https://github.com/sidequery/sidemantic
A universal metrics layer. Compatible with definitions in LookML, MetricFlow, Cube with DuckDB, Snowflake, Clickhouse, Bigquery & more!
https://github.com/sidequery/sidemantic
ai analytics analytics-engineering bigquery clickhouse data-engineering duckdb mcp postgresql semantic-layer snowflake spark sql visualization
Last synced: 7 days ago
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A universal metrics layer. Compatible with definitions in LookML, MetricFlow, Cube with DuckDB, Snowflake, Clickhouse, Bigquery & more!
- Host: GitHub
- URL: https://github.com/sidequery/sidemantic
- Owner: sidequery
- License: agpl-3.0
- Created: 2025-10-04T20:51:21.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-01-17T16:14:19.000Z (about 1 month ago)
- Last Synced: 2026-01-17T17:53:57.632Z (about 1 month ago)
- Topics: ai, analytics, analytics-engineering, bigquery, clickhouse, data-engineering, duckdb, mcp, postgresql, semantic-layer, snowflake, spark, sql, visualization
- Language: Python
- Homepage: https://sidemantic.com
- Size: 4.53 MB
- Stars: 48
- Watchers: 3
- Forks: 7
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
- Agents: AGENTS.md
Awesome Lists containing this project
- awesome-duckdb - Sidemantic - A semantic layer with DuckDB integration. (Integrations / Web Clients (WebAssembly))
README
# Sidemantic
SQL-first semantic layer for consistent metrics across your data stack. Compatible with many other semantic model formats.
- **Formats:** Sidemantic, Cube, MetricFlow (dbt), LookML, Hex, Rill, Superset, Omni, BSL, GoodData LDM, Snowflake Cortex, Malloy, OSI, AtScale SML, ThoughtSpot TML
- **Databases:** DuckDB, MotherDuck, PostgreSQL, BigQuery, Snowflake, ClickHouse, Databricks, Spark SQL
[Documentation](https://sidemantic.com) | [GitHub](https://github.com/sidequery/sidemantic) | [Discord](https://discord.com/invite/7MZ4UgSVvF) | [Demo](https://sidemantic.com/demo) (50+ MB download)
## Should I use Sidemantic
Sidemantic is a very ambitious and young semantic layer project. You may encounter rough patches, especially with the more exotic features like converting between semantic model formats.
Issue reports are much appreciated if you try out Sidemantic and hit a snag 🫡
## Quickstart
Install:
```bash
uv add sidemantic
```
Malloy support (uv):
```bash
uv add "sidemantic[malloy]"
```
Notebook widget (uv):
```bash
uv add "sidemantic[widget]" jupyterlab
uv run jupyter lab
```
Marimo (uv):
```bash
uv add "sidemantic[widget]" marimo
uv run marimo edit
```
```python
import duckdb
from sidemantic.widget import MetricsExplorer
conn = duckdb.connect(":memory:")
conn.execute("create table t as select 1 as value, 'a' as category, date '2024-01-01' as d")
MetricsExplorer(conn.table("t"), time_dimension="d")
```
Define models in SQL, YAML, or Python:
SQL (orders.sql)
```sql
MODEL (name orders, table orders, primary_key order_id);
DIMENSION (name status, type categorical);
DIMENSION (name order_date, type time, granularity day);
METRIC (name revenue, agg sum, sql amount);
METRIC (name order_count, agg count);
```
YAML (orders.yml)
```yaml
models:
- name: orders
table: orders
primary_key: order_id
dimensions:
- name: status
type: categorical
- name: order_date
type: time
granularity: day
metrics:
- name: revenue
agg: sum
sql: amount
- name: order_count
agg: count
```
Python (programmatic)
```python
from sidemantic import Model, Dimension, Metric
orders = Model(
name="orders",
table="orders",
primary_key="order_id",
dimensions=[
Dimension(name="status", type="categorical"),
Dimension(name="order_date", type="time", granularity="day"),
],
metrics=[
Metric(name="revenue", agg="sum", sql="amount"),
Metric(name="order_count", agg="count"),
]
)
```
Query via CLI:
```bash
sidemantic query "SELECT revenue, status FROM orders" --db data.duckdb
```
Or Python API:
```python
from sidemantic import SemanticLayer, load_from_directory
layer = SemanticLayer(connection="duckdb:///data.duckdb")
load_from_directory(layer, "models/")
result = layer.sql("SELECT revenue, status FROM orders")
```
## CLI
```bash
# Query
sidemantic query "SELECT revenue FROM orders" --db data.duckdb
# Interactive workbench (TUI with SQL editor + charts)
sidemantic workbench models/ --db data.duckdb
# PostgreSQL server (connect Tableau, DBeaver, etc.)
sidemantic serve models/ --port 5433
# Validate definitions
sidemantic validate models/
# Model info
sidemantic info models/
# Pre-aggregation recommendations
sidemantic preagg recommend --db data.duckdb
# Migrate SQL queries to semantic layer
sidemantic migrator --queries legacy/ --generate-models output/
```
## Demos
**Workbench** (TUI with SQL editor + charts):
```bash
uvx sidemantic workbench --demo
```
**PostgreSQL server** (connect Tableau, DBeaver, etc.):
```bash
uvx sidemantic serve --demo --port 5433
```
**Colab notebooks:**
[](https://colab.research.google.com/github/sidequery/sidemantic/blob/main/examples/notebooks/sidemantic_sql_duckdb_demo.ipynb) SQL + DuckDB
[](https://colab.research.google.com/github/sidequery/sidemantic/blob/main/examples/notebooks/lookml_multi_entity_duckdb_demo.ipynb) LookML multi-entity
**SQL syntax:**
```bash
uv run https://raw.githubusercontent.com/sidequery/sidemantic/main/examples/sql/sql_syntax_example.py
```
**Comprehensive demo:**
```bash
uv run https://raw.githubusercontent.com/sidequery/sidemantic/main/examples/advanced/comprehensive_demo.py
```
**Symmetric aggregates:**
```bash
uv run https://raw.githubusercontent.com/sidequery/sidemantic/main/examples/features/symmetric_aggregates_example.py
```
**Superset with DuckDB:**
```bash
git clone https://github.com/sidequery/sidemantic.git && cd sidemantic
uv run examples/superset_demo/run_demo.py
```
**Cube Playground:**
```bash
git clone https://github.com/sidequery/sidemantic.git && cd sidemantic
uv run examples/cube_demo/run_demo.py
```
**Rill Developer:**
```bash
git clone https://github.com/sidequery/sidemantic.git && cd sidemantic
uv run examples/rill_demo/run_demo.py
```
**OSI (complex adtech semantic model):**
```bash
git clone https://github.com/sidequery/sidemantic.git && cd sidemantic
uv run examples/osi_demo/run_demo.py
```
**OSI widget notebook (percent-cell Python notebook):**
```bash
git clone https://github.com/sidequery/sidemantic.git && cd sidemantic
uv run examples/osi_demo/osi_widget_notebook.py
```
See `examples/` for more.
## Core Features
- SQL query interface with automatic rewriting
- Automatic joins across models
- Multi-format adapters (Cube, MetricFlow, LookML, Hex, Rill, Superset, Omni, BSL, GoodData LDM, OSI, AtScale SML, ThoughtSpot TML)
- SQLGlot-based SQL generation and transpilation
- Pydantic validation and type safety
- Pre-aggregations with automatic routing
- Predicate pushdown for faster queries
- Segments and metric-level filters
- Jinja2 templating for dynamic SQL
- PostgreSQL wire protocol server for BI tools
## Multi-Format Support
Auto-detects: Sidemantic (SQL/YAML), Cube, MetricFlow (dbt), LookML, Hex, Rill, Superset, Omni, BSL, GoodData LDM, OSI, AtScale SML, ThoughtSpot TML
```bash
sidemantic query "SELECT revenue FROM orders" --models ./my_models
```
```python
from sidemantic import SemanticLayer, load_from_directory
layer = SemanticLayer(connection="duckdb:///data.duckdb")
load_from_directory(layer, "my_models/") # Auto-detects formats
```
## Databases
| Database | Status | Installation |
|----------|:------:|--------------|
| DuckDB | ✅ | built-in |
| MotherDuck | ✅ | built-in |
| PostgreSQL | ✅ | `uv add sidemantic[postgres]` |
| BigQuery | ✅ | `uv add sidemantic[bigquery]` |
| Snowflake | ✅ | `uv add sidemantic[snowflake]` |
| ClickHouse | ✅ | `uv add sidemantic[clickhouse]` |
| Databricks | ✅ | `uv add sidemantic[databricks]` |
| Spark SQL | ✅ | `uv add sidemantic[spark]` |
## Testing
```bash
uv run pytest -v
```