jupyter-anywidget-pglite 0.1.8

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jupyter_anywidget_pglite

Jupyter anywidget and magic for working with pglite (single use postgres wasm build).

Install from PyPi as: pip install jupyter_anywidget_pglite

Usage:

• import package and magic:

%load_ext jupyter_anywidget_pglite
from jupyter_anywidget_pglite import pglite_panel

pg = pglite_panel()
#This should open a panel in the right-hand sidebar
# (`split-right`) by default.
# Close the panel either manually or via:
# pg.close()

# w = pglite_panel("example panel title)`
# w = pglite_panel(None, "split-bottom")`

# Headless mode (no HTML UI, works in:
# Jupyter Lab, Jupyter Notebook, VS Code w/ Jupyter notebook support)
#from jupyter_anywidget_pglite import pglite_headless
#pg = pglite_headless()

# Inline display
# Display HTML UI as initialising code cell output
# Display will be updated with consequent queries
#from jupyter_anywidget_pglite import pglite_inline
#pg = pglite_inline()

Use pg.ready() / pg.ready(timeout=TIME_IN_S) function that will block until the pglite widget is loaded and ready to accept requests (not JupyterLite).

Persisting data in browser storage

To persist the database in browser storage, set the `idb='DBNAME`` parameter when creating a widget. For example:

pg_headless_persist = pglite_headless(idb="pglitetest1")

Running queries

To run a query, place the query insde a %%pglite cell block magic.

• use the -w / --widget-name setting to set the widget within the magic and it does not need to be passed again (for example, %%pglite -w pg)
• alternatively, prior to calling the block magic, set the widget used in the magic via a line magic: %setwidget pg

Running queries on the database using IPython cell block magic %%pglite WIDGET_VARIABLE:

%%pglite_magic -w pg
CREATE TABLE IF NOT EXISTS test  (
        id serial primary key,
        title varchar not null
      );

#----
%%pglite_magic
INSERT INTO test (title) VALUES ('dummy');

#----
%%pglite_magic
SELECT * FROM test;

To run multiple SQL statements in the same cell:

• use the -m / --multiple-statements flag (default: False) when calling the cell block magic [NOT RECOMMENDED]. This will naively split the query on each ; character, and then run each split item as a separate command. The response will be set to the response from the final query;
• use the -M / --multiple-statement-block flag to run all the tems using the pglite .exec() command.

We can also run queries (with the same arguments) using the %pglite_query line magic, with the query set via the -q / --query parameter:

%pglite_query -r -q 'SELECT * FROM test LIMIT 1;'

Having made a query onto the database via a magic cell, we can retrieve the response:

pg.response

If pandas is installed, we can get rows returned from a query response as a dataframe:

pg.df()

Note that the pglite query runs asynchronously, so how do we know on the Python side when the repsonse is ready?

Using the jupyter_ui_poll package (not JupyterLite), we can run a blocking wait on a response from pglite (not JupyterLite):

response = pg.blocking_reply()

Optionally provide a timeout period (seconds):

response = pg.blocking_reply(timeout=5)

We can also use a blocking trick to return a response from the magic cell (not JupyterLite). Set -r / --response flag when calling the magic. Optionally set the -t / --timeout to the timeout period in seconds (default 5s; if the timeout is explicitly set, -r is assumed): %%pglite -r, %pglite -t 10

Note: I think that IPython notebook cells should have cell run IDs cleared prior to running. I have seen errors if there are non-unique cell run IDs for the blocking cell.

Exporting data to file / reloading from file

Save and load data to / from a file.

We can get an export of the data using the pglite data exporter (.dumpdatadir()) in the database by calling:

pg.create_data_dump()

For a blocking wait until the datadump is read, use pg.create_data_dump(True) or pg.create_data_dump(wait=True) (not JupyterLite). You can also pass a timeout parameter in seconds (wait=True is assumed if the timeout parameter is explicitly set).

After a moment or two, the data will appear in a dictionary on: pg.file_package

If we make a copy of that data, we can then create a new pglite widget with a new pglite instance that can load in the data using the pglite data load option (loadDataDir).

Use the data= argument when creating the widget to pass in the data:

datadump = pg.file_package.copy()
# View info
#datadump["file_info"]
# Actual data is in: datadump["file_content"]
pg1 = pglite_inline(data=datadump)

We can export the datadump to a file using:

pg.save_datadump_to_file()

Or pass a filename: pg.save_datadump_to_file('myfile.tar.gz')

Load data into a datadump object:

from jupyter_anywidget_pglite import load_datadump_from_file

dd = load_datadump_from_file("pgdata.tar.gz")
dd["file_info"]

Or create a new widget with the pglite database seeded from the file:

pg2 = pglite_panel(data="pgdata.tar.gz")

Audible alerts

To provide an audible alert when a query or a data dump generation operation has completed, set: pg.audio = True,

TO DO

• options to display outputs in the panel;
• button to clear input history;
• button to reset database;
• explore possibility of a JuptyerLab extension to load pglite "centrally" and then connect to the same instance from any notebook.