This is the kind of question that looks manageable until you actually think about ties in the sort order.
Start by clarifying requirements: data source type, ordering guarantees, consistency needs, and memory constraints. Then propose a keyset (seek) pagination approach using a stable, unique sort key (e.g., timestamp + id) to avoid offset inefficiency and handle data shifts gracefully. Discuss trade-offs between keyset and offset pagination, and outline how to stream results in batches without loading everything into memory.
Pro tip: Mention that offset pagination breaks when data shifts (e.g., new rows inserted) and is O(n) in offset, while keyset pagination is O(log n) with proper indexing and gives stable results. Also, highlight the need for a tie-breaker in the sort key to ensure deterministic ordering.
Ask about the data source (SQL, NoSQL, streaming), ordering requirements, consistency expectations, and memory limits. Confirm whether the data can change between page requests and how that should be handled.
Compare offset vs. keyset pagination. Recommend keyset for large datasets and shifting data, explaining that it uses a WHERE clause on the last seen sort key to fetch the next page efficiently.
Define a stable, unique sort key (e.g., created_at + id) and show how to construct the query: SELECT ... WHERE (sort_key > last_sort_key) ORDER BY sort_key LIMIT page_size. Ensure the key is indexed.
Explain that keyset pagination naturally handles inserts/deletes by continuing from the last seen key. Discuss snapshot isolation or read replicas if strict consistency is needed.
Describe using a cursor or iterator that fetches one page at a time, processing results incrementally. Avoid caching all pages; use lazy evaluation and close resources promptly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The aggregation follow-up tripped me up more than I expected.
First, explain how to adapt pagination for a derived or aggregated query, such as using a subquery or materialized view, and ensure the pagination key is stable and unique. Then, compare offset-based and cursor-based pagination in terms of performance, consistency, and use cases, highlighting trade-offs relevant to Coinbase's scale and data integrity needs.
Pro tip: Emphasize that cursor-based pagination is generally preferred for large, frequently changing datasets like cryptocurrency transactions, but acknowledge that offset-based can be simpler for static or small datasets. Mention that Coinbase likely uses cursor-based pagination for its API to ensure consistent results and avoid performance degradation.
Clarify what the derived or aggregated query entails, such as grouping transactions by user or calculating daily volumes, and identify the need for a stable ordering key.
For derived queries, use a subquery or CTE to compute the aggregated results, then apply pagination on the outer query using a unique and sequential key (e.g., timestamp + ID).
Discuss offset-based pagination's simplicity and random access versus its performance issues (deep offsets) and inconsistency (data changes). Contrast with cursor-based pagination's efficiency and consistency but limited random access.
Apply the trade-offs to Coinbase's use case: high-volume, real-time data where consistency and performance are critical, making cursor-based pagination more suitable.
Summarize that for derived/aggregated queries, cursor-based pagination on a stable key is often better, but offset-based may suffice for static reports.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.