Dash Enterprise’s persistent filesystem feature provides persistent, fast storage for data files for apps and workspaces.
In many cases, it is faster to store a copy of your data close to your app rather than download it on-the-fly
within your app code. When these datasets are large, it isn’t always possible to store them in git or in memory.
Dash Enterprise’s persistent filesystem allows you to store these datasets in a filesystem that is shared
between your workspace and your deployed app (including every process declared in your Procfile).
Use a persistent filesystem to store:
- Large datasets that are too big to store in Git and/or in memory. In particular, HDF5 or Parquet files analyzed with Vaex (a file-based, out-of-memory dataframe library).
- Cached files that can be persisted across deploys.
- Dynamic datasets that are updated by periodic background processes and read by your app.
- Manually updated datasets that you update in your workspace without redeploying your app.
Each app and its workspace share a persistent filesystem. The persistent filesystem is retained even if you delete the workspace.
In summary, files stored in the persistent filesystem folder are:
- Shared between the deployed app and the workspace
- Shared between every container process listed in the Procfile
- Not version controlled in Git
- Located at ~/mount or at ../mount relative to the app folder
- Limited to 25 GB in total
- Unique to the app and workspace. Not shared with other apps or workspaces.
- Persisted to a persistent volume internally
This page describes how to enable and disable the persistent filesystem using the App Manager, but you can also use the Dash Enterprise CLI.
To enable a persistent filesystem:
Find and select the app in the App Manager.
Go to the Persistent Filesystem tab.
Select Edit Persistent Filesystem.
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Select Enabled and then Save.
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The persistent filesystem becomes available to your app and its workspace.
When you disable a persistent filesystem, all files stored in it are removed and are not recoverable.
To disable a persistent filesystem:
Find and select the app in the App Manager.
Go to the Persistent Filesystem tab.
Select Edit Persistent Filesystem.
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Select Disabled and then Save.
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Files can be added to a persistent filesystem manually within the workspace or
dynamically on deployment or in a background task in your app code.
To add files to the persistent filesystem in the workspace UI:
Select the persistent filesystem from the menu:
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Drag files from your device to the file explorer (you’ll see a README.md file already there).
You can also add files by going to File > Upload Files… and selecting files to upload.
The persistent filesystem is available at ../mount.
To add files to the persistent filesystem from the workspace terminal, you can run commands like curl
that retrieve data from a URL and save it to a csv file in the persistent filesystem.
curl -o ../mount/1962_2006_walmart_store_openings.csv <a href="https://raw.githubusercontent.com/plotly/datasets/master/1962_2006_walmart_store_openings.csv">https://raw.githubusercontent.com/plotly/datasets/master/1962_2006_walmart_store_openings.csv</a>
You can also open a Python or IPython console within the workspace and write code that downloads data and writes it to a file:
$ python
>>> import pandas as pd
>>> pd.DataFrame({'a': [1, 2, 3], 'b': [3, 1, 2]}).to_csv('../mount/data.csv')
Or run a script or Jupyter notebook within the workspace.
Instead of updating files manually within the workspace, you can update these files programmatically on deploy.
On App Boot
In your app code, your app can write or read files to the ../mount folder on boot:
app.py
from dash import Dash, html
import pandas as pd
# Write and/or read files from mount on app start
pd.DataFrame({'a': [1, 2, 3], 'b': [3, 1, 2]}).to_csv('../mount/data.csv')
df = pd.read_csv('../mount/data.csv')
app = Dash()
# ...
In Predeploy Script
Alternatively, you can run a Bash script before the web command is run by creating
a predeploy script that contains commands to fetch the data
and reference the script in a project.toml file.
Here, we add the command we ran in the terminal to a file fetchdata.sh, and then reference that file
under predeploy in our project.toml file. The system runs fetchdata.sh as a Bash script
before our app is deployed.
fetchdata.sh
curl -o ../mount/gapminderDataFiveYear.csv <a href="https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv">https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv</a>
project.toml
[scripts]
predeploy = "fetchdata.sh"
Update files while the app is running if your datasets change over time.
To add or update a file when an app is running:
Write a script that creates or updates a file in the persistent filesystem.
Add the script in your Procfile.
Here, we write data periodically to a CSV file in the persistent filesystem in task.py.
The app, app.py, reads the data when an app user selects the Get Data button.
The Procfile has a line worker: python task.py that runs the task.py script in the background.
app.py
This example has not been ported to Julia yet - showing the Python version instead.
Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20
from dash import Dash, html, dcc, Input, Output, callback
import plotly.express as px
import pandas as pd
from pathlib import Path
app = Dash()
server = app.server
app.layout = html.Div(
children=[
html.Button("Get Data", id="get-data", n_clicks=0),
dcc.Graph(
id="graph",
)
]
)
@callback(
Output("graph", "figure"),
Input("get-data", "n_clicks"),
)
def update_output(n_clicks):
data_path = Path.cwd().parent / 'mount' / 'data.csv'
data = pd.read_csv(data_path)
figure = px.scatter(data, x="x", y="y")
return figure
if __name__ == "__main__":
app.run(debug=True)
task.py
This example has not been ported to Julia yet - showing the Python version instead.
Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20
## Writes random numbers to data.csv every 2 seconds
import random
import time
from pathlib import Path
data_path = Path.cwd().parent / 'mount' / 'data.csv'
with open(data_path, 'w+') as f:
f.write(('x,y\n'))
while True:
with open(data_path, 'a') as f:
x = (random.randint(0,9))
y = (random.randint(0,100))
f.write((str(x) + ',' + str(y) + '\n'))
time.sleep(2)
Procfile
web: gunicorn app:server --workers 4
worker: python task.py
Note that your app code will need to read the file on-the-fly in callback functions or a layout function
when it needs to use the data; if you load the data in advance on app boot, the data will only be read
into memory when the app is deployed and will not read the updated file until the app is restarted or redeployed.
The persistent filesystem is available at ~/mount or ../mount relative to your app folder.
Known issue: When Dash Enterprise is installed on a multi-node cluster, deployed apps are unable to read or write to the persistent filesystem folder if the apps don’t have a workspace. To work around this issue, create a workspace for the app.
To access a file in the persistent filesystem, you can use any of Python’s built-in modules and functions for handling files.
These are all valid ways to read files from the persistent filesystem:
import pandas as pd
df = pd.read_csv('../mount/gapminderDataFiveYear.csv')
or using pathlib to construct the path of the file:
from pathlib import Path
import pandas as pd
gapminder_path = Path.cwd().parent / 'mount' / 'gapminderDataFiveYear.csv'
df = pd.read_csv(gapminder_path)
If your dataset is too large to fit in Git, it may also be too large to fit in memory.
In this case, the Vaex library can be a good alternative to pandas.
Store your datasets as HDF5 files in the ../mount folder and use vaex instead of pandas
to process the data. Vaex processes the data “row by row” instead of reading the file
all at once into memory.
The mount folder is not meant to be version controlled and so should not be part of
your app project folder. To run your app locally, you will need to
mimic the mount folder structure of Dash Enterprise by creating a mount folder
that is a sibling to your app project folder.
Without a ../mount folder, your project code might look like this:
└── my-project
├── Procfile
├── app.py
├── data.csv
└── requirements.txt
where the root of my-project is where you run git commands (the entire folder is version controlled).
To use the ../mount folder, move the contents of my-project into a folder called app/ that
is on the same level as the new mount/ folder:
└── my-project
├── app
│ ├── Procfile
│ ├── app.py
│ └── requirements.txt
└── mount
└── data.csv
With this structure, you run git commands at app/, ensuring that the app files
remain version controlled but mount/ is not.
Alternatively, you can keep the my-project/ folder name and create a new parent folder like
my-parent-project:
└── my-parent-project
├── my-project
| ├── Procfile
| ├── app.py
| └── requirements.txt
└── mount
└── data.csv
With this structure, you run git commands at my-project/, ensuring that the app files
remain version controlled but mount/ is not.
The folder containing the app code (app/ in the first example, my-project/ in the second example)
should not be referenced in your code so it can be named anything.
The mount/ folder’s name is referenced in code, so it needs to be called mount.
Its position is also referenced (app.py accesses it at ../mount), so it always needs to a sibling of your app folder.
The files in the app folder that you commit and push to Dash Enterprise are not persisted between deploys.
On each deploy, Dash Enterprise creates a new Docker image using the most recent commit’s files
and discards the previous image.
Subsequent deploys use a new image with the new set of files and
any changes to the app folder in the previous deploy are discarded.
Because the files in the persistent filesystem are not version controlled (you only run git commands against
your app folder), the files in the persistent filesystem persist across app deployments.
The persistent filesystem is created using a persistent volume.
This persistent volume creates a volume on the cloud provider that Dash Enterprise is running on (AWS, Azure, or GCP).
The persistent volume uses the default storage class of the cloud provider:
- AWS: EBS gp2 general purpose solid state drive (SSD)
- Azure: Azure Disk Standard solid state drive (SSD)
- GCP: pd-standard hard disk (HDD)