Issue Overview: Incorrect Row Numbering Across Joined Table Groups

The core challenge arises when attempting to generate sequential row numbers within specific groups of joined data in SQLite. The original query utilized the ROW_NUMBER() window function with an ORDER BY clause but lacked a mechanism to reset the counter when transitioning between logical groups defined by a foreign key relationship (t1_id). This resulted in a monotonically increasing sequence across the entire result set instead of restarting the count for each new t1_id group.

The problem is rooted in the interaction between window function partitioning and ordering. Window functions operate over a defined partition (a subset of rows) and apply ordering within that partition. Without explicit partitioning, the entire result set becomes a single partition, causing row numbers to increment globally. The desired behavior requires partitioning by t1_id to isolate groups and enforce numbering restarts. However, subtler issues may emerge when the ordering within partitions is ambiguous or improperly defined, leading to unstable or unintended row numbering sequences.

A secondary consideration involves persisting these computed row numbers into the t2 table via an UPDATE operation. This introduces challenges related to data integrity, performance, and maintaining consistency during concurrent modifications. While storing precomputed values can optimize read-heavy workflows, it demands careful handling to avoid desynchronization with the underlying data.

Possible Causes: Misconfigured Window Partitions and Ordering Ambiguity

1. Absence of Partitioning in Window Function Definitions
The primary cause of the continuous row numbering is the omission of a PARTITION BY clause in the OVER() specification. Without partitioning, ROW_NUMBER() treats all rows as part of a single group, incrementing the counter indefinitely. This is appropriate for global sequencing but incompatible with group-specific resets.

2. Inadequate Ordering Within Partitions
Even with correct partitioning, row numbering depends on the ORDER BY clause within the window function. If the ordering criterion is undefined or overly broad (e.g., ordering by a non-unique column), the sequence may exhibit instability—rows with identical ORDER BY values receive non-deterministic row numbers. In the provided example, ordering by t1.id within a partition where all t1.id values are identical (due to partitioning) renders the ordering ineffective, leaving the sequence vulnerable to SQLite’s internal row handling.

3. Misalignment Between Partitioning and Join Logic
The join between t1 and t2 introduces a dependency where t1_id acts as the grouping anchor. If the join condition or data model allows nulls, duplicates, or orphaned t2 records (those without a corresponding t1 entry), partitioning by t1.id may produce unexpected groups or exclude rows entirely, skewing row numbers.

4. Overlooking Persistence Challenges in Table Updates
Storing computed row numbers in t2.num introduces risks:

• Data Redundancy: The num column duplicates derivable information, inviting drift if t2 is modified without recalculating num.
• Concurrency Issues: Simultaneous updates to t2 could race against row number assignments, causing conflicts.
• Performance Overheads: Recomputing num during batch updates or inserts demands careful transaction handling to avoid locks or partial updates.

Troubleshooting Steps, Solutions & Fixes

Step 1: Correct Window Function Configuration

Solution: Incorporate PARTITION BY and refine the ORDER BY clause to enforce group-specific numbering.

Example Query:

SELECT 
  t2.id AS t2_id, 
  t1.id AS t1_id, 
  ROW_NUMBER() OVER (
    PARTITION BY t1.id 
    ORDER BY t2.id  -- Or another column defining intra-group order
  ) AS t2_num  
FROM t2 
JOIN t1 ON t2.t1_id = t1.id;  

Key Adjustments:

• PARTITION BY t1.id: Segregates rows into groups based on t1_id, resetting ROW_NUMBER() for each group.
• ORDER BY t2.id: Dictates the sequence within each partition. Replace t2.id with any column(s) reflecting the desired row order (e.g., t2.created_at).

Validation:

• Confirm that partitions align with t1_id values by inspecting intermediate results.
• Verify that intra-group ordering matches expectations (e.g., ascending t2_id).

Step 2: Addressing Edge Cases and Stability

Scenario 1: Duplicate Ordering Values Within Partitions
If multiple rows in a partition share identical ORDER BY values, ROW_NUMBER() assigns arbitrary ranks due to undefined ordering. To stabilize numbering:

• Augment the ORDER BY clause with additional columns (e.g., t2.id as a tiebreaker).
• Use deterministic sorting (e.g., always ascending/descending).

Example:

ORDER BY t2.created_at, t2.id  

Scenario 2: Handling Sparse or Missing t1_id References
Orphaned t2 rows (where t1_id doesn’t exist in t1) are excluded from the join. To include them:

• Use a LEFT JOIN and partition by COALESCE(t1.id, -1) (or another sentinel value).

Example:

FROM t2 
LEFT JOIN t1 ON t2.t1_id = t1.id  
...  
PARTITION BY COALESCE(t1.id, -1)  

Step 3: Persisting Row Numbers to t2.num

Approach 1: CTE-Based Update with Window Function
Leverage a Common Table Expression (CTE) to compute row numbers and update t2 in a single transaction.

Example:

WITH cte AS (
  SELECT 
    t2.id, 
    ROW_NUMBER() OVER (
      PARTITION BY t1_id 
      ORDER BY t2.id  -- Match the desired order
    ) AS computed_num  
  FROM t2  
)  
UPDATE t2  
SET num = cte.computed_num  
FROM cte  
WHERE t2.id = cte.id;  

Considerations:

• Isolation: Run during low-activity periods or within transactions to prevent mid-update conflicts.
• Indexes: Ensure indexes on t2.id and t1_id exist to accelerate joins and partitioning.

Approach 2: Incremental Updates with Triggers
For ongoing maintenance, employ triggers to recalculate num on insert/update.

Example Trigger:

CREATE TRIGGER update_t2_num AFTER INSERT ON t2  
BEGIN  
  UPDATE t2  
  SET num = (
    SELECT COUNT(*) + 1  
    FROM t2 AS inner_t2  
    WHERE inner_t2.t1_id = NEW.t1_id  
      AND inner_t2.id < NEW.id  -- Assumes ascending insertion order  
  )  
  WHERE id = NEW.id;  
END;  

Caveats:

• Trigger-based methods may suffer performance degradation with high write volumes.
• Logic must account for deletions or updates altering t1_id, necessitating recalibration of affected groups.

Step 4: Performance Optimization

Indexing Strategy:

• Composite Index on (t1_id, id): Accelerates PARTITION BY t1_id ORDER BY id operations.
• Covering Indexes: Include all columns referenced in the query to enable index-only scans.

Example Index:

CREATE INDEX idx_t2_t1_id_id ON t2(t1_id, id);  

Benchmarking:

• Use EXPLAIN QUERY PLAN to analyze the query’s execution steps.
• Monitor query duration with and without indexes to quantify improvements.

Step 5: Validation and Regression Testing

Test Cases:

1. Single t1_id Group: Verify numbering starts at 1 and increments sequentially.
2. Multiple t1_id Groups: Confirm restarts occur at group boundaries.
3. Empty Groups: Ensure no errors when a t1_id has zero t2 entries.
4. Concurrent Writes: Stress-test trigger-based solutions under parallel insert/update loads.

Automated Checks:
Implement unit tests using SQLite’s .test files or external testing frameworks to validate numbering logic across schema changes.

Final Recommendation: Compute-On-Demand vs. Persistence

While storing num denormalizes the data, it may be justified for read performance. However, prefer computed columns (via views or generated columns) where possible:

SQLite Generated Columns (3.31+):

ALTER TABLE t2  
ADD COLUMN num INTEGER GENERATED ALWAYS AS (
  ROW_NUMBER() OVER (
    PARTITION BY t1_id 
    ORDER BY id  
  )
);  

Note: SQLite does not support window functions in generated columns directly. Instead, use a view:

View-Based Solution:

CREATE VIEW t2_enriched AS  
SELECT 
  id, 
  name, 
  t1_id, 
  ROW_NUMBER() OVER (
    PARTITION BY t1_id 
    ORDER BY id  
  ) AS num  
FROM t2;  

This approach eliminates redundancy and ensures real-time accuracy, albeit at the cost of recalculating during each query.

By systematically addressing partitioning, ordering, persistence strategies, and performance considerations, this guide resolves the row numbering challenge while safeguarding data integrity and system efficiency.