Distributed databases are databases that are spread across multiple physical locations, which can be on different servers or even in different geographical areas. They are designed to improve data availability, fault tolerance, and performance by distributing the data and the workload across multiple nodes.

Distributed databases can be categorized based on their architecture and data distribution methods:

• Homogeneous Distributed Databases: All physical locations use the same DBMS and operating system.
• Heterogeneous Distributed Databases: Different locations may use different DBMSs and operating systems.
• Federated Databases: A type of heterogeneous database where each database remains autonomous but can be queried as a single entity.

Data in distributed databases can be distributed using various strategies:

• Replication: Copies of the same data are stored on multiple nodes to ensure high availability and fault tolerance.
• Partitioning (Sharding): Data is divided into distinct subsets, each stored on different nodes to balance the load and improve performance.

Distributed databases must ensure data consistency across nodes. Common consistency models include:

• Strong Consistency: Ensures that all nodes see the same data at the same time.
• Eventual Consistency: Guarantees that, given enough time, all nodes will converge to the same value.
• Causal Consistency: Ensures that operations that are causally related are seen by all nodes in the same order.

MongoDB is a popular NoSQL database that supports distributed data storage through sharding and replication.

Step-by-Step Guide

1. Install MongoDB on Multiple Servers:

   sudo apt-get install -y mongodb
   

2. Configure Sharding:

   • Start Config Servers:

     mongod --configsvr --replSet configReplSet --dbpath /data/configdb --port 27019
     

   • Start Shard Servers:

     mongod --shardsvr --replSet shardReplSet1 --dbpath /data/shard1 --port 27018
     mongod --shardsvr --replSet shardReplSet2 --dbpath /data/shard2 --port 27018
     

   • Start a Mongos Router:

     mongos --configdb configReplSet/localhost:27019 --port 27017
     

3. Connect to the Mongos Router and Add Shards:

   mongo --port 27017
   sh.addShard("shardReplSet1/localhost:27018")
   sh.addShard("shardReplSet2/localhost:27018")
   

4. Enable Sharding for a Database:

   sh.enableSharding("myDatabase")
   

5. Shard a Collection:

   sh.shardCollection("myDatabase.myCollection", { "shardKey": 1 })
   

Apache Cassandra is a distributed NoSQL database designed for handling large amounts of data across many commodity servers.

Step-by-Step Guide

1. Install Cassandra on Multiple Nodes:

   sudo apt-get install cassandra
   

2. Configure Cassandra Cluster:

   • Edit cassandra.yaml:

     cluster_name: 'MyCluster'
     seeds: '192.168.1.1,192.168.1.2'
     listen_address: '192.168.1.x'
     rpc_address: '192.168.1.x'
     

   • Start Cassandra:

     sudo service cassandra start
     

3. Create a Keyspace and Table:

   CREATE KEYSPACE myKeyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 3};
   USE myKeyspace;
   CREATE TABLE myTable (id UUID PRIMARY KEY, name text, value text);
   

Task: Set up a simple MongoDB sharded cluster with two shards and a single config server.

Steps:

1. Install MongoDB on three servers.
2. Configure one server as the config server.
3. Configure the other two servers as shard servers.
4. Start a mongos router and add the shards.
5. Enable sharding for a database and shard a collection.

Solution: Follow the step-by-step guide provided in Example 1.

Task: Configure a Cassandra cluster with three nodes and create a keyspace with a replication factor of 3.

Steps:

1. Install Cassandra on three nodes.
2. Configure the cassandra.yaml file on each node to join the same cluster.
3. Start Cassandra on each node.
4. Create a keyspace and a table.

Solution: Follow the step-by-step guide provided in Example 2.

• Network Configuration: Ensure that all nodes can communicate with each other over the network. Firewalls and security groups should allow necessary ports.
• Consistency Trade-offs: Understand the trade-offs between consistency, availability, and partition tolerance (CAP theorem) when designing your distributed database.
• Monitoring and Maintenance: Regularly monitor the health of your distributed database and perform maintenance tasks such as backups and updates.

In this section, we explored the fundamental concepts of distributed databases, including their types, data distribution strategies, and consistency models. We also provided practical examples using MongoDB and Apache Cassandra, along with exercises to reinforce the concepts. Understanding distributed databases is crucial for managing and processing data on a large scale efficiently and securely. In the next module, we will delve into distributed computing frameworks like MapReduce and Hadoop.