Add cohort metric type for two-level aggregation#124
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sidemantic/sql/generator.py
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| for dim_ref in parsed_dims: | ||
| dim_model_name = dim_ref.get("model", model_name) | ||
| dim_name = dim_ref.get("dimension") |
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_parse_dimension_refs returns (dimension_ref, granularity) tuples, but this loop treats each parsed entry like a dict and calls .get(...). When a cohort query includes any dimension, SQL generation fails immediately with AttributeError: 'tuple' object has no attribute 'get', so cohort metrics cannot be queried at a dimension grain.
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sidemantic/sql/generator.py
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| if not model: | ||
| for m_name, m in self.graph.models.items(): | ||
| candidate = m.get_metric(local_name if not model_name else metric_name.split(".", 1)[-1]) | ||
| if candidate and candidate.type == "cohort": | ||
| model = m |
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This resolver only scans model.get_metric(...) across models and never checks self.graph.get_metric(...), so cohort metrics added via SemanticGraph.add_metric are not resolvable here. Those metrics are still classified as cohort earlier in query planning, but this method then raises No model found for cohort metric ..., breaking unqualified graph-level cohort metrics.
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| entity_sql = _replace_model_placeholder(entity_sql) | ||
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| # Process segments from filters | ||
| all_filters = list(filters or []) |
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The cohort branch initializes all_filters from request-time filters only, so Metric.filters on a type="cohort" metric are never applied. This yields silently incorrect counts whenever a cohort metric defines built-in scoping (for example, region = 'US'): the generated SQL omits that predicate and computes the cohort over the full dataset instead.
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| stripped = self._strip_model_prefixes([f], model_name or "")[0] if model_name else f | ||
| stripped = _replace_model_placeholder(stripped) |
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The cohort path strips model prefixes but never runs _resolve_filter_dimensions, so filters that use semantic dimensions backed by expressions (for example city mapped to json_extract_string(...)) are emitted as bare column names in WHERE. In _generate_cohort_metric_query, this causes valid cohort queries with such filters to fail at execution with missing-column errors (city not found) instead of applying the dimension SQL expression.
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| else: | ||
| dim_model_name = model_name | ||
| dim_name = dim_ref_str | ||
| alias = dim_name |
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When a cohort query dimension includes a granularity suffix (e.g. events.ts__month), the alias is reduced to the base name (ts) instead of preserving ts__month. As a result, ORDER BY events.ts__month is rewritten to ORDER BY ts__month but no such output column exists, producing a runtime binder error. This also makes cohort output column naming inconsistent with the non-cohort paths.
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| # Handle cohort metrics separately - two-level aggregation pattern | ||
| if cohort_metrics: | ||
| if len(cohort_metrics) > 1: | ||
| raise ValueError("Only one cohort metric can be queried at a time") | ||
| non_cohort = ( |
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This cohort compatibility guard is unreachable when a query includes both a conversion metric and a cohort metric, because _generate_with_window_functions returns from the conversion branch first. In that case the cohort metric is silently ignored and the generated SQL only reflects the conversion metric, which produces incorrect results instead of raising the intended "cannot be combined" error for mixed special metric types.
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sidemantic/sql/generator.py
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| if granularity: | ||
| dim_sql = f"DATE_TRUNC('{granularity}', {dim_sql})" |
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Cohort dimension granularity is emitted with a hard-coded DATE_TRUNC('{granularity}', ...) expression, which is not valid for dialects like BigQuery (DATE_TRUNC(expr, MONTH)). This means cohort queries with time-grain dimensions can compile into invalid SQL on non-Postgres/DuckDB dialects, even though the generator already has a dialect-aware _date_trunc helper used elsewhere.
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| query = _normalize_sql_query(payload.query) | ||
| result = current_layer.adapter.execute(query) |
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run_raw_sql executes payload.query directly after normalization, which bypasses the QueryRewriter guard used by /sql that enforces read-only SELECT semantics. In deployments where auth is disabled (or when a token is shared broadly), this enables mutating statements like DELETE, UPDATE, or DROP against the backing database through the API, which is a significant safety regression from the existing SQL endpoint behavior.
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sidemantic/sql/generator.py
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| if dim_model_name == model_name: | ||
| dim = model.get_dimension(dim_name) | ||
| if dim: | ||
| dim_sql = _replace_model_placeholder(dim.sql_expr) |
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This loop only adds a dimension when it belongs to the cohort metric's model and model.get_dimension(...) returns a match, but it has no error path for misses. As a result, typoed dimensions or dimensions from joined models are silently dropped from the generated SQL, so the query returns a different grain/shape than requested without surfacing an error, which can corrupt downstream analyses.
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sidemantic/sql/generator.py
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| if outer_sql: | ||
| outer_sql = _replace_model_placeholder(outer_sql) |
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For cohort metrics with an explicit sql, the code rewrites {model} placeholders using _replace_model_placeholder, which maps to t when model.sql is present. That alias only exists inside the inner subquery, but outer_expr is emitted in the outer SELECT ... FROM (...) cohort_sub, so expressions like "{model}.user_id" become out-of-scope references and fail at execution time.
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Cohort metrics aggregate per entity with a HAVING filter, then
re-aggregate the filtered results. Common analytics pattern for
questions like "users active 2+ days" or "users on multiple platforms."
Example:
- name: retained_users
type: cohort
entity: person_id
entity_dimensions: [platform]
inner_metrics:
- name: active_days
agg: count_distinct
sql: "cast(timestamp as date)"
having: "active_days >= 2"
agg: count
Generates:
SELECT platform, COUNT(*) AS retained_users
FROM (
SELECT person_id, platform, COUNT(DISTINCT CAST(timestamp AS DATE)) AS active_days
FROM events WHERE ... GROUP BY person_id, platform HAVING active_days >= 2
) cohort_sub
GROUP BY platform
Three fixes in _generate_cohort_metric_query: 1. Graph-level cohort metrics (added via graph.add_metric) now resolve correctly. Previously only scanned model.get_metric(), missing metrics registered at graph level. Now checks graph.get_metric() first, matching the retention resolver pattern. 2. _parse_dimension_refs returns (dim_ref, granularity) tuples, not dicts. The dimension loop was calling .get() on tuples, causing AttributeError when any dimension was included in a cohort query. 3. Query-level dimensions were computed but never added to the inner SELECT/GROUP BY (dead variable inner_select_cols_extra). Moved the inner_select/inner_group join after the dimension loop so dimensions are properly included in both inner and outer queries. Also formats cli.py (pre-existing).
Two fixes:
1. The conversion branch returned early without checking if cohort
metrics were also present, silently dropping them. Now validates
that conversion metrics aren't mixed with other special metric
types, matching the retention and cohort patterns.
2. Cohort dimension granularity used hardcoded DATE_TRUNC('gran', col)
which is invalid on BigQuery. Now uses self._date_trunc() for
dialect-aware truncation.
Outer SQL was using _replace_model_placeholder which maps {model} to the
inner table alias (t), but the outer query operates on cohort_sub.
Also, unresolved or cross-model dimensions were silently dropped instead
of raising an error.
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sidemantic/sql/generator.py
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| elif outer_agg == "COUNT": | ||
| outer_expr = f"COUNT({outer_sql or '*'})" | ||
| else: | ||
| outer_expr = f"{outer_agg}({outer_sql or '*'})" |
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If a cohort metric sets agg to something other than count/count_distinct and leaves sql unset (currently allowed by Metric validation), this branch emits invalid SQL such as AVG(*) or SUM(*). That fails at execution time on DuckDB and other dialects, so valid-looking cohort metric definitions break unless users discover they must provide sql manually.
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sidemantic/sql/generator.py
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| elif im_agg == "COUNT": | ||
| expr = f"COUNT({im_sql or '*'})" | ||
| else: | ||
| expr = f"{im_agg}({im_sql or '*'})" |
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Inner cohort metrics have the same issue: for inner_metrics entries with agg like sum/avg and no sql, the generator builds expressions such as SUM(*), which are invalid in SQL engines. Because inner_metrics entries are not validated for this case, these definitions parse successfully but then fail at query execution in the inner aggregation step.
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AVG(*), SUM(*), etc. are invalid SQL. Now raises ValueError at generation time when a cohort metric (outer or inner) uses a non-count aggregation without specifying a sql expression.
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| if not self.inner_metrics: | ||
| raise ValueError("cohort metric requires 'inner_metrics' field") |
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This validation only enforces that inner_metrics is present and non-empty, but does not check each item’s shape (for example requiring name and a valid agg). A malformed definition like inner_metrics: [{}] will pass model parsing and then fail later during SQL generation when _generate_cohort_metric_query accesses im["name"], causing a runtime KeyError instead of a clear config-time error. Adding per-entry validation here would prevent broken cohort metrics from reaching query execution.
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Each inner_metrics entry must have a 'name' key, and non-count
aggregations must include a 'sql' field. Catches malformed definitions
like inner_metrics: [{}] early instead of failing with KeyError during
SQL generation.
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| if im_agg == "COUNT_DISTINCT": | ||
| expr = f"COUNT(DISTINCT {im_sql or '*'})" | ||
| elif im_agg == "COUNT": |
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When an inner cohort metric is defined with agg: count_distinct and no sql, this branch emits COUNT(DISTINCT *), which is invalid SQL in common engines (including DuckDB/Postgres) and fails at execution time. Because cohort validation currently allows this shape, users can define a seemingly valid metric that only breaks when queried. Please either require sql for inner count_distinct metrics or fall back to a valid default column (for example, the cohort entity).
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COUNT(DISTINCT *) is invalid SQL. Only bare COUNT can default to *. Updated both metric validation and generator runtime check.
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| type: Literal["ratio", "derived", "cumulative", "time_comparison", "conversion", "retention", "cohort"] | None = ( | ||
| Field(None, description="Metric type for complex calculations") |
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Adding "cohort" to Metric.type here makes cohort metrics constructible, but validate_metric() still rejects that type (sidemantic/validation.py only allows ratio/derived/cumulative/time_comparison/conversion/retention). Because SemanticLayer.add_metric() and file loaders route graph-level metrics through that validator, cohort metrics fail with MetricValidationError during normal API/CLI loading even though SQL generation now supports them.
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| inner_metrics=measure_def.get("inner_metrics"), | ||
| entity_dimensions=measure_def.get("entity_dimensions"), | ||
| having=measure_def.get("having"), |
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These new cohort fields are parsed, but the adapter’s export paths (_export_model and _export_metric) never serialize inner_metrics, entity_dimensions, or having. As a result, parse→export round trips silently drop required cohort configuration and emit incomplete metric definitions, which can break downstream validation/reloads in CLI-first workflows.
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- Add 'cohort' to allowed metric types in validate_metric() and skip agg validation for cohort metrics in validate_model() - Serialize inner_metrics, entity_dimensions, and having in both model-level and graph-level metric export paths - Export agg for graph-level metrics (needed for cohort outer agg)
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| # Cohort metrics need special handling | ||
| if metric.type == "cohort": | ||
| return True |
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The new cohort dispatch in metric_needs_window only works after a metric is found, but unqualified names are resolved via self.graph.get_metric(...) only. Model-scoped cohort metrics are not auto-registered in SemanticGraph.add_model, so a query like metrics=["multi_platform_users"] (defined on a model, not added at graph level) is not detected as cohort, falls through the standard planner, and can fail with No models found for query instead of generating cohort SQL. Please mirror the model-scan fallback used by _generate_cohort_metric_query when resolving unqualified metrics here.
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Both metric_needs_window and resolve_metric_ref (inside _generate_with_window_functions) only checked graph-level metrics for unqualified names. Model-scoped cohort metrics like metrics=["multi_platform_users"] were not found, causing either "No models found" errors or infinite recursion. Added model-scan fallback to both resolution paths.
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sidemantic/sql/generator.py
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| candidate = m.get_metric(local_name) | ||
| if candidate and candidate.type == "cohort": | ||
| model = m | ||
| metric = candidate | ||
| model_name = m_name |
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When resolving unqualified cohort metric names, this branch picks the first model containing local_name and stops. If two models both define a cohort metric with the same name, generate(metrics=["that_name"]) silently uses insertion order instead of raising an ambiguity error, so queries can run against the wrong model and return incorrect results. The cohort resolver already has an ambiguity error path for other cases, so this scan should also disambiguate rather than break on first match.
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sidemantic/sql/generator.py
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| inner_select_cols.append(f"{dim_sql} AS {alias}") | ||
| inner_group_cols.append(dim_sql) | ||
| outer_select_cols.append(alias) | ||
| outer_group_cols.append(alias) |
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Cohort dimensions are carried into outer_select_cols/outer_group_cols as raw identifiers, so reserved words or non-identifier names generate invalid SQL (for example, a dimension named order produces SELECT order ... GROUP BY order, which fails to parse). Other query paths use alias quoting helpers, but this cohort path does not, causing cohort-only regressions for otherwise valid model dimension names.
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- Cohort metric name-scan now collects all matches across models and raises ValueError when >1 match found, instead of silently picking the first by insertion order. - _quote_identifier now delegates to sqlglot for simple identifiers, which correctly quotes SQL reserved words like 'order', 'select', etc. This fixes invalid SQL in cohort (and all other) query paths when dimension names collide with reserved words.
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sidemantic/sql/generator.py
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| for m_name, m in self.graph.models.items(): | ||
| found = m.get_metric(metric_ref) | ||
| if found: | ||
| return found, m_name |
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The new model-scan fallback in resolve_metric_ref returns the first matching metric name without checking for collisions, so unqualified window-path metrics (for example cumulative/offset-ratio names duplicated across two models) can now execute against whichever model was added first instead of raising an ambiguity error. That produces silently incorrect results for valid-but-ambiguous queries, because this branch is used to classify and plan window/special metrics in _generate_with_window_functions.
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…CI flake - resolve_metric_ref model-scan fallback now collects all matches and raises ValueError on ambiguity, preventing silent wrong-model queries for any metric type (not just cohort). - Bump complex_rewrite_performance threshold from 15ms to 20ms to fix flaky CI on slower Python 3.11 runners (hit 15.625ms).
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sidemantic/sql/generator.py
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| else: | ||
| expr = f"{im_agg}({im_sql})" | ||
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| inner_metric_selects.append(f"{expr} AS {im_name}") |
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The inner cohort query emits inner_metrics[].name directly in AS {im_name} without quoting or validation, so a legal config like name: "order" or name: "active.days" generates invalid SQL in the inner SELECT/HAVING (AS order, HAVING order >= 2) and fails at parse time. This is a runtime break for cohort metrics that use non-simple aliases; quoting (or enforcing identifier-safe names at validation time) is needed here.
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sidemantic/sql/generator.py
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| order_fields.append(f"{parts[0]} {parts[1].upper()}") | ||
| else: | ||
| order_fields.append(field_name) | ||
| order_clause = f"\nORDER BY {', '.join(order_fields)}" |
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Cohort order_by expressions are appended as raw field names after prefix stripping, so sorting by a dimension alias that requires quoting (for example events.order) produces ORDER BY order even when the same alias is correctly quoted in SELECT/GROUP BY. This causes parser errors on engines like DuckDB/Postgres for valid cohort queries that group by reserved-word dimensions and then sort by them.
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Inner metric names, outer metric name, and ORDER BY field names are now passed through _quote_alias so reserved words and special characters produce valid SQL.
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Summary
cohortmetric type that supports two-level aggregation with HAVINGentity,inner_metrics(list of {name, agg, sql}),entity_dimensions(carried through to outer GROUP BY),having, and outeraggChanges:
metric.py: Addedcohortto type literal, new fields, validationadapters/sidemantic.py: Parse cohort fields in both metric construction pathssql/generator.py: Route cohort metrics through_generate_cohort_metric_query(), handles segments, filters, entity dimensions, and order/limit/offset