Blog System Design

Understanding Load Balancers, Distributed Systems, and Consistent Hashing

In modern software architecture, designing scalable and reliable systems is a key challenge. This article will explore the core responsibilities of load balancers, the design of distributed systems, and the role of consistent hashing in addressing key challenges. We’ll use the example of a hypothetical user-order management system to illustrate these concepts.


What is a Load Balancer?

A load balancer is a critical component in distributed systems that routes incoming requests to different servers. Its primary responsibilities include:

  1. Routing Requests: Directing client requests to an appropriate server.
  2. Distributing Load Equally: Ensuring all servers handle a balanced share of the load.
  3. Ensuring High Availability: Sending requests only to live and healthy servers.

A load balancer maintains a list of servers and their statuses (e.g., IP addresses marked as “live” or “dead”). Common algorithms used by load balancers are:

  • Round Robin: Routes requests sequentially to each server.
  • Weighted Round Robin: Distributes requests based on server capacity.
  • Least Response Time: Routes requests to the server with the shortest average response time.

To determine server health, the load balancer can use:

  • Health Checks: Regularly pinging servers or hitting an API endpoint (e.g., /check_health) to verify their status.
  • Heartbeat Mechanism: Servers periodically send “pings” to the load balancer to indicate they’re operational.

Distributed Systems and Scalability

A distributed system comprises multiple servers, each hosting the same application code and synchronized data to handle requests. This design offers redundancy—if one server fails, others can continue serving requests. However, this approach introduces challenges:

  1. Consistency Issues: When multiple servers maintain copies of the same database, delays in propagating updates can lead to inconsistencies. For example, if a user places an order, one server updates the database, but another server might not have the updated data immediately.
  2. Redundancy and Storage: Replicating the same data across multiple servers increases storage requirements, especially as the user base grows.
  3. Scalability Challenges: As the system grows, adding more servers without efficient data distribution leads to inefficiencies.

Sharding: Distributing Data Effectively

To address scalability, data can be divided across multiple servers using a technique called sharding. In a user-order management system, sharding ensures that data for a given user is stored on the same server. This reduces the need to query all servers for user-specific data.

Sharding Example:

Suppose we shard data based on user_id. A simple mapping might be:

user_id % number_of_shards

While this approach is efficient, it breaks when the number of shards changes, as it causes a massive redistribution of data. A better solution is consistent hashing.


Consistent Hashing

Consistent hashing is a technique to distribute data across servers (or shards) in a way that minimizes data movement when servers are added or removed.

How It Works:

  1. Represent a range of hash values (e.g., 0 to 101810^{18}) as a circular space.
  2. Hash each server ID to a point on the circle.
  3. Hash each user ID (or key) to a point on the circle.
  4. Assign a key to the first server in the clockwise direction from its hash.

If a server is removed, only the data mapped to that server needs to be reassigned to the next server. Adding a server similarly affects only a small portion of the data.

Improvement with Virtual Nodes:

To prevent overloading a single server after a failure, servers are represented multiple times on the circle using different hash values (virtual nodes). This ensures more uniform data distribution.

Example:

In a consistent hashing setup:

  • User data for hash values falling between virtual nodes of Server A and Server B will be reassigned to the next active server if Server A fails.
  • Virtual nodes distribute the impact of failure across multiple servers, avoiding cascading failures.

Consistency vs. Availability

Distributed systems often face trade-offs between consistency and availability:

  1. High Availability (e.g., Social Media): Systems prioritize uptime and quick responses, allowing temporary inconsistencies.
  2. High Consistency (e.g., Banking): Systems prioritize data accuracy and consistency, even at the cost of latency.

Technologies like Kafka and Cassandra use consistent hashing to achieve scalable and fault-tolerant designs while balancing these trade-offs.


System Design: Putting It All Together

For a scalable user-order management system, the architecture might look like this:

  1. Load Balancer: Distributes incoming requests among application servers.
  2. Application Servers: Handle business logic but do not store data.
  3. Database Servers (Sharded): Store data distributed using consistent hashing. Each server manages a subset of users/orders.

This design ensures:

  • Scalability: Adding more database servers distributes the load effectively.
  • Fault Tolerance: Consistent hashing minimizes data movement during server changes.
  • Flexibility: Virtual nodes balance load distribution.

Conclusion

Distributed systems and load balancers are essential for building scalable, reliable applications. Techniques like consistent hashing address critical challenges such as scalability and fault tolerance, ensuring systems can grow while maintaining performance. By understanding these concepts, developers can design systems that handle both high traffic and massive data efficiently.

Avatar

Neelabh

About Author

As Neelabh Singh, I am a Senior Software Engineer with 6.6 years of experience, specializing in Java technologies, Microservices, AWS, Algorithms, and Data Structures. I am also a technology blogger and an active participant in several online coding communities.

You may also like

Blog Design Pattern

Understanding the Builder Design Pattern in Java | Creational Design Patterns | CodeTechSummit

Overview The Builder design pattern is a creational pattern used to construct a complex object step by step. It separates
Blog Tech Toolkit

Base64 Decode

Base64 encoding is a technique used to encode binary data into ASCII characters, making it easier to transmit data over