What is distributed?

Distributed refers to a system or process that is spread across multiple computers, servers, or devices that work together over a network. Instead of everything running on a single machine, the workload, data, or services are divided among many nodes, each handling a part of the job.

Let's break it down

  • Multiple nodes: Individual computers or devices that each run a piece of the overall system.
  • Network: The communication layer that lets nodes talk to each other, share data, and coordinate actions.
  • Shared responsibility: Tasks like processing, storage, or decision‑making are split so no single node does everything.
  • Coordination mechanisms: Protocols (e.g., consensus algorithms) ensure nodes stay in sync and agree on the system’s state.

Why does it matter?

Because a single machine can become a bottleneck or a single point of failure. Distributing work lets you handle more users, process larger data sets, and keep the service running even if one node crashes. It also lets you place parts of the system closer to where they’re needed, reducing delays.

Where is it used?

  • Cloud platforms (AWS, Azure, Google Cloud) that run services on many servers.
  • Large‑scale web applications like Netflix, Facebook, and Amazon.
  • Distributed databases such as Cassandra, MongoDB, and CockroachDB.
  • Blockchain networks (Bitcoin, Ethereum).
  • Content Delivery Networks (CDNs) that cache files worldwide.
  • Internet of Things (IoT) setups where many sensors collaborate.

Good things about it

  • Scalability: Add more nodes to handle more load.
  • Fault tolerance: The system can survive individual node failures.
  • Performance: Work can be done in parallel, often faster than a single machine.
  • Geographic proximity: Data can be processed close to users, lowering latency.
  • Cost efficiency: Use cheaper commodity hardware instead of one massive server.

Not-so-good things

  • Complexity: Designing, deploying, and maintaining distributed systems is harder than single‑node setups.
  • Debugging difficulty: Problems may appear only when multiple nodes interact.
  • Network latency: Communication between nodes adds delay and can become a bottleneck.
  • Consistency challenges: Keeping data synchronized across nodes can require trade‑offs (e.g., CAP theorem).
  • Security surface: More nodes and network traffic mean more points for potential attacks.