Calculating Rolling Sums Relative to Latest Date in SQLite

Dynamic Date Range Calculation for Accurate Rolling Aggregates

Issue Overview: Anchoring Rolling Periods to the Latest Available Data

The core challenge arises when generating rolling aggregates (e.g., 28-day sums) in SQLite where the latest date in the dataset doesn’t align with the current system date. This occurs when:

1. Data ingestion latency: New daily data becomes available at inconsistent times
2. Default date logic: The dayDate column uses date('now', '-1 day') as its default value, making yesterday’s date the automatic entry for new rows
3. Temporal boundary ambiguity: Direct use of date('now') in queries risks including partial/future dates where data doesn’t exist

Consider this initial problematic query:

SELECT SUM(dayViews) AS Sum, COUNT(dayViews) AS Count
FROM dayViews
WHERE dayDate >= date('now', '-28 days');

When executed on 2023-11-01 with data through 2023-10-31, this would attempt to aggregate from 2023-10-04 to 2023-11-01. However, since 2023-11-01’s data won’t exist yet, it creates a 28-day window with one missing day, potentially causing undercounting.

Possible Causes: Temporal Misalignment and Offset Miscalculations

Three primary factors contribute to this issue:

1. Static Date Anchoring

• date('now') references query execution time rather than dataset state
• Creates dependency on external system clock consistency
• Fails to adapt to data pipeline delays or backfills

2. Calendar vs. Operational Day Discrepancy

• The default date('now', '-1 day') creates a 1-day offset between data collection and availability
• Assumes continuous daily data ingestion without gaps
• Doesn’t account for holidays/weekends in non-time-series contexts

3. Window Boundary Arithmetic Errors

• Misalignment between day offsets and inclusive/exclusive date comparisons
• Original -28 days adjustment needed recalibration when switching from now to max(dayDate)
• Boundary case handling at month/year transitions

Structural Amplifiers:

• TEXT-typed dates without CHECK constraints risk invalid formats
• Missing composite indexes on date ranges and view counts
• Implicit casting of date strings in comparisons

Resolution Framework: Adaptive Date Anchoring with Calibrated Offsets

Step 1: Establish Dynamic Date Anchoring

Replace static now references with the dataset’s actual maximum date:

SELECT MAX(dayDate) AS anchor_date FROM dayViews;

Validate date format consistency:

SELECT dayDate 
FROM dayViews 
WHERE date(dayDate) IS NULL 
LIMIT 5;

Any returned rows indicate invalid date formats requiring cleanup.

Step 2: Calibrate Window Offsets

The original adjustment from -28 to -27 days stems from anchor point relocation:

• With now as anchor: 28 days including today
• With max(dayDate) (yesterday) as anchor: 27 days gets equivalent span

Mathematical proof:

System date: 2023-11-01
max(dayDate): 2023-10-31

Using 'now' -28 days: 2023-10-04 to 2023-11-01 (28 days)
Using max(dayDate) -27 days: 2023-10-04 to 2023-10-31 (28 days)

Implementation:

WHERE dayDate >= date(
  (SELECT max(dayDate) FROM dayViews), 
  '-27 days'
)

Step 3: Implement Boundary-Aware Aggregation

Enhanced query with diagnostic fields:

SELECT 
  SUM(dayViews) AS TotalViews,
  COUNT(*) AS DayCount,
  MIN(dayDate) AS WindowStart,
  MAX(dayDate) AS WindowEnd,
  julianday(MAX(dayDate)) - julianday(MIN(dayDate)) + 1 AS SpanDays
FROM dayViews
WHERE dayDate >= date(
  (SELECT max(dayDate) FROM dayViews), 
  '-27 days'
);

Key additions:

• WindowStart/End visualizes actual date range
• SpanDays calculates exact day count using Julian day numbers
• COUNT(*) since dayViews is NOT NULL

Step 4: Handle Non-Contiguous Dates

For datasets with potential gaps, generate date series dynamically:

WITH RECURSIVE all_dates(query_date) AS (
  SELECT date((SELECT MAX(dayDate) FROM dayViews), '-27 days')
  UNION ALL
  SELECT date(query_date, '+1 day')
  FROM all_dates
  WHERE query_date < (SELECT MAX(dayDate) FROM dayViews)
)
SELECT 
  ad.query_date,
  dv.dayViews
FROM all_dates ad
LEFT JOIN dayViews dv ON ad.query_date = dv.dayDate;

This CTE:

1. Generates all dates in the target window
2. Left-joins with actual data to expose missing dates

Step 5: Optimize Performance

Add covering index for date-based queries:

CREATE INDEX idx_dayviews_date_views 
ON dayViews(dayDate DESC, dayViews);

Execution plan analysis:

EXPLAIN QUERY PLAN
SELECT SUM(dayViews) 
FROM dayViews 
WHERE dayDate >= date((SELECT max(dayDate) FROM dayViews), '-27 days');

Verify output includes USING INDEX idx_dayviews_date_views.

Step 6: Parameterize Window Size

For flexible period adjustments:

SELECT 
  SUM(dayViews) AS TotalViews,
  date((SELECT MAX(dayDate) FROM dayViews), '-' || :days || ' days') AS calc_start
FROM dayViews
WHERE dayDate >= calc_start;

Bind parameters like :days enable runtime customization.

Step 7: Implement Data Quality Monitoring

Daily sanity checks:

SELECT
  COUNT(*) AS total_days,
  MAX(dayDate) AS latest_date,
  julianday('now') - julianday(MAX(dayDate)) AS days_since_last
FROM dayViews;

Alert when:

• days_since_last > 2 (missing data)
• total_days < expected (data loss)

Edge Case Handling:

1. Empty Table

SELECT 
  SUM(dayViews) AS TotalViews,
  COUNT(*) AS DayCount
FROM dayViews
WHERE dayDate >= COALESCE(
  date((SELECT max(dayDate) FROM dayViews), '-27 days'), 
  '0000-00-00'
);

2. Cross-Year Boundaries
   Test with known leap years and February dates.

3. Time Zone Awareness
   Convert dates to UTC if needed:

SELECT MAX(dayDate) 
FROM dayViews 
WHERE date(dayDate) = date('now', '-1 day', 'utc');

Final Production-Grade Query:

WITH latest AS (
  SELECT 
    MAX(dayDate) AS anchor,
    date(MAX(dayDate), '-27 days') AS start_date
  FROM dayViews
)
SELECT 
  COALESCE(SUM(dv.dayViews), 0) AS TotalViews,
  COUNT(dv.dayDate) AS DaysReported,
  l.anchor AS PeriodEnd,
  l.start_date AS PeriodStart,
  (julianday(l.anchor) - julianday(l.start_date)) + 1 AS ExpectedDays
FROM latest l
LEFT JOIN dayViews dv 
  ON dv.dayDate BETWEEN l.start_date AND l.anchor;

This version:

• Uses explicit date range with BETWEEN
• Separates anchor date calculation
• Provides completeness metrics
• Handles empty table scenarios

Performance Considerations:

• Subquery materialization in CTE improves plan caching
• BETWEEN may utilize indexes better than >=
• COALESCE prevents NULL sums

Migration Strategy:

For existing systems using static now() references:

1. Phase 1: Dual-write both query versions
2. Compare results daily for discrepancy detection
3. Gradual cutover after 28-day validation cycle

Alternative Approaches:

1. Materialized View with Precomputed Windows

CREATE TABLE rolling_windows (
  period_start TEXT,
  period_end TEXT PRIMARY KEY,
  total_views INTEGER,
  days_count INTEGER
);

INSERT INTO rolling_windows
SELECT 
  date(dayDate, '-27 days'),
  dayDate,
  SUM(dayViews) OVER (
    ORDER BY julianday(dayDate)
    RANGE BETWEEN 27 PRECEDING AND CURRENT ROW
  ),
  COUNT(*) OVER (
    ORDER BY julianday(dayDate)
    RANGE BETWEEN 27 PRECEDING AND CURRENT ROW
  )
FROM dayViews;

Requires trigger-based maintenance but enables O(1) queries.

2. Programmatic Adjustment

Application-side date mathematics:

max_date = db.execute("SELECT MAX(dayDate) FROM dayViews").fetchone()[0]
start_date = datetime.strptime(max_date, "%Y-%m-%d") - timedelta(days=27)
query = f"""
  SELECT SUM(dayViews) 
  FROM dayViews 
  WHERE dayDate >= '{start_date.strftime("%Y-%m-%d")}'
"""

Shifts complexity to app layer but allows richer date logic.

Conclusion:

Dynamic date anchoring in SQLite requires careful coordination between dataset characteristics, date functions, and windowing logic. By shifting from absolute date references to data-driven anchors, we achieve resilient aggregation windows that automatically adapt to data arrival patterns. The comprehensive solution combines SQLite’s date manipulation capabilities with subquery anchoring, Julian day arithmetic, and diagnostic metadata to ensure accurate rolling calculations even with irregular data cadence.