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This is a hard difficulty System Design question, commonly asked during Technical Screen rounds at Databricks.

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

Identify and handle race conditions

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of concurrency, race conditions, synchronization mechanisms, and performance trade-offs in designing thread-safe in-memory data structures like key-value stores and hit counters.

Databricks

Sep 6, 2025, 12:00 AM

Software Engineer

Technical Screen

System Design

Concurrency in an In-Memory Key-Value Store and Hit Counter

Context and Assumptions

Assume you are implementing an in-memory key-value store and a hit counter within a single process, shared by multiple threads:

KeyValueStore supports: get(k), put(k, v), delete(k), and optionally compareAndSet(k, expected, new).
HitCounter supports: increment([key]), getCount([key]) — either a single global counter or per-key counters.
Data structure backing the store is a hash map with buckets and possible resizing.
High read-to-write ratio is common, but hot keys and heavy write bursts can occur.

Tasks

Identify the race conditions that can occur under concurrent access.
Propose thread-safety mechanisms (coarse-/fine-grained locks, read/write locks, atomic operations, lock-free designs) and explain their correctness.
Discuss performance trade-offs of the chosen mechanisms.
Outline how to test and validate concurrency behavior.

Solution

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