# Aggregate

Combine and process data from multiple inputs.

---

## Overview

The Aggregate node is a powerful data processing tool that combines multiple input items into a consolidated output. It's particularly useful when you need to merge data from different sources, extract specific fields, or combine lists from multiple items.

## Operations

The Aggregate node supports two main modes of operation:

### Individual Fields Aggregation

Aggregate specific fields from multiple input items with precise control over the output.

#### Configuration Options:
- **Field Selection**:
  - Source Field: The field to aggregate from input items
  - New Field Name: Custom name for the aggregated field
  - Dot Notation Support: Access nested fields (e.g., "user.name")

- **Processing Options**:
  - Keep Missing Values: Include fields even if missing in some items
  - Keep Only Unique: Remove duplicate values from the output
  - Merge Lists: Combine arrays from different items
  - Include Binaries: Option to include binary data

### All Fields Aggregation

Combine all fields from multiple input items into a single output structure.

#### Configuration Options:
- **Destination Settings**:
  - Destination Field: Name of the field to store combined data (default: "data")
  - Include Fields: Specify fields to include in the aggregation
  - Exclude Fields: Specify fields to exclude from the aggregation

- **Processing Options**:
  - Keep Missing Values: Include fields even if missing in some items
  - Keep Only Unique: Remove duplicate values from the output
  - Merge Lists: Combine arrays from different items
  - Include Binaries: Option to include binary data

## Common Use Cases

1. **Data Consolidation**
   - Merge user data from multiple sources
   - Combine related records into a single view
   - Aggregate statistics or metrics

2. **List Processing**
   - Merge arrays from different items
   - Remove duplicates from combined lists
   - Process nested data structures

3. **Field Extraction**
   - Extract specific fields from multiple records
   - Rename fields for better organization
   - Handle complex nested data structures

## Best Practices

1. **Field Naming**
   - Use clear, descriptive names for output fields
   - Avoid duplicate field names in the output
   - Use dot notation carefully for nested structures

2. **Data Processing**
   - Consider enabling "Keep Only Unique" when dealing with potential duplicates
   - Use "Keep Missing Values" when all data points are important
   - Be mindful of memory usage when processing large datasets

3. **Performance**
   - Limit the number of fields when using All Fields Aggregation
   - Use Individual Fields Aggregation for specific needs
   - Consider the impact of binary data inclusion

## Example
For example, if you have three input items with the following data(can generate using the [`Suger Code`](/workflow/suger-code) node):

```json
[
  { "json": { "num": 1 } },
  { "json": { "num": 2 } },
  { "json": { "num": 3 } }
]
```
You can configure the Aggregate node to combine these into a single output item with the following settings:
![Aggregate Node Configuration](images/aggregate.png)

then, it will generate the following output:
```json
[
  { "json": { "num2": [1, 2, 3] } }
]
```