Enum datafusion::logical_plan::LogicalPlan

pub enum LogicalPlan {
    Projection {
        expr: Vec<Expr>,
        input: Arc<LogicalPlan>,
        schema: DFSchemaRef,
    },
    Filter {
        predicate: Expr,
        input: Arc<LogicalPlan>,
    },
    Aggregate {
        input: Arc<LogicalPlan>,
        group_expr: Vec<Expr>,
        aggr_expr: Vec<Expr>,
        schema: DFSchemaRef,
    },
    Sort {
        expr: Vec<Expr>,
        input: Arc<LogicalPlan>,
    },
    Join {
        left: Arc<LogicalPlan>,
        right: Arc<LogicalPlan>,
        on: Vec<(String, String)>,
        join_type: JoinType,
        schema: DFSchemaRef,
    },
    Repartition {
        input: Arc<LogicalPlan>,
        partitioning_scheme: Partitioning,
    },
    TableScan {
        table_name: String,
        source: Arc<dyn TableProvider + Send + Sync>,
        projection: Option<Vec<usize>>,
        projected_schema: DFSchemaRef,
        filters: Vec<Expr>,
    },
    EmptyRelation {
        produce_one_row: bool,
        schema: DFSchemaRef,
    },
    Limit {
        n: usize,
        input: Arc<LogicalPlan>,
    },
    CreateExternalTable {
        schema: DFSchemaRef,
        name: String,
        location: String,
        file_type: FileType,
        has_header: bool,
    },
    Explain {
        verbose: bool,
        plan: Arc<LogicalPlan>,
        stringified_plans: Vec<StringifiedPlan>,
        schema: DFSchemaRef,
    },
    Extension {
        node: Arc<dyn UserDefinedLogicalNode + Send + Sync>,
    },
}

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.

Variants

Projection

Evaluates an arbitrary list of expressions (essentially a SELECT with an expression list) on its input.

Fields of Projection

expr: Vec<Expr>

The list of expressions

input: Arc<LogicalPlan>

The incoming logical plan

schema: DFSchemaRef

The schema description of the output

Filter

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.

Fields of Filter

predicate: Expr

The predicate expression, which must have Boolean type.

input: Arc<LogicalPlan>

The incoming logical plan

Aggregate

Aggregates its input based on a set of grouping and aggregate expressions (e.g. SUM).

Fields of Aggregate

input: Arc<LogicalPlan>

The incoming logical plan

group_expr: Vec<Expr>

Grouping expressions

aggr_expr: Vec<Expr>

Aggregate expressions

schema: DFSchemaRef

The schema description of the aggregate output

Sort

Sorts its input according to a list of sort expressions.

Fields of Sort

expr: Vec<Expr>

The sort expressions

input: Arc<LogicalPlan>

The incoming logical plan

Join

Join two logical plans on one or more join columns

Fields of Join

left: Arc<LogicalPlan>

Left input

right: Arc<LogicalPlan>

Right input

on: Vec<(String, String)>

Equijoin clause expressed as pairs of (left, right) join columns

join_type: JoinType

Join type

schema: DFSchemaRef

The output schema, containing fields from the left and right inputs

Repartition

Repartition the plan based on a partitioning scheme.

Fields of Repartition

input: Arc<LogicalPlan>

The incoming logical plan

partitioning_scheme: Partitioning

The partitioning scheme

TableScan

Produces rows from a table provider by reference or from the context

Fields of TableScan

table_name: String

The name of the table

source: Arc<dyn TableProvider + Send + Sync>

The source of the table

projection: Option<Vec<usize>>

Optional column indices to use as a projection

projected_schema: DFSchemaRef

The schema description of the output

filters: Vec<Expr>

Optional expressions to be used as filters by the table provider

EmptyRelation

Produces no rows: An empty relation with an empty schema

Fields of EmptyRelation

produce_one_row: bool

Whether to produce a placeholder row

schema: DFSchemaRef

The schema description of the output

Limit

Produces the first n tuples from its input and discards the rest.

Fields of Limit

n: usize

The limit

input: Arc<LogicalPlan>

The logical plan

CreateExternalTable

Creates an external table.

Fields of CreateExternalTable

schema: DFSchemaRef

The table schema

name: String

The table name

location: String

The physical location

file_type: FileType

The file type of physical file

has_header: bool

Whether the CSV file contains a header

Explain

Produces a relation with string representations of various parts of the plan

Fields of Explain

verbose: bool

Should extra (detailed, intermediate plans) be included?

plan: Arc<LogicalPlan>

The logical plan that is being EXPLAIN'd

stringified_plans: Vec<StringifiedPlan>

Represent the various stages plans have gone through

schema: DFSchemaRef

The output schema of the explain (2 columns of text)

Extension

Extension operator defined outside of DataFusion

Fields of Extension

node: Arc<dyn UserDefinedLogicalNode + Send + Sync>

The runtime extension operator

Implementations

impl LogicalPlan

pub fn schema(&self) -> &DFSchemaRef

Get a reference to the logical plan's schema

pub fn explain_schema() -> SchemaRef

Returns the (fixed) output schema for explain plans

impl LogicalPlan

pub fn accept<V>(&self, visitor: &mut V) -> Result<bool, V::Error> where
    V: PlanVisitor, 

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

impl LogicalPlan

pub fn display_indent(&self) -> impl Display + '_

Return a formatable structure that produces a single line per node. For example:

Projection: #id
   Filter: #state Eq Utf8(\"CO\")\
      CsvScan: employee.csv 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("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: #id Eq Int32(5)\
             \n  TableScan: foo.csv projection=None",
            display_string);

pub fn display_indent_schema(&self) -> impl Display + '_

Return a formatable structure that produces a single line per node that includes the output schema. For example:

Projection: #id [id:Int32]\
   Filter: #state Eq Utf8(\"CO\") [id:Int32, state:Utf8]\
     TableScan: employee.csv 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("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: #id Eq Int32(5) [id:Int32]\
            \n  TableScan: foo.csv projection=None [id:Int32]",
            display_string);

pub fn display_graphviz(&self) -> impl Display + '_

Return a formatable structure that produces lines meant for graphical display using the DOT language. This format can be visualized using software from graphviz

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("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:

  dot -Tpdf < /tmp/example.dot  > /tmp/example.pdf

pub fn display(&self) -> impl Display + '_

Return a formatable structure with the a human readable description of this LogicalPlan node per node, not including children. For example:

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("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);

Trait Implementations

impl Clone for LogicalPlan

fn clone(&self) -> LogicalPlan

Returns a copy of the value.

pub fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LogicalPlan

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.