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plan.rs
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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you 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.
//! This module contains the `LogicalPlan` enum that describes queries
//! via a logical query plan.
use super::display::{GraphvizVisitor, IndentVisitor};
use super::expr::{Column, Expr};
use super::extension::UserDefinedLogicalNode;
use crate::datasource::TableProvider;
use crate::error::DataFusionError;
use crate::logical_plan::dfschema::DFSchemaRef;
use crate::sql::parser::FileType;
use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
use std::{
collections::HashSet,
fmt::{self, Display},
sync::Arc,
};
/// Join type
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum JoinType {
/// Inner Join
Inner,
/// Left Join
Left,
/// Right Join
Right,
/// Full Join
Full,
/// Semi Join
Semi,
/// Anti Join
Anti,
}
/// Join constraint
#[derive(Debug, Clone, Copy)]
pub enum JoinConstraint {
/// Join ON
On,
/// Join USING
Using,
}
/// Evaluates an arbitrary list of expressions (essentially a
/// SELECT with an expression list) on its input.
#[derive(Clone)]
pub struct Projection {
/// The list of expressions
pub expr: Vec<Expr>,
/// The incoming logical plan
pub input: Arc<LogicalPlan>,
/// The schema description of the output
pub schema: DFSchemaRef,
/// Projection output relation alias
pub alias: Option<String>,
}
/// Filters rows from its input that do not match an
/// expression (essentially a WHERE clause with a predicate
/// expression).
///
/// Semantically, `<predicate>` is evaluated for each row of the input;
/// If the value of `<predicate>` is true, the input row is passed to
/// the output. If the value of `<predicate>` is false, the row is
/// discarded.
#[derive(Clone)]
pub struct Filter {
/// The predicate expression, which must have Boolean type.
pub predicate: Expr,
/// The incoming logical plan
pub input: Arc<LogicalPlan>,
}
/// Window its input based on a set of window spec and window function (e.g. SUM or RANK)
#[derive(Clone)]
pub struct Window {
/// The incoming logical plan
pub input: Arc<LogicalPlan>,
/// The window function expression
pub window_expr: Vec<Expr>,
/// The schema description of the window output
pub schema: DFSchemaRef,
}
/// Produces rows from a table provider by reference or from the context
#[derive(Clone)]
pub struct TableScan {
/// The name of the table
pub table_name: String,
/// The source of the table
pub source: Arc<dyn TableProvider>,
/// Optional column indices to use as a projection
pub projection: Option<Vec<usize>>,
/// The schema description of the output
pub projected_schema: DFSchemaRef,
/// Optional expressions to be used as filters by the table provider
pub filters: Vec<Expr>,
/// Optional limit to skip reading
pub limit: Option<usize>,
}
/// Apply Cross Join to two logical plans
#[derive(Clone)]
pub struct CrossJoin {
/// Left input
pub left: Arc<LogicalPlan>,
/// Right input
pub right: Arc<LogicalPlan>,
/// The output schema, containing fields from the left and right inputs
pub schema: DFSchemaRef,
}
/// Repartition the plan based on a partitioning scheme.
#[derive(Clone)]
pub struct Repartition {
/// The incoming logical plan
pub input: Arc<LogicalPlan>,
/// The partitioning scheme
pub partitioning_scheme: Partitioning,
}
/// Union multiple inputs
#[derive(Clone)]
pub struct Union {
/// Inputs to merge
pub inputs: Vec<LogicalPlan>,
/// Union schema. Should be the same for all inputs.
pub schema: DFSchemaRef,
/// Union output relation alias
pub alias: Option<String>,
}
/// Creates an in memory table.
#[derive(Clone)]
pub struct CreateMemoryTable {
/// The table name
pub name: String,
/// The logical plan
pub input: Arc<LogicalPlan>,
}
/// Creates an external table.
#[derive(Clone)]
pub struct CreateExternalTable {
/// The table schema
pub schema: DFSchemaRef,
/// The table name
pub name: String,
/// The physical location
pub location: String,
/// The file type of physical file
pub file_type: FileType,
/// Whether the CSV file contains a header
pub has_header: bool,
}
/// Drops a table.
#[derive(Clone)]
pub struct DropTable {
/// The table name
pub name: String,
/// If the table exists
pub if_exist: bool,
/// Dummy schema
pub schema: DFSchemaRef,
}
/// Produces a relation with string representations of
/// various parts of the plan
#[derive(Clone)]
pub struct Explain {
/// Should extra (detailed, intermediate plans) be included?
pub verbose: bool,
/// The logical plan that is being EXPLAIN'd
pub plan: Arc<LogicalPlan>,
/// Represent the various stages plans have gone through
pub stringified_plans: Vec<StringifiedPlan>,
/// The output schema of the explain (2 columns of text)
pub schema: DFSchemaRef,
}
/// Runs the actual plan, and then prints the physical plan with
/// with execution metrics.
#[derive(Clone)]
pub struct Analyze {
/// Should extra detail be included?
pub verbose: bool,
/// The logical plan that is being EXPLAIN ANALYZE'd
pub input: Arc<LogicalPlan>,
/// The output schema of the explain (2 columns of text)
pub schema: DFSchemaRef,
}
/// Extension operator defined outside of DataFusion
#[derive(Clone)]
pub struct Extension {
/// The runtime extension operator
pub node: Arc<dyn UserDefinedLogicalNode + Send + Sync>,
}
/// Produces no rows: An empty relation with an empty schema
#[derive(Clone)]
pub struct EmptyRelation {
/// Whether to produce a placeholder row
pub produce_one_row: bool,
/// The schema description of the output
pub schema: DFSchemaRef,
}
/// Produces the first `n` tuples from its input and discards the rest.
#[derive(Clone)]
pub struct Limit {
/// The limit
pub n: usize,
/// The logical plan
pub input: Arc<LogicalPlan>,
}
/// Values expression. See
/// [Postgres VALUES](https://www.postgresql.org/docs/current/queries-values.html)
/// documentation for more details.
#[derive(Clone)]
pub struct Values {
/// The table schema
pub schema: DFSchemaRef,
/// Values
pub values: Vec<Vec<Expr>>,
}
/// Aggregates its input based on a set of grouping and aggregate
/// expressions (e.g. SUM).
#[derive(Clone)]
pub struct Aggregate {
/// The incoming logical plan
pub input: Arc<LogicalPlan>,
/// Grouping expressions
pub group_expr: Vec<Expr>,
/// Aggregate expressions
pub aggr_expr: Vec<Expr>,
/// The schema description of the aggregate output
pub schema: DFSchemaRef,
}
/// Sorts its input according to a list of sort expressions.
#[derive(Clone)]
pub struct Sort {
/// The sort expressions
pub expr: Vec<Expr>,
/// The incoming logical plan
pub input: Arc<LogicalPlan>,
}
/// Join two logical plans on one or more join columns
#[derive(Clone)]
pub struct Join {
/// Left input
pub left: Arc<LogicalPlan>,
/// Right input
pub right: Arc<LogicalPlan>,
/// Equijoin clause expressed as pairs of (left, right) join columns
pub on: Vec<(Column, Column)>,
/// Join type
pub join_type: JoinType,
/// Join constraint
pub join_constraint: JoinConstraint,
/// The output schema, containing fields from the left and right inputs
pub schema: DFSchemaRef,
/// If null_equals_null is true, null == null else null != null
pub null_equals_null: bool,
}
/// A LogicalPlan represents the different types of relational
/// operators (such as Projection, Filter, etc) and can be created by
/// the SQL query planner and the DataFrame API.
///
/// A LogicalPlan represents transforming an input relation (table) to
/// an output relation (table) with a (potentially) different
/// schema. A plan represents a dataflow tree where data flows
/// from leaves up to the root to produce the query result.
#[derive(Clone)]
pub enum LogicalPlan {
/// Evaluates an arbitrary list of expressions (essentially a
/// SELECT with an expression list) on its input.
Projection(Projection),
/// Filters rows from its input that do not match an
/// expression (essentially a WHERE clause with a predicate
/// expression).
///
/// Semantically, `<predicate>` is evaluated for each row of the input;
/// If the value of `<predicate>` is true, the input row is passed to
/// the output. If the value of `<predicate>` is false, the row is
/// discarded.
Filter(Filter),
/// Window its input based on a set of window spec and window function (e.g. SUM or RANK)
Window(Window),
/// Aggregates its input based on a set of grouping and aggregate
/// expressions (e.g. SUM).
Aggregate(Aggregate),
/// Sorts its input according to a list of sort expressions.
Sort(Sort),
/// Join two logical plans on one or more join columns
Join(Join),
/// Apply Cross Join to two logical plans
CrossJoin(CrossJoin),
/// Repartition the plan based on a partitioning scheme.
Repartition(Repartition),
/// Union multiple inputs
Union(Union),
/// Produces rows from a table provider by reference or from the context
TableScan(TableScan),
/// Produces no rows: An empty relation with an empty schema
EmptyRelation(EmptyRelation),
/// Produces the first `n` tuples from its input and discards the rest.
Limit(Limit),
/// Creates an external table.
CreateExternalTable(CreateExternalTable),
/// Creates an in memory table.
CreateMemoryTable(CreateMemoryTable),
/// Drops a table.
DropTable(DropTable),
/// Values expression. See
/// [Postgres VALUES](https://www.postgresql.org/docs/current/queries-values.html)
/// documentation for more details.
Values(Values),
/// Produces a relation with string representations of
/// various parts of the plan
Explain(Explain),
/// Runs the actual plan, and then prints the physical plan with
/// with execution metrics.
Analyze(Analyze),
/// Extension operator defined outside of DataFusion
Extension(Extension),
}
impl LogicalPlan {
/// Get a reference to the logical plan's schema
pub fn schema(&self) -> &DFSchemaRef {
match self {
LogicalPlan::EmptyRelation(EmptyRelation { schema, .. }) => schema,
LogicalPlan::Values(Values { schema, .. }) => schema,
LogicalPlan::TableScan(TableScan {
projected_schema, ..
}) => projected_schema,
LogicalPlan::Projection(Projection { schema, .. }) => schema,
LogicalPlan::Filter(Filter { input, .. }) => input.schema(),
LogicalPlan::Window(Window { schema, .. }) => schema,
LogicalPlan::Aggregate(Aggregate { schema, .. }) => schema,
LogicalPlan::Sort(Sort { input, .. }) => input.schema(),
LogicalPlan::Join(Join { schema, .. }) => schema,
LogicalPlan::CrossJoin(CrossJoin { schema, .. }) => schema,
LogicalPlan::Repartition(Repartition { input, .. }) => input.schema(),
LogicalPlan::Limit(Limit { input, .. }) => input.schema(),
LogicalPlan::CreateExternalTable(CreateExternalTable { schema, .. }) => {
schema
}
LogicalPlan::Explain(explain) => &explain.schema,
LogicalPlan::Analyze(analyze) => &analyze.schema,
LogicalPlan::Extension(extension) => extension.node.schema(),
LogicalPlan::Union(Union { schema, .. }) => schema,
LogicalPlan::CreateMemoryTable(CreateMemoryTable { input, .. }) => {
input.schema()
}
LogicalPlan::DropTable(DropTable { schema, .. }) => schema,
}
}
/// Get a vector of references to all schemas in every node of the logical plan
pub fn all_schemas(&self) -> Vec<&DFSchemaRef> {
match self {
LogicalPlan::TableScan(TableScan {
projected_schema, ..
}) => vec![projected_schema],
LogicalPlan::Values(Values { schema, .. }) => vec![schema],
LogicalPlan::Window(Window { input, schema, .. })
| LogicalPlan::Projection(Projection { input, schema, .. })
| LogicalPlan::Aggregate(Aggregate { input, schema, .. }) => {
let mut schemas = input.all_schemas();
schemas.insert(0, schema);
schemas
}
LogicalPlan::Join(Join {
left,
right,
schema,
..
})
| LogicalPlan::CrossJoin(CrossJoin {
left,
right,
schema,
}) => {
let mut schemas = left.all_schemas();
schemas.extend(right.all_schemas());
schemas.insert(0, schema);
schemas
}
LogicalPlan::Union(Union { schema, .. }) => {
vec![schema]
}
LogicalPlan::Extension(extension) => vec![extension.node.schema()],
LogicalPlan::Explain(Explain { schema, .. })
| LogicalPlan::Analyze(Analyze { schema, .. })
| LogicalPlan::EmptyRelation(EmptyRelation { schema, .. })
| LogicalPlan::CreateExternalTable(CreateExternalTable { schema, .. }) => {
vec![schema]
}
LogicalPlan::Limit(Limit { input, .. })
| LogicalPlan::Repartition(Repartition { input, .. })
| LogicalPlan::Sort(Sort { input, .. })
| LogicalPlan::CreateMemoryTable(CreateMemoryTable { input, .. })
| LogicalPlan::Filter(Filter { input, .. }) => input.all_schemas(),
LogicalPlan::DropTable(_) => vec![],
}
}
/// Returns the (fixed) output schema for explain plans
pub fn explain_schema() -> SchemaRef {
SchemaRef::new(Schema::new(vec![
Field::new("plan_type", DataType::Utf8, false),
Field::new("plan", DataType::Utf8, false),
]))
}
/// returns all expressions (non-recursively) in the current
/// logical plan node. This does not include expressions in any
/// children
pub fn expressions(self: &LogicalPlan) -> Vec<Expr> {
match self {
LogicalPlan::Projection(Projection { expr, .. }) => expr.clone(),
LogicalPlan::Values(Values { values, .. }) => {
values.iter().flatten().cloned().collect()
}
LogicalPlan::Filter(Filter { predicate, .. }) => vec![predicate.clone()],
LogicalPlan::Repartition(Repartition {
partitioning_scheme,
..
}) => match partitioning_scheme {
Partitioning::Hash(expr, _) => expr.clone(),
_ => vec![],
},
LogicalPlan::Window(Window { window_expr, .. }) => window_expr.clone(),
LogicalPlan::Aggregate(Aggregate {
group_expr,
aggr_expr,
..
}) => group_expr.iter().chain(aggr_expr.iter()).cloned().collect(),
LogicalPlan::Join(Join { on, .. }) => on
.iter()
.flat_map(|(l, r)| vec![Expr::Column(l.clone()), Expr::Column(r.clone())])
.collect(),
LogicalPlan::Sort(Sort { expr, .. }) => expr.clone(),
LogicalPlan::Extension(extension) => extension.node.expressions(),
// plans without expressions
LogicalPlan::TableScan { .. }
| LogicalPlan::EmptyRelation(_)
| LogicalPlan::Limit(_)
| LogicalPlan::CreateExternalTable(_)
| LogicalPlan::CreateMemoryTable(_)
| LogicalPlan::DropTable(_)
| LogicalPlan::CrossJoin(_)
| LogicalPlan::Analyze { .. }
| LogicalPlan::Explain { .. }
| LogicalPlan::Union(_) => {
vec![]
}
}
}
/// returns all inputs of this `LogicalPlan` node. Does not
/// include inputs to inputs.
pub fn inputs(self: &LogicalPlan) -> Vec<&LogicalPlan> {
match self {
LogicalPlan::Projection(Projection { input, .. }) => vec![input],
LogicalPlan::Filter(Filter { input, .. }) => vec![input],
LogicalPlan::Repartition(Repartition { input, .. }) => vec![input],
LogicalPlan::Window(Window { input, .. }) => vec![input],
LogicalPlan::Aggregate(Aggregate { input, .. }) => vec![input],
LogicalPlan::Sort(Sort { input, .. }) => vec![input],
LogicalPlan::Join(Join { left, right, .. }) => vec![left, right],
LogicalPlan::CrossJoin(CrossJoin { left, right, .. }) => vec![left, right],
LogicalPlan::Limit(Limit { input, .. }) => vec![input],
LogicalPlan::Extension(extension) => extension.node.inputs(),
LogicalPlan::Union(Union { inputs, .. }) => inputs.iter().collect(),
LogicalPlan::Explain(explain) => vec![&explain.plan],
LogicalPlan::Analyze(analyze) => vec![&analyze.input],
LogicalPlan::CreateMemoryTable(CreateMemoryTable { input, .. }) => {
vec![input]
}
// plans without inputs
LogicalPlan::TableScan { .. }
| LogicalPlan::EmptyRelation { .. }
| LogicalPlan::Values { .. }
| LogicalPlan::CreateExternalTable(_)
| LogicalPlan::DropTable(_) => vec![],
}
}
/// returns all `Using` join columns in a logical plan
pub fn using_columns(&self) -> Result<Vec<HashSet<Column>>, DataFusionError> {
struct UsingJoinColumnVisitor {
using_columns: Vec<HashSet<Column>>,
}
impl PlanVisitor for UsingJoinColumnVisitor {
type Error = DataFusionError;
fn pre_visit(&mut self, plan: &LogicalPlan) -> Result<bool, Self::Error> {
if let LogicalPlan::Join(Join {
join_constraint: JoinConstraint::Using,
on,
..
}) = plan
{
self.using_columns.push(
on.iter()
.map(|entry| [&entry.0, &entry.1])
.flatten()
.cloned()
.collect::<HashSet<Column>>(),
);
}
Ok(true)
}
}
let mut visitor = UsingJoinColumnVisitor {
using_columns: vec![],
};
self.accept(&mut visitor)?;
Ok(visitor.using_columns)
}
}
/// Logical partitioning schemes supported by the repartition operator.
#[derive(Debug, Clone)]
pub enum Partitioning {
/// Allocate batches using a round-robin algorithm and the specified number of partitions
RoundRobinBatch(usize),
/// Allocate rows based on a hash of one of more expressions and the specified number
/// of partitions.
/// This partitioning scheme is not yet fully supported. See <https://issues.apache.org/jira/browse/ARROW-11011>
Hash(Vec<Expr>, usize),
}
/// Trait that implements the [Visitor
/// pattern](https://en.wikipedia.org/wiki/Visitor_pattern) for a
/// depth first walk of `LogicalPlan` nodes. `pre_visit` is called
/// before any children are visited, and then `post_visit` is called
/// after all children have been visited.
////
/// To use, define a struct that implements this trait and then invoke
/// [`LogicalPlan::accept`].
///
/// For example, for a logical plan like:
///
/// ```text
/// Projection: #id
/// Filter: #state Eq Utf8(\"CO\")\
/// CsvScan: employee.csv projection=Some([0, 3])";
/// ```
///
/// The sequence of visit operations would be:
/// ```text
/// visitor.pre_visit(Projection)
/// visitor.pre_visit(Filter)
/// visitor.pre_visit(CsvScan)
/// visitor.post_visit(CsvScan)
/// visitor.post_visit(Filter)
/// visitor.post_visit(Projection)
/// ```
pub trait PlanVisitor {
/// The type of error returned by this visitor
type Error;
/// Invoked on a logical plan before any of its child inputs have been
/// visited. If Ok(true) is returned, the recursion continues. If
/// Err(..) or Ok(false) are returned, the recursion stops
/// immediately and the error, if any, is returned to `accept`
fn pre_visit(&mut self, plan: &LogicalPlan)
-> std::result::Result<bool, Self::Error>;
/// Invoked on a logical plan after all of its child inputs have
/// been visited. The return value is handled the same as the
/// return value of `pre_visit`. The provided default implementation
/// returns `Ok(true)`.
fn post_visit(
&mut self,
_plan: &LogicalPlan,
) -> std::result::Result<bool, Self::Error> {
Ok(true)
}
}
impl LogicalPlan {
/// returns all inputs in the logical plan. Returns Ok(true) if
/// all nodes were visited, and Ok(false) if any call to
/// `pre_visit` or `post_visit` returned Ok(false) and may have
/// cut short the recursion
pub fn accept<V>(&self, visitor: &mut V) -> std::result::Result<bool, V::Error>
where
V: PlanVisitor,
{
if !visitor.pre_visit(self)? {
return Ok(false);
}
let recurse = match self {
LogicalPlan::Projection(Projection { input, .. }) => input.accept(visitor)?,
LogicalPlan::Filter(Filter { input, .. }) => input.accept(visitor)?,
LogicalPlan::Repartition(Repartition { input, .. }) => {
input.accept(visitor)?
}
LogicalPlan::Window(Window { input, .. }) => input.accept(visitor)?,
LogicalPlan::Aggregate(Aggregate { input, .. }) => input.accept(visitor)?,
LogicalPlan::Sort(Sort { input, .. }) => input.accept(visitor)?,
LogicalPlan::Join(Join { left, right, .. })
| LogicalPlan::CrossJoin(CrossJoin { left, right, .. }) => {
left.accept(visitor)? && right.accept(visitor)?
}
LogicalPlan::Union(Union { inputs, .. }) => {
for input in inputs {
if !input.accept(visitor)? {
return Ok(false);
}
}
true
}
LogicalPlan::Limit(Limit { input, .. }) => input.accept(visitor)?,
LogicalPlan::CreateMemoryTable(CreateMemoryTable { input, .. }) => {
input.accept(visitor)?
}
LogicalPlan::Extension(extension) => {
for input in extension.node.inputs() {
if !input.accept(visitor)? {
return Ok(false);
}
}
true
}
LogicalPlan::Explain(explain) => explain.plan.accept(visitor)?,
LogicalPlan::Analyze(analyze) => analyze.input.accept(visitor)?,
// plans without inputs
LogicalPlan::TableScan { .. }
| LogicalPlan::EmptyRelation(_)
| LogicalPlan::Values(_)
| LogicalPlan::CreateExternalTable(_)
| LogicalPlan::DropTable(_) => true,
};
if !recurse {
return Ok(false);
}
if !visitor.post_visit(self)? {
return Ok(false);
}
Ok(true)
}
}
// Various implementations for printing out LogicalPlans
impl LogicalPlan {
/// Return a `format`able structure that produces a single line
/// per node. For example:
///
/// ```text
/// Projection: #employee.id
/// Filter: #employee.state Eq Utf8(\"CO\")\
/// CsvScan: employee projection=Some([0, 3])
/// ```
///
/// ```
/// use arrow::datatypes::{Field, Schema, DataType};
/// use datafusion::logical_plan::{lit, col, LogicalPlanBuilder};
/// let schema = Schema::new(vec![
/// Field::new("id", DataType::Int32, false),
/// ]);
/// let plan = LogicalPlanBuilder::scan_empty(Some("foo_csv"), &schema, None).unwrap()
/// .filter(col("id").eq(lit(5))).unwrap()
/// .build().unwrap();
///
/// // Format using display_indent
/// let display_string = format!("{}", plan.display_indent());
///
/// assert_eq!("Filter: #foo_csv.id = Int32(5)\
/// \n TableScan: foo_csv projection=None",
/// display_string);
/// ```
pub fn display_indent(&self) -> impl fmt::Display + '_ {
// Boilerplate structure to wrap LogicalPlan with something
// that that can be formatted
struct Wrapper<'a>(&'a LogicalPlan);
impl<'a> fmt::Display for Wrapper<'a> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let with_schema = false;
let mut visitor = IndentVisitor::new(f, with_schema);
self.0.accept(&mut visitor).unwrap();
Ok(())
}
}
Wrapper(self)
}
/// Return a `format`able structure that produces a single line
/// per node that includes the output schema. For example:
///
/// ```text
/// Projection: #employee.id [id:Int32]\
/// Filter: #employee.state = Utf8(\"CO\") [id:Int32, state:Utf8]\
/// TableScan: employee projection=Some([0, 3]) [id:Int32, state:Utf8]";
/// ```
///
/// ```
/// use arrow::datatypes::{Field, Schema, DataType};
/// use datafusion::logical_plan::{lit, col, LogicalPlanBuilder};
/// let schema = Schema::new(vec![
/// Field::new("id", DataType::Int32, false),
/// ]);
/// let plan = LogicalPlanBuilder::scan_empty(Some("foo_csv"), &schema, None).unwrap()
/// .filter(col("id").eq(lit(5))).unwrap()
/// .build().unwrap();
///
/// // Format using display_indent_schema
/// let display_string = format!("{}", plan.display_indent_schema());
///
/// assert_eq!("Filter: #foo_csv.id = Int32(5) [id:Int32]\
/// \n TableScan: foo_csv projection=None [id:Int32]",
/// display_string);
/// ```
pub fn display_indent_schema(&self) -> impl fmt::Display + '_ {
// Boilerplate structure to wrap LogicalPlan with something
// that that can be formatted
struct Wrapper<'a>(&'a LogicalPlan);
impl<'a> fmt::Display for Wrapper<'a> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let with_schema = true;
let mut visitor = IndentVisitor::new(f, with_schema);
self.0.accept(&mut visitor).unwrap();
Ok(())
}
}
Wrapper(self)
}
/// Return a `format`able structure that produces lines meant for
/// graphical display using the `DOT` language. This format can be
/// visualized using software from
/// [`graphviz`](https://graphviz.org/)
///
/// This currently produces two graphs -- one with the basic
/// structure, and one with additional details such as schema.
///
/// ```
/// use arrow::datatypes::{Field, Schema, DataType};
/// use datafusion::logical_plan::{lit, col, LogicalPlanBuilder};
/// let schema = Schema::new(vec![
/// Field::new("id", DataType::Int32, false),
/// ]);
/// let plan = LogicalPlanBuilder::scan_empty(Some("foo.csv"), &schema, None).unwrap()
/// .filter(col("id").eq(lit(5))).unwrap()
/// .build().unwrap();
///
/// // Format using display_graphviz
/// let graphviz_string = format!("{}", plan.display_graphviz());
/// ```
///
/// If graphviz string is saved to a file such as `/tmp/example.dot`, the following
/// commands can be used to render it as a pdf:
///
/// ```bash
/// dot -Tpdf < /tmp/example.dot > /tmp/example.pdf
/// ```
///
pub fn display_graphviz(&self) -> impl fmt::Display + '_ {
// Boilerplate structure to wrap LogicalPlan with something
// that that can be formatted
struct Wrapper<'a>(&'a LogicalPlan);
impl<'a> fmt::Display for Wrapper<'a> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
writeln!(
f,
"// Begin DataFusion GraphViz Plan (see https://graphviz.org)"
)?;
writeln!(f, "digraph {{")?;
let mut visitor = GraphvizVisitor::new(f);
visitor.pre_visit_plan("LogicalPlan")?;
self.0.accept(&mut visitor).unwrap();
visitor.post_visit_plan()?;
visitor.set_with_schema(true);
visitor.pre_visit_plan("Detailed LogicalPlan")?;
self.0.accept(&mut visitor).unwrap();
visitor.post_visit_plan()?;
writeln!(f, "}}")?;
writeln!(f, "// End DataFusion GraphViz Plan")?;
Ok(())
}
}
Wrapper(self)
}
/// Return a `format`able structure with the a human readable
/// description of this LogicalPlan node per node, not including
/// children. For example:
///
/// ```text
/// Projection: #id
/// ```
/// ```
/// use arrow::datatypes::{Field, Schema, DataType};
/// use datafusion::logical_plan::{lit, col, LogicalPlanBuilder};
/// let schema = Schema::new(vec![
/// Field::new("id", DataType::Int32, false),
/// ]);
/// let plan = LogicalPlanBuilder::scan_empty(Some("foo.csv"), &schema, None).unwrap()
/// .build().unwrap();
///
/// // Format using display
/// let display_string = format!("{}", plan.display());
///
/// assert_eq!("TableScan: foo.csv projection=None", display_string);
/// ```
pub fn display(&self) -> impl fmt::Display + '_ {
// Boilerplate structure to wrap LogicalPlan with something
// that that can be formatted
struct Wrapper<'a>(&'a LogicalPlan);
impl<'a> fmt::Display for Wrapper<'a> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
match &*self.0 {
LogicalPlan::EmptyRelation(_) => write!(f, "EmptyRelation"),
LogicalPlan::Values(Values { ref values, .. }) => {
let str_values: Vec<_> = values
.iter()
// limit to only 5 values to avoid horrible display
.take(5)
.map(|row| {
let item = row
.iter()
.map(|expr| expr.to_string())
.collect::<Vec<_>>()
.join(", ");
format!("({})", item)
})
.collect();
let elipse = if values.len() > 5 { "..." } else { "" };
write!(f, "Values: {}{}", str_values.join(", "), elipse)
}
LogicalPlan::TableScan(TableScan {
ref table_name,
ref projection,
ref filters,
ref limit,
..
}) => {
write!(
f,
"TableScan: {} projection={:?}",
table_name, projection
)?;
if !filters.is_empty() {
write!(f, ", filters={:?}", filters)?;
}
if let Some(n) = limit {
write!(f, ", limit={}", n)?;
}
Ok(())
}
LogicalPlan::Projection(Projection {
ref expr, alias, ..
}) => {
write!(f, "Projection: ")?;
for (i, expr_item) in expr.iter().enumerate() {
if i > 0 {
write!(f, ", ")?;
}
write!(f, "{:?}", expr_item)?;
}
if let Some(a) = alias {
write!(f, ", alias={}", a)?;
}
Ok(())
}
LogicalPlan::Filter(Filter {
predicate: ref expr,
..
}) => write!(f, "Filter: {:?}", expr),
LogicalPlan::Window(Window {
ref window_expr, ..
}) => {
write!(f, "WindowAggr: windowExpr=[{:?}]", window_expr)
}
LogicalPlan::Aggregate(Aggregate {
ref group_expr,
ref aggr_expr,
..
}) => write!(
f,
"Aggregate: groupBy=[{:?}], aggr=[{:?}]",
group_expr, aggr_expr
),
LogicalPlan::Sort(Sort { expr, .. }) => {
write!(f, "Sort: ")?;
for (i, expr_item) in expr.iter().enumerate() {
if i > 0 {
write!(f, ", ")?;
}
write!(f, "{:?}", expr_item)?;
}
Ok(())
}
LogicalPlan::Join(Join {
on: ref keys,
join_constraint,
..
}) => {
let join_expr: Vec<String> =
keys.iter().map(|(l, r)| format!("{} = {}", l, r)).collect();
match join_constraint {
JoinConstraint::On => {
write!(f, "Join: {}", join_expr.join(", "))
}
JoinConstraint::Using => {
write!(f, "Join: Using {}", join_expr.join(", "))
}
}
}
LogicalPlan::CrossJoin(_) => {
write!(f, "CrossJoin:")
}
LogicalPlan::Repartition(Repartition {
partitioning_scheme,
..
}) => match partitioning_scheme {
Partitioning::RoundRobinBatch(n) => write!(
f,
"Repartition: RoundRobinBatch partition_count={}",
n
),
Partitioning::Hash(expr, n) => {
let hash_expr: Vec<String> =
expr.iter().map(|e| format!("{:?}", e)).collect();
write!(
f,
"Repartition: Hash({}) partition_count={}",
hash_expr.join(", "),
n
)
}
},
LogicalPlan::Limit(Limit { ref n, .. }) => write!(f, "Limit: {}", n),
LogicalPlan::CreateExternalTable(CreateExternalTable {
ref name,
..
}) => {
write!(f, "CreateExternalTable: {:?}", name)
}
LogicalPlan::CreateMemoryTable(CreateMemoryTable {
name, ..
}) => {
write!(f, "CreateMemoryTable: {:?}", name)
}
LogicalPlan::DropTable(DropTable { name, if_exist, .. }) => {
write!(f, "DropTable: {:?} if not exist:={}", name, if_exist)
}
LogicalPlan::Explain { .. } => write!(f, "Explain"),
LogicalPlan::Analyze { .. } => write!(f, "Analyze"),
LogicalPlan::Union(_) => write!(f, "Union"),
LogicalPlan::Extension(e) => e.node.fmt_for_explain(f),
}
}
}
Wrapper(self)
}
}
impl fmt::Debug for LogicalPlan {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {