← Databricks Interview Insights
The constraint about not using an object store tripped me up early.
Start by clarifying requirements and constraints, then design a layered file system: begin with the block device abstraction, then define the superblock, inode table, data blocks, and directory structure. Discuss trade-offs for each component (e.g., inode allocation, directory entry format, free-space management) and how they impact performance, scalability, and reliability.
Pro tip: Emphasize crash consistency and recovery mechanisms (e.g., journaling or copy-on-write) early, as Databricks values robust data systems. Also, relate design choices to real-world workloads like large-scale analytics.
Ask about expected scale, workload characteristics (e.g., file sizes, access patterns), durability, and performance goals. State assumptions to guide the design.
Define the superblock, inode structure (metadata, pointers to data blocks), and data block allocation. Decide between inode-based (e.g., ext4) or other layouts.
Choose a directory entry format (e.g., linear list, B-tree) and explain how path resolution works. Discuss trade-offs between simplicity and lookup performance.
Select a method (e.g., bitmap, free list, B-tree) to track free blocks and inodes. Explain allocation and deallocation strategies, and how to handle fragmentation.
Discuss crash consistency (journaling, COW), caching, and concurrency. Consider how the design scales with large files and many files.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cursor-based pagination was the right answer here and I got there eventually, but I wasted time talking about offset-based pagination first.
Start by clarifying requirements such as directory size, expected latency, and consistency needs. Then propose a design that avoids full scans by using an index or sorted structure, and discuss pagination techniques like cursor-based pagination with stable ordering. Finally, address scalability and trade-offs, including caching and distributed storage considerations.
Pro tip: Mention that cursor-based pagination with a stable sort key (e.g., inode number or name) avoids the O(n) offset problem and handles concurrent modifications gracefully. Also, highlight that Databricks often deals with cloud storage, so leveraging object store listing APIs with continuation tokens is key.
Ask about directory size, expected read/write patterns, latency requirements, and consistency guarantees. This scopes the problem and shows you think before coding.
Suggest maintaining a sorted index of directory entries (e.g., B-tree or LSM-tree) to allow efficient range scans. For cloud storage, use the object store's native listing with continuation tokens.
Use cursor-based pagination where the cursor encodes the last seen key (e.g., name or inode). This avoids offset scans and provides stable results even with concurrent modifications.
Discuss partitioning the index, caching frequently accessed pages, and using asynchronous prefetching. Consider distributed systems like HDFS or S3 and how they handle large listings.
Compare cursor vs. offset pagination, mention handling of deletions/insertions during pagination, and consistency models (e.g., snapshot isolation).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the meatiest part of the whole interview.
Start by clarifying the requirements: what 'atomic' means (all-or-nothing visibility, crash consistency), the scale of subtrees, and concurrency model. Then propose a design that uses rename-based atomicity for the top-level directory, an iterative traversal to avoid stack overflow, and a locking or versioning scheme to handle concurrent access. Finally, discuss trade-offs between simplicity, performance, and safety.
Pro tip: Mention that true atomicity for recursive deletion is often achieved by renaming the directory to a temporary name first, making it invisible to other operations, then deleting it asynchronously. This avoids partial visibility and reduces lock contention.
Ask about the definition of atomicity (e.g., crash consistency, isolation from concurrent readers/writers), expected subtree sizes, and concurrency patterns. This ensures you address the right problem.
Propose using an atomic rename of the target directory to a unique temporary name (e.g., .trash-uuid) to instantly remove it from the namespace. Then delete the renamed directory in the background, ensuring that if a crash occurs, a cleanup process can resume.
Use an iterative traversal (e.g., explicit stack or queue) instead of recursion to avoid stack overflow. Consider parallel deletion of independent subdirectories to improve throughput, but bound concurrency to avoid resource exhaustion.
Employ locking (e.g., per-directory locks or a global lock with fine-grained granularity) or use versioning/leases to prevent concurrent modifications during deletion. Discuss how to handle operations that race with deletion (e.g., open file handles, new file creation).
Compare approaches: rename-based vs. in-place deletion, locking vs. optimistic concurrency. Explain how to recover from partial deletions (e.g., journaling, idempotent cleanup) and the impact on performance and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I defaulted to journaling because it's what I know best.
Start by defining crash consistency and why it matters, then compare journaling, copy-on-write, and fsync semantics in terms of their mechanisms, trade-offs, and guarantees. Conclude by discussing how you would choose or combine these techniques based on workload requirements and performance constraints.
Pro tip: Emphasize that fsync is not just about flushing data but also about ordering and durability guarantees, and mention that many real-world systems (e.g., databases) use a combination of techniques like journaling with fsync to balance performance and consistency.
Explain that crash consistency ensures the file system remains in a valid state after a crash, preventing data corruption or loss. Highlight that it's critical for reliability and data integrity.
Describe how journaling writes metadata (and optionally data) to a log before committing changes to the main file system, allowing recovery by replaying the log. Mention trade-offs: performance overhead vs. faster recovery and consistency.
Describe how COW never overwrites data in place; instead, it writes new data to unused blocks and atomically updates pointers. This provides inherent crash consistency and enables snapshots, but can cause fragmentation and write amplification.
Detail that fsync forces a file's data and metadata to persistent storage, ensuring durability. Discuss that it doesn't guarantee ordering of writes to different files unless combined with other mechanisms, and note performance implications.
Summarize trade-offs: journaling is common for general-purpose file systems, COW is used in systems like ZFS and Btrfs for snapshots and integrity, and fsync is a tool for applications to enforce durability. Discuss how to combine them (e.g., journaling + fsync) for specific workloads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Saved this for the end and we were running low on time, which hurt me.
Start by outlining the core components of a distributed file system: a scalable metadata service, data servers for storage, replication for fault tolerance, and garbage collection for space reclamation. Then discuss how to partition and replicate metadata, distribute data blocks, ensure consistency, and handle failures. Finally, address trade-offs and real-world examples like HDFS or GFS.
Pro tip: Emphasize the separation of metadata and data planes, and discuss how to handle metadata scalability (e.g., sharding, caching) and consistency (e.g., using Paxos/Raft). Mention that garbage collection should be asynchronous and consider reference counting or mark-and-sweep with leases.
Ask about scale (number of files, size, read/write ratio), consistency needs, and fault tolerance. Assume a large-scale system like HDFS or GFS.
Propose a distributed metadata service using sharding (e.g., by namespace) and replication (e.g., via Raft) for high availability. Consider caching and hierarchical namespaces.
Store file data in fixed-size blocks across data servers. Use replication (e.g., 3x) or erasure coding for durability. Discuss placement policies (rack awareness) and consistency models.
Implement asynchronous GC: track orphaned blocks via reference counting or mark-and-sweep. Use leases to avoid deleting blocks still in use. Consider delayed deletion for safety.
Compare replication vs. erasure coding, strong vs. eventual consistency, and centralized vs. decentralized metadata. Explain how to handle node failures, network partitions, and rebalancing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.