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Reworks ccflow/examples/tpch as a more illustrative ccflow example: a registry of typed providers wired together in YAML, instead of a single context-dispatched data generator and query runner. The discriminator (table name / query id) becomes a Pydantic field on each model instance, so each registered provider has a fixed output schema. A shared TPCHDuckDBBackend (BaseModel) owns the DuckDB connection and runs dbgen exactly once; every TPCHTableProvider and TPCHAnswerProvider references it via /tpch/backend, so a single scale_factor override on the backend flows through to all 22+8 providers. TPCHQuery is a generic CallableModel parameterised by query_id and an explicit tuple of input table providers, replacing the previous side-table mapping. The bundled config/conf.yaml is heavily commented as a teaching example of the BaseModel/CallableModel distinction, registry cross-references via /abs/path strings, and per-instance dependency wiring. Signed-off-by: Pascal Tomecek <pascal.tomecek@cubistsystematic.com>
hintse
approved these changes
Apr 30, 2026
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Reworks
ccflow/examples/tpchto be more illustrative of how ccflow is used in practice — a registry of typed providers wired together in YAML — rather than a single context-dispatched data generator and query runner.Why
The old example had two anti-patterns:
TPCHDataGeneratorproduced 8 different output schemas depending onTPCHTableContext.table— one model whose return type changed by context.TPCHQueryRunnerdispatched byTPCHQueryContext.query_id, with the table dependencies of each query hidden in a_QUERY_TABLE_MAPside-table rather than expressed as model fields.Both shapes are convenient but obscure how ccflow is actually used in practice, where each registered model has a fixed output schema and dependencies are explicit Pydantic fields.