# Run Evidently on Spark

{% hint style="info" %}
**You are looking at the old Evidently documentation**: this API is available with versions 0.6.7 or lower. Check the newer docs version [here](https://docs.evidentlyai.com/introduction).
{% endhint %}

You can run distributed computation using Spark if you work with large datasets.

## Supported metrics

Currently, the following Tests, Metrics and Presets are supported:

* `ColumnDriftMetric()`
* `DataDriftTable()`
* `DatasetDriftMetric()`
* `DataDriftPreset()`
* `TestColumnDrift()`
* `TestShareOfDriftedColumns()`
* `TestNumberOfDriftedColumns()`
* `DataDriftTestPreset()`

For drift calculation, the following methods are supported:

* `chisquare`
* `jensen shannon`
* `psi`
* `wasserstein`

The following data types are supported:

* `numerical_features`
* `categorical_features`

## Code example

You can refer to an example How-to-notebook showing how to use Evidently on Spark:

{% embed url="<https://github.com/evidentlyai/evidently/blob/ad71e132d59ac3a84fce6cf27bd50b12b10d9137/examples/how_to_questions/how_to_run_calculations_on_spark.ipynb>" %}

## Run Evidently with Spark

To run Evidently on a Spark DataFrame, you need to specify the corresponding engine in the `run()` method for the Report calculation:

To import `SparkEngine` from Evidently, use the following command:

```
from evidently.spark.engine import SparkEngine
```

Pass the `SparkEngine` to the `run` method when you create the Report:

```
spark_report_table = Report(metrics=[
    DataDriftTable()
])
spark_report_table.run(reference_data=reference, current_data=current, engine=SparkEngine)

spark_report_table.show()  # OR spark_report_table.show()
```


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