I went straight for a naive merge and only thought about deduplication halfway through.
Start by clarifying requirements: what defines a duplicate subscription, expected data volume, and error handling needs. Then outline a modular design with separate readers for file and HTTP, a merge function with deduplication logic, and a unified in-memory store. Discuss trade-offs like synchronous vs asynchronous fetching, deduplication key choice, and conflict resolution.
Pro tip: Mention idempotency and data consistency: since Stripe values reliability, highlight how you'd handle partial failures (e.g., file read succeeds but HTTP fails) and ensure the merge is idempotent. Also, consider using a streaming approach for large files to avoid memory issues.
Ask about the JSON schema, what constitutes a duplicate subscription (e.g., same ID, same customer+plan), expected data sizes, and error handling expectations. Confirm whether the merge should be real-time or batch.
Describe how to read and parse JSON from a local file (e.g., using fs.readFile or streaming) and from an HTTP endpoint (e.g., fetch with error handling). Ensure both sources are parsed into a common in-memory representation.
Choose a deduplication key (e.g., subscription ID) and merge the two collections, resolving conflicts (e.g., prefer the most recent or the HTTP source). Use a hash map for O(n) deduplication.
Discuss handling malformed JSON, network failures, empty sources, and duplicate keys within a single source. Decide on fallback behavior (e.g., proceed with partial data or fail fast).
Compare approaches: in-memory vs streaming, synchronous vs asynchronous, and the impact of large datasets. Mention potential optimizations like pagination for HTTP or chunked file reading.
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Blanked for a second on whether to raise exceptions or return error objects.
Start by clarifying the context and requirements, then describe a consistent error-handling strategy that distinguishes between transient and permanent errors for both file and HTTP operations. Explain how you wrap errors with context, use appropriate retry/backoff for transient issues, and surface actionable information to the caller without leaking sensitive details.
Pro tip: Emphasize idempotency and observability: ensure retries are safe and log errors with correlation IDs so you can trace failures across services. Also, mention that you avoid catching generic exceptions and instead handle specific error types to prevent masking bugs.
Ask about the caller's expectations, error tolerance, and whether operations are idempotent. This shows you tailor solutions to the use case.
Distinguish between transient (e.g., network timeouts, temporary file locks) and permanent (e.g., file not found, 404) errors, as they require different handling.
For transient errors, use retries with exponential backoff and jitter; for permanent errors, fail fast. Wrap errors with context (operation, resource, cause) using custom error types or error codes.
Return structured errors to the caller with a clear message, error code, and whether it's retryable. Avoid exposing internal details like stack traces or file paths in production.
Log errors with sufficient context (correlation ID, user ID) and metrics. Write tests for error scenarios, including retries and timeouts.
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Talked through validating required fields and types before merging.
Start by clarifying the context: are we detecting mismatches at runtime, during CI/CD, or both? Then propose a layered validation strategy that combines schema validation, contract testing, and observability, emphasizing trade-offs between strictness and flexibility. Conclude with how you would surface mismatches to developers and operators for quick resolution.
Pro tip: At Stripe, reliability and developer experience are paramount. Show you understand that surfacing mismatches isn't just about logging errors—it's about providing actionable, contextual feedback (e.g., which field, which source, sample payload) and integrating with existing alerting/on-call systems.
Ask about the system: Is this batch or streaming? What are SLAs? Who consumes the output? This determines whether validation should be synchronous or asynchronous, and how strict it should be.
Establish a single source of truth for the output format (e.g., JSON Schema, Avro, Protobuf). Ensure it's versioned and accessible to both producers and consumers.
Validate parsed JSON against the schema at ingestion, and also validate the transformed output before emission. Use libraries like Ajv for JSON Schema, or custom validators for complex rules.
Log mismatches with details: source identifier, expected vs. actual, JSON path, and sample data. Emit metrics for mismatch rates and set up alerts for anomalies.
Decide on fallback behavior: reject, quarantine, or coerce with warnings. Use mismatches to drive schema evolution and improve upstream data quality.
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This was the part I actually felt decent about.
Clarify the structure of the subscription data and expected JSON, then design a recursive comparison that identifies and reports differences with precise paths. Focus on handling nested objects, arrays, and type mismatches, and discuss how to surface differences clearly for debugging.
Pro tip: Mention that you would use a library like deep-diff or implement a custom comparator with early exit for performance, and emphasize the importance of deterministic ordering when comparing arrays to avoid false positives.
Ask about the structure of the subscription data (e.g., nested objects, arrays) and the expected JSON format. Confirm whether order matters for arrays and how to handle missing keys.
Outline a recursive function that traverses both structures, comparing keys and values. For arrays, decide on order-sensitive or order-insensitive comparison based on requirements.
When a mismatch is found, record the path (e.g., using dot notation or JSON Pointer) and the expected vs actual values. Handle type mismatches and missing/extra keys.
Return a boolean indicating match, and if not, a list of differences with clear descriptions. Consider formatting for readability or machine parsing.
Mention handling of null, undefined, special types (dates, numbers), and performance considerations for large datasets (e.g., early exit, streaming).
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