Anthropic

Design systems for processing large-scale data across multiple machines.

Tasks:

1. Find Duplicate Files

• Content-based comparison
• Handle terabyte-scale directories
• Optimize memory usage

2. Find Mode in Distributed Dataset

• Distribute workload across machines
• Compute local frequencies
• Aggregate for global mode

3. Find Median in Distributed Dataset

• Use distributed selection algorithms
• Minimize data transfer
• Ensure result accuracy

Implementation Strategy:

• Use parallel processing
• Implement efficient hashing
• Handle data consistency

Design a system to find mode and median in a very large dataset using distributed computing.

Part 1: Mode Calculation

Input:
- Large dataset (size >> single machine memory)
- Array of available machines
- Each machine has limited memory

Requirements:
- Distribute workload efficiently
- Handle machine failures
- Minimize network communication
- Ensure accurate results

Approach:
1. Data Partitioning
2. Local Mode Calculation
3. Result Aggregation
4. Final Mode Selection

Part 2: Median Calculation (Follow-up)

Additional Challenges:
- Sorting distributed data
- Memory constraints
- Network bandwidth optimization
- Maintaining accuracy

Considerations:
- Approximate vs exact solutions
- Sampling techniques
- Parallel processing strategies
- Data skew handling

System Design Requirements:

• Scalable architecture
• Fault tolerance
• Load balancing
• Progress monitoring
• Result verification