DatabricksTools CLI
A simple commandline application to keep in sync between databricks and your local filesystem. This project uses the databricks workspace api.
Key features:
- List and MkDir commands.
- Download databricks notebooks as HTML, Jupyter, or Source format.
- Upload file from your filesystem (source format) to create/overwrite databricks notebooks.
- Automatically transform markdown files to source!
- Using markdown syntax instead of source is more convenient.
- Use codeblocks to change between languages (e.g.
python
, scala
, sql
, sh
). - Integrate local development and execution with databricks notebooks.
$ databrickstools ls --path /Shared
$ databrickstools upload markdown \
--from-path exploration.md \
--to-path /Shared/merchants/exploration
Setup
Install via pip
:
$ pip install databrickstools
Or clone the repository and install the commandline application!
- Clone:
git clone https://github.com/rhdzmota/databrickstools-cli
- Install:
pip install -e databrickstools-cli
Note: you'll need python 3.6 or greater.
Create the .env
file containing your environment variables.
- Create
.env
following the example .env.example
:
- Load the variables into the environment:
export $(cat .env | xargs)
Note: the .env
file can be on any place of your machine. The commandline application just needs those variables to be present on the environment.
Test with a simple command:
$ databrickstools ls --path /Shared
Use case!
Assume you have a file named exploration.md
with the following markdown content:
# Exporatory Data Analysis
This is a file containing the initial EDA work.
## Setup
```python
import pandas as pd
from sqlalchemy import create_engine
connection_string = "..."
engine = create_engine(connection_string)
```
## Getting the data
Consider the following invalid merchant data:
```python
SQL_INVALID_MERCHANTS = "SELECT * FROM merchants WHERE invalid = 1"
df = pd.read_sql(query, engine)
df.head()
```
Location distribution:
```python
df.groupby("state").size().to_frame()
```
...
You could use rmarkdown + reticulate for local development and execution of that report. And then use databrickstools
to deploy and transform this markdown file into a databricks notebook!
$ databrickstools upload markdown \
--from-path exploration.md \
--to-path /Shared/merchants/exploration \
--base-language python \
--overwrite
Usage
The CLI contains groups
or commands
. A command
is a method call that received zero or more arguments. A group
contains one or more commands
with common functionality.
Available groups:
Top Level Commands
cmd: ls
List the databricks resources for a give path.
$ databrickstools ls --path <value>
Where:
--path
(string)
Path to folder inside databricks.
Example:
$ databrickstools ls --path /Shared
cmd: mkdir
Create a directory on databricks.
$ databrickstools mkdir --path <value>
Where:
--path
(string)
Path to folder inside databricks.
Example:
$ databrickstools mkdir --path /Shared/temp
Group: Download
cmd: file
Download a given file from databricks!
$ databrickstools download file \
--from-path <value> \
--to-path <value> \
--file-format <value>
Where:
--from-path
(string)
Path to file inside databricks.
--to-path
(string)
Path in local machine.
--file-format
(string)
Format used to download file.
Default: DATABRICKSTOOLS_DEFAULT_FORMAT
Example:
databrickstools download file \
--from-path /Shared/example \
--to-path example \
--file-format SOURCE
cmd: html
Download a notebook as an HTML file.
$ databrickstools download html \
--from-path <value> \
--to-path <value>
Where:
--from-path
(string)
Path to file inside databricks.
--to-path
(string)
Path in local machine.
Example:
$ databrickstools download html \
--from-path /Shared/example \
--to-path example.html
cmd: ipynb
Download a notebook as a Jupyter file. Only works for python-based notebooks.
$ databrickstools download ipynb \
--from-path <value> \
--to-path <value>
Where:
--from-path
(string)
Path to file inside databricks.
--to-path
(string)
Path in local machine.
Example:
$ databrickstools download ipynb \
--from-path /Shared/example \
--to-path example.ipynb
cmd: source
Download a notebook as a source file. This can be either a .py
file for Python or a .sc
file for Scala.
$ databrickstools download source \
--from-path <value> \
--to-path <value>
Where:
--from-path
(string)
Path to file inside databricks.
--to-path
(string)
Path in local machine.
Example:
$ databrickstools download source \
--from-path /Shared/example \
--to-path example.py
Group: Upload
cmd: markdown
Upload a markdown file to databricks as a notebook.
$ databrickstools upload markdown \
--from-path <value> \
--to-path <value> \
--base-language <value> \
--overwrite
Where:
--from-path
(string)
Path to file.md
in your local machine.
--to-path
(string)
Path to notebook on databricks.
--base-language
(string)
The markdown might contain multiple languages, but we'll need to define (or know) the base language of the notebook. If not present, the CLI will try to infer the base language by looking into the file-ending or fallback to: DATABRICKSTOOLS_DEFAULT_LANGUAGE
.
--overwrite
(flag)
If present, the new file will overwrite the current one on databricks.
Example:
$ databrickstools markdown \
--from-path markdown-file.md \
--to-path /Shared/test \
--overwrite
cmd: source
Same as markdown
but with the SOURCE
format.
Recommendations
To get the most out of markdown please consider taking a look into: