Optimizing Performance on JSON Data: A PostgreSQL Query Review

The provided query already seems optimized, considering the use of a CTE to improve performance on JSON data. However, there are still some potential improvements that can be explored.

Here’s an updated version of your query:

WITH cf as (
  SELECT 
    cfiles.property_values::jsonb AS prop_vals,
    users.email,
    cfiles.name AS cfile_name,
    cfiles.id AS cfile_id
  FROM cfiles
  LEFT JOIN user_permissions ON (user_permissions.cfile_id = cfiles.id)
  LEFT JOIN users on users.id = user_permissions.user_id
  ORDER BY email NULLS LAST
  LIMIT 20
)
SELECT 
  cf.cfile_name,
  jsonb_array_elements(cf.prop_vals) AS project_ids,
  jsonb_array_elements(cf.prop_vals) AS sample_names,
  string_agg(DISTINCT g.permissions, ', ') OVER (PARTITION BY cfile_id ORDER BY g.user_email) AS permissions
FROM (
  SELECT 
    cfile_id,
    string_agg(DISTINCT(user_email), ', ') as permissions
  FROM cf 
  LEFT JOIN user_permissions ON (user_permissions.cfile_id = cf.cfile_id)
  LEFT JOIN users on users.id = user_permissions.user_id
  GROUP BY cfile_id, user_email
) g
JOIN cf ON (cf.cfile_id = g.cfile_id)
GROUP BY cfile_name, project_ids, sample_names, permissions;

Here’s what changed:

We cast cfiles.property_values to jsonb to enable the use of JSON functions.
We added an additional subquery to calculate the aggregate values separately for performance reasons and consistency with your original query. However, this might add unnecessary overhead depending on the amount of data.

As it turns out your final CTE solution should work fine and can be very efficient as long as it’s well-indexed and tuned accordingly. The use of JSON functions in Postgres often provides significant performance improvements compared to string-based operations for handling JSON data.

Last modified on 2024-08-06