← Snowflake Interview Insights
Knocked out the base version in around 20 minutes, felt pretty clean.
Start by modeling the tasks and dependencies as a directed acyclic graph (DAG), then use topological sorting to determine a valid execution order. For parallel scheduling, compute the earliest start time for each task using longest path in the DAG, and discuss resource constraints. For the harder variant, consider limited resources (e.g., number of workers) and propose a greedy or priority-based scheduling algorithm, analyzing its complexity and optimality.
Pro tip: Explicitly state assumptions about the number of workers and whether tasks can be preempted; this shows you understand real-world scheduling constraints. Also, mention that the problem is NP-hard when resources are limited, so you'll discuss heuristics and trade-offs.
Ask about the number of workers, task durations, whether tasks can be preempted, and the goal (minimize makespan vs. maximize throughput). This ensures you solve the right problem.
Represent tasks as nodes and dependencies as directed edges. Perform topological sort to detect cycles and get a valid order.
Compute earliest start and finish times for each task using longest path from source. The makespan is the maximum finish time. This gives an optimal schedule when resources are unlimited.
When the number of workers is limited, the problem becomes NP-hard. Propose a greedy list scheduling algorithm: at each step, assign ready tasks to available workers using a priority (e.g., critical path). Discuss approximation bounds.
For unlimited resources, O(V+E) for topological sort and longest path. For limited resources, list scheduling is O(V log V) with a priority queue, but may not be optimal. Mention alternative approaches like integer programming for small instances.
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