# Databricks User Guide

---

You can run BigDL program on the [Databricks](https://databricks.com/) cluster as follows.
### **1. Create a Databricks Cluster**

- Create either an [AWS Databricks](https://docs.databricks.com/getting-started/try-databricks.html) workspace or an [Azure Databricks](https://docs.microsoft.com/en-us/azure/azure-databricks/) workspace. 
- Create a Databricks [cluster](https://docs.databricks.com/clusters/create.html) using the UI. Choose Databricks runtime version. This guide is tested on Runtime 9.1 LTS (includes Apache Spark 3.1.2, Scala 2.12).

### 2. Download BigDL Libraries

Download the BigDL package from [here](https://oss.sonatype.org/content/repositories/snapshots/com/intel/analytics/bigdl/bigdl-assembly-spark_3.1.2/2.1.0-SNAPSHOT/), scroll down to the bottom, choose the **latest** release **bigdl-assembly-spark_3.1.2-2.1.0-*-fat-jars.zip**.

![](images/fat-jars.png)

Unzip the zip file, we only need two files:

- jars/**bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar**
- python/**bigdl-spark_3.1.2-2.1.0-SNAPSHOT-python-api.zip**

### 3. Install BigDL Java dependencies

In the Databricks left panel, click **Compute** and select your cluster.

![](images/compute.png)

Install BigDL java packages using **bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar** from [step 2](#2-download-bigdl-libraries). Click **Libraries > Install New > Library Source(Upload) > Library Type (Jar)**. Drop the jar on Databricks.

![](images/assembly-jar.png)

After upload finishes, click **Install**.

> Tips: if you find your upload process is really slow, try to use **Databricks CLI** to upload, see [Appendix B](#appendix-b) for details.

### 4. Install BigDL Python libraries

Install BigDL python environment using **bigdl-spark_3.1.2-2.1.0-SNAPSHOT-python-api.zip** from [step 2](#2-download-bigdl-libraries). However, Databricks can only upload **Jar**, **Python Egg** and **Python Whl**, but doesn't support **Zip**, so we can not simply upload the python api zip and install it like what we do in [step 3](#3-install-bigdl-java-dependencies). You can upload and install the zip package in one of the following ways.

#### 4.1 Upload and Install through DBFS

**First, upload the zip package to [DBFS](https://docs.databricks.com/dbfs/index.html).** In the left panel, click **Data > DBFS**, if your panel don't have DBFS, see [Appendix A](#appendix-a). then choose or create a folder and right click in the folder, choose **Upload here**.

![](images/upload.png)

Upload your zip package.

![](images/upload-success.png)

Right click the uploaded zip package and choose **Copy path**, copy the **Spark API Format** path.

![](images/copy-path.png)

**Then install the zip package from DBFS.** In the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(DBFS/ADLS) > Library Type(Python Egg) > paste the path > Install**

![](images/install-zip.png)

#### 4.2 Change the File Extension Name

You can simply change the **bigdl-spark_3.1.2-2.1.0-SNAPSHOT-python-api.zip** extension name(**.zip**) to **.egg**, since Egg is essentially a zip format package. Then in the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(Upload) > Library Type(Python Egg) > Install**

![](images/egg.png)

### **5. Set Spark configuration**

On the cluster configuration page, click the **Advanced Options** toggle. Click the **Spark** tab. You can provide custom [Spark configuration properties](https://spark.apache.org/docs/latest/configuration.html) in a cluster configuration. Please set it according to your cluster resource and program needs.

![](images/Databricks5.PNG)

See below for an example of Spark config setting needed by BigDL. Here it sets 2 core per executor. Note that "spark.cores.max" needs to be properly set below.

```
spark.executor.cores 2
spark.cores.max 4
```

### **6. Run BigDL on Databricks**

Open a new notebook, and call `init_orca_context` at the beginning of your code (with `cluster_mode` set to "spark-submit").

```python
from bigdl.orca import init_orca_context, stop_orca_context
init_orca_context(cluster_mode="spark-submit")
```

Output on Databricks:

![](images/init-orca-context.png)


### **7. Install other third-party libraries on Databricks if necessary**

If you want to use other third-party libraries, check related Databricks documentation of [libraries for AWS Databricks](https://docs.databricks.com/libraries/index.html) and [libraries for Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/libraries/).

### Appendix A

If there is no DBFS in your panel,  go to **User profile > Admin Console > Workspace settings > Advanced > Enabled DBFS File Browser**

![](images/dbfs.png)

### Appendix B

Use **Databricks CLI** to upload file to DBFS.

**Install and config Azure Databricks CLI**

1. Install Python, need Python version 2.7.9 and above if you’re using Python 2 or Python 3.6 and above if you’re using Python 3.

2. Run `pip install databricks-cli`

3. Set authentication, Click **user profile icon > User Settings > Access tokens > Generate new token > generate > copy the token**, make sure to **copy** the token and store it in a secure location, **it won't show again**.

   ![](images/token.png)

4. Copy the URL of Databricks host, the format is `https://adb-<workspace-id>.<random-number>.azuredatabricks.net`, you can copy it from your Databricks web page URL.

   ![](images/url.png)

5. In cmd run `dbfs config --token` as shown below:

   ```
   dbfs configure --token
   Databricks Host (should begin with https://): https://your.url.from.step.4
   Token: your-token-from-step-3
   ```

6. Verify whether you are able to connect to DBFS, run "databricks fs ls".

   ![](images/verify-dbfs.png)

**Upload through Databricks CLI**

Now, we can use Databricks CLI to upload file to DBFS. run command:

```
dbfs cp /your/local/filepath/bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar dbfs:/FileStore/jars/stable/bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar
```

After command finished, check DBFS in Databricks, in left panel, click **Data > DBFS > your upload directory**, if you do not see DBFS in your panel, see [Appendix A](#appendix-a).

**Install package from DBFS**

In the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(DBFS/ADLS) > Library Type(your package type)**.

![](images/install-zip.png)