Recursive JSON Generation in SQLite: Troubleshooting Circular View Definitions and Hierarchical Data Aggregation

Issue Overview: Circular View Definitions and Hierarchical JSON Aggregation

The core issue revolves around generating a recursive JSON structure from a hierarchical table in SQLite. The table, named patriarchal, represents a family tree where each person has a PersonId, a FatherId (which references another PersonId), and a Name. The goal is to produce a JSON object that recursively nests each person under their father, forming a tree-like structure. The desired output should look like this:

[
  {
    "Name": "David",
    "Begat": [
      {
        "Name": "Solomon",
        "Begat": [
          {
            "Name": "Rehoboam",
            "Begat": []
          }
        ]
      }
    ]
  }
]

The initial attempt involved creating a view named Begats that uses a recursive JSON aggregation. However, this approach resulted in a "circularly defined" view error because SQLite does not support recursive views or recursive CTEs (Common Table Expressions) with aggregate functions like JSONB_GROUP_ARRAY. This limitation is a significant hurdle because the problem inherently requires recursion to traverse the hierarchical data and aggregation to build the nested JSON structure.

The discussion explores various solutions, including the use of temporary tables, triggers, and advanced CTE techniques. Each approach has its own trade-offs in terms of complexity, performance, and maintainability. The primary challenge is to find a method that can handle the recursive nature of the data while adhering to SQLite’s limitations on recursive aggregation.

Possible Causes: Limitations of SQLite in Recursive Aggregation and Circular Definitions

The root cause of the issue lies in SQLite’s inability to handle recursive views or recursive CTEs with aggregate functions. SQLite’s recursive CTEs are powerful for traversing hierarchical data, but they cannot perform aggregations within the recursive part of the CTE. This limitation makes it impossible to directly construct a nested JSON object using recursive CTEs alone.

Additionally, the initial attempt to create a recursive view (Begats) resulted in a circular definition error. This error occurs because the view references itself in its definition, which SQLite does not support. Views in SQLite are essentially stored queries, and they cannot be self-referential. This limitation forces developers to seek alternative approaches, such as using temporary tables or procedural logic (e.g., triggers) to achieve the desired result.

Another potential cause of confusion is the handling of siblings in the hierarchical data. The initial solutions provided in the discussion did not account for cases where a father has multiple children (siblings). This oversight can lead to incomplete or incorrect JSON structures, as the recursive logic must correctly nest all children under their respective fathers.

Troubleshooting Steps, Solutions & Fixes: Advanced CTEs, Temporary Tables, and JSON Manipulation

To address the issue, several solutions were proposed, each with its own strengths and weaknesses. Below, we explore these solutions in detail, focusing on their implementation, performance implications, and suitability for different use cases.

Solution 1: Temporary Tables and Triggers

The first solution involves using a temporary table and a trigger to handle the recursive aggregation. The temporary table, named flattened, stores intermediate JSON objects, and a trigger ensures that the JSON structure is built from the bottom up. Here’s how it works:

1. Create the Temporary Table: The flattened table is created to store the JSON objects. It has two columns: FlatId (which corresponds to PersonId) and jsonb (which stores the JSON object).

2. Define the Trigger: A trigger named flatten is created to handle the insertion of JSON objects into the flattened table. The trigger ensures that the JSON structure is built recursively by inserting the JSON object for each person and their children.

3. Recursive CTE for Traversal: A recursive CTE named level is used to traverse the hierarchical data from the top down. This CTE ensures that the JSON objects are inserted into the flattened table in the correct order.

4. Final JSON Aggregation: After the temporary table is populated, the final JSON object is constructed using json_group_array and json_pretty.

Here is the complete implementation:

-- Step 1: Create the temporary table
CREATE TEMP TABLE flattened (
  FlatId INTEGER PRIMARY KEY,
  jsonb BLOB
);

-- Step 2: Define the trigger
CREATE TRIGGER temp.flatten
  BEFORE INSERT ON flattened
  WHEN NEW.jsonb IS NULL
BEGIN
  INSERT INTO flattened
    SELECT NEW.FlatId,
      jsonb_object(
        'Name', (SELECT Name FROM patriarchal WHERE PersonId = NEW.FlatId),
        'Begat', (SELECT jsonb_group_array(jsonb)
                  FROM patriarchal
                  JOIN flattened ON FlatId = PersonId
                  WHERE FatherId = NEW.FlatId)
      );
  DELETE FROM flattened
    WHERE FlatId IN (SELECT PersonId FROM patriarchal WHERE FatherId = NEW.FlatId);
  SELECT RAISE(IGNORE);
END;

-- Step 3: Recursive CTE for traversal
WITH RECURSIVE
  level (LevelId, depth) AS (
    SELECT PersonId, 0 FROM patriarchal WHERE FatherId IS NULL
    UNION ALL
    SELECT PersonId, depth + 1
    FROM level
    JOIN patriarchal ON FatherId = LevelId
  )
INSERT INTO temp.flattened
  SELECT LevelId, NULL FROM level ORDER BY depth DESC;

-- Step 4: Final JSON aggregation
SELECT json_pretty(json_group_array(jsonb)) AS Begat
  FROM temp.flattened;

-- Clean up
DROP TABLE temp.flattened;

Pros: This solution effectively handles the recursive aggregation by using a temporary table and a trigger. It ensures that the JSON structure is built correctly from the bottom up.

Cons: The use of a temporary table and a trigger adds complexity to the solution. Additionally, the trigger may have performance implications for large datasets, as it performs multiple inserts and deletes for each row.

Solution 2: Advanced CTE with Path Construction

The second solution leverages advanced CTE techniques to construct the JSON object incrementally. This approach avoids the need for temporary tables and triggers by using a series of CTEs to build the JSON structure step by step. Here’s how it works:

1. Path Construction: The first CTE, named path, constructs a path for each person in the hierarchy. This path represents the position of each person in the JSON structure.

2. Process Order: The second CTE, named process_order, assigns a processing order to each person based on their path. This ensures that the JSON object is built in the correct order.

3. JSON Construction: The third CTE, named json, incrementally constructs the JSON object by inserting each person into the appropriate position in the JSON structure.

Here is the complete implementation:

WITH
  path AS (
    SELECT PersonId,
           FatherId,
           Name,
           Concat('$[', Row_Number() OVER () - 1, ']') AS path
    FROM patriarchal
    WHERE FatherId IS NULL
    UNION ALL
    SELECT p.PersonId,
           p.FatherId,
           p.Name,
           Concat(d.path, '.Begat[', (SELECT COUNT(*)
                                      FROM patriarchal
                                      WHERE FatherId = p.FatherId
                                        AND PersonId < p.PersonId), ']') AS path
    FROM path d
    JOIN patriarchal p ON d.PersonId = p.FatherId
  ),
  process_order AS (
    SELECT *,
           Row_Number() OVER (ORDER BY path) AS process_order_id
    FROM path
  ),
  json AS (
    SELECT process_order_id,
           json_array(json_object('Name', Name, 'Begat', json_array())) AS obj
    FROM process_order
    WHERE process_order_id = 1
    UNION ALL
    SELECT p.process_order_id,
           json_insert(j.obj, p.path, json_object('Name', p.Name, 'Begat', json_array())) AS obj
    FROM json j
    JOIN process_order p ON j.process_order_id + 1 = p.process_order_id
  )
SELECT json_pretty(obj, ' ')
FROM json
WHERE process_order_id = (SELECT MAX(process_order_id) FROM process_order);

Pros: This solution is more elegant and avoids the need for temporary tables and triggers. It uses advanced CTE techniques to construct the JSON object incrementally, which can be more efficient for large datasets.

Cons: The solution is more complex and may be harder to understand and maintain. Additionally, it requires a deep understanding of SQLite’s JSON functions and CTE capabilities.

Solution 3: Minimal Code Solution

The third solution is a more compact version of the second solution, eliminating the process_order CTE and simplifying the path construction. This approach is more concise but may be harder to follow for those unfamiliar with advanced SQLite features.

Here is the complete implementation:

WITH
  b AS (
    SELECT FatherId AS a,
           PersonId AS c,
           '{"Name":"' || Name || '","Begat":[]}' AS o,
           Row_Number() OVER (PARTITION BY FatherId) - 1 AS i
    FROM patriarchal
  ),
  p AS (
    SELECT *, '$[' || i || ']' AS p
    FROM b
    WHERE a IS NULL
    UNION
    SELECT b.*, p.p || '.Begat[' || b.i || ']'
    FROM p
    JOIN b ON p.c = b.a
  ),
  j AS (
    SELECT 0 AS p, '[]' AS o
    UNION
    SELECT p.p, json_insert(j.o, p.p, json(p.o))
    FROM j
    JOIN p ON p.p = (SELECT MIN(p) FROM p WHERE p > j.p)
  )
SELECT IfNull(json_pretty(o, ' '), MAX(LENGTH(o)))
FROM j;

Pros: This solution is the most concise and leverages SQLite’s JSON functions and CTEs to their fullest extent. It is a good option for those who prefer minimal code.

Cons: The solution is highly compact and may be difficult to understand and debug. It also assumes a deep familiarity with SQLite’s JSON and CTE features.

Conclusion

Generating recursive JSON structures from hierarchical data in SQLite is a challenging task due to the database’s limitations on recursive views and aggregations. However, by leveraging advanced CTE techniques, temporary tables, and triggers, it is possible to achieve the desired result. Each solution has its own trade-offs in terms of complexity, performance, and maintainability, and the best approach will depend on the specific requirements of the project.