Exploring the Data model

There are several options in python for exploring a data model. Here are some examples of their usage:

Views

When listing views, the default is to return a list of View objects. More information is available in the view section of the docs

from metrics_layer import MetricsLayerConnection
# Connect to the repo we're at the root of right nowconn = MetricsLayerConnection('./')
# Lists of *all* the views in your data modelviews = conn.list_views()
# You can also get a single view based on it's name.view = conn.get_view("order_lines")

Metrics

When listing metrics, the default is to return a list of Field objects. Listing metrics will return all measures associated with your data model.

from metrics_layer import MetricsLayerConnection
# Connect to the repo we're at the root of right nowconn = MetricsLayerConnection('./')
# Lists of *all* the metrics in your data modelmetrics = conn.list_metrics()
# List of metrics in this viewmetrics_in_orders_customers_view = conn.list_metrics(view_name="customers")
# You can also get a single metric based on it's name.# The below three calls return the same thing
# Metric namemetric = conn.get_metric("total_revenue")
# View and metric namemetric = conn.get_metric("orders.total_revenue")

Dimensions

When listing dimensions, like listing metrics, the default is to return a list of Field objects. Listing dimensions will return all dimensions and dimension_groups associated with your data model.

# Lists of *all* the dimensions in your data modeldimensions = conn.list_dimensions()

# List of dimensions in this viewdimensions_in_orders_customers_view = conn.list_dimensions(view_name="customers")
# You can also get a single dimension based on it's name.# The below three calls return the same thing
# Dimension namedimension = conn.get_dimension("total_revenue")
# View and dimension namedimension = conn.get_dimension("orders.total_revenue")