mentat/query-projector/src/lib.rs

// Copyright 2016 Mozilla
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not use
// this file except in compliance with the License. You may obtain a copy of the
// License at http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#[macro_use]
extern crate error_chain;
extern crate rusqlite;

extern crate mentat_core;
extern crate mentat_db;                 // For value conversion.
extern crate mentat_query;
extern crate mentat_query_algebrizer;
extern crate mentat_query_sql;
extern crate mentat_sql;

use std::iter;
use rusqlite::{
    Row,
    Rows,
};

use mentat_core::{
    SQLValueType,
    TypedValue,
};

use mentat_db::{
    TypedSQLValue,
};

use mentat_query::{
    Element,
    FindSpec,
    Limit,
    Variable,
};

use mentat_query_algebrizer::{
    AlgebraicQuery,
    ColumnName,
    VariableColumn,
};

use mentat_query_sql::{
    ColumnOrExpression,
    Name,
    Projection,
    ProjectedColumn,
};

error_chain! {
    types {
        Error, ErrorKind, ResultExt, Result;
    }

    foreign_links {
        Rusqlite(rusqlite::Error);
    }

    links {
        DbError(mentat_db::Error, mentat_db::ErrorKind);
    }
}

#[derive(Debug)]
pub enum QueryResults {
    Scalar(Option<TypedValue>),
    Tuple(Option<Vec<TypedValue>>),
    Coll(Vec<TypedValue>),
    Rel(Vec<Vec<TypedValue>>),
}

impl QueryResults {
    pub fn len(&self) -> usize {
        use QueryResults::*;
        match self {
            &Scalar(ref o) => if o.is_some() { 1 } else { 0 },
            &Tuple(ref o)  => if o.is_some() { 1 } else { 0 },
            &Coll(ref v)   => v.len(),
            &Rel(ref v)    => v.len(),
        }
    }

    pub fn is_empty(&self) -> bool {
        use QueryResults::*;
        match self {
            &Scalar(ref o) => o.is_none(),
            &Tuple(ref o)  => o.is_none(),
            &Coll(ref v)   => v.is_empty(),
            &Rel(ref v)    => v.is_empty(),
        }
    }

    pub fn empty(spec: &FindSpec) -> QueryResults {
        use self::FindSpec::*;
        match spec {
            &FindScalar(_) => QueryResults::Scalar(None),
            &FindTuple(_)  => QueryResults::Tuple(None),
            &FindColl(_)   => QueryResults::Coll(vec![]),
            &FindRel(_)    => QueryResults::Rel(vec![]),
        }
    }

    pub fn empty_factory(spec: &FindSpec) -> Box<Fn() -> QueryResults> {
        use self::FindSpec::*;
        match spec {
            &FindScalar(_) => Box::new(|| QueryResults::Scalar(None)),
            &FindTuple(_)  => Box::new(|| QueryResults::Tuple(None)),
            &FindColl(_)   => Box::new(|| QueryResults::Coll(vec![])),
            &FindRel(_)    => Box::new(|| QueryResults::Rel(vec![])),
        }
    }
}

type Index = i32;            // See rusqlite::RowIndex.
type ValueTypeTag = i32;
enum TypedIndex {
    Known(Index, ValueTypeTag),
    Unknown(Index, Index),
}

impl TypedIndex {
    /// Look up this index and type(index) pair in the provided row.
    /// This function will panic if:
    ///
    /// - This is an `Unknown` and the retrieved type code isn't an i32.
    /// - If the retrieved value can't be coerced to a rusqlite `Value`.
    /// - Either index is out of bounds.
    ///
    /// Because we construct our SQL projection list, the code that stored the data, and this
    /// consumer, a panic here implies that we have a bad bug — we put data of a very wrong type in
    /// a row, and thus can't coerce to Value, we're retrieving from the wrong place, or our
    /// generated SQL is junk.
    ///
    /// This function will return a runtime error if the type code is unknown, or the value is
    /// otherwise not convertible by the DB layer.
    fn lookup<'a, 'stmt>(&self, row: &Row<'a, 'stmt>) -> Result<TypedValue> {
        use TypedIndex::*;

        match self {
            &Known(value_index, value_type) => {
                let v: rusqlite::types::Value = row.get(value_index);
                TypedValue::from_sql_value_pair(v, value_type).map_err(|e| e.into())
            },
            &Unknown(value_index, type_index) => {
                let v: rusqlite::types::Value = row.get(value_index);
                let value_type_tag: i32 = row.get(type_index);
                TypedValue::from_sql_value_pair(v, value_type_tag).map_err(|e| e.into())
            },
        }
    }
}

fn candidate_column(query: &AlgebraicQuery, var: &Variable) -> (ColumnOrExpression, Name) {
    // Every variable should be bound by the top-level CC to at least
    // one column in the query. If that constraint is violated it's a
    // bug in our code, so it's appropriate to panic here.
    let columns = query.cc
                       .column_bindings
                       .get(var)
                       .expect("Every variable has a binding");

    let qa = columns[0].clone();
    let name = VariableColumn::Variable(var.clone()).column_name();
    (ColumnOrExpression::Column(qa), name)
}

fn candidate_type_column(query: &AlgebraicQuery, var: &Variable) -> (ColumnOrExpression, Name) {
    let extracted_alias = query.cc
                               .extracted_types
                               .get(var)
                               .expect("Every variable has a known type or an extracted type");
    let type_name = VariableColumn::VariableTypeTag(var.clone()).column_name();
    (ColumnOrExpression::Column(extracted_alias.clone()), type_name)
}

/// Walk an iterator of `Element`s, collecting projector templates and columns.
///
/// Returns a pair: the SQL projection (which should always be a `Projection::Columns`)
/// and a `Vec` of `TypedIndex` 'keys' to use when looking up values.
///
/// Callers must ensure that every `Element` is distinct -- a query like
///
/// ```edn
/// [:find ?x ?x :where [?x _ _]]
/// ```
///
/// should fail to parse. See #358.
fn project_elements<'a, I: IntoIterator<Item = &'a Element>>(
    count: usize,
    elements: I,
    query: &AlgebraicQuery) -> (Projection, Vec<TypedIndex>) {

    let mut cols = Vec::with_capacity(count);
    let mut i: i32 = 0;
    let mut templates = vec![];
    let mut with = query.with.clone();

    for e in elements {
        match e {
            // Each time we come across a variable, we push a SQL column
            // into the SQL projection, aliased to the name of the variable,
            // and we push an annotated index into the projector.
            &Element::Variable(ref var) => {
                // If we're projecting this, we don't need it in :with.
                with.remove(var);

                let (column, name) = candidate_column(query, var);
                cols.push(ProjectedColumn(column, name));
                if let Some(t) = query.cc.known_type(var) {
                    let tag = t.value_type_tag();
                    templates.push(TypedIndex::Known(i, tag));
                    i += 1;     // We used one SQL column.
                } else {
                    templates.push(TypedIndex::Unknown(i, i + 1));
                    i += 2;     // We used two SQL columns.

                    // Also project the type from the SQL query.
                    let (type_column, type_name) = candidate_type_column(query, &var);
                    cols.push(ProjectedColumn(type_column, type_name));
                }
            }
        }
    }

    for var in with {
        // We need to collect these into the SQL column list, but they don't affect projection.
        // If a variable is of a non-fixed type, also project the type tag column, so we don't
        // accidentally unify across types when considering uniqueness!
        let (column, name) = candidate_column(query, &var);
        cols.push(ProjectedColumn(column, name));
        if query.cc.known_type(&var).is_none() {
            let (type_column, type_name) = candidate_type_column(query, &var);
            cols.push(ProjectedColumn(type_column, type_name));
        }
    }

    (Projection::Columns(cols), templates)
}

pub trait Projector {
    fn project<'stmt>(&self, rows: Rows<'stmt>) -> Result<QueryResults>;
}

/// A projector that produces a `QueryResult` containing fixed data.
/// Takes a boxed function that should return an empty result set of the desired type.
struct ConstantProjector {
    results_factory: Box<Fn() -> QueryResults>,
}

impl ConstantProjector {
    fn new(results_factory: Box<Fn() -> QueryResults>) -> ConstantProjector {
        ConstantProjector { results_factory: results_factory }
    }
}

impl Projector for ConstantProjector {
    fn project<'stmt>(&self, _: Rows<'stmt>) -> Result<QueryResults> {
        Ok((self.results_factory)())
    }
}

struct ScalarProjector {
    template: TypedIndex,
}

impl ScalarProjector {
    fn with_template(template: TypedIndex) -> ScalarProjector {
        ScalarProjector {
            template: template,
        }
    }

    fn combine(sql: Projection, mut templates: Vec<TypedIndex>) -> CombinedProjection {
        let template = templates.pop().expect("Expected a single template");
        CombinedProjection {
            sql_projection: sql,
            datalog_projector: Box::new(ScalarProjector::with_template(template)),
            distinct: false,
        }
    }
}

impl Projector for ScalarProjector {
    fn project<'stmt>(&self, mut rows: Rows<'stmt>) -> Result<QueryResults> {
        if let Some(r) = rows.next() {
            let row = r?;
            let binding = self.template.lookup(&row)?;
            Ok(QueryResults::Scalar(Some(binding)))
        } else {
            Ok(QueryResults::Scalar(None))
        }
    }
}

/// A tuple projector produces a single vector. It's the single-result version of rel.
struct TupleProjector {
    len: usize,
    templates: Vec<TypedIndex>,
}

impl TupleProjector {
    fn with_templates(len: usize, templates: Vec<TypedIndex>) -> TupleProjector {
        TupleProjector {
            len: len,
            templates: templates,
        }
    }

    // This is exactly the same as for rel.
    fn collect_bindings<'a, 'stmt>(&self, row: Row<'a, 'stmt>) -> Result<Vec<TypedValue>> {
        assert_eq!(row.column_count(), self.len as i32);
        self.templates
            .iter()
            .map(|ti| ti.lookup(&row))
            .collect::<Result<Vec<TypedValue>>>()
    }

    fn combine(column_count: usize, sql: Projection, templates: Vec<TypedIndex>) -> CombinedProjection {
        let p = TupleProjector::with_templates(column_count, templates);
        CombinedProjection {
            sql_projection: sql,
            datalog_projector: Box::new(p),
            distinct: false,
        }
    }
}

impl Projector for TupleProjector {
    fn project<'stmt>(&self, mut rows: Rows<'stmt>) -> Result<QueryResults> {
        if let Some(r) = rows.next() {
            let row = r?;
            let bindings = self.collect_bindings(row)?;
            Ok(QueryResults::Tuple(Some(bindings)))
        } else {
            Ok(QueryResults::Tuple(None))
        }
    }
}

/// A rel projector produces a vector of vectors.
/// Each inner vector is the same size, and sourced from the same columns.
/// One inner vector is produced per `Row`.
/// Each column in the inner vector is the result of taking one or two columns from
/// the `Row`: one for the value and optionally one for the type tag.
struct RelProjector {
    len: usize,
    templates: Vec<TypedIndex>,
}

impl RelProjector {
    fn with_templates(len: usize, templates: Vec<TypedIndex>) -> RelProjector {
        RelProjector {
            len: len,
            templates: templates,
        }
    }

    fn collect_bindings<'a, 'stmt>(&self, row: Row<'a, 'stmt>) -> Result<Vec<TypedValue>> {
        assert_eq!(row.column_count(), self.len as i32);
        self.templates
            .iter()
            .map(|ti| ti.lookup(&row))
            .collect::<Result<Vec<TypedValue>>>()
    }

    fn combine(column_count: usize, sql: Projection, templates: Vec<TypedIndex>) -> CombinedProjection {
        let p = RelProjector::with_templates(column_count, templates);
        CombinedProjection {
            sql_projection: sql,
            datalog_projector: Box::new(p),
            distinct: true,
        }
    }
}

impl Projector for RelProjector {
    fn project<'stmt>(&self, mut rows: Rows<'stmt>) -> Result<QueryResults> {
        let mut out: Vec<Vec<TypedValue>> = vec![];
        while let Some(r) = rows.next() {
            let row = r?;
            let bindings = self.collect_bindings(row)?;
            out.push(bindings);
        }
        Ok(QueryResults::Rel(out))
    }
}

/// A coll projector produces a vector of values.
/// Each value is sourced from the same column.
struct CollProjector {
    template: TypedIndex,
}

impl CollProjector {
    fn with_template(template: TypedIndex) -> CollProjector {
        CollProjector {
            template: template,
        }
    }

    fn combine(sql: Projection, mut templates: Vec<TypedIndex>) -> CombinedProjection {
        let template = templates.pop().expect("Expected a single template");
        CombinedProjection {
            sql_projection: sql,
            datalog_projector: Box::new(CollProjector::with_template(template)),
            distinct: true,
        }
    }
}

impl Projector for CollProjector {
    fn project<'stmt>(&self, mut rows: Rows<'stmt>) -> Result<QueryResults> {
        let mut out: Vec<TypedValue> = vec![];
        while let Some(r) = rows.next() {
            let row = r?;
            let binding = self.template.lookup(&row)?;
            out.push(binding);
        }
        Ok(QueryResults::Coll(out))
    }
}

/// Combines the two things you need to turn a query into SQL and turn its results into
/// `QueryResults`.
pub struct CombinedProjection {
    /// A SQL projection, mapping columns mentioned in the body of the query to columns in the
    /// output.
    pub sql_projection: Projection,

    /// A Datalog projection. This consumes rows of the appropriate shape (as defined by
    /// the SQL projection) to yield one of the four kinds of Datalog query result.
    pub datalog_projector: Box<Projector>,

    /// True if this query requires the SQL query to include DISTINCT.
    pub distinct: bool,
}

impl CombinedProjection {
    fn flip_distinct_for_limit(mut self, limit: &Limit) -> Self {
        if *limit == Limit::Fixed(1) {
            self.distinct = false;
        }
        self
    }
}

/// Compute a suitable SQL projection for an algebrized query.
/// This takes into account a number of things:
/// - The variable list in the find spec.
/// - The presence of any aggregate operations in the find spec. TODO: for now we only handle
///   simple variables
/// - The bindings established by the topmost CC.
/// - The types known at algebrizing time.
/// - The types extracted from the store for unknown attributes.
pub fn query_projection(query: &AlgebraicQuery) -> CombinedProjection {
    use self::FindSpec::*;

    if query.is_known_empty() {
        // Do a few gyrations to produce empty results of the right kind for the query.
        let empty = QueryResults::empty_factory(&query.find_spec);
        let constant_projector = ConstantProjector::new(empty);
        CombinedProjection {
            sql_projection: Projection::One,
            datalog_projector: Box::new(constant_projector),
            distinct: false,
        }
    } else {
        match query.find_spec {
            FindColl(ref element) => {
                let (cols, templates) = project_elements(1, iter::once(element), query);
                CollProjector::combine(cols, templates).flip_distinct_for_limit(&query.limit)
            },

            FindScalar(ref element) => {
                let (cols, templates) = project_elements(1, iter::once(element), query);
                ScalarProjector::combine(cols, templates)
            },

            FindRel(ref elements) => {
                let column_count = query.find_spec.expected_column_count();
                let (cols, templates) = project_elements(column_count, elements, query);
                RelProjector::combine(column_count, cols, templates).flip_distinct_for_limit(&query.limit)
            },

            FindTuple(ref elements) => {
                let column_count = query.find_spec.expected_column_count();
                let (cols, templates) = project_elements(column_count, elements, query);
                TupleProjector::combine(column_count, cols, templates)
            },
        }
    }
}