Document-oriented NoSQL database

Certainly! Let's provide examples for each of the concepts you mentioned within the context of non-relational databases, specifically focusing on a document-oriented NoSQL database like MongoDB.

1. Data Modeling (MongoDB - Document-Oriented):

// Example of data modeling in MongoDB
db.customers.insertOne({
  _id: 1,
  name: "John Doe",
  email: "[e-mail]",
  orders: [
    { order_id: 101, product: "Laptop", quantity: 2 },
    { order_id: 102, product: "Monitor", quantity: 1 }
  ]
});

// This document represents a customer with embedded orders, a common practice in document-oriented databases.

2. CAP Theorem:

The CAP theorem states that a distributed system can achieve at most two out of three guarantees: Consistency, Availability, and Partition Tolerance.

• Consistency:

  • Ensuring that all nodes in a distributed system see the same data at the same time.

• Availability:

  • Ensuring that every request to a non-failing node in the system receives a response, without guarantee that it contains the most recent version of the data.

• Partition Tolerance:

  • The system continues to operate despite network partitions that may cause communication breakdowns between nodes.

In a distributed database system, you need to make trade-offs based on your application's requirements.

3. Scaling Strategies:

Horizontal Scaling (Sharding):

// Sharding example in MongoDB
sh.shardCollection("mydatabase.mycollection", { "shard_key": 1 });

// This command shards the collection based on the specified shard key.

Replication for High Availability:

// Replication example in MongoDB
rs.initiate({
   _id: "rs0",
   members: [
      { _id: 0, host: "mongodb1.example.net:27017" },
      { _id: 1, host: "mongodb2.example.net:27017" },
      { _id: 2, host: "mongodb3.example.net:27017" }
   ]
});

4. Schema-less Design (MongoDB):

// Schema-less design example in MongoDB
db.products.insertOne({
  name: "Smartphone",
  brand: "XYZ",
  specifications: {
    display: "6.5 inches",
    storage: "128GB"
  }
});

// In MongoDB, each document in a collection can have a different structure, allowing for flexibility in data representation.

5. Consistency Models:

MongoDB supports various consistency models:

• Eventual Consistency:
  • The system will become consistent over time.

db.collection.find({}).readPref('secondary');
// Reading from secondary nodes for eventual consistency.

• Strong Consistency:
  • Ensures that all reads reflect the most recent write.

db.collection.find({}).readPref('primary');
// Reading from the primary node for strong consistency.

These examples illustrate how these concepts are implemented in MongoDB, which is a popular document-oriented NoSQL database. Keep in mind that other NoSQL databases may have different implementations or terminologies.