Building Datastores From Scratch: An Introduction

SQL, or Structured Query Language, is a language designed specifically for communicating with databases. SQL databases, also known as relational databases, have been a prevailing technology for over four decades. They organize data into one or more tables, each with a unique key identifying every record.

An SQL database functions on the principles of ACID (Atomicity, Consistency, Isolation, Durability). ACID compliance reduces anomalies and protects the integrity of your database by prescribing exactly how transactions interact with the database. This is particularly helpful in sectors such as finance where consistency of data is paramount.

SQL databases support complex querying, thus allowing intricate filtering within the data. This makes SQL databases suitable for applications where complex transactions are involved. This might actually ring a bell to our AI enthusiasts who frequently need to handle complex numerical datasets.

The Python code given as an example demonstrates a basic transaction using Python’s sqlite3 module, a light-weight disk-based database, well suited for testing and prototyping. Here, a table named 'stocks' is created, a record is inserted, and then we query all records, ordered by the price.

SQL databases require a fixed schema defining the data structure before you can store anything. This feature ensures the necessary rigidity for applications that need consistent data, building the foundation for a strong, predictable model. This is particularly relevant in traditional client-server applications where consistency is key.

In conclusion, SQL databases prove a good choice when the problem domain is well understood, and if strong consistency in data is required. They are an excellent choice for applications involving complex transactions, or for analytics solutions requiring complex querying.

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if __name__ == "__main__":

  # Python logic here

  # For instance, this is how a typical transaction could be implemented using Python’s sqlite3 module

  import sqlite3

​

  conn = sqlite3.connect(':memory:')

  c = conn.cursor()

  c.execute('''CREATE TABLE stocks (date text, trans text, symbol text, qty real, price real)''')

​

  # Insert a row of data

  c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")

​

  # To save the changes

  conn.commit()

​

  # Do some operations with the data

  for row in c.execute('SELECT * FROM stocks ORDER BY price'):

    print(row)

​

  conn.close()

​

  print("This is how a basic SQL database operation using python looks like.")

Understanding NoSQL Databases

NoSQL (Not Only SQL) databases are another common datastore category that came about as an answer to the limitations of SQL databases. Unlike SQL databases, they can scale across multiple servers easily. They do not enforce a fixed schema, offering increased flexibility in managing your data.

One of their most valued features for AI enthusiasts is how they handle large data volumes, a common scenario in data science and AI projects. Coupled with versatility in data modeling, NoSQL databases have an edge in storing, querying, and processing large datasets. Their performance does not degrade with increased data volume, providing a relevant solution for use cases where scalability and flexibility are critical.

A common misconception is that NoSQL databases completely replace SQL. However, they each have their own use-cases and are especially powerful when utilized together. In fact, the term 'NoSQL' has been rightly revisited to 'Not Only SQL' to acknowledge this fact.

Specifically focusing on finance, NoSQL databases can serve us remarkably when we have to work with unstructured data, like parsing news to predict stock trends.

In the provided Python code, we demonstrate a simple interaction with a NoSQL database using PyMongo, a Python driver for MongoDB. We connect to a MongoDB instance running locally, create a 'collection' (equivalent to an SQL table), insert a document, and then print all documents in the collection.

Please note that NoSQL includes a wide range of database technologies and MongoDB is just one of them. The specifics of interacting with NoSQL databases may vary depending on the technology used.

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if __name__ == "__main__":

  from pymongo import MongoClient

  client = MongoClient('localhost', 27017)

  db = client['ai_database']

  collection = db['ai_projects']

  collection.insert_one({"name": "predictive_analysis", "budget": 10000})

  for project in collection.find():

    print(project)

Comparing SQL and NoSQL Databases

Given your experience, you'd know that both SQL and NoSQL have their merits and disadvantages. Their use depends largely on the specific requirements of the project at hand.

SQL databases are known for their reliability, effective management of structured data, and robust support for ACID transactions. They excel in cases where the data is relational and where consistency is crucial, such as in most financial applications. A typical SQL query might be as follows:

1SELECT * FROM users WHERE age > 25

However, SQL databases have their limitations in terms of scalability and flexibility.

For its part, NoSQL databases like MongoDB or Redis emerged as a solution to these limitations, providing easy scalability and flexibility in dealing with varied data types (structured, semi-structured, and unstructured data). A downside to NoSQL databases can be the lack of ACID transactions support, depending on the specific NoSQL system being used. While SQL queries are expressed as strings, NoSQL databases like MongoDB uses JSON-like documents for queries:

1{ 'age': { '$gt': 25 } }

Both have their sweet spots in software development, and the challenge lies in knowing when to use one over the other.

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if __name__ == "__main__":

  # Python logic here

  # define a SQL query

 sql_query = 'SELECT * FROM users WHERE age > 25'

 print('SQL query:', sql_query)

​

 # define similar query in NoSQL (MongoDB)

 nosql_query = { 'age': { '$gt': 25 } }

 print('NoSQL query:', nosql_query)

Introduction to PostgreSQL

PostgreSQL, also known as Postgres, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and technical standards compliance. It is designed to handle a range of workloads, from single machines to data warehouses or Web services with many concurrent users. It's a powerful, enterprise class database system which is highly customizable and offers a wide variety of features. It is designed to handle high volume workloads and is capable of supporting both SQL (relational) and JSON (non-relational) querying.

A key feature of PostgreSQL is its atomicity, consistency, isolation, durability (ACID) compliance which ensures data validity even in situations like power failures or system crashes. It supports complex queries with multiple joined relations and subqueries, and has full support for transactions—important for financial systems—making it a versatile choice for many different types of projects, including those in the financial and data analysis realms. Here's an example of how to connect to a PostgreSQL database and retrieve its version using Python:

1if __name__ == "__main__":
2  import psycopg2
3  #connect to your postgres DB server
4  conn = psycopg2.connect("dbname=test user=postgres password=secret")
5  #create a cursor object
6  cur = conn.cursor()
7  #execute a statement
8  print("PostgreSQL database version:")
9  cur.execute("SELECT version()")
10  #display the PostgreSQL database server version
11  db_version = cur.fetchone()
12  print(db_version)

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if __name__ == "__main__":

  import psycopg2

  #connect to your postgres DB server

  conn = psycopg2.connect("dbname=test user=postgres password=secret")

  #create a cursor object

  cur = conn.cursor()

  #execute a statement

  print("PostgreSQL database version:")

  cur.execute("SELECT version()")

  #display the PostgreSQL database server version

  db_version = cur.fetchone()

  print(db_version)

Introduction to Redis

Redis is an open-source, in-memory data structure store used as a database, cache, and message broker. As an experienced engineer, it's essential to have Redis in your tool chest, particularly if you are working in financial systems or high-volume data processing like AI.

Compared to a relational database like PostgreSQL, Redis stores data in memory which allows it to provide blazing fast read and write operations – an essential feature for real-time applications. Furthermore, you can run atomic operations on these types, like appending to a string; incrementing the value in a hash; pushing an element to a list; computing set intersection, union and difference; or getting the member with highest ranking in a sorted set.

Several high-profile tech companies like GitHub and Snapchat have been known to leverage Redis due to its flexibility, performance, and a rich set of features.

In the Python script below, we demonstrate a simple interaction with Redis. We connect to a Redis server, set a key-value pair, and retrieve the value by its key.

1if __name__ == '__main__':
2  import redis
3  # connect to Redis server
4  r = redis.Redis(host='localhost', port=6379, db=0)
5  # set a key and value
6  r.set('example_key', 'example_value')
7  # get the value by the key
8  print(r.get('example_key'))

In future screens, we will explore more features of Redis and their use cases.

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if __name__ == '__main__':

  import redis

  # connect to Redis server

  r = redis.Redis(host='localhost', port=6379, db=0)

  # set a key and value

  r.set('example_key', 'example_value')

  # get the value by the key

  print(r.get('example_key'))

Introduction to MongoDB

MongoDB is a popular NoSQL datastore that provides a high-performance, scalable, and flexible data storage solution for various applications. It stores data in Binary JSON (BSON) format, providing a binary representation where more type-primitives are supported, making it easy to store and process large sets of data. It is highly suited for applications that require large data processing such as finance and AI.

Compared to SQL databases, MongoDB is schema-less, meaning that documents in the same collection can have different structures. This flexibility can greatly speed up development process in scenarios where data structure can change over time.

The Python script below demonstrates basic interaction with MongoDB. We connect to a MongoDB server, create a new collection, insert a document, and query the document by one of its fields.

Referencing the code block, you can see how MongoDB makes use of the BSON format to integrate natural to programming languages. We only needed a simple dictionary to represent a complex structure that would have required creating a table schema in traditional SQL. The code demonstrates the flexibility and ease of use that MongoDB provides, making it a good fit for modern, agile software development.

In the following screens, we will deep dive into MongoDB and how we can leverage it in building data stores.

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if __name__ == '__main__':

  from pymongo import MongoClient

  # create a connection to MongoDB

  client = MongoClient('localhost', 27017)

  db = client['python-db']

  # create a new collection

  collection = db['python-collection']

  # insert a document

  collection.insert_one({'name': 'John', 'age': 30, 'profession': 'Engineer'})

  # query the document

  q_res = collection.find_one({'name': 'John'})

  print(q_res)

Introduction to ElasticSearch

Elasticsearch is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time. It is generally used as the underlying engine/technology that powers applications that have complex search features and requirements. For example, in e-commerce applications, Elasticsearch could be used to serve real-time product recommendations to customers. It could also be used as a complement to other database systems (like SQL or NoSQL based) to enable full-text searching and more complex querying. ElasticSearch works exceptionally well with text data, making it ideal for use-cases involving natural language processing or machine learning.

In finance, for instance, you could use ElasticSearch to develop stock price tracking applications. It would enable the storage, processing, and visualization of large financial datasets. Its powerful analytical capabilities make it a strong choice for developing big data applications and visualizations.

To illustrate the primary interaction with ElasticSearch, consider the below Python code snippet:

1if __name__ == '__main__':
2    from elasticsearch import Elasticsearch
3    es = Elasticsearch([{'host':'localhost', 'port':9200}])
4    print('Connection: ',es.ping())

The code connects to an ElasticSearch server and print whether it succeeded by using a simple ping call. Subsequent code pieces can search, update, delete or add indices using this connection as per ElasticSearch's rich querying API.

Advanced learners like software engineers can utilize ElasticSearch for creating data-intensive applications in fields like finance, e-commerce, and AI. They can benefit significantly from the real-time data analysis and visualization capabilities that this tool offers.

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if __name__ == '__main__':

    from elasticsearch import Elasticsearch

    es = Elasticsearch([{'host':'localhost', 'port':9200}])

    print('Connection: ',es.ping())

Conclusion: The Road to Building Datastores

Having explored different types of datastores like SQL, NoSQL, PostgreSQL, Redis, MongoDB, and ElasticSearch, you are now at the brink of a fascinating journey. The foundation is laid for you to understand and build datastores from scratch. Regardless of the domain you are focusing on such as software development, AI, or finance, proficiency in handling datastores can give you an edge in optimizing data management and understanding the complexity behind these powerful tools.

Whether you aim to build a full-text search feature for an e-commerce application using ElasticSearch, or design a stock price tracking application using time series database like InfluxDB, understanding the principles and mechanisms of these datastores is crucial. Remember, these databases are not just 'black boxes', but are understandable and tangible systems which you can master.

Let's start embracing the adventure of building datastores from scratch!

1print('The road to building datastores begins!')

This is the beginning of an exciting journey. Jump in and start coding your datastores today. For further reading and understanding, refer to the respective documentations of these datastores and experiment with their interfaces. Happy coding!

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if __name__ == '__main__':

    print('The road to building datastores begins!')