Json explosion and correct grain comparison

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Summary

The problem at hand involves flattening a complex JSON object into a table format while following specific logic rules. The JSON object contains nested arrays and objects, and the goal is to transform it into a table with unique rows for each personId and feeType or correspondenceValue. The challenge lies in handling the cartesian explosion and ensuring that correspondence values and feeType values do not appear on the same row.

Root Cause

The root cause of the issue is the complexity of the JSON object and the nested arrays and objects it contains. The use of JSON_TABLE and NESTED PATH can lead to a cartesian explosion, resulting in too many rows. Additionally, the requirement to keep correspondence values and feeType values separate adds to the complexity.

Why This Happens in Real Systems

This issue occurs in real systems when dealing with complex data structures and nested data. The use of JSON and XML data formats can lead to nested arrays and objects, making it challenging to transform the data into a flat table format. The cartesian explosion can result in performance issues and data inconsistencies if not handled properly.

Real-World Impact

The real-world impact of this issue can be significant, leading to:

  • Performance issues: The cartesian explosion can result in slow query performance and increased resource utilization.
  • Data inconsistencies: The incorrect transformation of the data can lead to data inconsistencies and errors in downstream applications.
  • Difficulty in data analysis: The complex data structure can make it challenging to analyze and gain insights from the data.

Example or Code

WITH json_data AS (
  SELECT *
  FROM JSON_TABLE(
    '{"metadata": {"eventPayloadType": "JSON"}, "data": {"groupActions": [{"action": "UPDATE", "group": {"groupId": "G001", "arrangements": [{"businessKey": {"personGroupId": "SRC01", "personId": "ARR001"}, "arrangementFees": [{"feeType": "TYPE1", "feeValue": 10.0}, {"feeType": "TYPE2", "feeValue": 20.0}], "correspondence": [{"correspondenceTypeCode": "TYPE1", "correspondenceValueCode": "VAL001"}]}]}}]}',
    '$'
    COLUMNS (
      metadata VARCHAR2(4000) PATH '$.metadata',
      data VARCHAR2(4000) PATH '$.data'
    )
  )
),
arrangement_fees AS (
  SELECT *
  FROM JSON_TABLE(
    (SELECT data FROM json_data),
    '$.data.groupActions[0].group.arrangements[*]'
    COLUMNS (
      business_key VARCHAR2(4000) PATH '$.businessKey',
      arrangement_fees VARCHAR2(4000) PATH '$.arrangementFees[*]',
      correspondence VARCHAR2(4000) PATH '$.correspondence[*]'
    )
  )
)
SELECT *
FROM arrangement_fees;

How Senior Engineers Fix It

Senior engineers fix this issue by:

  • Breaking down the complex JSON object into smaller, manageable pieces using CTEs or subqueries.
  • Using JSON_TABLE and NESTED PATH carefully to avoid the cartesian explosion.
  • Applying filters and conditions to ensure that correspondence values and feeType values do not appear on the same row.
  • Optimizing the query for performance and data consistency.

Why Juniors Miss It

Juniors may miss this issue due to:

  • Lack of experience with complex JSON data structures and nested arrays and objects.
  • Insufficient understanding of the cartesian explosion and its impact on query performance.
  • Inadequate knowledge of JSON_TABLE and NESTED PATH and how to use them effectively.
  • Failure to test the query thoroughly and validate the results.

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