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* Pre: use debugcli in VSCode. * Pre: wrap subqueries in parentheses in output SQL. * Pre: add ExistingColumn. This lets us make reference to columns by name, rather than only pointing to qualified aliases. * Pre: add Into for &str to TypedValue. * Pre: add Store.transact. * Pre: cleanup. * Parse and algebrize simple aggregates. (#312) * Follow-up: print aggregate columns more neatly in the CLI. * Useful ValueTypeSet helpers. * Allow for entity inequalities. * Add 'differ', which is a ref-specialized not-equals. * Add 'unpermute', a function for getting unique, distinct pairs from bindings. * Review comments. * Add 'the' pseudo-aggregation operator. This allows for a corresponding value to be returned when a query includes one 'min' or 'max' aggregate. |
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| README.md | ||
This crate turns a parsed query, as defined by the query crate and produced by the query-parser crate, into an algebrized tree, also called a query processor tree.
This is something of a wooly definition: a query algebrizer in a traditional relational database is the component that combines the schema — including column type constraints — with the query, resolving names and that sort of thing. Much of that work is unnecessary in our model; for example, we don't need to resolve column aliases, deal with table names, or that sort of thing. But the similarity is strong enough to give us the name of this crate.
The result of this process is traditionally handed to the query optimizer to yield an execution plan. In our case the execution plan is deterministically derived from the algebrized tree, and the real optimization (such as it is) takes place within the underlying SQLite database.