mentat/query-algebrizer
Richard Newman e33fe71c47 Rework caching and use it inside the query engine. (#553) r=emily
This puts caching in mentat_db, adds a reverse lookup capability for
unique attributes, and populates bidirectional caches with a single
SQL cursor walk.

Differentiate between begin_read and begin_uncached_read.

Note that we still allow toggling within InProgress, because there might be
transient local state that makes starting a new transaction impossible.
2018-02-21 11:51:45 -08:00
..
src Rework caching and use it inside the query engine. (#553) r=emily 2018-02-21 11:51:45 -08:00
tests Rework caching and use it inside the query engine. (#553) r=emily 2018-02-21 11:51:45 -08:00
Cargo.toml Partial work from simple aggregates work (#497) r=nalexander 2017-11-30 15:02:07 -08:00
README.md Partly flesh out query algebrizer. (#243) r=nalexander 2017-02-15 16:10:59 -08:00

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.