Distributed Systems
Distributed systems coordinate work across processes or machines that communicate over fallible networks. The competency is designing for partial failure, latency, concurrency and uncertain ordering, so an AI pipeline or service remains correct when one component retries, slows down or loses contact with another.
What it is
A distributed system lacks a single instantaneous view of all state. Messages can be delayed, duplicated or lost, and one service can fail while others continue. Replication, partitioning, queues and coordination protocols make it possible to distribute storage and computation, but they introduce consistency and recovery choices. Parallel execution alone is not the whole subject: correctness depends on state ownership, ordering and what participants know. AI workloads add expensive model calls and large artifacts, making retry semantics, capacity control and transfer cost particularly consequential.
What the work involves
The practitioner identifies state and failure boundaries, specifies whether operations must be idempotent and chooses consistency appropriate to the decision. They set deadlines, limit retries and apply backpressure when downstream capacity is exhausted. They use correlation identifiers and durable records to trace work across components. Failure exercises check duplicate delivery, network loss and partial completion. Useful work delivers a system whose recovery rules preserve intended outcomes, including a clear answer to what happens if a caller cannot tell whether its request succeeded.
Illustrative example
A queue distributes document embedding jobs across workers. One worker stores vectors but loses contact before acknowledging the message, causing a retry. The engineer uses a stable job identifier and document version so the next worker can recognize completed output instead of duplicating it. A fault test pauses storage responses and confirms that queue consumers slow down rather than starting an unbounded number of inference calls.
Limits and common mistakes
Retries can amplify overload, and a timeout does not prove an operation was never completed. Stronger consistency may increase latency or reduce availability under particular failures. Queue delivery labels do not guarantee exactly-once business effects across unrelated systems. Check state transitions, duplicate handling and bounded resource use under faults. Distributed systems competence means making these semantics explicit; adding more machines without that reasoning can reduce reliability.
Prerequisites
Related skills
- ← is an instance of: Dask
- ← is subcategory of: HPC Cluster Computing
- ← is an instance of: Ray
- ← is subcategory of: Cloud Platforms
Sources and further reading
- Google SRE: handling overload
Documents overload, bounded queues, load shedding and retry-related operational failure modes.
Last updated: 2026-10-10