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This is a hard difficulty System Design question, commonly asked during Onsite rounds at Anthropic.

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This question is commonly asked for Software Engineer candidates at Anthropic during technical interviews.

Design distributed median and mode | Anthropic Interview Question

Quick Overview

This question evaluates skills in distributed systems and large-scale analytics, covering data partitioning, streaming and sliding-window processing, quantile and heavy-hitter summarization, fault tolerance, and trade-off analysis for time, space, and network I/O.

Distributed System Design: Global Median and Global Mode at Massive Scale

Context

You are designing a distributed analytics system that must compute the global median and the global mode over a dataset with hundreds of billions of records stored across many machines. The system should support both batch and streaming (including sliding windows) and be efficient, fault-tolerant, and scalable.

Assume records each contain a single value x (numeric for median; categorical or integer for mode). You may make minimal, explicit assumptions as needed.

Tasks

Specify data partitioning strategies (batch and streaming) to compute:
- Global median
- Global mode
Define local sketches/summaries computed on each worker.
Describe how reducers aggregate partial results.
Minimize network I/O (explain how and why).
Compare exact vs approximate approaches (quantile sketches, heavy-hitter sketches) with error/resource trade-offs.
Discuss handling data skew and hot keys.
Address fault tolerance and recovery.
Support incremental/streaming updates.
Provide time, space, and communication complexity.
Extend the design to a sliding-window median and mode over an unbounded stream.

Quick Overview

Context

Assume records each contain a single value x (numeric for median; categorical or integer for mode). You may make minimal, explicit assumptions as needed.

Tasks

Specify data partitioning strategies (batch and streaming) to compute:

Global median
Global mode

Define local sketches/summaries computed on each worker.

Describe how reducers aggregate partial results.

Minimize network I/O (explain how and why).

Compare exact vs approximate approaches (quantile sketches, heavy-hitter sketches) with error/resource trade-offs.

Discuss handling data skew and hot keys.

Address fault tolerance and recovery.

Support incremental/streaming updates.

Provide time, space, and communication complexity.

Extend the design to a sliding-window median and mode over an unbounded stream.

Design distributed median and mode

Quick Overview

Distributed System Design: Global Median and Global Mode at Massive Scale

Context

Tasks

Solution

Comments (0)

Design distributed median and mode

Quick Overview

Distributed System Design: Global Median and Global Mode at Massive Scale

Context

Tasks

Solution

Comments (0)