Distributed Systems

A distributed system is a collection of independent computers that appear to their users as a single coherent system. Components run on separate nodes, communicate via a network, and coordinate to achieve a common goal.

Distributed systems introduce a class of problems absent in single-process applications: partial failures (one node fails while others continue), network partitions (nodes cannot communicate), clock skew (no shared global time), and concurrency across nodes.

The CAP theorem states that a distributed system can guarantee at most two of three properties simultaneously: Consistency (all nodes see the same data), Availability (every request gets a response), and Partition tolerance (the system continues despite network splits). Real networks always partition, so the practical choice is between CP (consistency over availability) and AP (availability over consistency, i.e., eventual consistency).

In PHP, distributed systems arise in microservices, multi-region deployments, distributed caches (Redis Cluster, Memcached), and queue-based architectures.

<?php

// PHP interacts with distributed systems through clients:

// Redis Cluster — distributed key-value store
$redis = new Redis();
$redis->connect('redis-cluster.internal', 6379);
$redis->set('session:abc', json_encode($sessionData), 3600);

// RabbitMQ / SQS — distributed message passing
$channel->basic_publish(
    new AMQPMessage(json_encode($payload)),
    exchange: '',
    routingKey: 'orders',
);

// HTTP service call — network may fail, retry with backoff
$response = $httpClient->request('POST', 'https://payments.internal/charge', [
    'json'    => $payload,
    'timeout' => 3.0,
]);

?>

Documentation

See also Wikipedia: Distributed computing.

Related : Microservice, Eventual Consistency, Fault Isolation, Message Queue, Resilience, CAP Theorem, Distributed Tracing, Service Discovery, Service Registry