bsl-model-builder

name	bsl-model-builder
description	Build BSL semantic models with dimensions, measures, joins, and YAML config. Use for creating/modifying data models.

BSL Model Builder

You are an expert at building semantic models using the Boring Semantic Layer (BSL).

Core Concepts

A Semantic Table transforms a raw Ibis table into a reusable data model:

• Dimensions: Attributes to group by (categorical data)
• Measures: Aggregations and calculations (quantitative data)

Creating a Semantic Table

from boring_semantic_layer import to_semantic_table

# Start with an Ibis table
flights_st = to_semantic_table(flights_tbl, name="flights")

with_dimensions()

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"
    )
)

Time Dimensions

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"

with_measures()

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"
    )
)

Percent of Total with all(ref)

Pass a declared measure or reduction to t.all(...) to reference its value over the entire dataset. There is no zero-argument t.all() form:

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
)

Joins

join_many() - One-to-Many (LEFT JOIN)

# One carrier has many flights
carriers_with_flights = carriers_st.join_many(
    flights_st,
    lambda c, f: c.code == f.carrier
)

join_one() - At-Most-One Right Match (LEFT JOIN)

# Each flight matches at most one carrier
flights_with_carrier = flights_st.join_one(
    carriers_st,
    lambda f, c: f.carrier == c.code
)

join_cross() - Cartesian Product

all_combinations = flights_st.join_cross(carriers_st)

Join Predicates and Source-Aware Aggregation

join_one() and join_many() are left joins. Their on= argument can be a column-name string, Deferred key, sequence of equality keys, or a lambda whose predicate is a direct field equality (or conjunction of direct field equalities). Source-aware aggregation rejects inequality, OR, cast, and transformed join predicates because pre-aggregating those predicates from key columns could change the matched rows. Aggregate each model first or restate the relationship as equality keys. Use join_cross() for Cartesian products.

After joins: Fields are prefixed with table names (e.g., flights.origin, carriers.name)

Unique aliases are required: Every base model in a composed join tree must have a distinct name. When the same physical table plays multiple roles, use both .view() and explicit model aliases:

pickup_locs = to_semantic_table(locs_tbl.view(), "pickup_locs")
dropoff_locs = to_semantic_table(locs_tbl.view(), "dropoff_locs")

YAML Configuration

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.

Loading YAML Models

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"]

Best Practices

1. Add descriptions to dimensions/measures for AI-friendly models
2. Use meaningful names that reflect business concepts
3. Define composed measures to avoid repetition
4. Use YAML for production models (version control, collaboration)
5. Use profiles for database connections (see Profile docs)
6. Choose join cardinality from the left side and give every joined source a unique name

Common Patterns

Derived Dimensions

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"
    )
)

Ratio Measures

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
)

Additional Information

Available documentation:

• Getting Started: Introduction to BSL, installation, and basic usage with semantic tables
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/getting-started.md
• Semantic Tables: Building semantic models with dimensions, measures, and expressions
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/semantic-table.md
• YAML Configuration: Defining semantic models in YAML files for better organization
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/yaml-config.md
• Profiles: Database connection profiles for connecting to data sources
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/profile.md
• Composing Models: Joining multiple semantic tables together
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/compose.md
• Query Methods: Complete API reference for group_by, aggregate, filter, order_by, limit, mutate
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/query-methods.md
• Window Functions: Running totals, moving averages, rankings, lag/lead, and cumulative calculations
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/windowing.md
• Bucketing with Other: Create categorical buckets and consolidate long-tail into 'Other' category
  • URL: https://github.com/boringdata/boring-semantic-layer/blob/main/docs/md/doc/bucketing.md
• Nested Subtotals: Rollup calculations with subtotals at each grouping level