edit_model

Edit an existing model in a single call — update metadata, upsert columns/measures/aggregations/joins, manage filters, and remove entities.

Host a column/measure on the model whose row grain is 1:1 with what it describes — not merely one where its input columns live. Choose join keys by column Description (author intent); on ties take the shortest declared join path (long chains through lookup/log tables fan out rows). Encode definitions in dependency order, referencing already-defined entities by name rather than re-deriving them inline; in row-level SQL parenthesise weighted sums in comparisons ((a*w1 + b*w2) > t).

Args: model_name: Name of the model to edit. description: New model description. data_source: Lookup key — the datasource the model belongs to. Required when the same name exists in multiple datasources (otherwise the priority list / single-match rules apply). new_data_source: Move the model to a different datasource (rare; renames its storage location). Pass None (default) to leave the data_source unchanged. default_time_dimension: Default time dimension (a column of type date/time) for time-dependent transforms. sql_table: Database table name. Setting this clears sql and source_queries. sql: Custom SQL expression for the model source. Setting this clears sql_table and source_queries. source_queries: Replace the model's backing query with this list of stages. Each stage is a SlayerQuery dict; non-final stages must have a name. Setting this clears sql_table and sql, makes the model query-backed, and refreshes the cached columns and backing_query_sql. query_variables: Replace the model's default {var} placeholder values for its backing query. Pass null/None to clear. Only meaningful for query-backed models. hidden: Whether this model is hidden from discovery. meta: Arbitrary JSON metadata for the model (replaces existing meta). Pass null/None to clear. columns: Columns to create or update (upsert by name). Each dict: {"name": "col", "type": "string", "sql": "col", "description": "...", "primary_key": false, "unique": false, "hidden": false, "allowed_aggregations": ["sum", "avg"], "filter": "status = 'active'", "label": "..."}. If a column with this name exists, only the provided fields are updated. Types: string, number, time, date, boolean. unique marks single-column uniqueness that is not the primary key (primary_key already implies it); it is used to infer join cardinality. measures: Named formula measures to create or update (upsert by name). Each dict: {"name": "aov", "formula": "sum(revenue) / count(*)", "label": "...", "description": "...", "meta": {...}}. Queries can reference these by bare name (e.g. {"formula": "aov"}). meta is an optional opaque dict for caller bookkeeping. aggregations: Aggregations to create or update (upsert by name). Each dict: {"name": "weighted_avg", "formula": "SUM({value} * {weight}) / NULLIF(SUM({weight}), 0)", "params": [{"name": "weight", "sql": "quantity"}], "description": "...", "meta": {...}}. meta is an optional opaque dict for caller bookkeeping. joins: Joins to create or update (upsert by target_model). Each dict: {"target_model": "customers", "join_pairs": [["customer_id", "id"]], "cardinality": "many_to_one", "description": "...", "meta": {...}}. A composite key is one join with several join_pairs entries, not one join per column. cardinality is the join's arity read source->target, one of one_to_one / one_to_many / many_to_one / many_to_many; omit it when undetermined. It is descriptive metadata only — it changes neither join_type nor query results. add_filters: SQL filter strings to add (e.g. ["deleted_at IS NULL"]). Duplicates ignored. remove_filters: SQL filter strings to remove (exact match). remove: Named entities to delete, keyed by type: {"columns": ["col_name"], "measures": ["measure_name"], "aggregations": ["agg_name"], "joins": ["target_model_name"]}. Removals are processed before upserts.

Example — update a column and add a named measure: edit_model(model_name="orders", columns=[{"name": "status", "type": "string"}], measures=[{"name": "aov", "formula": "sum(revenue) / count(*)"}]) Example — remove a measure: edit_model(model_name="orders", remove={"measures": ["old_metric"]})