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Complex `or`s are translated to SQL as a subquery -- in particular, a subquery that's a UNION. Conceptually, that subquery is a computed table: `all_datoms` and `datoms` yield rows of e/a/v/tx, and each computed table yields rows of variable bindings. The table itself is a type, `ComputedTable`. Its `Union` case contains everything a subquery needs: a `ConjoiningClauses` and a projection list, which together allow us to build a SQL subquery, and a list of variables that need type code extraction. (This is discussed further in a later commit.) Naturally we also need a way to refer to columns in a computed table. We model this by a new enum case in `DatomsTable`, `Computed`, which maintains an integer value that uniquely identifies a computed table. |
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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.