Coordination in Distributed Systems
Learn how distributed systems coordinate across nodes to ensure reliable collaboration without conflicts or duplication.
A monolithic application operates within a single memory space and maintains a unified source of truth. Distributed systems often sacrifice scalability and resilience, but this creates a core challenge: how can many independent machines maintain the same state?
Without a reliable way to coordinate, the system can run into serious problems:
It may not know which machine should handle certain tasks.
Different machines might overwrite each other’s data.
Some machines might not even realize when others have been disconnected or stopped working.
This challenge is called the coordination problem.
It sits at the core of distributed systems and frequently appears in System Design interviews because it directly impacts a system’s reliability and correctness. In this lesson, we’ll examine the key building blocks that enable distributed services to coordinate, transforming a set of independent machines into a powerful, unified system.
Introduction to distributed system coordination
When we break an application into distributed services, we gain fault tolerance and scalability.
However, these services must still collaborate. For instance, a cluster of database replicas needs to perform a leader election to agree on which node is the primary writer. A set of workers processing a queue needs to avoid processing the same job twice, which requires careful collaboration.
This act of getting multiple nodes to agree on a state or a course of action is called coordination. It often involves state replication, which ensures that all nodes have the same data, and heartbeats, which are regular signals nodes send to confirm they are alive and reachable.
The primary challenge in distributed coordination lies in balancing consistency, availability, and partition tolerance. When a network failure splits the cluster, the system must decide whether to continue running with possibly outdated data or shut down to ensure full correctness.
Note: The CAP theorem says that a distributed system can only deliver two out of three guarantees: Consistency, Availability, and Partition Tolerance. Because network partitions are unavoidable in practice, system designers usually face a trade-off between consistency and availability.
To address these challenges, engineers rely on a small set of foundational tools called coordination primitives. The next section examines these primitives in detail, including how they enable services to elect leaders, agree on values, and ...