Grouping Data by Custom 7 AM to 7 AM Day Windows in SQLite

Understanding Date Grouping with Custom Day Boundaries in SQLite

The core challenge involves grouping time-series data into 24-hour windows that start at 7:00 AM instead of midnight. A typical scenario might involve industrial shift operations, laboratory test schedules, or business metrics where the "day" is defined as 7:00 AM to 6:59:59 AM the following calendar day. The initial implementation attempts to achieve this grouping using SQLite’s date/time functions encountered inconsistencies in the aggregated results due to logical flaws in boundary calculations and grouping strategies.

Key Concepts and Data Structure

Table Schema:
The testinfo table stores timestamps in start_time (ISO8601-compatible datetime format) alongside other metadata. The critical fields are:

CREATE TABLE testinfo(
  ID integer primary key autoincrement,
  serial varchar(11),
  start_time datetime
);

Sample Data:
Entries span multiple days with timestamps both before and after 7:00 AM:

2023-10-31 11:17:48 (after 7 AM)
2023-11-02 06:31:04 (before 7 AM)
2023-11-04 13:48:26 (after 7 AM)

Desired Output:
Group counts should reflect entries within each 7 AM – 6:59:59 AM window:

2|2023-10-31  -- Oct 31 7 AM to Nov 1 6:59:59
4|2023-11-01  -- Nov 1 7 AM to Nov 2 6:59:59
1|2023-11-02  -- Nov 2 7 AM to Nov 3 6:59:59

Critical Analysis of Original Query Flaws

The original query contained multiple overlapping issues that prevented accurate grouping:

1. Misuse of Correlated Subqueries in Date Boundaries

The BETWEEN clause attempted to generate dynamic date ranges using uncorrelated subqueries:

where start_time between 
  (select strftime('%Y-%m-%d 07:00:00', start_time) ... group by start) 
  and 
  (select strftime('%Y-%m-%d 06:59:00', date(start_time, '+1 days')) ... group by end)

Problem:

• Subqueries compute global start and end values by grouping all start_time values without correlation to the outer query’s rows.
• This results in static boundary dates (e.g., the earliest 7 AM and latest 6:59 AM across all data), not per-row window calculations.
• The BETWEEN clause filters all rows to a single fixed range instead of partitioning data into per-day windows.

2. Incorrect Grouping Key Definition

The GROUP BY yr_mon clause used strftime('%Y-%m-%d', start_time), which groups by calendar days (midnight-to-midnight), ignoring the 7 AM offset requirement.

3. Time Zone and Daylight Saving Time Ambiguity

While not explicitly mentioned in the discussion, using hard-coded time offsets without accounting for time zones or DST changes could introduce errors in regions observing such rules. SQLite’s datetime functions operate in UTC or local time based on the datetime string format and modifiers.

Step-by-Step Solutions for Custom Day Grouping

1. Defining Custom Day Windows with Date Shifting

Core Logic:
For any given timestamp, subtract 1 calendar day if its time component is before 07:00:00. This effectively shifts timestamps between midnight and 6:59:59 AM into the previous "business day" window ending at 7 AM.

SQL Implementation:
Use a CASE expression with time(start_time) and date(start_time, '-1 day'):

SELECT 
  *,
  CASE 
    WHEN time(start_time) < '07:00:00' 
    THEN date(start_time, '-1 day') 
    ELSE date(start_time) 
  END AS workday
FROM testinfo;

Intermediate Output:

ID  | start_time           | workday
----|----------------------|---------
1366|2023-10-31 11:17:48 |2023-10-31
1367|2023-10-31 11:44:47 |2023-10-31
1368|2023-11-01 07:24:36 |2023-11-01
1371|2023-11-02 06:31:04 |2023-11-01  -- shifted to Nov 1
1373|2023-11-03 12:59:57 |2023-11-03

2. Aggregating by the Adjusted Workday Column

Incorrect Approach (Original Query):
Grouping by yr_mon (the unadjusted date) led to misassignment of shifted rows:

-- Flawed: groups by original date, not workday
SELECT 
  count(*) serial_num, 
  strftime('%Y-%m-%d', start_time) yr_mon,
  workday
FROM ( ... ) 
GROUP BY yr_mon;

Correct Approach:
Group by the computed workday column directly:

SELECT 
  count(*) AS serial_num,
  workday
FROM (
  SELECT 
    CASE 
      WHEN time(start_time) < '07:00:00' 
      THEN date(start_time, '-1 day') 
      ELSE date(start_time) 
    END AS workday
  FROM testinfo
)
GROUP BY workday;

Output:

serial_num | workday
-----------|---------
2         |2023-10-31
4         |2023-11-01  -- includes shifted row from Nov 2 6:31 AM
1         |2023-11-02
3         |2023-11-03
3         |2023-11-04

3. Handling Edge Cases and Validation

Scenario: Timestamp exactly at 07:00:00
The CASE expression’s WHEN time(start_time) < '07:00:00' correctly excludes 07:00:00, assigning it to the current workday.

Leap Days and Month Boundaries:
SQLite’s date() function correctly handles date arithmetic across month/year boundaries (e.g., date('2024-02-29', '+1 day') → 2024-03-01).

Validation Query:
To verify individual date assignments:

SELECT 
  start_time,
  CASE 
    WHEN time(start_time) < '07:00:00' 
    THEN date(start_time, '-1 day') 
    ELSE date(start_time) 
  END AS workday
FROM testinfo
ORDER BY start_time;

Advanced Considerations and Optimizations

1. Indexing for Performance

Without an index on start_time, full table scans are required for filtering and date computations. Add an index:

CREATE INDEX idx_testinfo_start_time ON testinfo(start_time);

2. Time Zone-Aware Adjustments

If start_time is stored in UTC but needs local time adjustments, use datetime() modifiers:

-- Convert UTC to US/Eastern time
SELECT 
  datetime(start_time, 'localtime', 'UTC', 'localtime', '-5 hours') AS local_time
FROM testinfo;

3. Window Function Alternatives

For cumulative counts or rolling windows, use SUM() OVER with custom frames:

SELECT 
  workday,
  count(*) OVER (ORDER BY workday ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative
FROM (
  -- workday calculation subquery
);

Final Query and Output

Optimized Query:

SELECT 
  count(*) AS serial_num,
  workday
FROM (
  SELECT 
    CASE 
      WHEN time(start_time) < '07:00:00' 
      THEN date(start_time, '-1 day') 
      ELSE date(start_time) 
    END AS workday
  FROM testinfo
)
GROUP BY workday
ORDER BY workday;

Result:

serial_num | workday
-----------|---------
2         |2023-10-31  -- Oct 31 7 AM – Nov 1 6:59:59
4         |2023-11-01  -- Nov 1 7 AM – Nov 2 6:59:59
1         |2023-11-02  -- Nov 2 7 AM – Nov 3 6:59:59
3         |2023-11-03
3         |2023-11-04

This approach ensures accurate grouping for custom day windows, handles edge cases, and can be extended with additional optimizations for large datasets.