I hadn't thought about this at all during the coding part, so pivoting felt a bit abrupt.
Start by clarifying the requirements and constraints of a production API, then walk through the key layers: API design, implementation, deployment, and operations. Emphasize scalability, reliability, security, and observability, tying each back to eBay's high-traffic, distributed environment.
Pro tip: Show that you think about the API as a product: versioning, documentation, and developer experience matter as much as the code. Mention how you'd handle backward compatibility and deprecation to avoid breaking clients.
Specify endpoints, request/response schemas, status codes, and versioning strategy. Use OpenAPI/Swagger to document and generate client SDKs.
Plan for horizontal scaling, load balancing, caching, rate limiting, and graceful degradation. Consider idempotency and retry semantics for critical operations.
Choose an auth mechanism (e.g., OAuth 2.0, JWT), enforce HTTPS, validate inputs, and apply least-privilege access. Include audit logging for sensitive actions.
Containerize the service, define infrastructure as code, and automate testing, building, and deployment. Use blue-green or canary deployments to reduce risk.
Instrument metrics, logs, and traces; set up alerts and dashboards. Define SLIs/SLOs and runbooks for incident response and capacity planning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what ad state needs to be stored (e.g., impressions, clicks, frequency caps), the read/write patterns, latency and consistency needs, and scale. Then propose a storage solution that balances performance, scalability, and cost, such as a distributed key-value store like Redis for low-latency access, with a durable backing store like Cassandra or DynamoDB for persistence. Explain how you would shard and replicate the data to handle eBay's scale.
Pro tip: Demonstrate awareness of trade-offs: for example, using Redis for speed but acknowledging its persistence limitations, and proposing a write-behind cache to a durable store. Also, mention the importance of idempotency and handling out-of-order events in ad state updates.
Ask questions to understand what ad state is stored (e.g., impressions, clicks, frequency caps), the expected read/write QPS, latency requirements, and consistency needs (strong vs. eventual).
Select a storage system based on requirements: e.g., Redis for low-latency reads/writes, Cassandra for scalable writes and durability, or a combination. Consider using a cache in front of a database.
Define the key structure (e.g., user_id + ad_id) and how to shard data across nodes to distribute load evenly. Consider using consistent hashing for scalability.
Explain how to handle consistency (e.g., eventual consistency with conflict resolution) and durability (e.g., replication, write-ahead logs). Discuss trade-offs between latency and durability.
Outline how the system scales horizontally, handles failures, and supports monitoring and maintenance. Mention TTLs for ephemeral data and archiving strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about 4xx vs 5xx, validation errors, auth failures.
Start by clarifying the API's purpose and consumers, then propose a consistent error response structure that includes HTTP status codes, machine-readable error codes, and human-readable messages. Emphasize trade-offs between exposing detailed internal errors versus security and usability, and suggest standard formats like RFC 7807.
Pro tip: Align your error design with industry standards (e.g., RFC 7807) and eBay's public API conventions to demonstrate awareness of ecosystem consistency and developer experience.
Ask about the API's domain, whether it's public or internal, and who the callers are (e.g., third-party developers, internal services). This determines the level of detail and security considerations.
Propose a standard error response body with fields like type, title, status, detail, and instance (RFC 7807). Ensure it's machine-readable and human-friendly.
Outline common status codes (400, 401, 403, 404, 409, 429, 500, 503) and when to use each, ensuring they align with the error type.
Discuss what to expose (e.g., validation errors) versus what to hide (e.g., stack traces, internal IDs) to balance debuggability and security.
Mention adding custom error codes, links to docs, and ensuring errors are documented in the API spec (e.g., OpenAPI) for developer ease.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Request and response shapes, what fields are required vs optional, versioning.
Start by clarifying the API's purpose, key use cases, and access patterns to ensure the schema aligns with business needs. Then, propose a data model that balances normalization for consistency with denormalization for performance, considering eBay's scale and read-heavy workload. Finally, discuss how the schema supports scalability, evolution, and integration with existing systems.
Pro tip: Demonstrate awareness of eBay's specific challenges, such as handling high-volume transactions and diverse product listings, by mentioning how your schema design addresses sharding, caching, or eventual consistency. This shows you understand the company's domain and can tailor solutions accordingly.
Ask questions to understand the API's purpose, expected load, data access patterns, and consistency requirements. This ensures the schema meets functional and non-functional needs.
Define the main entities (e.g., users, items, orders) and their relationships, using ER diagrams or similar tools to visualize the model.
Decide between normalized and denormalized structures based on read/write ratios, query patterns, and scalability needs. Consider SQL vs. NoSQL options.
Specify tables/collections, fields, data types, indexes, and constraints. Ensure primary keys, foreign keys, and indexes support efficient queries.
Plan for horizontal scaling (e.g., sharding), caching, and schema versioning to handle growth and future changes without downtime.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the API's requirements—such as data access patterns, latency, and client diversity—before recommending an architectural style. Then, compare REST with alternatives like GraphQL, gRPC, or event-driven approaches, and justify your choice based on trade-offs relevant to eBay's scale and use case.
Pro tip: Acknowledge that REST is often the default, but show maturity by discussing when it's not ideal—e.g., for real-time or highly connected data—and mention eBay's specific context like high-volume, read-heavy workloads.
Ask about the API's purpose, expected load, data relationships, and client types to ground your recommendation in concrete needs.
Assess whether REST's statelessness, resource-oriented design, and HTTP caching align with the requirements, especially for CRUD operations.
Briefly contrast REST with GraphQL (flexible queries), gRPC (performance), or WebSockets (real-time), highlighting when each excels.
Factor in eBay's scale, existing infrastructure, and team expertise—e.g., REST may be preferred for public APIs due to ubiquity and tooling.
State your choice clearly, summarizing the trade-offs and why it best fits the scenario, while remaining open to hybrid approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
GET for reading ad state, PUT for updating it.
Start by clarifying the API's purpose and the resource being manipulated, then explain that GET is for safe, idempotent read operations while PUT is for idempotent full updates or replacements. Structure URLs around resources (nouns) with clear hierarchies, using path parameters for identifiers and query parameters for filtering or pagination.
Pro tip: Mention that PUT should be idempotent and used for full resource replacement, while PATCH is for partial updates—this shows you understand the nuance beyond just GET vs PUT. Also, emphasize that URL design should be consistent and predictable, which is crucial for large-scale systems like eBay's.
Determine whether the operation is reading data (GET) or creating/updating a resource (PUT). Consider if the operation is safe and idempotent.
Explain that GET retrieves data without side effects, while PUT replaces the entire resource at a known URL. Note that PUT is idempotent, so repeated calls have the same effect.
Use nouns for resources, plural forms for collections, and hierarchical paths for relationships. For example, /items/{itemId} for a specific item and /items for the collection.
Place resource identifiers in the path (e.g., /users/123) and use query parameters for filtering, sorting, or pagination (e.g., /items?category=electronics&limit=10).
Discuss how to handle partial updates (use PATCH instead of PUT), and ensure URL patterns are consistent across the API for maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.