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Add Databricks setup instructions. #713

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22 changes: 22 additions & 0 deletions docs/examples/how-to-run.md
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Expand Up @@ -26,3 +26,25 @@ cd lithops/aws # or whichever executor/cloud combination you are using
| | Google | [modal/gcp/README.md](https://github.com/cubed-dev/cubed/blob/main/examples/modal/gcp/README.md) |
| Coiled | AWS | [coiled/aws/README.md](https://github.com/cubed-dev/cubed/blob/main/examples/coiled/aws/README.md) |
| Beam | Google | [dataflow/README.md](https://github.com/cubed-dev/cubed/blob/main/examples/dataflow/README.md) |

## Databricks

If you want to run Cubed on Databricks, we recommend using the Spark executor (experimental stage, see [#499](https://github.com/cubed-dev/cubed/issues/499)).

You will need to setup your compute cluster with [Dedicated Access Mode](https://docs.databricks.com/aws/en/compute/single-user-fgac), as Spark executor requires use of Spark RDDs that are not supported by [Serverless](https://docs.databricks.com/aws/en/compute/serverless/limitations#limitations-overview) or [Standard mode](https://docs.databricks.com/aws/en/compute/access-mode-limitations#standard-access-mode-limitations-on-unity-catalog).

### Configuration

Note that if you are using a local directory for `work_dir`, you can only use a single node Spark cluster since the Spark worker nodes will not have access to your driver node local directory.

Using Unity Catalog Volume is not recommended for `work_dir` since it is significantly slower.

```py
spec = cubed.Spec(
executor_name="spark",
work_dir="/tmp/", # this is using local directory of the driver node, your cluster will need to run in single node
allowed_mem="2GB"
)
```


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