Databricks·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Databricks system design round focused entirely on concurrent data structures. One long question about thread-safe hash maps that went pretty deep into JVM internals. Left feeling like I covered the surface but probably missed some nuance they were looking for.

Questions Asked (1)

Design and implement a thread-safe hash map that handles many concurrent readers and writers efficiently. Walk through why a plain HashMap fails under concurrency, what coarse-grained alternatives exist, and how ConcurrentHashMap actually works internally.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I started with the obvious stuff, HashMap isn't thread-safe because concurrent rehashing can corrupt the internal structure, and in older JDKs you could get threads stuck in infinite loops reading a circular linked list.

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AI HintsAI Generated

Suggested Approach

Start by explaining why a plain HashMap fails under concurrency, then compare coarse-grained synchronization approaches, and finally dive into ConcurrentHashMap's internal design. Emphasize trade-offs and how the design achieves high concurrency.

Pro tip: Mention that ConcurrentHashMap uses lock striping and CAS operations to minimize contention, and that its iterators are weakly consistent, which avoids ConcurrentModificationException. This shows deep understanding beyond textbook knowledge.

1. Identify concurrency issues with plain HashMap

Explain that HashMap is not thread-safe: concurrent writes can cause data corruption, infinite loops (pre-Java 8), and lost updates. Also, its fail-fast iterators throw ConcurrentModificationException.

2. Discuss coarse-grained synchronization

Describe using Collections.synchronizedMap or a single lock around the entire map. This ensures thread safety but serializes all operations, severely limiting concurrency.

3. Introduce ConcurrentHashMap and its goals

Explain that ConcurrentHashMap is designed for high concurrency with thread safety, allowing multiple readers and a configurable number of writers to proceed concurrently.

4. Detail ConcurrentHashMap internals

Describe lock striping (segments in Java 7, bins in Java 8+), use of CAS for lock-free reads and some writes, and synchronization on individual bins for writes. Mention that reads are typically lock-free and writes lock only the affected bin.

5. Summarize trade-offs and best practices

Highlight that ConcurrentHashMap offers better scalability than synchronized maps, but has overhead for certain operations (e.g., size() is not constant-time). Mention weakly consistent iterators and when to choose it.

Key Points to Mention

HashMap's lack of thread safety leads to data races and corruption.
Coarse-grained locking (e.g., synchronizedMap) serializes access, hurting performance.
ConcurrentHashMap uses lock striping (segments) in Java 7 and finer-grained bin-level locking with CAS in Java 8+.
Reads are generally lock-free, and writes lock only the specific bin being modified.
Iterators are weakly consistent, meaning they don't throw ConcurrentModificationException and may reflect some but not all modifications.
Trade-offs: higher concurrency but size() and isEmpty() are not atomic snapshots; suitable for high-read, high-write scenarios.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.