Caching systems are usually in-memory key-value databases that provide very fast access to data. They sit between your application and the database.
Depending on your use case, you can either cache only the results of the database queries, or cache the entire response objects (such as fully rendered web pages).
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Sharding means splitting your data across multiple nodes. This can be done in different ways:
1. Vertical sharding
2. Key-based sharding
3. Directory-based sharding
Systems can be scaled:
Joins in relational databases get very slow as the system grows bigger. You should denormalize your data to avoid joins and speed up your queries.
Normalized databases are designed to minimize redundancy, while denormalized databases are designed to optimize read time.
Slow operations should be done asynchronously to avoid making users wait for long periods of time until their requests are processed.
Sometimes this can be done in advance, by pre-processing data that you know users will need. For example, a forum might periodically re-render its page of most popular posts and the number of comments.
Asynchronous processing usually requires 2 components:
A successful value proposition requires fit between what the company offers and what customers want.
Fit happens in three stages:
Fear of death is the most common fear among everyone.
But the question is why do we fear death?
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