Caching is basically the process of storing copies of files in a cache, or a temporary storage location, so that they can be accessed more quickly.
The key caching strategies include write-through, read-through, lazy loading, and time-to-live (TTL). In this post, we will delve into these strategies and uncover what makes each unique:
1. Write-Through Caching
Write-through caching is a caching strategy in which data is simultaneously written into the cache and the corresponding database. Every write operation goes to the cache and then it is written through to the underlying disk. While this adds latency to write operations, it ensures that nothing will get lost even if crash occurs immediately after a write.
2. Read-Through Caching
In this strategy, when the cache is missing data, it reads it from the slower backing storage, populates the cache and returns it to the requesting client. Operations are read through the cache, which acts as a shock absorber for the underlying disk thus providing low latency reads. A read-through cache is more efficient when a large percentage of the stored data is read frequently
3. Lazy Loading
Lazy loading (or lazy initialization) is the tactic of delaying the initialization of an object until the point at which it is needed. In the context of caching, it can significantly improve performance by only loading or calculating data when it’s needed. If the application never needs the data, it is never loaded. However, the downside is that the first access may suffer from latency as the data must be loaded or computed.
4. Time-to-Live (TTL)
TTL is a value in a data record defining the lifespan or expiry of that data after a certain period of time. The data is discarded after the expiry time. TTL is typically used in situations where data needs to be refreshed frequently, like DNS servers or web applications.
Here’s a comparison of the different caching strategies:
| Strategy | Pros | Cons |
|---|---|---|
| Write-Through | Data is always present in the cache, there are no delays | Introduces latency on writes |
| Read-Through | Low latency reads, the cache is always populated with frequently used data | First read can be slow |
| Lazy Loading | Improved performance by only loading data when needed, can save computational power | First access usually slows due to need to load the data |
| Time-to-Live | Data is automatically refreshed, useful in fast changing data environments | Unnecessary re-fetching of data which can lead to increased latency |
In the context of AWS, caching is a crucial part of several services. For example, with Amazon DynamoDB, accelerating the performance of web applications can be achieved by adding in-memory cache. You can choose a read-through or lazy loading strategy for the application and set a TTL value on DynamoDB items. Similarly, you can employ caching strategies in API Gateway to improve performance and reduce the number of calls made to your backend.
In conclusion, understanding caching strategies is key for optimizing access to data and enhancing the performance of your applications — knowledge that’s important for passing the AWS Certified Developer – Associate (DVA-C02) exam as well as for your day-to-day as a developer.
Practice Test
True or False: The write-through caching strategy means that data is written into the cache and the corresponding database at the same time.
- True
- False
Answer: True
Explanation: The write-through caching strategy aims to ensure data consistency by writing data into storage and cache simultaneously.
In a read-through caching strategy, where are the data first read from: Cache or Main memory?
- A) Cache
- B) Main Memory
Answer: A) Cache
Explanation: In a read-through strategy, the cache handles all read operations. If requested data is not in the cache, it fetches it from the main memory, stores it in the cache and serves it to the client.
True or False: Lazy loading is a strategy where data is loaded only when it is requested.
- True
- False
Answer: True
Explanation: As the name suggests, lazy loading delays the initialization of resources until they are needed, which can result in improved performance.
When using the TTL caching strategy, what does “TTL” stand for?
- A) Time To Load
- B) Time To Live
- C) Total Time Lag
Answer: B) Time To Live
Explanation: TTL, or Time To Live, is a caching strategy where each cache item has a set time limit after which it expires, encouraging a regular cycle of refreshing data.
True or False: AWS Elasticache supports lazy loading as a caching strategy.
- True
- False
Answer: True
Explanation: AWS ElastiCache does support the lazy loading strategy, which allows for the fetching of data only when it is required.
Which of the following is not a type of caching strategy supported by AWS?
- A) Write-Through
- B) Read-Through
- C) Time-To-Load
- D) Lazy Loading
Answer: C) Time-To-Load
Explanation: Time-To-Load is not a supported caching strategy for AWS. The supported strategies are Write-Through, Read-Through, and Lazy Loading.
What caching strategy does not serve stale data?
- A) Write-Through
- B) Read-Through
- C) Lazy Loading
Answer: A) Write-Through
Explanation: Write-through caching prevents serving stale (outdated) data because every write to the cache causes a write to storage, providing immediate consistency between cache and storage.
True or False: The lazy loading caching strategy does not have an issue with delay.
- True
- False
Answer: False
Explanation: Lazy loading can introduce a delay when the requested data is not in the cache, as the data needs to be loaded from the database into the cache before it can be presented to the client.
Which caching strategy will have an increased latency with a ‘cache-miss’?
- A) Write-Through
- B) Read-Through
- C) Lazy Loading
- D) TTL
Answer: C) Lazy Loading
Explanation: Lazy Loading strategy could have increased latency during a ‘cache-miss’ because it then has to fetch data from the database.
In which of the following caching strategies is data written into the cache only when a change is made?
- A) Write-Through
- B) Read-Through
- C) Lazy Loading
- D) TTL
Answer: A) Write-Through
Explanation: In write-through, data is written into the cache every time a change occurs in the main memory or back-end database. It ensures consistency between data stored in the cache and the underlying storage.
Interview Questions
What is a write-through caching strategy in AWS?
A write-through caching strategy ensures that all the writes to the cache are also written to the backing store. It is used to ensure data consistency and integrity.
What is the major advantage of write-through caching strategy?
The major advantage of write-through caching strategy is that data is always up to date. Any successful write request means that both the cache and the backing store have updated data.
What is a read-through caching strategy?
In a read-through caching strategy, the cache takes responsibility for loading data. If data is not present in the cache, it will be loaded from the backing store and delivered.
Why is lazy loading caching strategy used?
Lazy loading caching strategy is used to save system resources. Rather than loading all the data into the cache at once, lazy loading only loads data as and when it is required.
What does TTL stand for in AWS caching strategies?
In AWS caching strategies, TTL stands for Time to Live. It is used to specify how long data should stay in the cache before it is automatically removed.
What is the impact of a lower TTL in AWS caching mechanism?
A lower TTL would mean that data would be refreshed more often. This might lead to more load on the database but ensures that the most recent data is served from cache.
What happens if cache data expires and the subsequent database request fails in a lazy loading caching strategy?
In a lazy loading caching strategy, if cache data expires and the subsequent database request fails, an error would be returned. But, in some strategies, the stale data from the cache might be returned.
In a write-through cache, what happens when a data is updated?
In a write-through cache, when a data item is updated, the cache is updated first and then the update is written through to the backing store.
What might be the potential downside of a write-through caching strategy?
The potential downside of a write-through caching strategy is that it might increase the latency of write operations because every write must be done twice.
Is it possible to use multiple cache strategies together in AWS?
Yes, it is possible to use different caching strategies for different tasks, or even mix strategies for a single task based on specific conditions.
How is cache invalidation handled in read-through caching?
In read-through caching, when the client requests an update or delete, the cache invalidates or updates the corresponding entry. The next read will re-fetch and cache the new value.
What type of caching strategy AWS ElastiCache use?
AWS ElastiCache supports TTL-based invalidation which is a common feature in lazy loading and write-through/read-through strategies. Exact strategy can depend on specific use-case and configuration.
How is read traffic handled in a read-through caching strategy?
In a read-through caching strategy, all read requests are directed to the cache. If requested data is not found in the cache, it will be retrieved from the backing store, placed in the cache, and then returned to the requester.
What could be a potential disadvantage of using a lazy loading caching strategy?
A potential disadvantage of a lazy loading caching strategy is the “cache miss penalty,” where a user’s request might experience more latency in returning data as the request has to populate the cache first.
How does TTL help in managing the size of the cache in AWS?
TTL helps in managing the size of the cache by limiting the lifespan of the data in the cache. After the set period of time, the data is automatically removed from the cache, thus managing its size.
extutorials.com