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Python SDK Preview

The dbt-sl-sdk Python software development kit (SDK) is a Python library that provides you with easy access to the dbt Semantic Layer with Python. It allows developers to interact with the dbt Semantic Layer APIs and query metrics and dimensions in downstream tools.

Installation

To install the Python SDK, you'll need to specify optional dependencies depending on whether you want to use it synchronously, backed by requests, or with asynchronous (asyncio backed by aiohttp).

The Python SDK supports the Long-Term Support (LTS) versions of Python, such as 3.9, 3.10, 3.11, and 3.12. When Python discontinues support for a version, the Python SDK will also discontinue support for that version. If you’re using a non-supported version, you may experience compatibility issues and won’t receive updates or security patches from the SDK.

Sync installation means your program waits for each task to finish before moving on to the next one.

It's simpler, easier to understand, and suitable for smaller tasks or when your program doesn't need to handle many tasks at the same time.

pip install dbt-sl-sdk[sync]

If you're using async frameworks like FastAPI or Strawberry, installing the sync version of the SDK will block your event loop and can significantly slow down your program. In this case, we strongly recommend using async installation.

Usage

To run operations against the Semantic Layer APIs, instantiate (create an instance of) a SemanticLayerClient with your specific API connection parameters:

from dbtsl import SemanticLayerClient

client = SemanticLayerClient(
environment_id=123,
auth_token="<your-semantic-layer-api-token>",
host="semantic-layer.cloud.getdbt.com",
)

# query the first metric by `metric_time`
def main():
with client.session():
metrics = client.metrics()
table = client.query(
metrics=[metrics[0].name],
group_by=["metric_time"],
)
print(table)

main()

Note: All method calls that reach out to the APIs need to be within a client.session() context manager. This allows the client to establish a connection to the APIs only once and reuse the same connection between API calls.

We recommend creating an application-wide session and reusing the same session throughout the application for optimal performance. Creating a session per request is discouraged and inefficient.

asyncio usage

If you're using asyncio, import AsyncSemanticLayerClient from dbtsl.asyncio. The SemanticLayerClient and AsyncSemanticLayerClient APIs are identical, but the async version has async methods that you need to await.

import asyncio
from dbtsl.asyncio import AsyncSemanticLayerClient

client = AsyncSemanticLayerClient(
environment_id=123,
auth_token="<your-semantic-layer-api-token>",
host="semantic-layer.cloud.getdbt.com",
)

async def main():
async with client.session():
metrics = await client.metrics()
table = await client.query(
metrics=[metrics[0].name],
group_by=["metric_time"],
)
print(table)

asyncio.run(main())

Integrate with dataframe libraries

The Python SDK returns all query data as pyarrow tables.

The Python SDK library doesn't come bundled with Polars or Pandas. If you use these libraries, add them as dependencies in your project.

To use the data with libraries like Polars or Pandas, manually convert the data into the desired format. For example:

If you're using pandas

# ... initialize client

arrow_table = client.query(...)
pandas_df = arrow_table.to_pandas()

If you're using polars

import polars as pl

# ... initialize client

arrow_table = client.query(...)
polars_df = pl.from_arrow(arrow_table)

Usage examples

For additional usage examples, check out the usage examples, some of which include:

Disable telemetry

By default, the Python SDK sends some platform-related information to dbt Labs. To opt-out, set the PLATFORM.anonymous attribute to True:

from dbtsl.env import PLATFORM
PLATFORM.anonymous = True

# ... initialize client

Contribute

To contribute to this project, check out our contribution guidelines and open a GitHub issue or pull request.

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