Training
Module
Use Spark Notebooks in an Azure Synapse Pipeline - Training
This module describes how Apache Spark notebooks can be integrated into an Azure Synapse Analytics pipeline.
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This page describes how to develop code in Databricks notebooks, including autocomplete, automatic formatting for Python and SQL, combining Python and SQL in a notebook, and tracking the notebook version history.
For more details about advanced functionality available with the editor, such as autocomplete, variable selection, multi-cursor support, and side-by-side diffs, see Navigate the Databricks notebook and file editor.
When you use the notebook or the file editor, Databricks Assistant is available to help you generate, explain, and debug code. See Use Databricks Assistant for more information.
Databricks notebooks also include a built-in interactive debugger for Python notebooks. See Debug notebooks.
With Databricks Runtime 11.3 LTS and above, you can create and manage source code files in the Azure Databricks workspace, and then import these files into your notebooks as needed.
For more information on working with source code files, see Share code between Databricks notebooks and Work with Python and R modules.
Azure Databricks provides tools that allow you to format Python and SQL code in notebook cells quickly and easily. These tools reduce the effort to keep your code formatted and help to enforce the same coding standards across your notebooks.
Important
This feature is in Public Preview.
Azure Databricks supports Python code formatting using black within the notebook. The notebook must be attached to a cluster with black
and tokenize-rt
Python packages installed.
On Databricks Runtime 11.3 LTS and above, Azure Databricks preinstalls black
and tokenize-rt
. You can use the formatter directly without needing to install these libraries.
On Databricks Runtime 10.4 LTS and below, you must install black==22.3.0
and tokenize-rt==4.2.1
from PyPI on your notebook or cluster to use the Python formatter. You can run the following command in your notebook:
%pip install black==22.3.0 tokenize-rt==4.2.1
or install the library on your cluster.
For more details about installing libraries, see Python environment management.
For files and notebooks in Databricks Git folders, you can configure the Python formatter based on the pyproject.toml
file. To use this feature, create a pyproject.toml
file in the Git folder root directory and configure it according to the Black configuration format. Edit the [tool.black] section in the file. The configuration is applied when you format any file and notebook in that Git folder.
You must have CAN EDIT permission on the notebook to format code.
Azure Databricks uses the Gethue/sql-formatter library to format SQL and the black code formatter for Python.
You can trigger the formatter in the following ways:
Format a single cell
%sql
language magic.%python
language magic.Format multiple cells
Select multiple cells and then select Edit > Format Cell(s). If you select cells of more than one language, only SQL and Python cells are formatted. This includes those that use %sql
and %python
.
Format all Python and SQL cells in the notebook
Select Edit > Format Notebook. If your notebook contains more than one language, only SQL and Python cells are formatted. This includes those that use %sql
and %python
.
The default language for the notebook appears next to the notebook name.
To change the default language, click the language button and select the new language from the dropdown menu. To ensure that existing commands continue to work, commands of the previous default language are automatically prefixed with a language magic command.
By default, cells use the default language of the notebook. You can override the default language in a cell by clicking the language button and selecting a language from the dropdown menu.
Alternately, you can use the language magic command %<language>
at the beginning of a cell. The supported magic commands are: %python
, %r
, %scala
, and %sql
.
Note
When you invoke a language magic command, the command is dispatched to the REPL in the execution context for the notebook. Variables defined in one language (and hence in the REPL for that language) are not available in the REPL of another language. REPLs can share state only through external resources such as files in DBFS or objects in object storage.
Notebooks also support a few auxiliary magic commands:
%sh
: Allows you to run shell code in your notebook. To fail the cell if the shell command has a non-zero exit status, add the -e
option. This command runs only on the Apache Spark driver, and not the workers. To run a shell command on all nodes, use an init script.%fs
: Allows you to use dbutils
filesystem commands. For example, to run the dbutils.fs.ls
command to list files, you can specify %fs ls
instead. For more information, see Work with files on Azure Databricks.%md
: Allows you to include various types of documentation, including text, images, and mathematical formulas and equations. See the next section.Syntax highlighting and SQL autocomplete are available when you use SQL inside a Python command, such as in a spark.sql
command.
In a Databricks notebook, results from a SQL language cell are automatically made available as an implicit DataFrame assigned to the variable _sqldf
. You can then use this variable in any Python and SQL cells you run afterward, regardless of their position in the notebook.
Note
This feature has the following limitations:
_sqldf
variable is not available in notebooks that use a SQL warehouse for compute._sqldf
in subsequent Python cells is supported in Databricks Runtime 13.3 and above._sqldf
in subsequent SQL cells is only supported on Databricks Runtime 14.3 and above.CACHE TABLE
or UNCACHE TABLE
, the _sqldf
variable is not available.The screenshot below shows how _sqldf
can be used in subsequent Python and SQL cells:
Important
The variable _sqldf
is reassigned each time a SQL cell is run. To avoid losing reference to a specific DataFrame result, assign it to a new variable name before you run the next SQL cell:
new_dataframe_name = _sqldf
ALTER VIEW _sqldf RENAME TO new_dataframe_name
While a command is running and your notebook is attached to an interactive cluster, you can run a SQL cell simultaneously with the current command. The SQL cell is executed in a new, parallel session.
To execute a cell in parallel:
Click Run now. The cell is immediately executed.
Because the cell is run in a new session, temporary views, UDFs, and the implicit Python DataFrame (_sqldf
) are not supported for cells that are executed in parallel. In addition, the default catalog and database names are used during parallel execution. If your code refers to a table in a different catalog or database, you must specify the table name using three-level namespace (catalog
.schema
.table
).
You can run SQL commands in a Databricks notebook on a SQL warehouse, a type of compute that is optimized for SQL analytics. See Use a notebook with a SQL warehouse.
Training
Module
Use Spark Notebooks in an Azure Synapse Pipeline - Training
This module describes how Apache Spark notebooks can be integrated into an Azure Synapse Analytics pipeline.
Documentation
Introduction to Databricks notebooks - Azure Databricks
Learn what an Azure Databricks notebook is, and how to use and manage notebooks to process, analyze, and visualize your data.
Basic editing in Databricks notebooks - Azure Databricks
Learn how to edit Databricks notebooks.
Navigate the Databricks notebook and file editor - Azure Databricks
Learn to use the notebook editor based on VS Code, supporting code suggestions and autocomplete, variable inspection, code folding, and diffs.