> For the complete documentation index, see [llms.txt](https://upsolver.gitbook.io/content/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://upsolver.gitbook.io/content/articles-1/jobs/transformation/flattening-arrays.md).

# Flattening Arrays

The `FLATTEN_PATHS` feature enables you to flatten the results of a query based on the provided paths. This feature can transform nested arrays within your data into separate rows in the output, providing a simple and efficient way to handle hierarchical data structures.

## Syntax

The basic syntax to use `FLATTEN_PATHS` in a transformation job is as follows:

```sql
CREATE sync JOB job_name
    FLATTEN_PATHS = (path1, path2, ...)
AS INSERT INTO target_table
      SELECT *
      FROM source_table;
```

{% hint style="info" %}
The `FLATTEN_PATHS` feature is currently only supported when writing to Upsolver tables.
{% endhint %}

## Example: Flattening a Single Path

The following example demonstrates how to flatten the `items` array.

### SQL Code

```sql
CREATE sync JOB flatten_single_path_example
    FLATTEN_PATHS = (items)
AS INSERT INTO orders_database.flattened_orders
       SELECT *
       FROM orders_database.raw_orders;
```

### Input Row

```json
{
  "name": "Order1",
  "items": [
    {"product": "apple", "quantity": 3, "categories": ["fruit", "fresh"]},
    {"product": "sugar", "quantity": 2, "categories": ["pantry"]}
  ]
}
```

### Output Rows

```json
{
  "name": "Order1",
  "product": "apple",
  "quantity": 3,
  "categories": ["fruit", "fresh"]
}
```

```json
{
  "name": "Order1",
  "product": "sugar",
  "quantity": 2,
  "categories": ["pantry"]
}
```

## Example: Flattening a Nested Array

### SQL Code

```sql
CREATE sync JOB flatten_nested_path_example
    FLATTEN_PATHS = (items[].categories)
AS INSERT INTO orders_database.flattened_categories
      SELECT *
      FROM orders_database.raw_orders;
```

### Input Row

```json
{
  "name": "Order1",
  "items": [
    {"product": "apple", "quantity": 3, "categories": ["fruit", "fresh"]},
    {"product": "sugar", "quantity": 2, "categories": ["pantry"]}
  ]
}
```

### Output Rows

```json
{
  "name": "Order1",
  "product": "apple",
  "quantity": 3,
  "categories": "fruit"
}
```

```json
{
  "name": "Order1",
  "product": "apple",
  "quantity": 3,
  "categories": "fresh"
}
```

```json
{
  "name": "Order1",
  "product": "sugar",
  "quantity": 2,
  "categories": "pantry"
}
```

## Example: Flattening Multiple Paths Creating a Cartesian Product

In this example, we flatten both `categories` and `pricing`, resulting in a Cartesian product since two independent arrays are used in the flattening.

### SQL Code

```sql
CREATE sync JOB flatten_multiple_paths_example
    FLATTEN_PATHS = (categories, pricing)
AS INSERT INTO products_database.flattened_products
      SELECT *
      FROM products_database.raw_products;
```

### Input Row

```json
{
  "name": "Apple",
  "categories": ["fruit", "fresh"],
  "pricing": [ {"members": 2.99}, {"general": 3.99}]
}
```

### Output Rows

Here, flattening both `categories` and `pricing` creates a Cartesian product of the two arrays, leading to combinations of each category with each pricing option.

```json
{
  "name": "Apple",
  "categories": "fruit",
  "pricing": {"members": 2.99}
}
```

```json
{
  "name": "Apple",
  "categories": "fruit",
  "pricing": {"general": 3.99}
}
```

```json
{
  "name": "Apple",
  "categories": "fresh",
  "pricing": {"members": 2.99}
}
```

```json
{
  "name": "Apple",
  "categories": "fresh",
  "pricing": {"general": 3.99}
}
```

The `FLATTEN_PATHS` feature provides an intuitive way to handle complex data structures, making it easier to work with nested arrays in your data processing tasks.


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