Distributed vs Cluster: Key Differences and When to Use Each

Distributed vs Cluster: Core Differences

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 not every cluster is 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, nodes are loosely organized; if one server fails, its specific service becomes unavailable, unlike a cluster where other nodes can take over.

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

Illustration: A task composed of 10 sub‑tasks takes 1 hour each on a single server (10 hours total). Using a distributed approach with 10 servers, each handles one sub‑task simultaneously, completing in 1 hour (e.g., Hadoop Map/Reduce). Using a cluster with 10 servers handling 10 independent tasks concurrently also finishes in 1 hour.

Cluster Concept

1. Two key features

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

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

2. Two essential capabilities

Load balancing – distributes tasks evenly across nodes.

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

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

3. Two core technologies

Cluster address – a single address that clients use to reach the cluster; a load balancer maps this address to internal node addresses.

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

Cluster Types

Linux clusters are mainly divided 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”). It ensures continuous service availability by minimizing 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.

3. High‑Performance Computing Cluster

Provides computational power beyond a single machine, supporting workloads such as high‑throughput computing (e.g., SETI@HOME) and distributed computing that requires tight inter‑node communication.

Distributed vs Cluster Relationship

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 is not necessarily distributed.

In a distributed setup, each node may run a different service; failure of a node makes its specific service unavailable. In a cluster, nodes are organized to provide redundancy, so a failed node can be compensated by others.

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