Pretty straightforward once you figure out the join key is message_type plus id together.
Clarify the data format and assumptions (e.g., unique message IDs, timestamp format, file sizes). Then propose a hash join approach: load the smaller file into a hash map keyed by message ID, iterate through the larger file, and for matching IDs compute the latency. Discuss handling of missing messages, timestamp parsing, and output format.
Pro tip: Mention that you would validate the assumption of synchronized timestamps and consider edge cases like duplicate message IDs or clock skew, showing attention to data quality and real-world robustness.
Ask about file sizes, message ID uniqueness, timestamp format, and whether all messages in one file are expected in the other. Confirm output format (e.g., CSV with message ID and latency).
Given typical file sizes, a hash join is optimal: load the smaller file into a hash map (message ID -> send time), then stream the larger file to find matches and compute latency. If files are sorted by timestamp, a merge join could be considered, but hash join is simpler and O(n+m).
Address missing messages (skip or report), duplicate IDs (decide on first/last or error), timestamp parsing (e.g., ISO 8601), and potential clock skew. Ensure latency is non-negative; if negative, flag as anomaly.
Write code to read CSV, build hash map, compute latencies, and write output. Test with small sample files, including cases with no matches, duplicates, and large files to verify performance.
If files are too large for memory, propose external sorting or streaming with a database. Mention using efficient CSV parsers and parallel processing if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use DFS with backtracking to explore all possible paths from each cell that matches the first character of the target string. At each step, check if the current cell matches the corresponding character, mark it as visited, and recursively explore its orthogonal neighbors. If the entire string is matched, return true; otherwise, backtrack by unmarking the cell.
Pro tip: Before diving into code, clarify edge cases with the interviewer, such as empty grid, empty string, or when the target length exceeds the number of cells. Also, mention that you can optimize by checking if the grid contains enough of each character to form the target, or by pruning paths early if the remaining characters cannot be matched.
Confirm the problem constraints: grid dimensions, character set, and whether the target string can be empty. Check if the target length is greater than the total number of cells; if so, return false immediately.
Explain that you will use depth-first search (DFS) with backtracking to explore all possible paths. This is because the problem requires checking sequences of adjacent cells without reuse, which naturally fits a recursive backtracking approach.
Define a recursive function that takes the current cell coordinates and the current index in the target string. If the index equals the target length, return true. If the current cell is out of bounds, already visited, or does not match the target character, return false. Otherwise, mark the cell as visited, recursively explore all four orthogonal neighbors, then unmark the cell (backtrack) and return the result.
Loop through each cell in the grid. If the cell matches the first character of the target, invoke the recursive function starting from that cell. If any call returns true, return true. If no starting cell yields a valid path, return false.
Discuss the time complexity: O(N * 3^L) in the worst case, where N is the number of cells and L is the target length, since each step explores up to 3 directions (excluding the previous cell). Mention potential optimizations like early termination if the target length exceeds the number of cells or if the grid lacks sufficient characters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.