Sumator

Type: sumator • Category: flow • Tags: join, merge, data

Description

Join two lists of dicts by key

Parameters

Name	Type	Description	Required	Default
leftExpr	string	Expr -> list[dict] (left) lub bezpośrednio lista	no
rightExpr	string	Expr -> list[dict] (right) lub bezpośrednio lista	no
key	string	Common key present in both dicts	no
joinType	string	Join type	no	"inner"
conflictStrategy	string	Conflict: prefer_left / prefer_right / rename_right (suffix _r)	no	"prefer_left"

Help

Join Lists of Dictionaries Worker

Merges two collections of dictionary objects based on a common key. Supports various join types and conflict resolution strategies.

How it works:

1. Data Resolution: Evaluates expressions to get left and right lists
2. Key Matching: Joins dictionaries based on specified key field
3. Conflict Resolution: Handles overlapping fields according to strategy
4. Result: Returns merged list of dictionaries

Parameters:

• leftExpr: Expression returning left list of dictionaries
• rightExpr: Expression returning right list of dictionaries
• key: Common field name present in both lists
• joinType: Type of join operation
• conflictStrategy: How to handle field conflicts

Join Types:

• inner: Only matching keys (intersection)
• left: All left records, matching right records
• right: All right records, matching left records
• outer: All records from both lists

Conflict Strategies:

• prefer_left: Keep left value when fields conflict
• prefer_right: Keep right value when fields conflict
• rename_right: Keep both, rename right field with '_r' suffix

Examples:

• Basic join: leftExpr="data.users", rightExpr="data.profiles", key="user_id"
• Expression join: leftExpr="vars.api_data", rightExpr="[{'id': 1, 'status': 'active'}]", key="id"

Input Format:

• Lists of dictionaries with consistent key field
• Key field must exist in all dictionaries
• Values can be any type (strings, numbers, objects, arrays)

Output:

• List of merged dictionaries
• Count of resulting records
• Preserves all fields from matched records

Common Use Cases:

• Combine user data with profile information
• Merge API responses with local data
• Join database query results
• Consolidate related datasets
• Data enrichment and normalization

Notes:

• Key field values must be hashable (strings, numbers)
• Missing key fields in records are ignored
• Complex nested objects are fully supported