Pre: export AttributeBuilder from mentat_db.
Pre: fix module-level comment for tx/src/entities.rs.
Pre: rename some `to_` conversions to `into_`.
Pre: make AttributeBuilder::unique less verbose.
Pre: split out a HasSchema trait to abstract over Schema.
Pre: rename SchemaMap/schema_map to AttributeMap/attribute_map.
Pre: TypedValue/NamespacedKeyword conversions.
Pre: turn Unique and ValueType into TypedValue::Keyword.
Pre: export IntoResult.
Pre: export NamespacedKeyword from mentat_core.
Pre: use intern_set in tx.
Pre: add InternSet::len.
Pre: comment gardening.
Pre: remove inaccurate TODO from TxReport comment.
This was done using the following shell script:
```
find . -type f -not -path "*target*" \
'(' -name '*.rs' -o -name '*.md' -o -name '*.toml' ')' -print0 | \
xargs -0 sed -i '' -E 's/[[:space:]]*$//'
```
Which is admittedly imperfect, but manages to hit everything that was a problem in this repo.
* Pre: make FindQuery, FindSpec, and Element non-Clone.
* Pre: make query translator return a Result.
* Pre: make projection return a Result.
* Pre: refactor query parser in preparation for parsing aggregates.
* Pre: rename PredicateFn -> QueryFunction.
* Pre: expose more about bound variables from CC.
* Pre: move ValueTypeSet to core.
* Update some dependencies.
* Update rusqlite to 0.12.
* Update error-chain to a forked version that implements Sync.
* Fix some compiler warnings.
* Remove unused imports in tests.
* Parse errors no longer naturally print with the expected symbol.
This commit lifts some logic out of the scalar ground handler to apply
elsewhere.
When a new value binding is encountered for a variable to which column
bindings have already been established, we do two things:
- We apply a new constraint to the primary column. This ensures that the
behavior for ground-first and ground-second is equivalent.
- We eliminate any existing column type extraction: it won't be
necessary now that a constant value and constant type are known.
This version removes nalexander's lovely matrix code. It turned out
that scalar and tuple bindings are sufficiently different from coll
and rel -- they can directly apply as values in the query -- that
there was no point in jumping through hoops to turn those single
values into a matrix.
Furthermore, I've standardized us on a Vec<TypedValue>
representation for rectangular matrices, which should be much
more efficient, but would have required rewriting that code.
Finally, coll and rel are sufficiently different from each other
-- coll doesn't require processing nested collections -- that
my attempts to share code between them fell somewhat flat. I had
lots of nice ideas about zipping together cycles and such, but
ultimately I ended up with relatively straightforward, if a bit
repetitive, code.
The next commit will demonstrate the value of this work -- tests
that exercised scalar and tuple grounding now collapse down to
the simplest possible SQL.
Datomic accepts mostly-arbitrary EDN, and it is actually used: for
example, the following are all valid, and all mean different things:
* `(ground 1 ?x)`
* `(ground [1 2 3] [?x ?y ?z])`
* `(ground [[1 2 3] [4 5 6]] [[?x ?y ?z]])`
We could probably introduce new syntax that expresses these patterns
while avoiding collection arguments, but I don't see one right now.
I've elected to support only vectors for simplicity; I'm hoping to
avoid parsing edn::Value in the query-algebrizer.
* Pre: unused import in translate.rs.
* Part 2: take a dependency on rusqlite for query arguments.
* Part 1: flatten V2 schema into V1. Add UUID and URI.
Bump expected ident and bootstrap datom count in tests.
* Part 5: parse edn::Value::Uuid.
* Part 3: extend ValueType and TypedValue to include Uuid.
* Part 4: add Uuid to query arguments.
* Part 6: extend db to support Uuid.
* Part 8: add a tx-parser test for #f NaN and #uuid.
* Part 7: parse and algebrize UUIDs in queries.
* Part 1: parse #inst in EDN and throughout query engine.
* Part 3: handle instants in db.
* Part 2: instants never matches integers in queries.
* Part 4: use DateTime for tx_instants.
* Add a test for adding and querying UUIDs and instants.
* Review comments.
* Part 1 - Parse `not` and `not-join`
* Part 2 - Validate `not` and `not-join` pre-algebrization
* Address review comments rnewman.
* Remove `WhereNotClause` and populate `NotJoin` with `WhereClause`.
* Fix validation for `not` and `not-join`, removing tests that were invalid.
* Address rustification comments.
* Rebase against `rust` branch.
* Part 3 - Add required types for NotJoin.
* Implement `PartialEq` for
`ConjoiningClauses` so `ComputedTable` can be included inside `ColumnConstraint::NotExists`
* Part 4 - Implement `apply_not_join`
* Part 5 - Call `apply_not_join` from inside `apply_clause`
* Part 6 - Translate `not-join` into `NOT EXISTS` SQL
* Address review comments.
* Rename `projected` to `unified` to better describe the fact that we are not projecting any variables.
* Check for presence of each unified var in either `column_bindings` or `input_bindings` and bail if not there.
* Copy over `input_bindings` for each var in `unified`.
* Only copy over the first `column_binding` for each variable in `unified` rather than the whole list.
* Update tests.
* Address review comments.
* Make output from Debug for NotExists more useful
* Clear up misunderstanding. Any single failing clause in the not will cause the entire not to be considered empty
* Address review comments.
* Remove Limit requirement from cc_to_exists.
* Use Entry.or_insert instead of matching on the entry to add to column_bindings.
* Move addition of value_bindings to before apply_clauses on template.
* Tidy up tests with some variable reuse.
* Addressed nits,
* Address review comments.
* Move addition of column_bindings to above apply_clause.
* Update tests.
* Add test to ensure that unbound vars fail
* Improve test for unbound variable to check for correct variable and error
* address nits
* Part 1: define ValueTypeSet.
We're going to use this instead of `HashSet<ValueType>` so that we can clearly express
the empty set and the set of all types, and also to encapsulate a switch to `EnumSet`."
* Part 2: use ValueTypeSet.
* Part 3: fix type expansion.
* Part 4: add a test for type extraction from nested `or`.
* Review comments.
* Review comments: simplify ValueTypeSet.
* Pre: put query parts in alphabetical order.
* Pre: rename 'input' to 'query' in translate tests.
* Part 1: parse :limit.
* Part 2: validate and escape variable parameters in SQL.
* Part 3: algebrize and translate limits.
This is for two reasons.
Firstly, we need to track the types of inputs, their values, and also
the input variables; adding a struct gives us a little more clarity.
Secondly, when we come to implement prepared statements, we'll be
algebrizing queries without having the values available. We'll be able
to do a better job of algebrizing, and also do more validating, if we
allow callers to specify the types of variables in advance, even if the
values aren't known.
This adds an `:order` keyword to `:find`.
If present, the results of the query will be an ordered set, rather than
an unordered set; rows will appear in an ordered defined by each
`:order` entry.
Each can be one of three things:
- A var, `?x`, meaning "order by ?x ascending".
- A pair, `(asc ?x)`, meaning "order by ?x ascending".
- A pair, `(desc ?x)`, meaning "order by ?x descending".
Values will be ordered in this sequence for asc, and in reverse for desc:
1. Entity IDs, in ascending numerical order.
2. Booleans, false then true.
3. Timestamps, in ascending numerical order.
4. Longs and doubles, intermixed, in ascending numerical order.
5. Strings, in ascending lexicographic order.
6. Keywords, in ascending lexicographic order, considering the entire
ns/name pair as a single string separated by '/'.
Subcommits:
Pre: make bound_value public.
Pre: generalize ErrorKind::UnboundVariable for use in order.
Part 1: parse (direction, var) pairs.
Part 2: parse :order clause into FindQuery.
Part 3: include order variables in algebrized query.
We add order variables to :with, so we can reuse its type tag projection
logic, and so that we can phrase ordering in terms of variables rather
than datoms columns.
Part 4: produce SQL for order clauses.
* Pre: refactor projector code.
* Part 1: maintain 'with' variables in AlgebrizedQuery.
* Part 2: include necessary 'with' variables in SQL projection list.
The test produces projection elements for `:with`, even though there are
no aggregates in the query. This test will need to be adjusted when we
optimize this away!
This commit turns complex `or` -- `or`s in which not all variables are
unified, or in which not all arms are the same shape -- into a
computed table.
We do this by building a template CC that shares some state with the
destination CC, applying each arm of the `or` to a copy of the template
as if it were a standalone query, then building a projection list and
creating a `ComputedTable::Union`. This is pushed into the destination
CC's `computed_tables` list.
Finally, the variables projected from the UNION are bound in the
destination CC, so that unification occurs, and projection of the
outermost query can use bindings established by the `or-join`.
This commit includes projection of type codes from heterogeneous `UNION`
arms: we compute a list of variables for which a definite type is
unknown in at least one arm, and force all arms to project either a type
tag column or a fixed type. It's important that each branch of a UNION
project the same columns in the same order, hence the projection of
fixed values.
The translator is similarly extended to project the type tag column name
or the known value_type_tag to support this.
Review comment: clarify union type extraction.
This commit:
- Defines a new kind of column, distinct from the eavt columns in
`DatomsColumn`, to model the rows projected from subqueries. These
always name one of two things: a variable, or a variable's type tag.
Naturally the two cases are thus `Variable` and `VariableTypeTag`.
These are cheap to clone, given that `Variable` is an `Rc<String>`.
- Defines `Column` as a wrapper around `DatomsColumn` and
`VariableColumn`. Everywhere we used to use `DatomsColumn` we now
allow `Column`: particularly in constraints and projections.
- Broadens the definition of a table list in the intermediate
"query-sql" representation to include a SQL UNION. A UNION is
represented as a list of queries and an alias.
- Implements translation from a `ComputedTable` to the query-sql
representation. In this commit we only project vars, not type tags.
Review comment: discuss bind_column_to_var for ValueTypeTag.
Review comment: implement From<Vec<T>> for ConsumableVec<T>.
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.
