How do you design clustering systems for large-scale distributed data processing?

Distributed clustering systems split data across nodes, compute partial clusters, and merge results using aggregation strategies.

At scale, clustering cannot run on a single machine due to memory and compute constraints. Distributed systems partition data across multiple workers using frameworks like Spark or Flink. Each node performs local clustering (e.g., mini-batch K-Means), then a coordinator aggregates centroids or cluster summaries. Challenges include maintaining consistency, handling data skew, and ensuring convergence across distributed updates.