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The self-join on reciprocal rows is straightforward enough, but the u1 < u2 constraint to avoid duplicate pairs is the part I almost forgot.
Start by clarifying the schema and defining 'mutual connection' as a pair of users who follow each other. Then write a self-join on the edge table to find reciprocal follows, and use a CASE expression or LEAST/GREATEST to order the pair so the smaller user ID comes first. Finally, deduplicate the result to return each friendship once.
Pro tip: Mention that using LEAST and GREATEST is the cleanest way to enforce the ordering, but if the database doesn't support them, a CASE expression works. Also, explicitly state that you'd add a condition like user1 < user2 in the join to avoid duplicate pairs and improve performance.
Confirm the table name and columns (e.g., follower_id, followee_id) and define 'mutual connection' as two users following each other. Ask if the output should include only mutual pairs or all friendships.
Join the edge table to itself on follower_id = followee_id and followee_id = follower_id to find pairs where both directions exist. This identifies mutual connections.
Use LEAST and GREATEST (or a CASE expression) to ensure the first user column is always less than the second. This normalizes the pair and avoids duplicates like (A,B) and (B,A).
Apply DISTINCT or GROUP BY to return each friendship only once. Since the self-join may produce two rows per mutual pair (one for each direction), deduplication is essential.
Assemble the query and mentally test with sample data, including cases where users follow themselves or where only one direction exists. Ensure the output matches the expected format.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a self-join on the friendship table to find all pairs of users who share a common friend, then filter to ensure the pair members are distinct and ordered (x < y). Exclude cases where the pair members are directly friends with each other, and ensure the common friend is not one of the pair members.
Pro tip: Clarify the schema and assumptions upfront (e.g., friendship is mutual, table name and columns). This shows you think about data modeling and avoids ambiguity, which is crucial in interviews.
Ask about the friendship table structure (e.g., columns: user1, user2) and whether friendships are mutual. Confirm that we need unordered pairs with x < y.
Join the friendship table to itself on the common friend column, ensuring that the two users are different and ordered (x < y). This yields candidate pairs (x, y) and their common friend z.
Filter out rows where x and y are directly friends (i.e., there exists a friendship between x and y). Also ensure z is not equal to x or y.
Group by x and y, and optionally list the common friends (e.g., using STRING_AGG or array_agg). Ensure the final output contains each unordered pair once.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
COUNT(DISTINCT f) wraps it up, but the interviewer pushed on why DISTINCT is necessary.
First, clarify the schema and whether the edge table contains reciprocal duplicates. Then propose a query that deduplicates edges (e.g., using a canonical ordering or SELECT DISTINCT) before joining to find common friends, and finally count distinct common friends per pair. Emphasize the importance of handling duplicates to avoid inflated counts.
Pro tip: Mention that you would verify the deduplication logic with a quick data audit (e.g., checking for reciprocal rows) and consider using a subquery or CTE to materialize the deduplicated edges for better performance and readability.
Ask about the edge table structure: are friendships stored as directed edges with possible reciprocal duplicates? Confirm the definition of 'common friends' (e.g., users who are friends with both members of a pair).
Use a method to ensure each undirected friendship appears once. For example, select rows where user_id < friend_id, or use SELECT DISTINCT with a canonical ordering. This prevents double-counting in the join.
Self-join the deduplicated edges to find users who are friends with both members of a pair. Use a join condition that matches the first user to one edge and the second user to another edge, ensuring the common friend is the same.
Group by the pair and count distinct common friend IDs. Use COUNT(DISTINCT friend_id) to avoid duplicates if a common friend appears multiple times due to other redundancies.
Test the query on a small sample to ensure correctness. Consider performance implications and suggest indexing or materializing the deduplicated edges if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said composite indexes on (user_from, user_to) and (user_to, user_from) to cover both join directions, which felt right.
Start by clarifying the common-friends query pattern (e.g., self-join on user_id to find mutual friends) and the table schema. Then propose a composite index on (user_id, friend_id) to support efficient lookups, and discuss whether a covering index or additional indexes on friend_id are needed based on query patterns. Finally, address scalability concerns like index size, write overhead, and potential partitioning strategies for 100M rows.
Pro tip: Mention that at 100M rows, index maintenance and storage become significant, so you'd validate the index with query plans and consider partitioning or denormalization if write throughput is critical. Also, note that Microsoft often values data-driven decisions, so suggest benchmarking with realistic data.
Ask or state assumptions about the common-friends query (e.g., finding friends of friends who are not already friends) and the FriendEdges table structure (e.g., user_id, friend_id, possibly directionality).
Determine the columns used in JOIN, WHERE, and ORDER BY clauses. For common-friends, this typically involves filtering by user_id and joining on friend_id.
Recommend a composite index on (user_id, friend_id) to quickly find all friends of a user. If the query also filters by friend_id, consider an index on (friend_id, user_id) or a covering index.
Discuss the impact on write performance, storage, and maintenance. At 100M rows, consider partitioning the table (e.g., by user_id range) and using included columns to make indexes covering.
Suggest using EXPLAIN plans, query profiling, and A/B testing to confirm the index improves performance without excessive overhead. Mention monitoring index usage and adjusting as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.