← Atlassian Interview Insights
I started with the data model which felt safe, tag table, join table for tag-to-object associations, nothing crazy.
Start by clarifying requirements and scale, then propose a data model with a many-to-many relationship between tags and objects, and design APIs for CRUD operations and search. Discuss indexing strategies, consistency, and performance trade-offs for high-scale scenarios.
Pro tip: Emphasize how your design handles tag deletions and renames without breaking existing associations, and consider using a graph or inverted index for efficient tag-based searches.
Ask about scale (number of tags, objects, queries per second), consistency needs, and whether tags are hierarchical or flat. Confirm if search should support multiple tags (AND/OR) and if tag suggestions are needed.
Propose a schema with a tags table (id, name, metadata) and a taggings table (tag_id, object_id, object_type) to support many-to-many relationships. Consider using a graph database if relationships are complex.
Outline RESTful endpoints: POST /tags, POST /objects/{id}/tags, DELETE /objects/{id}/tags/{tag}, GET /objects?tag={tag}. Include bulk operations and pagination for search results.
Discuss indexing (e.g., B-tree on tag name, inverted index for tag-object mapping), caching frequent queries, and sharding by object type or tag. Consider eventual consistency for high availability.
Cover tag deletion (cascade or soft delete), renaming, and permission checks. Compare SQL vs NoSQL vs graph DB, and explain your choice based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by mapping each operation to a RESTful verb and URL pattern, emphasizing resource-oriented design. Then explain idempotency guarantees for each verb and how you handle errors with appropriate status codes and consistent error bodies. Use a concrete example from a tag system to illustrate.
Pro tip: Mention that idempotency is not just about HTTP verbs but also about how you handle retries and duplicate requests, e.g., using idempotency keys for POST. Also, highlight that error responses should include a machine-readable code and human-readable message for better debugging.
List the CRUD operations for tags (create, read, update, delete, list) and assign the appropriate HTTP verb (POST, GET, PUT/PATCH, DELETE).
Design resource-oriented URLs, e.g., /tags for collection and /tags/{id} for individual resources, ensuring consistency and predictability.
Clarify which verbs are idempotent (GET, PUT, DELETE) and which are not (POST), and discuss strategies to make non-idempotent operations safe, such as idempotency keys.
Outline how to use HTTP status codes (e.g., 400, 404, 409, 500) and provide a consistent error response format with details like error code, message, and optional metadata.
Walk through a specific tag operation (e.g., creating a tag) to show the verb, URL, idempotency handling, and error responses in action.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with an inverted index approach and mentioned Elasticsearch as an option.
Start by clarifying requirements like scale, query patterns, and consistency needs. Then propose an inverted index mapping tags to document IDs, discuss sharding and replication for scalability, and compare storage options like Elasticsearch vs. custom solutions. Finally, address trade-offs around write vs. read performance and consistency.
Pro tip: Mention how Atlassian's products like Jira and Confluence handle tag-based search, and emphasize the importance of monitoring and iterating on the index design based on real usage patterns.
Ask about scale (number of documents, tags, queries per second), query patterns (single tag, multiple tags, boolean logic), and consistency requirements (eventual vs. strong).
Propose an inverted index where each tag maps to a posting list of document IDs. Discuss compression techniques like delta encoding and skip lists for efficient intersection.
Explain sharding strategies (e.g., by document ID or tag) and replication for fault tolerance. Consider using a distributed search engine like Elasticsearch or building on top of a KV store.
Discuss trade-offs between write and read performance, consistency models, and storage overhead. Mention caching frequent queries and using bloom filters for quick negative checks.
Cover monitoring, reindexing strategies, and handling updates/deletes. Emphasize the need for a feedback loop to optimize based on real query patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly came at the end and I was a bit fried by then.
Start by clarifying the requirements and constraints of the tag APIs, then propose a layered approach that combines authentication, authorization, and rate limiting. Emphasize scalability, security, and alignment with Atlassian's platform standards, and discuss trade-offs of different strategies.
Pro tip: Mention Atlassian's existing API gateway and identity services (e.g., OAuth 2.0, API tokens) to show familiarity with their ecosystem, and highlight the importance of per-user and per-app rate limits to prevent abuse while ensuring fair usage.
Ask about expected traffic patterns, client types (internal vs external), and security requirements to tailor the solution.
Implement OAuth 2.0 scopes and role-based access control (RBAC) to ensure only authorized users/apps can perform tag operations.
Use a distributed rate limiter (e.g., Redis) with sliding window or token bucket algorithms, applying limits per user, per app, and globally.
Leverage Atlassian's API gateway for centralized auth and rate limiting, ensuring consistency and reducing duplication.
Set up monitoring and alerting for rate limit breaches and auth failures, and adjust limits based on usage data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.