Distributed vs Cluster: What’s the Real Difference and When to Use Each?

First, the distinction:

In one sentence: distributed systems work in parallel, while clusters work in series.

1. Distributed systems spread different services across multiple locations, whereas a cluster groups several servers together to provide the same service.

Each node in a distributed system can act as a cluster, but a cluster is not necessarily distributed.

Example: a popular website may place a front‑end server that routes requests to several back‑end servers; the front‑end chooses the least loaded server, illustrating a cluster.

In a distributed setup, each node performs different tasks, so if a node fails, its specific service becomes unavailable.

2. Distributed systems aim to shorten the execution time of a single task, while clusters increase the number of tasks completed per unit time.

Example: a job with 10 sub‑tasks taking 1 hour each would need 10 hours on a single server. Using a distributed approach with 10 servers, each handling one sub‑task, the job finishes in 1 hour (e.g., Hadoop Map/Reduce). In a cluster, 10 servers can process 10 independent jobs simultaneously, also completing in 1 hour.

Cluster Concept

1. Two key characteristics

A cluster is a group of cooperating service entities that provides greater scalability and availability than a single entity. To the client, a cluster appears as a single service, but internally it consists of multiple nodes.

Scalability – performance is not limited to a single node; new nodes can be added dynamically.

High availability – redundant nodes ensure the service remains accessible; if one node fails, another takes over.

2. Two essential capabilities

Load balancing – distributes tasks evenly across compute and network resources.

Error recovery – if a node fails, another node transparently continues the task.

Both capabilities require that each node can execute the same task with identical context information.

3. Two core technologies

Cluster address – a single address (or virtual IP) that clients use to reach the cluster; a load balancer manages node membership and address translation.

Internal communication – nodes constantly exchange heartbeat and task context information to coordinate load balancing and error recovery.

Cluster Types

Linux clusters are mainly classified into three categories:

High‑availability cluster (HA)

Load‑balancing cluster

High‑performance computing (HPC) cluster

Detailed Introduction

1. High‑availability cluster

Typically a two‑node HA setup (often called dual‑machine hot standby). Its purpose is to ensure continuous service availability, not to protect data; it minimizes the impact of hardware, software, or human failures on the application.

2. Load‑balancing cluster

All nodes are active and share the workload. Commonly used for web servers, database servers, and application servers. The load balancer directs incoming requests to the least loaded node, providing both load distribution and fault tolerance.

3. High‑performance computing (HPC) cluster

Provides computational power beyond a single machine. Includes high‑throughput computing (many independent tasks) and distributed high‑performance computing (tightly coupled tasks requiring extensive data exchange).

4. Relationship and differences between distributed systems and clusters

Distributed systems distribute different services across locations, while clusters concentrate several servers to deliver the same service. Any node in a distributed system can form a cluster, but a cluster does not have to be distributed. In a distributed setup, a node failure makes its specific service unavailable; in a cluster, other nodes can take over.

Original source: http://blog.chinaunix.net/uid-7374279-id-4413214.html