← Databricks Interview Insights
The 'low-level' part is what trips people up.
Start by clarifying requirements (read/write ratio, consistency, eviction policy, TTL) and then propose a design using a hash map for O(1) lookups and a doubly linked list for LRU eviction. Write pseudocode for core operations (get, put, evict) and discuss trade-offs like thread safety, memory limits, and cache invalidation.
Pro tip: Mention that you would instrument the cache with hit/miss metrics and consider a two-tier design (e.g., in-memory + disk) if persistence is needed. Also, explicitly state assumptions about request patterns and data size to show you think about real-world constraints.
Ask about expected read/write ratio, data size, consistency needs, and eviction policy (e.g., LRU, LFU). Confirm whether the cache is per-process or shared, and if it needs to survive restarts.
Propose a hash map for O(1) key lookup and a doubly linked list to track access order for LRU eviction. Explain why this combination gives O(1) get and put operations.
Outline the get(key) and put(key, value) methods, including how to move accessed nodes to the front and evict the least recently used node when capacity is exceeded. Include thread-safety considerations (e.g., locks or concurrent data structures).
Compare LRU with other policies (FIFO, LFU) and explain why LRU is often suitable for web request caching. Address memory overhead, eviction cost, and potential contention in multithreaded environments.
Mention TTL support, cache stampede mitigation (e.g., single-flight), and metrics for monitoring. Discuss how to handle cache invalidation when underlying data changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came right after the main design question.
Start by explaining the importance of minimizing critical sections in a cache to improve concurrency and throughput. Then describe specific techniques like fine-grained locking, lock striping, or lock-free data structures, and discuss the trade-offs and concurrency risks such as race conditions, deadlocks, and increased complexity. Conclude by emphasizing the need for careful design and testing to balance performance and correctness.
Pro tip: Demonstrate awareness of real-world systems like Databricks' caching layers by mentioning how techniques like lock striping are used in high-performance systems (e.g., Java's ConcurrentHashMap) and how you would validate correctness with stress tests and formal reasoning.
Analyze the cache operations (e.g., get, put, evict) to pinpoint which parts require mutual exclusion and why they are critical.
Propose methods such as fine-grained locking, lock striping, read-write locks, or lock-free algorithms to shrink critical sections.
Discuss potential issues introduced: race conditions, deadlocks, livelocks, priority inversion, and memory consistency errors.
Explain how to address these risks through careful design, atomic operations, versioning, or transactional memory.
Emphasize testing with stress tests, race detectors, and performance benchmarks to ensure correctness and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the cache architecture and concurrency model, then systematically identify potential deadlock scenarios at both the shard level and across shards. Explain how you would prevent, detect, and recover from deadlocks, emphasizing trade-offs between consistency, latency, and complexity.
Pro tip: Proactively discuss how deadlocks can be avoided by design—such as using lock ordering, timeouts, or lock-free structures—rather than just detection and recovery. This shows you think about prevention first, which is highly valued in distributed systems.
Ask questions to understand the sharding strategy (e.g., consistent hashing), locking mechanisms (e.g., per-shard locks, global locks), and operations that require multiple locks (e.g., resizing, cross-shard transactions).
Enumerate situations where multiple threads or processes could hold locks and wait for each other, such as concurrent updates to multiple shards or lock acquisition during rebalancing.
Discuss strategies like imposing a global lock order, using timeouts with retries, or employing lock-free data structures to eliminate circular wait conditions.
If prevention is not fully possible, explain how to detect deadlocks (e.g., wait-for graphs, timeout-based detection) and recover (e.g., aborting and retrying transactions, releasing locks).
Compare the impact of different approaches on latency, throughput, consistency, and complexity, and mention monitoring/alerting for deadlock occurrences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like a bonus question, almost optional in tone.
Start by clarifying the requirements and constraints of the multi-machine setup, then discuss partitioning, replication, and consistency trade-offs. Focus on how to scale the cache while maintaining performance and correctness, and tie your answer to Databricks' data-intensive environment.
Pro tip: Emphasize that the right design depends on the workload (read-heavy vs write-heavy) and consistency requirements; showing you can adapt the design to different scenarios demonstrates maturity.
Ask about scale, latency, consistency, and failure tolerance to understand what the multi-machine cache must achieve.
Discuss how to distribute data across machines, e.g., consistent hashing, and how to handle rebalancing when nodes are added or removed.
Explain replication for fault tolerance and read scalability, and the trade-offs between strong and eventual consistency.
Describe how to keep caches coherent across nodes, e.g., via invalidation messages or versioning, and how to handle stale data.
Mention how to detect and recover from node failures, and how to monitor performance and hit rates in a distributed setup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.