Building Datastores From Scratch

Building a datastore from scratch is a bold task and an enlightening one. It's like crafting your own financial trading algorithm, you get a sense of deep understanding of the inner workings of the system.

The choice of language here matters. Considering Python, due to its simplicity and ample libraries, it can be a good choice. But remember, language is a tool and the concept remains the same even if the tool changes.

Let's consider a basic example - a Key Value Store is a well-known primitive in data storage. It is simple, handling key-value pairs. Redis, a popular datastore, uses this along with other data structures.

To build our own primitive version of this, let's start with Python's built-in dictionary data structure. You may perceive it as a simple Hashmap, but at its core, it represents the Key-Value data model.

The implementation seems deceivingly simple, but the catch lies in scaling such a system. Here is where Computer Science concepts kick in, such as Sharding, Replication, Consistent Hashing, etc. AI models could be leveraged for smart caching mechanisms in our datastore as well.

In the upcoming screens, we will delve deeper into these topics. By the end of it, you'll be more confident in understanding how datastores function beneath the surface, and you may even be inspired to start building one yourself.

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if __name__ == "__main__":

  # Python key-value datastore

  datastore = {}

  # Let's add some data

  datastore['Apple'] = 'AAPL'

  datastore['Microsoft'] = 'MSFT'

  datastore['Amazon'] = 'AMZN'

  # Let's retrieve some data

  print(datastore['Apple'])

  # Let's delete some data

  del datastore['Amazon']

  print(datastore)