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Build BSL semantic models with dimensions, measures, joins, and YAML config. Use for creating/modifying data models.
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Baseado na classificação ocupacional SOC
| name | bsl-model-builder |
| description | Build BSL semantic models with dimensions, measures, joins, and YAML config. Use for creating/modifying data models. |
You are an expert at building semantic models using the Boring Semantic Layer (BSL).
A Semantic Table transforms a raw Ibis table into a reusable data model:
from boring_semantic_layer import to_semantic_table
# Start with an Ibis table
flights_st = to_semantic_table(flights_tbl, name="flights")
Define groupable attributes using lambda, unbound syntax (_.), or Dimension class:
from ibis import _
from boring_semantic_layer import Dimension
flights_st = flights_st.with_dimensions(
# Lambda - explicit
origin=lambda t: t.origin,
# Unbound syntax - concise
destination=_.dest,
year=_.year,
# Dimension class - with description (AI-friendly)
carrier=Dimension(
expr=lambda t: t.carrier,
description="Airline carrier code"
)
)
Use .truncate() for time-based groupings:
flights_st = flights_st.with_dimensions(
# Year, Quarter, Month, Week, Day
arr_year=lambda t: t.arr_time.truncate("Y"),
arr_month=lambda t: t.arr_time.truncate("M"),
arr_date=lambda t: t.arr_time.truncate("D"),
)
Truncate units: "Y" (year), "Q" (quarter), "M" (month), "W" (week), "D" (day), "h", "m", "s"
Define aggregations using lambda or Measure class:
from boring_semantic_layer import Measure
flights_st = flights_st.with_measures(
# Simple aggregations
flight_count=lambda t: t.count(),
total_distance=lambda t: t.distance.sum(),
avg_delay=lambda t: t.dep_delay.mean(),
max_delay=lambda t: t.dep_delay.max(),
# Composed measures (reference other measures)
avg_distance_per_flight=lambda t: t.total_distance / t.flight_count,
# Measure class - with description
avg_distance=Measure(
expr=lambda t: t.distance.mean(),
description="Average flight distance in miles"
)
)
Use t.all() to reference the entire dataset:
flights_st = flights_st.with_measures(
flight_count=lambda t: t.count(),
market_share=lambda t: t.flight_count / t.all(t.flight_count) * 100
)
# One carrier has many flights
flights_with_carriers = flights_st.join_many(
carriers_st,
lambda f, c: f.carrier == c.code
)
# Each flight has exactly one carrier
flights_with_carrier = flights_st.join_one(
carriers_st,
lambda f, c: f.carrier == c.code
)
all_combinations = flights_st.join_cross(carriers_st)
flights_st.join(
carriers_st,
lambda f, c: f.carrier == c.code,
how="left" # "inner", "left", "right", "outer", "cross"
)
After joins: Fields are prefixed with table names (e.g., flights.origin, carriers.name)
Multiple joins to same table: Use .view() to create distinct references:
pickup_locs = to_semantic_table(locs_tbl.view(), "pickup_locs")
dropoff_locs = to_semantic_table(locs_tbl.view(), "dropoff_locs")
Define models in YAML for better organization:
# flights_model.yaml
profile: my_db # Optional: use a profile for connections
flights:
table: flights_tbl
dimensions:
origin: _.origin
destination: _.dest
carrier: _.carrier
arr_year: _.arr_time.truncate("Y")
measures:
flight_count: _.count()
total_distance: _.distance.sum()
avg_distance: _.distance.mean()
carriers:
table: carriers_tbl
dimensions:
code: _.code
name: _.name
measures:
carrier_count: _.count()
YAML uses unbound syntax only (_.field), not lambdas.
from boring_semantic_layer import from_yaml
# With profile (recommended)
models = from_yaml("flights_model.yaml")
# With explicit tables
models = from_yaml(
"flights_model.yaml",
tables={"flights_tbl": flights_tbl, "carriers_tbl": carriers_tbl}
)
flights_sm = models["flights"]
flights_st = flights_st.with_dimensions(
# Extract from timestamp
arr_year=lambda t: t.arr_time.truncate("Y"),
arr_month=lambda t: t.arr_time.truncate("M"),
# Categorize numeric values (use ibis.cases - PLURAL, not ibis.case)
distance_bucket=lambda t: ibis.cases(
(t.distance < 500, "Short"),
(t.distance < 1500, "Medium"),
else_="Long"
)
)
flights_st = flights_st.with_measures(
total_flights=lambda t: t.count(),
delayed_flights=lambda t: (t.dep_delay > 0).sum(),
delay_rate=lambda t: t.delayed_flights / t.total_flights * 100
)
Available documentation: