System Design: Distributed Word Frequency Counting (No MapReduce)
Context
Design a distributed system capable of computing word frequencies over terabytes of text data. The system must scale horizontally and produce both global word counts and global top-K words. A critical constraint is that the solution must not use MapReduce. The system should accommodate data arriving as either batch files or continuous streams.
Requirements
Your end-to-end design must cover the following aspects:
1. Ingestion
- Detail how raw text data enters the system (e.g., via a log or stream service).
- Describe assumptions regarding data chunking, schema, and ordering.
2. Partitioning/Sharding
- Explain how tokens (words) are partitioned across different shards or processing units (e.g., using consistent hashing).
- Propose strategies for mitigating the impact of "hot keys" (e.g., common words like "the") through techniques like salting or dynamic splitting.
3. Aggregation
- Describe the process of producing and aggregating partial word counts across the distributed system.
- Explain how counts from salted partitions are combined to yield accurate global per-word counts.
4. Global Results and Top-K
- Outline the method for computing exact global word counts.
- Detail how to determine the global top-K words across all shards.
- Clarify whether approximation is acceptable for top-K and, if so, which algorithm would be used.
5. Reliability and Correctness
- Address fault tolerance and recovery mechanisms in the event of node failures.
- Discuss idempotency and strategies for handling retries effectively.
- Analyze the trade-offs between exactly-once and at-least-once delivery semantics, and explain how each could be achieved.
- Describe mechanisms for backpressure and flow control within the system.
6. Storage and Serving
- Specify where the computed results (e.g., per-word counts, historical snapshots, system metadata) will be stored.
- Explain how these results will be exposed to consumers (e.g., via APIs), including expectations for latency and consistency.
General Instructions
- Make all reasonable assumptions explicit.
- You may describe diagrams verbally if they help clarify your design.
- Include small examples to illustrate concepts like top-K computation and salted key combination.
AI interview prep
Core dilemma
Designing a highly scalable, fault-tolerant, and consistent distributed aggregation system for both batch and streaming data, *without* relying on a pre-built framework like MapReduce. This forces the candidate to explicitly design the distributed coordination, state management, and fault recovery mechanisms that MapReduce abstracts away, particularly for accurate global counts and top-K while managing hot keys.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
System Design: Distributed Word Frequency Counting (No MapReduce)
Context
Design a distributed system capable of computing word frequencies over terabytes of text data. The system must scale horizontally and produce both global word counts and global top-K words. A critical constraint is that the solution must not use MapReduce. The system should accommodate data arriving as either batch files or continuous streams.
Requirements
Your end-to-end design must cover the following aspects:
1. Ingestion
- Detail how raw text data enters the system (e.g., via a log or stream service).
- Describe assumptions regarding data chunking, schema, and ordering.
2. Partitioning/Sharding
- Explain how tokens (words) are partitioned across different shards or processing units (e.g., using consistent hashing).
- Propose strategies for mitigating the impact of "hot keys" (e.g., common words like "the") through techniques like salting or dynamic splitting.
3. Aggregation
- Describe the process of producing and aggregating partial word counts across the distributed system.
- Explain how counts from salted partitions are combined to yield accurate global per-word counts.
4. Global Results and Top-K
- Outline the method for computing exact global word counts.
- Detail how to determine the global top-K words across all shards.
- Clarify whether approximation is acceptable for top-K and, if so, which algorithm would be used.
5. Reliability and Correctness
- Address fault tolerance and recovery mechanisms in the event of node failures.
- Discuss idempotency and strategies for handling retries effectively.
- Analyze the trade-offs between exactly-once and at-least-once delivery semantics, and explain how each could be achieved.
- Describe mechanisms for backpressure and flow control within the system.
6. Storage and Serving
- Specify where the computed results (e.g., per-word counts, historical snapshots, system metadata) will be stored.
- Explain how these results will be exposed to consumers (e.g., via APIs), including expectations for latency and consistency.
General Instructions
- Make all reasonable assumptions explicit.
- You may describe diagrams verbally if they help clarify your design.
- Include small examples to illustrate concepts like top-K computation and salted key combination.