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Query Engine

The query engine resolves channel selections and evaluates time-series expressions against the silver-layer data. It has two parts:

TSAL — the expression language you write to select channels, derive virtual signals, and define events and aggregations.
Query Solvers — the DefaultSolver that knows how your silver tables are laid out, reads them, and evaluates the expressions per container.

Two ways to use it

Directly, for ad-hoc analysis. Build a query against a MeasurementDB and solve it interactively — no reporting setup required:

eng_rpm = db.query.channel(channel_name='Engine RPM')
result = db.query.select(eng_rpm.mean().alias('rpm_mean')).solve(spark, solver=DefaultSolver(spark))
# or .toPandas(spark, solver=DefaultSolver(spark)) for a pandas DataFrame

As the foundation of the Report component. The reporting layer embeds TSAL expressions in events and aggregations, and the Report orchestrator batch-solves them through the query engine and persists the results to the gold layer.

Two ways to use it