← Walleye Capital Interview Insights
This one tripped me up more than it should have.
Start by describing the observable symptom (e.g., missing or duplicated rows) that would prompt investigation of the JOIN. Then trace the root cause to the specific JOIN type (e.g., INNER vs LEFT) and explain why it produces that symptom. Finally, propose the minimal fix—changing the JOIN type—and verify it resolves the issue without unintended side effects.
Pro tip: Mention that you would first reproduce the bug with a minimal test case and check row counts before and after the JOIN to confirm the symptom, showing a disciplined debugging approach.
Describe the observable incorrect behavior, such as missing rows, duplicate rows, or unexpected NULLs in the result set.
Find the specific JOIN clause in the SQL file and determine its current type (e.g., INNER JOIN, LEFT JOIN).
Articulate why the current JOIN type causes the symptom, referencing the intended relationship between tables and the expected result set.
Suggest changing the JOIN type to the correct one (e.g., from INNER to LEFT) and explain why this is the smallest change that resolves the issue.
Outline how to test the fix (e.g., run the query, compare row counts) and check for any unintended consequences on other parts of the query.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
NULL = NULL being unknown is the kind of thing you know abstractly but forget under pressure.
Start by explaining how NULLs behave in SQL (three-valued logic) and why they cause bugs in WHERE clauses and aggregations. Then walk through a systematic debugging process: identify the symptom, locate the problematic clause, explain the root cause, and propose a fix with trade-offs. Finally, discuss how to prevent similar issues with testing and schema design.
Pro tip: Mention that NULL handling bugs often stem from assumptions about data completeness; always validate with edge cases like empty tables or all-NULL columns. Also, show awareness that fixes like COALESCE can impact performance and index usage, so consider trade-offs.
Run the query and observe incorrect results (e.g., missing rows, wrong aggregates). Isolate the WHERE clause or aggregation causing the issue by testing with and without NULLs.
Explain how NULL comparisons yield UNKNOWN, and how aggregate functions like COUNT, SUM, AVG treat NULLs differently. Identify which specific condition or function is misbehaving.
Suggest a concrete fix: use IS NULL / IS NOT NULL, COALESCE, or adjust aggregation with FILTER or CASE. Explain why the fix works and any side effects.
Discuss performance implications (e.g., COALESCE may prevent index usage) and correctness (e.g., changing COUNT to COUNT(*) vs COUNT(column)). Consider alternative approaches.
Recommend adding tests for NULL scenarios, using NOT NULL constraints where appropriate, and documenting NULL behavior in data models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining the logical order of SQL evaluation, focusing on how GROUP BY creates groups and SELECT then projects columns. Then walk through a systematic debugging process: reproduce the error, inspect the query, and apply fixes like adding missing columns to GROUP BY or using aggregate functions. Emphasize prevention through best practices and testing.
Pro tip: Mention that in some databases (e.g., MySQL with ONLY_FULL_GROUP_BY disabled), the query might run but return non-deterministic results, so always enforce strict SQL modes. Also, suggest using window functions as an alternative when you need both aggregated and non-aggregated columns.
Explain that GROUP BY groups rows, and SELECT can only reference grouped columns or aggregates. The error occurs when SELECT includes non-aggregated columns not in GROUP BY.
Run the query to see the exact error message. Examine the SELECT list and GROUP BY clause to identify mismatched columns.
Clarify the business requirement: what should each row represent? This dictates which columns belong in GROUP BY.
Either add missing columns to GROUP BY or wrap non-grouped columns in aggregate functions (e.g., MAX, MIN) if appropriate. Consider using window functions if you need both detail and aggregates.
Test the corrected query for correctness and performance. Enforce strict SQL modes and follow best practices to avoid similar issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I actually like this one because it's subtle in a real way.
Choose a concrete bug scenario where HAVING and WHERE were swapped, then walk through the incorrect query, the unexpected result, and the corrected version. Explain the logical difference between filtering rows (WHERE) and filtering groups (HAVING) and why the bug was subtle.
Pro tip: Mention that some databases allow HAVING without GROUP BY, which can mask the bug until data changes. Also note that moving a condition from WHERE to HAVING can silently change results when NULLs or aggregates are involved.
Briefly describe the query's purpose, e.g., 'We needed to find customers with more than 5 orders in the last month.'
Present the SQL with the misplaced clause, e.g., using HAVING for a non-aggregate condition like HAVING order_date > '2023-01-01'.
Describe what the query returned and why it was wrong, e.g., it included customers with old orders because the filter applied after grouping.
Clarify that WHERE filters rows before grouping, while HAVING filters groups after aggregation; the condition belonged in WHERE.
Show the corrected query with the condition in WHERE, and mention how you'd prevent similar issues (e.g., code review, testing with edge data).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, scan the SQL for date/time functions and comparisons, focusing on implicit conversions, timezone assumptions, and boundary conditions. Identify the bug by tracing how the query behaves across different timezones or DST transitions. Then propose the minimal fix, such as using UTC functions or explicit timezone conversion, and explain why it resolves the issue without side effects.
Pro tip: Mention that you'd check for DST edge cases and use database-native timezone-aware types (e.g., TIMESTAMPTZ in PostgreSQL) to avoid silent data corruption. This shows you think beyond the immediate bug and consider long-term data integrity.
Scan the SQL for functions like NOW(), CURRENT_DATE, EXTRACT, DATE_TRUNC, and any comparisons involving dates or timestamps. Identify where timezone assumptions might be implicit.
Determine if the query uses local time instead of UTC, ignores timezone offsets, or mishandles DST transitions. Look for hardcoded timezones or missing AT TIME ZONE clauses.
Suggest a small change, such as replacing CURRENT_DATE with CURRENT_DATE AT TIME ZONE 'UTC' or using TIMESTAMPTZ instead of TIMESTAMP. Ensure the fix addresses the root cause without altering unrelated logic.
Describe how the fix resolves the bug and any potential side effects, such as performance or compatibility. Mention if the fix requires schema changes or data migration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Caught this one by looking at the WHERE clause closely.
Start by explaining how you would reproduce the issue and identify the implicit cast using query plans or logs. Then walk through the root cause, the impact on results, and the fix, emphasizing testing and prevention. Finally, discuss trade-offs of different solutions and how you would communicate the fix to stakeholders.
Pro tip: Demonstrate that you always check data types and query plans proactively, and mention that you add regression tests to catch similar issues in the future. This shows you think beyond the immediate fix and care about long-term code health.
Run the query and compare expected vs. actual results. Use EXPLAIN or query profiling to see where the implicit cast occurs and how it affects performance or correctness.
Examine the query's WHERE, JOIN, or SELECT clauses for comparisons or operations between different data types (e.g., string vs. integer). Check column definitions and literals to pinpoint the mismatch.
Explain why the implicit cast leads to incorrect behavior (e.g., data truncation, unexpected ordering, index bypass). Assess the scope: does it affect only this query or other parts of the system?
Propose an explicit cast or schema change to align types. Test the fix with representative data, ensuring correctness and performance. Consider edge cases like NULLs or locale-specific formats.
Suggest adding data type checks in code reviews, using static analysis tools, or writing unit tests that validate query results. Document the issue and share learnings with the team.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the hardest one for me to reason about live.
Start by explaining what a correlated subquery is and why it can cause performance issues or errors. Then walk through a systematic process to identify the problematic subquery, analyze its execution, and propose a fix such as rewriting it as a JOIN or using a derived table. Finally, validate the fix and discuss trade-offs.
Pro tip: Mention that you would check the execution plan to confirm the correlation and measure the performance improvement after the fix. This shows you not only fix the error but also ensure efficiency, which is crucial in a high-frequency trading environment like Walleye Capital.
Clarify what the SQL file is supposed to do and identify the specific error or performance issue related to the correlated subquery.
Scan the SQL for subqueries that reference columns from the outer query. Use tools like EXPLAIN to confirm correlation and its impact.
Determine why the correlated subquery is problematic: it might be returning multiple rows, causing performance degradation, or leading to incorrect results.
Refactor the correlated subquery into a more efficient construct, such as a JOIN, a derived table, or a window function, ensuring logical equivalence.
Run the rewritten query, compare results with the original (if possible), and check the execution plan to confirm improved performance and correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew this was a window function frame issue almost immediately because the running totals were wrong in a very specific pattern.
Start by explaining the purpose of window functions and how frame boundaries define the set of rows used for each calculation. Then, walk through a systematic debugging process: identify the expected vs. actual results, inspect the frame clause, and test with edge cases. Finally, discuss how to fix the bug and prevent similar issues.
Pro tip: Demonstrate deep understanding by mentioning that frame boundaries interact with ordering and partitioning, and that off-by-one errors are common. Also, highlight the importance of testing with small datasets to isolate the issue.
Clarify what the window function is supposed to compute and what the correct results should be. Identify the partitioning, ordering, and frame specifications.
Check for incorrect keywords (ROWS vs RANGE), missing or wrong bounds (e.g., UNBOUNDED PRECEDING, CURRENT ROW, 1 FOLLOWING), and whether the frame is compatible with the ordering.
Create a minimal test case with sample data that highlights the discrepancy. Compare results with and without the frame clause, or with different frame settings.
Adjust the frame boundaries to match the intended logic, then verify with edge cases (e.g., first/last rows, ties in ordering, empty partitions).
Suggest adding comments, unit tests for window functions, and code reviews focusing on frame specifications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.