dashml-lang 0.1.2

Declarative YAML-based compiler for data visualization dashboards. Write your dashboard once, compile to Streamlit, Plotly, Observable Plot, Apache Superset, Vega-Lite, or Grafana.

DashML

A declarative language for data visualization dashboards that compiles to multiple platforms.

Write your dashboard spec once in .dashml format, then compile it to Streamlit, Plotly, Observable Plot, Apache Superset, Vega-Lite, or Grafana.

Quick Start

# Generate a Streamlit app
python -m dashml_new.cli build dashboard.dashml --target streamlit --output app_dir
streamlit run app_dir/app.py

# Generate a standalone Plotly HTML dashboard
python -m dashml_new.cli build dashboard.dashml --target plotly --output dashboard.html

# Generate an Observable Plot HTML dashboard
python -m dashml_new.cli build dashboard.dashml --target observable --output dashboard.html

# Generate a Vega-Lite JSON specification
python -m dashml_new.cli build dashboard.dashml --target vegalite --output dashboard.json

# Generate a Grafana dashboard JSON
python -m dashml_new.cli build dashboard.dashml --target grafana --output dashboard.json

# Create a dashboard directly in Apache Superset
python -m dashml_new.cli build dashboard.dashml --target superset \
  --superset-user admin --superset-password admin

Database credentials

For SQL or BigQuery data sources, the generated artifact reads connection parameters from environment variables at runtime — never from baked-in literals. The generator emits .env.example, .gitignore, and SECRETS.md next to the generated app.py. Copy .env.example to .env, fill in the values, and run. The same artifact directory is safe to commit to a public repository.

python -m dashml_new.cli build dashboard.dashml --target plotly --output app_dir
cd app_dir
cp .env.example .env       # edit .env, set DASHML_DB_PASSWORD etc.
python app.py              # reads from env (or .env via python-dotenv)

See SECRETS.md in any generated SQL/BigQuery artifact for the full list of environment variables and recommended deployment patterns (Docker, Kubernetes, systemd, CI/CD, GCP Workload Identity).

Example

version: 0.1
title: "Sales Dashboard"
style: "styles/dracula.dmls"

data:
  type: csv
  path: "data/sales.csv"

charts:
  - id: "sales_by_country"
    type: "bar"
    title: "Sales by Country"
    x: "country"
    y: "sales"
    agg: "sum"
    sort: "y"
    sort_order: "desc"
    limit: 10

Documentation

• User Guide - Full specification reference, chart types, CLI, themes
• Architecture - System design, compiler pipeline, type system

Key Concepts

• Compiler, not runtime - DashML generates standalone code; it never loads or touches your data
• Credentials never baked - generated SQL/BigQuery artifacts read database credentials from environment variables at runtime; safe to version-control
• 12 chart types - bar, line, scatter, pie, area, histogram, stacked_bar, grouped_bar, bubble, heatmap, box, geo
• 3 data sources - CSV, SQL (PostgreSQL/MySQL/SQLite), Google BigQuery
• 6 backends - Streamlit, Plotly, Observable Plot, Apache Superset, Vega-Lite, Grafana
• 6 built-in themes - Dracula, Nord, Gruvbox, Monokai, One Dark, Solarized Light

Authors

• Dawid Olejniczak
• Szymon Nowaczyk

License

MIT