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This approach uses SQL's self-join capabilities. We join the Weather table with itself by matching the current day's date with the day after the previous date, effectively allowing us to compare temperatures between consecutive days.
This way, we filter for cases where the current day's temperature is higher than the previous day's temperature.
Time Complexity: O(n), where n is the number of records, as we are checking each record with its previous day's record.
Space Complexity: O(1), as no extra space is used except for the result set.
1SELECT w1.id FROM Weather w1 JOIN Weather w2 ON DATEDIFF(w1.recordDate, w2.recordDate) = 1 WHERE w1.temperature > w2.temperature;The SQL code joins the Weather table with itself. For each record in the first instance (w1), it compares with records in the second instance (w2) where the difference in days (DATEDIFF) is exactly one, meaning consecutive days. Then, it filters those records where the temperature of w1 is greater than w2.
This approach utilizes SQL window functions, specifically the LAG function, which allows you to access the value of a column from the previous row.
We use LAG to find the previous day's temperature for each record and then filter the results where the current day's temperature is higher than the previous day's temperature.
Time Complexity: O(n), due to scanning through all rows once with window function.
Space Complexity: O(n), due to the subquery which temporarily holds additional columns.
1SELECT id FROM (SELECT id, temperature, LAG(temperature, 1) OVER (ORDERThis approach involves performing a self-join on the Weather table. We join the table with itself where the recordDate of the second table (alias W2) is one day earlier than the recordDate of the first table (alias W1). This allows us to compare the temperatures of each date with its previous day.
Time Complexity: O(n log n) due to the use of joins, assuming a general sort operation for join implementation. Space Complexity: O(n) for storing join results.
1SELECT W1.id FROM Weather W1 JOIN Weather W2 ON W1.recordDate = DATE_ADD(W2.recordDate, INTERVAL 1This approach leverages window functions to compare each day's temperature with the previous day's temperature without explicitly joining the table. By using the LAG function, we can access the previous record's temperature for comparison.
Time Complexity: O(n) as window functions efficiently traverse the data in one pass. Space Complexity: O(n) to store intermediate results of window functions.
1SELECT id FROM (SELECT id, temperature, LAG(temperature) OVER (ORDER BY recordDate) The SQL code first creates a subquery with the Weather data, adding a new column prev_temp using the LAG window function. LAG(temperature, 1) gives the temperature of the previous day. Then, the outer query selects rows where the current day's temperature is greater than prev_temp.
This SQL solution utilizes a self-join to compare the temperature of each day with its previous day. We join the Weather table with itself using the condition that W1's recordDate should be exactly one day after W2's recordDate. Then, we select the IDs where W1's temperature is greater than W2's temperature.
This solution employs the LAG window function to access the temperature of the previous day for each record. We create a subquery that includes a column for the previous day's temperature calculated by LAG. Finally, we filter where the current temperature is greater than the previous day's temperature.