Manage Notebooks

You can manage notebooks using the UI, the CLI, and by invoking the Workspace API. This topic focuses on performing notebook tasks using the UI. For the other methods, see Databricks CLI and Workspace API.

Create a notebook

1. Click the Workspace button or the Home button in the sidebar. Do one of the following:

   • Next to any folder, click the on the right side of the text and select Create > Notebook.

     

   • In the Workspace or a user folder, click and select Create > Notebook.

2. In the Create Notebook dialog, enter a name and select the notebook’s primary language.

3. If there are running clusters, the Cluster drop-down displays. Select the cluster to attach the notebook to.

4. Click Create.

Delete a notebook

Since notebooks are contained inside the Workspace (and in folders in the Workspace), they follow the same rules as folders. See Folders and Workspace object operations for information about how to access the Workspace menu and delete notebooks or other items in the Workspace.

Copy notebook path

To copy a notebook file path without opening notebook, right-click the notebook name or click the to the right of the notebook name and select Copy File Path.

Control access to a notebook

If your Databricks account has the Databricks Operational Security Package, you can use Workspace access control to control who has access to a notebook.

Notebook external formats

Databricks supports several notebook external formats:

Source File

A source file with the extension .scala, .py, .sql, or .r.

HTML

A Databricks notebook with an .html extension.

DBC Archive

A Databricks archive.

IPython Notebook

A Jupyter notebook with the extension .ipynb.

RMarkdown

An R Markdown document with the extension .Rmd.

Import a notebook

You can import an external notebook from a URL or a file.

1. Click the Workspace button or the Home button in the sidebar. Do one of the following:

   • Next to any folder, click the on the right side of the text and select Import.

   • In the Workspace or a user folder, click and select Import.

     

2. Specify the URL or browse to a file containing a supported external format.

3. Click Import.

Export a notebook

In the notebook toolbar, select File > Export and a format.

Note

When you export a notebook as a Databricks notebook (HTML), IPython notebook, or archive (DBC), and you have not previously cleared the results, the results of running the notebook are included.

Publish a notebook

If you’re using Community Edition, you can publish a notebook so that you can share a URL path to the notebook. Subsequent publish actions update the notebook at that URL.

Notebooks and clusters

Before you can do any work in a notebook, you must first attach the notebook to a cluster. This section describes how to attach and detach notebooks to and from clusters and what happens behind the scenes when you perform these actions.

Execution contexts

When you attach a notebook to a cluster, Databricks creates an execution context. An execution context contains the state for a REPL environment for each supported programming language: Python, R, Scala, and SQL. When you run a cell in a notebook, the command is dispatched to the appropriate language REPL environment and run.

You can also use the REST 1.2 API to create an execution context and send a command to run in the execution context. Similarly, the command is dispatched to the language REPL environment and run.

A cluster has a maximum number of execution contexts (145). Once the number of execution contexts has reached this threshold, you cannot attach a notebook to the cluster or create a new execution context.

Idle execution contexts

An execution context is considered idle when the last completed execution occurred past a set idle threshold. Last completed execution is the last time the notebook completed execution of commands. The idle threshold is the amount of time that must pass between the last completed execution and any attempt to automatically detach the notebook. The default idle threshold is 24 hours.

When a cluster has reached the maximum context limit, Databricks removes (evicts) idle execution contexts (starting with the least recently used) as needed. Even when a context is removed, the notebook using the context is still attached to the cluster and appears in the cluster’s notebook list. Streaming notebooks are considered actively running, and their context is never evicted until their execution has been stopped. If an idle context is evicted, the UI displays a message indicating that the notebook using the context was detached due to being idle.

If you attempt to attach a notebook to cluster that has maximum number of execution contexts and there are no idle contexts (or if auto-eviction is disabled), the UI displays a message saying that the current maximum execution contexts threshold has been reached and the notebook will remain in the detached state.

If you fork a process, an idle execution context is still considered idle once execution of the request that forked the process returns. Forking separate processes is not recommended with Spark.

Configure context auto-eviction

You can configure context auto-eviction by setting the Spark property spark.databricks.chauffeur.enableIdleContextTracking.

• In Databricks 5.0 and above, auto-eviction is enabled by default. You disable auto-eviction for a cluster by setting spark.databricks.chauffeur.enableIdleContextTracking false.
• In Databricks 4.3, auto-eviction is disabled by default. You enable auto-eviction for a cluster by setting spark.databricks.chauffeur.enableIdleContextTracking true.

Attach a notebook to a cluster

To attach a notebook to a cluster:

1. In the notebook toolbar, click Detached .
2. From the drop-down, select a cluster.

Important

An attached notebook has the following Apache Spark variables defined.

Class	Variable Name
SparkContext	sc
SQLContext/HiveContext	sqlContext
SparkSession (Spark 2.x)	spark

Do not create a SparkSession, SparkContext, or SQLContext. Doing so will lead to inconsistent behavior.

Determine Spark and Databricks Runtime version

To determine the Spark version of the cluster your notebook is attached to, run:

spark.version

To determine the Databricks Runtime version of the cluster your notebook is attached to, run:

• Scala

  dbutils.notebook.getContext.tags("sparkVersion")
  

• Python

  spark.conf.get("spark.databricks.clusterUsageTags.sparkVersion")
  

Note

Both this sparkVersion tag and the spark_version property required by the endpoints in the Clusters API and Jobs API refer to the Databricks Runtime version, not the Spark version.

Detach a notebook from a cluster

1. In the notebook toolbar, click Attached <cluster-name> .

2. Select Detach.

   

You can also detach notebooks from a cluster using the Notebooks tab on the cluster details page.

When you detach a notebook from a cluster, the execution context is removed and all computed variable values are cleared from the notebook.

Tip

Databricks recommends that you detach unused notebooks from a cluster. This frees up memory space on the driver.

View all notebooks attached to a cluster

The Notebooks tab on the cluster details page displays all of the notebooks that are attached to a cluster. The tab also displays the status of each attached notebook, along with the last time a command was run from the notebook.

Schedule a notebook

To schedule a notebook job to run periodically:

1. In the notebook toolbar, click the button at the top right.
2. Click + New.
3. Choose the schedule.
4. Click OK.

Distribute notebooks

To allow you to easily distribute Databricks notebooks, Databricks supports the Databricks archive, which is a package that can contain a folder of notebooks or a single notebook. A Databricks archive is a JAR file with extra metadata and has the extension .dbc. The notebooks contained in the archive are in a Databricks internal format.

Import an archive

1. Click or to the right of a folder or notebook and select Import.
2. Choose File or URL.
3. Go to or drop a Databricks archive in the dropzone.
4. Click Import. The archive is imported into Databricks. If the archive contains a folder, Databricks recreates that folder.

Export an archive

Click or to the right of a folder or notebook and select Export > DBC Archive. Databricks downloads a file named <[folder|notebook]-name>.dbc.