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Execution model

An execution model is an abstract set of rules governing how a computational system runs, schedules, coordinates, orders, and triggers computations.

How 2 Manning books explain it

Fast Python: High performance techniques for large datasets

How it is used
Frames Dask-based analytics as a choice of execution strategy for computations that may exceed one machine's capacity.
How deep it goes
Explains execution independently from computation and previews scheduling alternatives, but defers detailed distributed implementation.
Examples
Uses a pandas-like property-tax analysis over US Census data from the 50 states.
What you can do after
Apply Dask interfaces while choosing among serial, parallel, and distributed execution approaches.

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Building User-Friendly DSLs

A specification of how business rules expressed as DSL content interact and are executed.

How it is used
Specifies how DSL-defined business rules interact, execute, and produce effects in the runtime.
How deep it goes
Explains effects, dependencies, ambiguity, ordering, and runtime collection while deferring concrete detection algorithms.
Examples
Uses discount-dependent conditions, contradictory attribute assignments, and collective runtime effect gathering as concrete cases.
What you can do after
Evaluate how rule effects, dependencies, ambiguities, and execution order shape a DSL’s runtime behavior.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.