets containing avatars Store avatars in an EC2 instance to reduce S3 latency

Correct Answer: Introduce CloudFront for caching avatars close to users

By introducing CloudFront, you cache the avatars closer to the users which will decrease the latency experienced during peak times. Other solutions, such as storing avatars in an EC2 instance or using AWS Lambda, do not solve the latency issue and may introduce new problems.

Question 2

You are hosting a web application on EC2 instances behind an Application Load Balancer. Which caching solution will provide the lowest latency for retrieving static assets?

Using AWS AppConfig Amazon CloudFront ElastiCache with Redis ElastiCache with Memcached

Correct Answer: Amazon CloudFront

Amazon CloudFront caches and delivers static assets (images, JavaScript, CSS) close to users reducing latency. ElastiCache is designed for in-memory caching, which isn't optimal for static assets, and the other options don't focus on static asset caching and delivery.

Question 3

Your website dynamically generates personalized content for users. The requests to generate content are causing slow page load times. Which caching strategy should you use?

Cache content using S3 ElastiCache with Redis ElastiCache with Memcached Use a CloudFront cache

Correct Answer: ElastiCache with Redis

ElastiCache with Redis provides in-memory caching to cache complex or frequently changing content which can reduce the time required to generate personalized content. The other options either provide caching for static content or do not improve performance in this scenario.

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