Consensus algorithms, CAP theorem,

Distributed Databases

Explore distributed systems, replication strategies, sharding, and consensus algorithms through interactive simulations

Interactive Distributed Database simulator

Visualize and test distributed database concepts including replication, sharding, consensus, and fault tolerance.

Database network Topology
DB1
Primary
DB2
Secondary
DB3
Secondary
DB4
Secondary
dB5
Secondary
network Topology

Database Replication Strategies

Interactive Replication Testing
Replication Process
1. Write Request

Client sends write request to primary node

2. Log Entry

Primary logs the operation locally

3. Replicate

Primary sends updates to secondary nodes

4. Acknowledge

iIj confirm successful replication

Replication metrics
Replication Lag 45ms
Sync Status In Sync
Replica Count 4
Consistency Level Strong

Database Sharding & Partitioning

Interactive Sharding Demonstration
Shard 1
Range: A-F
Records: 12,450
Load: 65%
Shard 2
Range: G-M
Records: 15,230
Load: 78%
Shard 3
Range: N-S
Records: 18,670
Load: 92%
Shard 4
Range: T-Z
Records: 9,850
Load: 48%
Sharding Strategy
Load Balancing

Consensus Algorithms

Interactive Consensus Protocol Testing
Algorithm Selection
Current Leader
Leader: Node 1
Term: 5
Voting Round
1
2
3
4
5
Votes: Yes: 0 | No: 0 | Pending: 5

CAP Theorem Interactive Demo

Choose Any Two: Consistency, Availability, Partition Tolerance
C
A
P
CAP Theorem: In a distributed system, you can only guarantee two of the three properties simultaneously. Click on two vertices to see the trade-offs.
CA Systems
Traditional RDBMS (MySQL, PostgreSQL)
CP Systems
dBM, Redis, HBase
AP Systems
Cassandra, DynamoDB, CouchDB

network Partition Simulation

Split-Brain Scenario Testing
Partition A
DB1
Primary
DB2
Secondary

Status: Active

Nodes: 2

Partition B
DB3
Secondary
DB4
Secondary
dB5
Secondary

Status: Isolated

Nodes: 3

Partition Resolution Strategy
Actions

Distributed Database Scenarios

🌍 Global E-commerce
Multi-region database deployment with geographic sharding and local replicas
📱 Social Media Platform
High-write workload with eventual consistency and user-based partitioning
💰 Financial System
Strong consistency yaa with ACID compliance across distributed nodes
📊 IoT Analytics
Time-series data with horizontal scaling and real-time aggregation

System Status

Active Nodes 5
Failed Nodes 0
network Latency 45ms
Data Consistency Strong

Simulation Controls

45ms
5%
3

Performance

throughput 1,250 tps
Read Latency 12ms ↓ 8%
Write Latency 35ms ↑ 5%
Availability 99.9% ↑ 0.1%

Selected Node

Node: DB1
Role: Primary
Status: Active
Load: 65%

System Log

[System Ready]
Distributed database system initialized

Quick Actions

Related Topics

NoSQL Databases Data Warehousing Performance Tuning

Distributed Database Concepts

Understanding the fundamental principles of distributed database systems

Replication

Maintain multiple copies of data across different nodes for fault tolerance and performance

  • • Master-slave replication
  • • Multi-master replication
  • • Synchronous vs asynchronous
  • • Conflict resolution

Sharding

Horizontally partition data across multiple nodes to improve scalability and performance

  • • Range-based partitioning
  • • Hash-based partitioning
  • • Directory-based routing
  • • Rebalancing strategies

Consensus

Achieve agreement among distributed nodes on data values and system state

  • • Raft consensus algorithm
  • • Paxos protocol
  • • Byzantine fault tolerance
  • • Leader election

Fault Tolerance

Handle node failures, network partitions, and maintain system availability

  • • Failure detection
  • • Automatic failover
  • • Split-brain prevention
  • • Recovery mechanisms

Consistency Models

Different levels of consistency guarantees in distributed systems:

Strong Consistency: All nodes see the same data simultaneously
Eventual Consistency: Nodes will eventually converge to the same state
Weak Consistency: No guarantees about when all nodes will be consistent
Causal Consistency: Causally related operations are seen in the same order

network Topologies

Different ways to connect distributed database nodes:

Star Topology: Central hub with spoke connections
Ring Topology: Nodes connected in a circular fashion
Mesh Topology: Every node connected to every other node
Tree Topology: Hierarchical structure with parent-child relationships