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I started with Product and Category and kind of worked outward from there.
Start by clarifying the scope and requirements of the analytics module, then define the core domain classes with their attributes and relationships, and finally discuss how they interact to support analytics queries. Emphasize trade-offs in data modeling and scalability, and relate to Blizzard's gaming context if possible.
Pro tip: Demonstrate awareness of real-world constraints by discussing how your design would handle high traffic and large datasets, and mention any relevant design patterns or technologies (e.g., caching, denormalization) that you would consider.
Ask questions to understand the expected scale, types of analytics (e.g., sales by category, popular products), and non-functional requirements like performance and consistency.
List the main classes such as Product, Category, Order, OrderItem, and possibly User, and define their key attributes and methods.
Explain how entities relate: e.g., a Category has many Products, an Order contains many OrderItems, each OrderItem references a Product.
Discuss how to support analytics queries efficiently, considering aggregation, indexing, and possibly denormalization or precomputed summaries.
Highlight trade-offs between normalization and performance, consistency vs. availability, and how you would scale the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward to write but I fumbled on the edge cases.
Clarify the data model and constraints first, then design a class with add/remove methods that handle edge cases like duplicates, non-existent products, and concurrency. Discuss trade-offs between data structures (e.g., hash set vs. list) and mention idempotency, error handling, and scalability.
Pro tip: Demonstrate production awareness by discussing concurrency control (e.g., locks or optimistic concurrency) and how you'd handle high-throughput scenarios, as Blizzard's systems often deal with massive concurrent user actions.
Ask about expected scale, concurrency needs, and whether operations should be idempotent. Confirm the data model: is a product unique within a category? Can a product belong to multiple categories?
Select a data structure that supports efficient add/remove and membership checks, such as a hash set for O(1) operations. Discuss trade-offs if ordering or duplicate handling is required.
Specify return types (e.g., boolean for success) and exceptions for invalid inputs. Decide how to handle adding an existing product or removing a non-existent one—whether to throw, return false, or ignore.
Cover null/empty inputs, duplicate additions, removal of absent products, and concurrent modifications. Use synchronization or atomic operations if thread safety is needed.
Mention how the solution scales with large categories, potential memory overhead, and alternatives like database-backed storage with transactions for distributed systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the OOP choices from earlier came back to bite me a little.
Start by clarifying the class structure and data model, then propose an aggregation strategy that iterates over sales records and groups by category. Emphasize efficiency and scalability, and discuss how you would handle edge cases like missing categories or returns.
Pro tip: Mention that you would pre-aggregate metrics in a materialized view or cache if the data is read-heavy, and highlight the importance of idempotent updates for real-time systems.
Ask about the class structure, data sources, and expected scale. Confirm whether metrics are computed on-demand or pre-aggregated.
Outline a method that iterates over sales records, groups by category, and sums revenue and item counts. Use a dictionary or map for O(n) efficiency.
Discuss handling of returns, discounts, missing categories, and null values. Ensure revenue and item counts are correctly adjusted.
Propose indexing, parallel processing, or pre-aggregation for large datasets. Mention caching or materialized views for read-heavy scenarios.
Describe how you would write unit tests for the aggregation logic, including edge cases, and integrate with the existing class structure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Recursive tree traversal, so the algorithm itself wasn't the hard part.
Start by clarifying the requirements: data volume, read/write patterns, and latency expectations. Then propose a schema and algorithm for recursive aggregation, discussing trade-offs between recursive queries, materialized paths, and precomputed aggregates. Finally, address scalability and consistency concerns.
Pro tip: Mention that Blizzard's game economies often require real-time analytics, so consider incremental updates or caching to avoid expensive recursive queries on every request.
Ask about data scale, query frequency, latency SLAs, and whether the hierarchy is static or dynamic. This determines the appropriate solution.
Discuss options like adjacency list, nested set, materialized path, or closure table. Explain how each supports recursive aggregation.
Outline a recursive traversal (DFS/BFS) or recursive SQL (WITH RECURSIVE) to sum metrics. Consider bottom-up precomputation for efficiency.
Propose indexing, caching, or incremental updates. Discuss sharding or denormalization if data is large.
Explain how to keep aggregates consistent when categories or metrics change, using transactions or eventual consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.