{
  "id": 7039594,
  "title": "Recursive CTEs: How SQL Secretly Learned to Loop",
  "url": "https://urgent.news/2026/09/13/recursive-ctes-how-sql-secretly-learned-to-loop",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-13T02:06:19.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/rahmanfrr/recursive-ctes-how-sql-secretly-learned-to-loop-457f"
  },
  "original_language": "en",
  "account": "Recursive CTEs are a powerful feature in SQL that allow for looping without the need for complex joins. This is particularly useful when dealing with hierarchical data structures, such as employee org charts or category trees. The basic concept is a recursive common table expression (CTE) that references itself, using UNION ALL to combine its anchor member (the initial row(s) of the hierarchy) with its recursive member (subsequent levels of the hierarchy).\n\nTo illustrate, consider an employees table with columns for the employee ID, name, and their manager's ID. A recursive CTE can start with a specific manager and recursively pull in all employees reporting under them. The query initializes with the anchor member, selecting the manager's details and assigning a depth of 1. The recursive member then joins the employees table back to the CTE, pulling in the next level of employees and incrementing the depth count.\n\nThe recursive process continues until no new rows are returned, effectively reaching the bottom of the hierarchy. This allows for the construction of an actual organizational chart or category tree, with each level properly indented and ordered. The depth column can also be useful for ordering the results, making the data more readable and visually appealing.\n\nAnother common use case for recursive CTEs is in building category trees, where data is naturally nested. The same recursive structure can be applied to product categories, comment threads, folder structures, or bill-of-materials breakdowns. By appending the parent's name to each child's name, a breadcrumb trail can be generated automatically.\n\nHowever, it's important to note that UNION ALL should be used instead of UNION when building recursive CTEs. This is because UNION ALL avoids the overhead of deduplicating intermediate results, which can be expensive and unnecessary given that the rows are inherently distinct.\n\nA potential pitfall with recursive CTEs is the possibility of cyclic data, where an entity manages another entity that somehow manages back. This would cause an infinite loop. To prevent this, one can track visited IDs in an array or impose a maximum depth limit.\n\nNot all SQL engines support recursive CTEs. PostgreSQL, SQL Server, and MySQL 8.0 and later versions do, while older MySQL versions do not. Additionally, recursive CTEs can be resource-intensive for very deep or wide hierarchies queried frequently. In such cases, alternative approaches like closure tables or materialized paths might be more efficient, even though they require more maintenance effort for inserts and updates.\n\nIn summary, recursive CTEs are a clever SQL feature that elegantly solve a common problem in data modeling involving nested structures. They allow for the looping effect without the need for complex or inefficient join structures, thereby simplifying the SQL code while achieving the desired result.",
  "summary": "Ask a SQL query to find \"all employees under this manager,\" and things get ugly fast if you don't know how many levels deep the org chart goes. A regular join handles one level. Two joins handle two levels. Nobody's writing seven joins for seven levels of middle management. This is exactly the problem recursive CTEs exist to solve — and most people who've written SQL for years have never touched…",
  "key_points": [
    "Recursive CTEs enable looping in SQL without complex joins.",
    "They are useful for hierarchical data like org charts or category trees.",
    "UNION ALL combines anchor and recursive members, avoiding unnecessary deduplication."
  ],
  "editors_take": "Recursive CTEs in SQL signify a shift towards more intuitive handling of hierarchical data, enabling simpler construction of organizational charts and category trees, and streamlining querying of nested structures.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}