Query Planning

Query planning is the process of transforming a query graph into an optimized expression tree that can be executed by the TypeScript interpreter.

Query Graph Structure

The query graph is a directed graph where:

Nodes represent operations (queries, computations, control flow)
Edges represent dependencies (data flow, execution order)

Node Types

pub enum Node {
    /// Database query (read or write)
    Query(Query),
    
    /// Empty placeholder node
    Empty,
    
    /// Control flow (if/else, return)
    Flow(Flow),
    
    /// In-memory computation (diff, etc.)
    Computation(Computation),
}

Edge Types

pub enum QueryGraphDependency {
    /// Execution ordering (A must run before B)
    ExecutionOrder,
    
    /// Data dependency with projection
    ProjectedDataDependency(FieldSelection, RowSink, Option<DataExpectation>),
    
    /// Raw data dependency
    DataDependency(RowCountSink, Option<DataExpectation>),
    
    /// Conditional branch (then)
    Then,
    
    /// Conditional branch (else)
    Else,
}

Translation Process

The translate function is the entry point:

pub fn translate(mut graph: QueryGraph, builder: &dyn QueryBuilder) -> TranslateResult<Expression> {
    let mut enums = EnumsMap::new();
    let mut result_node_builder = ResultNodeBuilder::new(&mut enums);
    let structure = map_result_structure(&graph, &mut result_node_builder);

    // Collect root nodes
    let root_nodes: Vec<NodeRef> = graph.root_nodes().collect();

    // Translate each root node
    let root = root_nodes
        .into_iter()
        .map(|node| NodeTranslator::new(&mut graph, node, &[], builder).translate())
        .collect::<TranslateResult<Vec<_>>>()
        .map(Expression::Seq)?;

    // Wrap with data mapping if needed
    let mut root = if let Some(structure) = structure {
        Expression::DataMap {
            expr: Box::new(root),
            structure,
            enums,
        }
    } else {
        root
    };

    // Optimize
    root.simplify();
    
    // Wrap in transaction if needed
    if graph.needs_transaction() {
        return Ok(Expression::Transaction(Box::new(root)));
    }
    
    Ok(root)
}

From query-compiler/src/translate.rs

Node Translation

The NodeTranslator walks the graph and generates expressions:

Mark Node as Visited

Prevent infinite loops in cyclic graphs:

fn translate(&mut self) -> TranslateResult<Expression> {
    self.graph.mark_visited(&self.node);
    // ...
}

Extract Node Content

Get the actual node data:

let node = self
    .graph
    .node_content(&self.node)
    .ok_or_else(|| TranslateError::NodeContentEmpty(self.node.id()))?;

Dispatch by Node Type

Different node types require different translation logic:

match node {
    Node::Query(_) => self.translate_query(),
    Node::Empty => self.translate_children(),
    Node::Flow(Flow::If { .. }) => self.translate_if(),
    Node::Flow(Flow::Return(_)) => self.translate_return(),
    Node::Computation(Computation::DiffLeftToRight(_)) => self.translate_diff_left_to_right(),
    Node::Computation(Computation::DiffRightToLeft(_)) => self.translate_diff_right_to_left(),
}

Query Translation

Translating a database query node:

fn translate_query(&mut self) -> TranslateResult<Expression> {
    // Translate child nodes first
    let children = self.translate_children()?;

    // Extract and transform the query node
    let node = self.graph.pluck_node(&self.node);
    let node = self.transform_node(node)?;

    // Convert to Query type
    let query: Query = node.try_into().expect("current node must be query");
    
    // Generate SQL expression
    let expr = translate_query(query, self.query_builder)?;

    // Wrap with children if any
    Ok(self.wrap_children_with_expr(expr, children))
}

Conditional Translation

Translating an if node creates a conditional expression:

fn translate_if(&mut self) -> TranslateResult<Expression> {
    let mut then_node = None;
    let mut else_node = None;

    // Find then/else branches
    for (edge, node) in self.graph.direct_child_pairs(&self.node) {
        match self.graph.edge_content(&edge) {
            Some(QueryGraphDependency::Then) => {
                self.graph.pluck_edge(&edge);
                then_node = Some(node);
            }
            Some(QueryGraphDependency::Else) => {
                self.graph.pluck_edge(&edge);
                else_node = Some(node);
            }
            _ => {}
        }
    }

    let then_expr = match then_node {
        Some(node) => self.process_child_with_dependencies(node)?,
        None => return Err(/* missing then branch */),
    };

    let else_expr = match else_node {
        Some(node) => self.process_child_with_dependencies(node)?,
        None => Expression::Unit,
    };

    // Extract the condition
    let Node::Flow(Flow::If { rule, data }) = node else {
        panic!("current node must be Flow::If");
    };

    let expr = Expression::If {
        value: Expression::Get {
            name: SelectionResults::new(data).into_placeholder()?.name,
        }.into(),
        rule,
        then: then_expr.into(),
        r#else: else_expr.into(),
    };

    Ok(expr)
}

Data Dependencies

Data dependencies flow through the graph via bindings:

Projected Dependencies
Row Count Dependencies

When a node needs specific fields from a parent:

QueryGraphDependency::ProjectedDataDependency(selection, sink, expectation)

Creates field-level bindings:

Binding::new(
    binding::projected_dependency(source, field),
    Expression::MapField {
        field: field.db_name().into(),
        records: Expression::Get {
            name: binding::node_result(source),
        }.into(),
    },
)

When a node needs to validate result counts:

QueryGraphDependency::DataDependency(RowCountSink::Discard, expectation)

Creates validation expressions:

Expression::Validate {
    expr: expr.into(),
    rules: expectation.rules().to_vec(),
    error_identifier: expectation.error().id(),
    context: expectation.error().context(),
}

Query Building

The QueryBuilder trait abstracts SQL generation:

pub trait QueryBuilder {
    fn build_select(&self, query: SelectQuery) -> Result<DbQuery>;
    fn build_insert(&self, query: InsertQuery) -> Result<DbQuery>;
    fn build_update(&self, query: UpdateQuery) -> Result<DbQuery>;
    fn build_delete(&self, query: DeleteQuery) -> Result<DbQuery>;
}

Database-specific builders implement this trait:

impl QueryBuilder for SqlQueryBuilder<visitor::Postgres<'_>> { /* ... */ }
impl QueryBuilder for SqlQueryBuilder<visitor::Mysql<'_>> { /* ... */ }
impl QueryBuilder for SqlQueryBuilder<visitor::Sqlite<'_>> { /* ... */ }
impl QueryBuilder for SqlQueryBuilder<visitor::Mssql<'_>> { /* ... */ }

In-Memory Processing

Some operations happen in-memory after data is fetched:

#[derive(Debug, Serialize)]
pub struct InMemoryOps {
    /// Pagination (cursor, take, skip)
    pub pagination: Option<Pagination>,
    
    /// Distinct on fields
    pub distinct: Option<Vec<String>>,
    
    /// Reverse result order
    pub reverse: bool,
    
    /// Nested operations for relations
    pub nested: BTreeMap<String, InMemoryOps>,
    
    /// Fields to use for linking parent/child
    pub linking_fields: Option<Vec<String>>,
}

These are wrapped in a Process expression:

Expression::Process {
    expr: query_expr.into(),
    operations: in_memory_ops,
}

Application-Level Joins

When relation joins can’t be done in SQL:

Expression::Join {
    parent: Box::new(parent_query),
    children: vec![
        JoinExpression {
            child: child_query,
            on: vec![("parent_id".into(), "id".into())],
            parent_field: "posts".into(),
            is_relation_unique: false,
        }
    ],
    can_assume_strict_equality: true,
}

The interpreter:

Executes parent query
Extracts join keys from parent results
Executes child query with WHERE key IN (...)
Merges results in-memory based on on conditions

Result Node Structure

The result structure describes how to shape the final output:

pub enum ResultNode {
    /// A record with named fields
    Record(BTreeMap<String, ResultNode>),
    
    /// A list of items
    List(Box<ResultNode>),
    
    /// A leaf value
    Leaf,
}

This enables the data mapper to transform flat database results into nested JSON.

Optimizations

The planner applies several optimizations:

Subquery Elimination

Flatten nested queries where possible to reduce round-trips.

Projection Pushdown

Only select fields that are actually needed.

Predicate Pushdown

Push WHERE clauses as deep as possible.

Join Coalescing

Combine multiple joins when safe to do so.

Benchmarking Query Planning

Benchmark the compilation process:

# Run all benchmarks
cargo bench -p query-compiler --profile profiling

# Save a baseline
cargo bench -p query-compiler --profile profiling -- --save-baseline main

# Compare against baseline
cargo bench -p query-compiler --profile profiling -- --baseline main

From the Makefile:

bench-qc:
	cargo bench -p query-compiler --profile profiling

bench-qc-baseline:
	cargo bench -p query-compiler --profile profiling -- --save-baseline $(NAME)

bench-qc-compare:
	cargo bench -p query-compiler --profile profiling -- --baseline $(NAME)

Query Graph Benchmarks

Benchmark just the graph building phase:

cargo bench -p core-tests --profile profiling --bench query_graph_bench

Profiling

Profile a specific query:

cargo run -p query-compiler --example profile_query --profile profiling

This runs the profile_query example which you can customize for your specific use case.

Playground

Explore query planning interactively:

cargo run -p query-compiler-playground

The playground lets you:

Input a Prisma schema
Write a GraphQL query
See the generated expression tree
Export Graphviz diagrams (if dot is installed)

Dependencies from query-compiler-playground/Cargo.toml:

[dependencies]
psl = { workspace = true, features = ["all"] }
query-compiler = { workspace = true, features = ["all"] }
request-handlers.workspace = true
query-core.workspace = true
quaint = { workspace = true, features = ["all-native"] }
sql-query-builder.workspace = true

Overview

Core Components

Query Compiler

Schema Engine

Prisma Schema Language

Development

Query Graph Structure

Node Types

Edge Types

Translation Process

Node Translation

Query Translation

Conditional Translation

Data Dependencies

Query Building

In-Memory Processing

Application-Level Joins

Result Node Structure

Optimizations

Subquery Elimination

Projection Pushdown

Predicate Pushdown

Join Coalescing

Benchmarking Query Planning

Query Graph Benchmarks

Profiling

Playground

Next Steps

Overview

WASM Build

Build docs developers (and LLMs) love

Overview

Core Components

Query Compiler

Schema Engine

Prisma Schema Language

Development

Documentation Index

​Query Graph Structure

​Node Types

​Edge Types

​Translation Process

​Node Translation

​Query Translation

​Conditional Translation

​Data Dependencies

​Query Building

​In-Memory Processing

​Application-Level Joins

​Result Node Structure

​Optimizations

Subquery Elimination

Projection Pushdown

Predicate Pushdown

Join Coalescing

​Benchmarking Query Planning

​Query Graph Benchmarks

​Profiling

​Playground

​Next Steps

Overview

WASM Build

Build docs developers (and LLMs) love

Query Graph Structure

Node Types

Edge Types

Translation Process

Node Translation

Query Translation

Conditional Translation

Data Dependencies

Query Building

In-Memory Processing

Application-Level Joins

Result Node Structure

Optimizations

Benchmarking Query Planning

Query Graph Benchmarks

Profiling

Playground

Next Steps