Knex: Simplifying Joins and Groupings

Understanding the Challenge: Flat Objects and Related Data

When performing joins between tables in Knex, the result is typically a series of flat objects, each representing a row returned by the query. This format, while straightforward, can be limiting when you need to manipulate and group related data for specific use cases.

Solution: Grouping Related Data Using SQL and Knex

Is it possible to directly construct and group related data using SQL and Knex? Absolutely! Let’s dive into a practical example to illustrate this.

Consider two tables in a one-to-many relationship: a Team table and a Players table.

Team Table Structure:
    id: Unique identifier for each team.
    name: The name of the team.

Players Table Structure:
    if: Unique identifier for each player.
    fullname: The player’s full name.
    team_id: The ID of the team to which the player belongs.

Objective

Our goal is to retrieve a list of teams along with the full names of all their players.

Initially, a standard Knex join query might look like this:

knex
  .from("team")
  .select("team.*", "players_team.name")
  .leftJoin("players", "players.team_id", "team.id");

This query returns a flat array of objects, where each object contains data from both the Team and Players tables.

However, for more efficient data handling, we aim to group the player data under each team, resulting in a more structured and usable object.

This can be achieved using the json_agg and json_build_object functions. Here’s how it can be done:

knex
  .from("team")
  .select(
    "team.*",
    knex.raw(
      "(case when count(players) = 0 then '[]' else json_agg(json_build_object('fullname', players.fullname)) end) as players"
    )
  )
  .leftJoin("players", "players.team_id", "team.id")
  .groupBy(["team.id", "players.team_id"]);

This query groups player data under their respective teams, creating a nested and more manageable data structure.

Conclusion

By leveraging the capabilities of SQL and Knex, we can effectively manage and group related data, enhancing data manipulation and improving overall efficiency.