datus-semantic-metricflow
MetricFlow adapter for Datus semantic layer.
Installation
pip install datus-semantic-metricflow
Dependencies (datus-metricflow, pydantic) will be installed automatically.
Requirements
- Python >= 3.12
Quick Start
import asyncio
from datus_semantic_metricflow import MetricFlowAdapter, MetricFlowConfig
config = MetricFlowConfig(
datasource="my_project",
config_path="/path/to/metricflow/config", # optional
)
adapter = MetricFlowAdapter(config)
async def main():
# List metrics
metrics = await adapter.list_metrics(limit=10)
for metric in metrics:
print(f"{metric.name}: {metric.description}")
# Get dimensions for a metric
dimensions = await adapter.get_dimensions("revenue")
for dim in dimensions:
print(f"{dim.name}: {dim.description}")
# Query metrics
result = await adapter.query_metrics(
metrics=["revenue", "orders"],
dimensions=["date", "region"],
limit=100,
)
print(f"Columns: {result.columns}")
print(f"Data: {result.data[:5]}")
asyncio.run(main())
Configuration
| Field | Type | Default | Description |
|---|---|---|---|
datasource |
str | Required | Datasource for this semantic layer instance |
config_path |
str | None | Path to MetricFlow configuration file |
timeout |
int | 300 | Query timeout in seconds |
db_config |
dict | None | Datus datasource config; Snowflake supports password or private_key / private_key_file with private_key_file_pwd plus optional role |
API
list_metrics(path=None, limit=100, offset=0)- List available metricsget_dimensions(metric_name, path=None)- Get dimensions for a metricquery_metrics(metrics, dimensions=[], ...)- Execute metric queriesvalidate_semantic()- Validate configuration files
Development
pip install -e ".[dev]"
pytest
1 maintainer
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