numeric·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Numeric had me work through a paginated API aggregation problem that sounds straightforward until you start thinking about all the ways it can fall apart in production. The conversation went pretty deep into failure handling and I wasn't fully prepared for how much they cared about the retry logic.

Questions Asked (1)

You have a paginated server endpoint that returns a page of records and a continuation token. Write code to consume all pages and group the results by a 'planet' field. Then walk through how you'd handle rate limits, transient failures with retry/backoff, and merging partial state if something fails mid-way.

API & IntegrationsSystem DesignTechnical Trade-offs

Author's notes

My loop logic was fine, the grouping was fine, but I stumbled when they asked about partial state merging.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by writing a clear, idiomatic pagination loop that accumulates records and groups them by 'planet' using a map. Then discuss resilience: rate limits, retries with exponential backoff and jitter, and idempotent merging of partial state. Finally, tie it together with a design that separates fetching, retry logic, and aggregation for testability.

Pro tip: Mention that you'd make the pagination loop resumable by persisting the continuation token and partial groups, so a mid-way failure doesn't force a full restart. This shows you think about production reliability, not just happy-path code.

1. Clarify requirements and constraints

Ask about expected page size, rate limit specifics, whether the endpoint is idempotent, and if ordering matters. Confirm the grouping key and desired output structure.

2. Implement the pagination loop

Write a loop that fetches pages using the continuation token, accumulates records, and groups them by 'planet' into a map. Stop when the token is null or empty.

3. Add retry with backoff and rate limit handling

Wrap each request in a retry mechanism with exponential backoff and jitter, respecting Retry-After headers for 429s. Distinguish between retryable (5xx, timeouts) and non-retryable (4xx) errors.

4. Design for partial failure and resumability

Persist the last successful continuation token and the current grouped state so you can resume without reprocessing. Ensure merging is idempotent to avoid duplicates on retry.

5. Discuss trade-offs and testing

Explain choices like in-memory vs. streaming aggregation, concurrency vs. sequential fetching, and how you'd test with mocked failures and rate limits.

Key Points to Mention

Exponential backoff with jitter to avoid thundering herd
Respecting Retry-After headers and handling 429 rate limits
Idempotent merging of partial results to prevent duplicates
Persisting continuation token and partial state for resumability
Distinguishing retryable vs. non-retryable errors
Memory considerations for large datasets (streaming vs. in-memory)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.