Data Docs

_quarto.yml

@@ -251,6 +251,7 @@ website:
           contents:
             - docs/overview.qmd
             - docs/user-interfaces.qmd
+            - docs/reading-data.qmd
         - section: "<span class='emoji-icon'>🤖</span> __Generative AI__"
           contents:
             - docs/genai-inspiration.qmd

docs/persistent-storage.qmd

@@ -0,0 +1,139 @@
+---
+title: Persistent storage & writing data
+editor:
+  markdown:
+    wrap: sentence
+lightbox:
+  effect: fade
+---
+
+## Intro
+* Callout connect cloud and support for secrets and persistent storage, link to docs, tehre's a talk that aleks chisolm did at conf
+## When do you actually need persistent storage
+* You want your application to collect and save information from your users. Example, forms.
+* You need to save a result that your users generate? (ex. save a copy of report generated)
+
+Note: What are we not talking about here: Auth. Link to any auth docs we have.
+
+CALLOUT: Connect does both Auth and supports secrets for storage. Link to those docs.
+
+### Example with pins and polars? (use a form, save the result)
+
+TODO: Update this
+```python
+from shiny import App, Inputs, Outputs, Session, ui, reactive, render
+import pandas as pd
+
+app_ui = ui.page_fluid(
+    ui.h2("Submitted Results"),
+    ui.output_data_frame("results_df"),
+    ui.input_text("text", "Enter text", value=""),
+    ui.input_checkbox("checkbox", "I like checkboxes"),
+    ui.input_slider("slider", "My favorite number is:", min=0, max=100, value=50),
+    ui.input_action_button("submit", "Submit"),
+)
+
+
+def server(input: Inputs, output: Outputs, session: Session):
+    df = reactive.value(pd.DataFrame(columns=["text", "checkbox", "slider"]))
+
+    @reactive.effect
+    @reactive.event(input.submit)
+    def results():
+        row = {
+            "text": input.text(),
+            "checkbox": input.checkbox(),
+            "slider": input.slider(),
+        }
+        row_df = pd.DataFrame([row])
+        new_df = pd.concat([df.get(), row_df], ignore_index=True)
+        df.set(new_df)
+
+    @render.data_frame
+    def results_df():
+        return render.DataGrid(df.get())
+
+
+app = App(app_ui, server)
+```
+
+
+### Example with DuckDB (use a form, save the result)
+
+#### DuckDB
+```python
+from shiny import App, Inputs, Outputs, Session, ui, reactive, render
+import duckdb
+
+con = duckdb.connect("test_db.db")
+
+app_ui = ui.page_fluid(
+    ui.h2("Submitted Results"),
+    ui.output_data_frame("results_df"),
+    ui.input_text("text", "Enter text", value=""),
+    ui.input_checkbox("checkbox", "I like checkboxes"),
+    ui.input_slider("slider", "My favorite number is:", min=0, max=100, value=50),
+    ui.input_action_button("submit", "Submit"),
+)
+
+def server(input: Inputs, output: Outputs, session: Session):
+    # Initialize a reactive value so we start with the table visible.
+    df = reactive.value(con.sql("SELECT * FROM data").df())
+
+    @reactive.effect
+    @reactive.event(input.submit)
+    def save():
+        row = [input.text(), input.checkbox(), input.slider()]
+
+        # We use a prepared statement here to avoid SQL injection
+        con.execute("INSERT INTO data VALUES (?, ?, ?)", row)
+        con.commit()
+
+        # Set the reactive value so the table updates
+        df.set(con.sql("SELECT * FROM data").df())
+
+    @render.data_frame
+    def results_df():
+        return df()
+
+
+app = App(app_ui, server, debug=True)
+```
+
+## Connecting to databases
+
+### Example connecting with Ibis and saving to db with a form
+
+## Deployment options
+
+### Auth - connect callout
+
+### SQL injection prevention
+Callout near wherever we first execute SQL, be careful, diff libraries have tooling
+
+
+### Connection pooling & transaction locking?
+
+### Note on credentials? (don't use your admin creds in your app, have user creds for db, scope them narrowly, etc.)
+
+
+
+### Example Applications
+::: {.panel-tabset .panel-pills}
+
+#### Polars
+
+
+#### DuckDB
+Outliers app: Reads and writes from DuckDB https://github.com/skaltman/outliers-app-db-python/blob/main/app.py
+Database Explorer: https://shiny.posit.co/py/templates/database-explorer/
+
+#### Pandas
+AWS-Community-Builders-App: Process data from a CSV and displays it https://github.com/robertgv/aws-community-builders-dashboard
+
+#### Sqlite3
+Database monitoring app: https://github.com/posit-dev/py-shiny-templates/tree/main/monitor-database
+
+
+
+