← Databricks Interview Insights
I started with a plain dict and got the basic operations working pretty fast.
Start by clarifying requirements: what operations are needed, expected scale, and whether thread-safety and eviction are required. Then design a simple, correct solution using a hash map for storage and a counter for hits, and discuss how to extend it with thread-safety (e.g., locks or concurrent data structures) and eviction policies (e.g., LRU).
Pro tip: Demonstrate awareness of trade-offs: for example, a global lock is simple but limits concurrency, while fine-grained locking or lock-free approaches improve throughput but add complexity. Also, mention that eviction policy choice depends on access patterns and memory constraints.
Ask about expected operations (put, get, hits), data types, scale, concurrency needs, and eviction requirements. Confirm whether hits should count total gets or only successful gets.
Propose using a hash map (dictionary) to store key-value pairs and a global counter for hits. For get, increment the counter if the key exists and return the value; otherwise return a sentinel (e.g., -1).
Discuss options: a single mutex for simplicity, or a read-write lock to allow concurrent reads. For higher concurrency, consider sharding the cache or using concurrent data structures like ConcurrentHashMap.
Explain that eviction is needed when the cache reaches capacity. Describe common policies (LRU, LFU, FIFO) and how to implement them, e.g., LRU with a doubly linked list and hash map.
Summarize time/space complexity for each operation and the trade-offs between simplicity, performance, and concurrency. Mention potential optimizations like lazy eviction or probabilistic counting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like a gut check more than a hard question.
Start by clarifying the cache's interface and expected behavior, then outline a test plan that covers put, get, hit, miss, overwrite, and repeated gets. Write concrete unit tests using a testing framework, ensuring each test isolates a specific behavior and uses assertions to verify outcomes.
Pro tip: Use parameterized tests to cover multiple scenarios efficiently and include edge cases like overwriting an existing key and getting a key multiple times to verify idempotency. Also, consider testing with different data types if the cache is generic.
Identify the methods (e.g., put(key, value), get(key)) and their expected return values or exceptions. Confirm whether the cache has a fixed size, eviction policy, or other constraints.
List the required behaviors: put then get (hit), get missing key (miss), overwrite existing key, repeated gets, and absent key lookups. Consider edge cases like null keys/values if allowed.
For each scenario, write a separate test method with a clear name. Use setup to create a fresh cache instance per test to avoid state leakage.
Use assertions to verify expected outcomes: e.g., assertEquals for values, assertNull for misses, and verify overwrites return the new value. For repeated gets, assert consistency.
Execute tests, ensure they pass, and consider adding more edge cases or parameterized tests for thoroughness. Discuss any trade-offs in test design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by contrasting the in-memory cache design with a persistent, disk-backed design, emphasizing the shift from hash-based lookups to LSM-tree write and read paths. Then walk through the key components: write path (memtable, WAL, SSTables), read path (bloom filters, sparse indexes, compaction), and trade-offs in latency, throughput, and durability. Finally, relate this to Databricks' needs for scalable, consistent storage.
Pro tip: Highlight that LSM-trees optimize for write-heavy workloads by batching writes in memory and flushing sequentially, but this introduces read amplification and compaction overhead—so you must tune for the workload. Mention that Databricks often deals with large-scale data, so compaction strategies and tiered storage are critical.
Ask about workload characteristics: read/write ratio, latency SLAs, data size, durability guarantees, and whether the cache must survive restarts. This determines if an LSM-tree is appropriate versus a B-tree or hybrid approach.
Explain how writes go to a write-ahead log (WAL) for durability, then to an in-memory memtable. When the memtable fills, it's flushed to disk as an immutable SSTable, and background compaction merges SSTables to reduce read amplification.
Detail how reads check the memtable first, then SSTables from newest to oldest, using bloom filters to skip SSTables that don't contain the key, and sparse indexes to locate the key within an SSTable. Mention that compaction and leveled strategies affect read performance.
Compare LSM-trees to traditional caches: higher write throughput but higher read latency due to multiple levels; compaction consumes I/O and CPU. Discuss tuning parameters like memtable size, compaction strategy (size-tiered vs. leveled), and bloom filter false positive rate.
Connect to Databricks' use cases: large-scale data processing, Delta Lake, and the need for efficient persistent storage. Mention how LSM-trees can be used in caching layers for cloud storage or as part of a larger data system, considering factors like cost and elasticity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.