# Profiles Optimizations


{{< announcement >}}
This feature is in **Beta**, where we work with early users and customers to test new features and get feedback before making them generally available. 

[Contact the Product team](mailto:product@rudderstack.com) if you have any questions.
{{< /announcement >}}

This guide introduces the available optimizations in Profiles and explains how to use them in your Profiles project to enhance execution efficiency.

## Optimization options

Profiles provides the following optimizations to reduce the execution time of a `pb run`:

### Input variable table optimization

Instead of copying the entire input table, Profiles automatically identifies and copies only the necessary columns, reducing data transfer and improving performance. PB identifies the necessary columns by parsing the SQL fragments provided in `input_vars` and `entity_vars`.

Set `enable_input_columns_filtering` to `true` in your [`optimizations.yaml` file](#usage) to enable this optimization.

**Limitation**

If Profiles fails to identify the necessary columns and the run fails, you need to specify the unidentified columns in the input model's contract - this workaround should help you bypass the run failure. An example is shown below:

```yaml
- name: rsIdentifies
   contract: 
        is_optional: false 
        is_event_stream: true 
        with_entity_ids: 
            - users
        with_columns: 
            - name: missed_column # PB was not able to identify this column, it is added to the contract.
    app_defaults: 
        table: identifies 
        occurred_at_col: timestamp 
        ids: 
            - select: user_id 
            type: user_id 
            entity: users
```

### Efficient main ID column addition

Instead of performing multiple joins between the input variable table and the ID stitcher, a case-based approach is used to add the main ID column. This method is particularly effective for large input tables. 

Set `main_id_addition_approach` to `case_based_join` in your [`optimizations.yaml` file](#usage) to enable this optimization.

### Input vars bundling

Profiles automatically groups the execution of related `input_vars` based on their definitions to optimize runtime performance.

Set `bundle_input_vars` to `true` in your [`optimizations.yaml` file](#usage) to enable this optimization.

### Entity vars bundling

Similar to input variable bundling, this optimization groups the execution of `entity_vars` based on their definitions to improve efficiency.

Set `bundle_entity_vars` to `true` in your [`optimizations.yaml` file](#usage) to enable this optimization.

There are two bundling strategies - `normal` and `aggressive`, with `normal` as the default strategy.

- In the `normal` strategy, PB groups similar bundles with inter-dependencies and with or without a window together.
- In the `aggressive` strategy, PB groups regardless of similarity.

#### Optimization options summary

The following table summarizes the available optimizations:

| Optimization | Allowed values | Default value            |
| :---------| :----| :-----| 
| `enable_input_columns_filtering`   | `true`, `false` | `false` |
| `main_id_addition_approach` | `plain_join`, `case_based_join`    | `plain_join` |
| `bundle_input_vars` | `true`, `false` | `false`                  |
| `bundle_entity_vars` | `true`, `false` | `false` |
| `bundle_entity_vars_strategy`      | `normal`, `aggressive`             | `normal` |

## Usage

Specify the optimizations in the `optimizations.yaml` file.

### Example configuration

```yaml
optimizations:
    main_id_addition_approach: case_based_join
    enable_input_columns_filtering: true
    bundle_entity_vars: true
    bundle_input_vars: true
```

{{< warning >}}
Make sure to place the `optimizations.yaml` file in the appropriate configuration directory of your Profiles project (within the `models` folder). Also, make sure that there is only one `optimizations.yaml` file in your project.
{{< /warning >}}