Expand description
§Substrait Text Format Grammar
This document describes the grammar for the human-readable Substrait text format used by substrait-explain. This format allows you to write Substrait query plans in a concise, readable text format that can be parsed back into full Substrait protobuf plans.
§Overview
The Substrait text format consists of three sections:
- Version Section (optional) - Declares the Substrait version of the plan
- Extensions Section (optional) - Defines URNs and function/type extensions
- Plan Section - Contains the actual query plan with relations
§Design Principles
This section describes the syntax and semantics principles of the text-format
DSL. For the repository-level design philosophy, compatibility expectations, and
format change guidance, see DESIGN.md.
The grammar is designed around several concrete choices that make it practical and consistent:
§1. Single-Line, Structured Relations
All relations follow the same structure: Name[arguments => columns]
- Name: The relation type (Read, Filter, Project, etc.)
- Arguments: Relation-specific: input expressions, field references, or function calls
- Arguments follow a regular pattern (tuple, input expression, etc.) or combination, and should map directly to Substrait proto fields. Uses tuples for compound arguments, with literals, expressions, and enums for values.
- Arrow:
=>separates arguments from output columns - Columns: Output column names and types
Every relation fits on one line with indentation showing hierarchy. This uniform pattern makes it easy to parse any relation, understand input/output structure, and add new relation types.
§2. SQL-Like References, Literals, and Enums
- Field references:
$0,$1, etc. - Types are shown inline with literals and column names:
42:i64,'hello':string - Nullability is explicit:
string?for nullable,stringfor non-nullable
This prevents ambiguity and makes plans self-documenting while being familiar to SQL developers.
§3. Extension Support and Structured Syntax
- Extensions section defines URNs and function/type mappings.
- Function calls can include anchors:
add#10@1($0, $1). - Clear structural boundaries:
[]for relations,<>for types,()for functions. - Maintains full Substrait compatibility while keeping the text format readable and parseable.
§4. Hierarchical Organization
- Section headers (
===) separate major components. - 2-space indentation shows query plan hierarchy.
- Consistent formatting across all document elements.
The format maps directly to Substrait protobuf messages, with relations, expressions, types, and extensions corresponding to their respective protobuf structures.
§Grammar Notation
This document uses PEG (Parsing Expression Grammar) notation:
"text"- Literal textelement?- Optional elementelement*- Zero or more repetitionselement+- One or more repetitionselement1 / element2- Choice (try element1 first)- Implementation Note: Pest uses
|instead of/
- Implementation Note: Pest uses
element1 element2- Sequence- Implementation Note: Pest uses
~for explicit concatenation
- Implementation Note: Pest uses
§Basic Example
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_arithmetic.yaml
Functions:
# 10 @ 1: add
# 11 @ 1: multiply
=== Plan
Root[result]
Project[$0, $1, add($0, $1):i64]
Read[orders => quantity:i32?, price:i64]§Document Structure
A Substrait text format document consists of two main sections with specific formatting rules.
§Sections
The document uses === headers to separate major sections:
=== Version- Declares the plan’s Substrait version (optional)=== Extensions- Defines URNs and function/type mappings (optional)=== Plan- Contains the actual query plan (required)
When present, the sections appear in this order, mirroring the field order of
the Substrait Plan protobuf.
§Version format
=== Version major.minor.patch
producer: producer string
git_hash: git hashThe header carries the version number as major.minor.patch (three
non-negative integers). The indented producer: and git_hash: lines are
optional and may appear in either order beneath the header.
The === Version section as a whole is optional; a document with no version
section is valid and denotes a plan without a declared version.
=== Version 0.55.0
producer: my-optimizer
=== Plan
Root[result]
Read[orders => quantity:i32?]§Extension format
=== Extensions
URNs:
@ urn_anchor: urn
…
Functions:
## anchor @ urn_anchor: name
…
Types:
## anchor @ urn_anchor: name
…
Type Variations:
## anchor @ urn_anchor: name
…Where anchor and urn_anchor are integers, urn is a text URN, and function, type, and type variation names are identifiers or quoted text.
§Plan Hierarchy and Indentation
Relations use indentation to show the query plan hierarchy:
- Root level: No indentation (typically
Rootrelation) - Child relations: Indented with 2 spaces per level
- Each relation: On its own line with format
Name[arguments => columns]
§Example
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_arithmetic.yaml
Functions:
# 10 @ 1: gt
=== Plan
Root[result] // Level 0 (no indentation)
Project[$0, $1] // Level 1 (2 spaces)
Filter[gt($0, 10):boolean => $0] // Level 2 (4 spaces)
Read[data => a:i64] // Level 3 (6 spaces)§Basic Terminals
§Character Classes
letter:= [a-zA-Z]- Alphabetic charactersdigit:= [0-9]- Numeric digits
§name and identifier
name:= identifier / quoted_name- Used for column names, function names, etc. It can be unquoted if it’s a valid identifier, or using “double quotes” if special characters are required (much like SQL)
- Examples:
function_name,"quoted name"
identifier:= letter (letter / digit / "_")*- Used for columns, function names, etc. that are proper identifiers.
- Examples:
table_name,my_function,col1
quoted_name:= '"' ("\\" . / !'"' .)* '"'- Used for columns, function names, etc. that are not valid as identifiers, and thus need quoting.
- Examples:
"function name","table.name","table\.name","function \"with some\nescapes\""
§enum
Enum fields in arguments are represented as &-prefixed variants (e.g., &AscNullsFirst), matching the Substrait proto definition. This applies to all enum fields in relation arguments.
§Syntax
enum := "&" identifier
§Examples
&AscNullsFirst,&AscNullsLast,&DescNullsFirst,&DescNullsLast- sort directions
§literal
A literal can be an integer, float, boolean, string, or null. Literals may include a type annotation:
literal := (float / integer / boolean / string / "null") (":" type)?
integer:= "-"? digit+- Examples:
42,-10,0 - Default to
i64type; other integer types may be assigned
- Examples:
float:= "-"? digit+ "." digit+- Examples:
3.14,-2.5,1.0 - Default to
fp64type; other float types may be assigned
- Examples:
boolean:= "true" / "false"- Examples:
true,false - May only be boolean type
- Examples:
string:= "'" ("\\" . / !"'" .)* "'"- Examples:
'hello','table name','C:\path\to\file','line1\nline2','quote\'s here' - Default to
stringtype; other types may also be assigned
- Examples:
null:= "null"- Examples:
null:i64?,null:string?,null:date? - A type annotation is required for
null
- Examples:
typed_literal:= string ":" type- String literals with type annotations for non-primitive types
- Examples:
'2023-01-01':date,'2023-12-25T14:30:45.123':timestamp,'2023-01-01T12:00:00.123456789':precisiontimestamp<9>,'14:30:45.123456':precisiontime<6>
All basic literal types (integer, float, boolean, and string) are supported, plus date, time, timestamp, precisiontime, precisiontimestamp, precisiontimestamptz, and typed null literals. Other Substrait literal types (e.g., interval_year, decimal, uuid) are not yet implemented. The deprecated timestamp_tz literal is also not yet implemented; use precisiontimestamptz<6> instead.
§Types
The type syntax in this grammar follows the standard Substrait type definition syntax, with extensions to support anchors and URN references for user-defined types.
§Type Syntax Overview
All types follow this general pattern:
type := ("u!")? identifier anchor? urn_anchor? nullability? parameters?Where:
identifier- The type name (case-insensitive, lowercase preferred), e.g.geo_pointormy_type.u!my_typesyntax is accepted, but not recommended; theu!will be dropped - e.g. bothu!jsonandjsonrefer to the same extension.anchor:= "#" integer- Extension anchor (e.g.,#10)urn_anchor:= "@" integer- URN anchor (e.g.,@1)nullability:= "?"- Optional nullability indicator (defaults to non-nullable)parameters:= "<" (param ("," param)*)? ">"- Optional type parametersparam:= type / integer / name- Type parameter (type, integer, or name)
§Simple Types
Simple types are the basic Substrait types with optional nullability.
§Syntax
simple_type_name nullability?
§Simple Type Names
From official Substrait grammar, simple_type_name can be any of these literal strings:
boolean,i8,i16,i32,i64fp32,fp64string,binarytimestamp,timestamp_tz,date,timeinterval_year,uuid
§Nullability
?- nullable⁉- unspecified nullability (not generally valid)- (nothing) - non-nullable
§Examples:
let plan_text = r#"
=== Plan
Root[result]
Project[$0, $1, $2, $3]
Read[data => int_field:i64, string_field:string?, created_at:timestamp?, user_id:uuid]
"#;§Compound Types
Compound types follow the same syntax as standard Substrait parameterized types.
§Precision Time And Timestamp Types
Precision time and timestamp types put the nullability marker before the precision parameter.
§Syntax
precision_time_type := "precisiontime" nullability? "<" integer ">"
precision_timestamp_type := "precisiontimestamp" nullability? "<" integer ">"
precision_timestamp_tz_type := "precisiontimestamptz" nullability? "<" integer ">"The precision parameter follows the Substrait unit convention: 0 means
seconds, 3 milliseconds, 6 microseconds, 9 nanoseconds, and 12
picoseconds. Two different subsets apply:
- Types accept the full Substrait range, any precision from
0through12. - Literals accept only
0,3,6, and9.
The literal restriction is a limitation of this implementation, not of Substrait, which supports every precision from 0 to 12.
Literals stop at 9 because chrono (the underlying date/time library) has no
sub-nanosecond resolution, so precision-12 literals cannot be parsed.
Textifying a precision-12 value from a protobuf plan is still supported: the
value is truncated to nanosecond resolution and a truncation diagnostic is
emitted, but the declared type is preserved as <12> (truncating the value
does not silently rewrite its type).
§Examples
precisiontime<6>
precisiontime?<6>
precisiontimestamp<9>
precisiontimestamp?<9>
precisiontimestamptz<3>
precisiontimestamptz?<3>§Examples
// TODO: This example uses map type, which is not yet implemented in the parser.
use substrait_explain::Parser;
let plan_text = r#"
=== Plan
Root[result]
Project[$0, $1, $2]
Read[data => list_field:list<i64>, map_field:map<string, i64>, struct_field:struct<i64, string?>]
"#;
let plan = Parser::parse(plan_text).unwrap();
assert_eq!(plan.relations.len(), 1);§User-Defined Types
User-defined types extend the standard Substrait UDT syntax to support anchors and URN references.
§Syntax
("u!")? identifier anchor? urn_anchor? nullability? parameters?
§Key differences from standard Substrait
- Adds optional
anchorandurn_anchorfor extension references - The
u!prefix is accepted in type declarations but normalized at storage time, sou!jsonandjsonin a declaration refer to the same type. It is an error to useu!on function or type-variation declarations. - In plan references both
u!jsonandjsonare accepted and resolve to the same anchor. The canonical output always uses the bare name.
§Examples
=== Extensions
URNs:
@ 1: https://example.com/types
@ 2: https://example.com/functions
Types:
# 8 @ 1: point
# 9 @ 1: custom_type
Functions:
# 10 @ 2: add
=== Plan
Root[result]
Project[$0, $1, $2]
Read[data => point_field:point#8@1?<i8>, custom_field:custom_type#9, prefixed_field:u!custom_type]§Expressions
§Syntax
expression := function_call / reference / literal / if_then
§Examples
add($3, 10):i64 // Simple function call with required output type
add#10@2($3, 10):i64 // Function call with anchors and output type§Field References
Currently, only references to fields in the Relations’ input are supported.
§Syntax
reference := "$" integer
§Examples
=== Plan
Root[result]
Project[$0, $1, $42]
Read[data => field0:i64, field1:string, field42:boolean]§Function Calls
§Syntax
function_call := function_signature anchor? urn_anchor? "(" (expression ("," expression)*)? ")" ":" type
where function_signature is the function base name with an optional colon-delimited type-signature suffix:
function_signature := identifier (":" argument_signature?)?
argument_signature := short_arg_type ("_" short_arg_type)*
short_arg_type := "u!" short_arg_name | short_arg_name
short_arg_name := ASCII_ALPHA ASCII_ALPHANUMERIC*Examples of function signatures: add, equal:any_any, count: (empty signature), json_extract_path:u!json_str, bar:u!arg_u!arg.
§Components
function_signature- function name with optional signature suffix (see above)anchor- optional anchor (e.g.,#10)urn_anchor- optional URN anchor (e.g.,@1)expression- as abovetype- required output type
§Function Name Resolution
Within the plan, a function name has three parts: a base name (e.g. abs), a type signature (prefixed with a colon, e.g. :i64), and anchor (prefixed with #, e.g. #4).
Both type signature and anchor are separably optional if the reference is unambiguous; either or both may be required to make the reference unambiguous. A function name (base, signature if present, and anchor if present) must map to exactly one function named in the Extensions section.
Where unambiguous, signature and anchor may both be left off, used separately, or together for completeness (abs:i64#4($0):fp64).
§Examples
// Simple: resolves if there is exactly one function named `add`
add($0, $1):i64
// Signature: resolves only if exactly one function named `add` is registered,
// with type signature `:i64_i64`
add:i64_i64($0, $1):i64
// Anchor: resolves if anchor 1 exists with base name `add`
add#1($0, $1):i64
// Anchor + full name: resolves if anchor 1 exists with name `add` and
// type signature `i64_i64`
add:i64_i64#1($0, $1):i64
// Simple: resolves if there is exactly one function named `count`
count():i64
// Signature: resolves if "count:" is registered exactly once with
// type signature "" (zero arguments)
count:():i64§Aggregate Measures
Aggregate measures are used in the output of Aggregate relations to compute aggregates. An aggregate measure is written as a plain function_call (see Function Calls section) in the output position of an Aggregate relation - the syntax is identical, e.g. sum($2):i64, count($1):i64, avg($3):fp64.
This syntax only captures a measure’s function_reference, arguments, and output_type. The Substrait Measure message also carries a filter (a per-measure filter expression, separate from the aggregate function) and the AggregateFunction itself has invocation (e.g. DISTINCT), phase, and sorts (for ordered aggregates), which are currently unsupported. Parsing always produces invocation: UNSPECIFIED, phase: UNSPECIFIED, no sorts, and no filter, regardless of what the original plan contained.
§IfThen
An IfThen expression is a conditional function or logical operator that evaluates to a boolean.
§Syntax
if_then := "if_then(" (if_clause ",")+ "_ ->" expression ")"
if_clause := expression "->" expression
§Examples
=== Plan
Root[status]
Fetch[limit=10, offset=0 => ]
Project[if_then(true -> $0, false -> $1, _ -> $2)]
Read[events.logs => status:string?]§Relations
Relations represent the operations in a query plan. Each relation is displayed on a single line with indentation showing the hierarchy.
§General Relation Grammar
All relations follow this general pattern:
§Syntax
relation := name "[" (arguments ("," named_arguments)? ("=>" columns)?)? "]"
columns := name ("," name)* / reference_listWhere:
name: The type of operation (Read, Filter, Project, Root, etc.)arguments: Input expressions, field references, function calls, or other parameters (optional)named_arguments: Named arguments (optional)=>: Separator between arguments and output columns (optional, only present when both arguments and columns are specified)columns: Output column names and types, or field references for pass-through (all relations specify outputs, but format varies)reference_list := reference ("," reference)*: comma-separated list of field references
§Example
RelationName[arguments, named_arguments => columns]§Special cases
- Root relation: Only specifies output column names, no arguments or
=>separator - Project relation: Only specifies expressions, no
=>separator or output columns - Some relations may use ‘…’ instead of column names when they pass through all fields
The exact structure varies by relation type, but all follow this basic pattern.
§Arguments
Arguments in relations can be literals, expressions, enums, or tuples thereof.
§Syntax
argument := enum / reference / literal / expression / tuple
tuple := "(" ")" // 0-tuple
/ "(" argument "," ")" // 1-tuple (trailing comma required)
/ "(" argument ("," argument)+ ","? ")" // 2+-tuple (trailing comma optional)
arguments := argument ("," argument)*
named_arguments := name "=" argument ("," name "=" argument)*Tuples follow the Python/Rust trailing-comma convention to disambiguate from parenthesised
expressions: (x) is a parenthesised expression, not a tuple. A trailing comma is required to
form a 1-element tuple: (x,). For 2+ elements the trailing comma is optional: (x, y) and
(x, y,) are equivalent.
§Examples
- Simple arguments:
$0,42,'hello',&AscNullsFirst - 0-tuple:
() - 1-tuple:
(&HASH,)— trailing comma required - 2+-tuple:
($0, &AscNullsFirst),(&HASH, &RANGE,) - Named arguments:
limit=10,offset=5
§Root Relation
§Syntax
"Root" "[" (name ("," name)*)? "]"
§Example
=== Plan
Root[c, d] // root with output columns c and d
Project[$0, $1]
Read[data => a:i64, b:string]§Read Relation
§Syntax
read_relation := "Read" "[" table_name output "]"
output := implicit_output / direct_output
implicit_output := "=>" named_column_list
direct_output := "+>" named_column_list ("|>" reference_list)?§Components
table_name := name ("." name)*- table name, optionally qualified with schema/databasenamed_column := name ":" type- column name with type annotationnamed_column_list := (named_column ("," named_column)*)?- the list of columns and their types to be read from the tabletable_name.=>is used to mean implicit column ordering; forRead, this translates toDirectcolumn ordering.- When used with
+>and no|>, thenamed_columns are in the expected order of the table, andDirectemit is used. - When used with
+> … |>, thenamed_columns are in the expected order of the table, and the emit order is a Remap specified byreference_list.
§Example
=== Plan
Root[result]
Project[$0, $1]
Read[schema.table => a:i64, b:string?]
Root[result2]
Project[$0, $1]
Read[orders => quantity:i32?, price:i64]Use +> when the read’s base schema records the direct output domain:
=== Plan
Root[a, b]
Read[my_table +> a:i64, b:string]Use +> ... |> to specify an Emit / output ordering different from the table’s base schema:
only some fields should flow downstream:
=== Plan
Root[b, a]
Read[my_table +> a:i64, b:string, c:i64 |> $1, $0]§VirtualTable Read Relation
A VirtualTable read embeds inline data directly in the plan, similar to SQL’s VALUES clause. Instead of referencing a catalog table, the data rows are specified as part of the relation.
The Read:Virtual relation uses the same ReadRel protobuf message with ReadType::VirtualTable, where each row is a nested::Struct containing expressions.
§Syntax
"Read:Virtual" "[" virtual_read_rows ("," "filter" "=" expression)? "=>" named_column_list "]"
Where virtual_read_rows := virtual_row ("," virtual_row)* / "_", and
virtual_row := "(" (expression ("," expression)*)? ")" — a parenthesized
tuple of expressions forming one row. Rows may be empty (()) for zero-column
tables. For an empty virtual table, write _ in place of the entire row list,
e.g. Read:Virtual[_ => id:i64] for zero rows.
§Components
virtual_row- parenthesized tuple of expressions, one per row;()for zero-column rowsexpression- any expression (literal, field reference, function call)filter- optionalReadRel.filterexpression, with field references over the virtual table output schemanamed_column_list- output column names with type annotations
§Examples
Inline form with two rows:
=== Plan
Root[id, name]
Read:Virtual[(1, 'alice'), (2, 'bob') => id:i64, name:string]Empty virtual table (no rows):
=== Plan
Root[id, name]
Read:Virtual[_ => id:i64, name:string]Inline form with a ReadRel.filter:
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_comparison.yaml
Functions:
# 10 @ 1: gt
=== Plan
Root[id, name]
Read:Virtual[(1, 'alice'), (2, 'bob'), filter=gt($0, 1:i64):boolean => id:i64, name:string]§Multi-line form
For readability, a Read:Virtual with many rows may be written across several
lines. Each continuation line is indented one level deeper than the relation and
prefixed with a - marker. Continuations are allowed after the opening [,
after each row or filter separator (,), and before =>:
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_comparison.yaml
Functions:
# 10 @ 1: gt
=== Plan
Root[id, name]
Read:Virtual[
- (1, 'alice'),
- (2, 'bob'),
- filter=gt($0, 1:i64):boolean
- => id:i64, name:string]The multi-line form is purely a layout convenience: it parses to exactly the
same plan as the inline form above. The standard output may use inline or multi-line form depending on the length of the VirtualTable.
§ExtensionTable Read Relation
An ExtensionTable read uses ReadRel with ReadType::ExtensionTable. The relation header carries the read output schema, while a required + Ext: addendum carries the custom table detail payload.
§Syntax
extension_table_read_relation := "Read:Extension" "[" named_column_list "]"
extension_table_detail := "+" "Ext" ":" name "[" (empty / extension_args)? "]"The + Ext: line is indented one level deeper than the Read:Extension line. It is required, and addendum lines must appear before any child relations. Canonical formatting writes + Ext: first, followed by + Enh: and + Opt: lines when present.
§Components
named_column_list- output column names with type annotations, stored inReadRel.base_schemaname- the extension name registered withExtensionRegistryextension_args- positional and/or named arguments encoded into theExtensionTable.detail: Any
§Example
=== Plan
Root[id, payload]
Read:Extension[id:i64, payload:string]
+ Ext:BlobStoreRead['path/to/file', limit=100, include_archived=true]Relation-level advanced extensions can still be attached to the same read relation:
=== Plan
Root[id]
Read:Extension[id:i64]
+ Ext:BlobStoreRead['path/to/file']
+ Enh:PartitionHint[&HASH, count=8]
+ Opt:PlanHint[hint='parallel']§Filter Relation
§Syntax
"Filter" "[" expression "=>" reference_list "]"
§Components
expression- boolean expression for filteringreference_list- field references to pass through
§Example
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_arithmetic.yaml
Functions:
# 10 @ 1: gt
=== Plan
Root[result]
Filter[gt($2, 100):boolean => $0, $1, $2]
Project[$0, $1, $2]
Read[data => a:i64, b:string, c:i32]§Project Relation
§Syntax
"Project" "[" (expression ("," expression)*)? "]"
§Components
expression- field reference, function call, or literal (see Expressions section)
§Example
=== Plan
Root[result]
Project[$1, 42] // project field 1 and literal 42
Read[data => a:i64, b:string]§Aggregate Relation
§Syntax
"Aggregate" "[" grouping_sets "=>" aggregate_output "]"
§Components
grouping_sets := grouping_set_list / expression_list- can be a list of grouping sets (each parenthesized), or a single unparenthesized list for the common, single-set casegrouping_set_list := grouping_set ("," grouping_set)*grouping_set := "(" expression_list ")" / "_"- a grouping set can be (1) a list of expressions, or (2)_, the standard we use for empty listsaggregate_output := expression ("," expression)*- comma-separated list of output items- Each output item is either an aggregate function call (which becomes a new measure), or an expression that must match one of the
grouping_setsexpressions by value - since an Aggregate relation’s output schema is always exactlygrouping_expressions + measures. See Aggregate Measures section
§Example
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_aggregate.yaml
Functions:
# 10 @ 1: sum
# 11 @ 1: count
=== Plan
Root[result]
Aggregate[($0), ($0, $1) => $0, $1, sum($2):i64, count($2):i64] // Group by field 0, and ($0, $1)
Read[orders => category:string, region:string?, amount:i64]§Sort Relation
The Sort relation specifies sort fields and directions for ordering the input:
Sort[($0, &AscNullsFirst), ($1, &DescNullsLast) => $0, $1]
§Syntax
sort_relation := "Sort" "[" sort_fields "=>" reference_list "]"
sort_fields := sort_field ("," sort_field)*
sort_field := "(" reference "," sort_direction ")"
sort_direction := "&AscNullsFirst" / "&AscNullsLast" / "&DescNullsFirst" / "&DescNullsLast"§Components
- Each sort field is a tuple:
(reference, sort_direction) - Sort directions follow the general
enumsyntax and specify null handling reference_list- comma-separated list of field references to pass through
§Join Relation
Syntax: "Join" "[" join_type "," expression ("," "post_filter" "=" expression)? "=>" reference_list "]"
Components:
join_type- Join type enum with&prefix (e.g.,&Inner,&Left,&Right,&Outer)expression- Join condition (boolean expression relating left and right inputs), with field references over input orderpost_filter- Optional post-join filter expression, applied after join matching, with field references over direct output orderreference_list- comma-separated list of field references for output columns, with field references over direct output order
Field Reference Mapping:
For join conditions, field references map to the combined schema of left and right inputs:
$0,$1, … refer to left input fields$n,$n+1, … refer to right input fields (where n = number of left fields)
For post_filter and reference_list, field references map to the join’s
direct output order:
- inner, left, right, and outer joins output left fields followed by right fields
- left semi, left anti, and left single joins output left fields only
- right semi, right anti, and right single joins output right fields only, renumbered from
$0 - left mark and right mark joins output the retained side’s fields followed by the mark column
Example:
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_comparison.yaml
Functions:
# 10 @ 1: eq
=== Plan
Root[user_orders]
Join[&Inner, eq($0, $2):boolean => $0, $1, $3]
Read[users => id:i64, name:string] // Fields $0, $1
Read[orders => user_id:i64, amount:i32] // Fields $2, $3§Set Relation
§Syntax
"Set" "[" set_op "=>" reference_list "]"
§Components
set_op- Set operation enum with&prefix, using Substrait’s protobufSetOpvariant names directlyreference_list- Comma-separated list of field references for output columns
A Set relation combines two or more inputs (written as indented children,
like any other multi-input relation), which must all share the same output
schema. Field references map to that common schema:
$0,$1, … refer to fields of the shared input schema
§Example
=== Plan
Root[id, name]
Set[&UnionAll => $0, $1]
Read[active_users => id:i64, name:string]
Read[archived_users => id:i64, name:string]§Cross Relation
§Syntax
"Cross" "[" reference_list "]"
§Components
reference_list- Comma-separated list of field references for output columns
A Cross relation is the Cartesian product of its two inputs (written as
indented children). It takes no arguments, so the bracket body is just the
output columns. The output concatenates the left and right inputs, so field
references map to the combined schema:
$0,$1, … refer to left input fields$n,$n+1, … refer to right input fields (where n = number of left fields)
§Example
=== Plan
Root[id, name, order_id, amount]
Cross[$0, $1, $2, $3]
Read[users => id:i64, name:string]
Read[orders => order_id:i64, amount:i32]§Extension Relations
Extension relations allow custom relation types with user-defined protobuf payloads. They enable integration with custom data sources, optimizations, or specialized operations beyond standard Substrait relations.
§Types
There are three extension relation types, based on their input cardinality:
ExtensionLeaf- No child relations (e.g., custom data sources)ExtensionSingle- Exactly one child relation (e.g., custom transformations)ExtensionMulti- Zero or more child relations (e.g., custom joins)
§Syntax
extension_relation := extension_type ":" name "[" (empty / extension_args)? ("=>" extension_columns)? "]"
extension_type := "ExtensionLeaf" / "ExtensionSingle" / "ExtensionMulti"
extension_args := (positional_args ("," named_args)?) / named_args
positional_args := extension_arg ("," extension_arg)*
extension_arg := enum / reference / literal / expression / tuple
named_args := named_arg ("," named_arg)*
named_arg := name "=" extension_arg
extension_columns := (extension_column ("," extension_column)*)?
extension_column := named_column / reference / expressionNote: the parser also accepts the => section being omitted entirely (e.g. ExtensionLeaf:Foo[_]), treating it as zero output columns. The canonical form always includes =>.
§Components
extension_type- One ofExtensionLeaf,ExtensionSingle, orExtensionMultiname- The extension name (registered withExtensionRegistry)empty(_) - Explicitly marks an extension with no argumentsextension_args- Positional arguments (enums, references, literals, expressions, or tuples) and/or named arguments (key=valuepairs); both are optionalextension_columns- Output column definitions: named columns (name:type), field references ($0), or expressions
Untyped scalar extension arguments such as 2, 2.4, true, and 'path'
are treated as extension scalar values and render without expression type
suffixes, even in verbose output. They can still be consumed by extension
handlers as expressions, in which case they widen to default non-nullable
Substrait literal expressions. Typed literals such as 2:i16 or
'2024-01-01':date, field references, function calls, and casts are expression
values.
§Examples
=== Plan
Root[result]
ExtensionSingle:CustomFilter[threshold=100 => $0, $1]
ExtensionLeaf:ParquetScan[path='data/users.parquet', batch_size=1024 => id:i64, name:string]Extension with positional arguments and no output columns:
ExtensionSingle:VectorNormalize[$0, $1, method='l2' => ]Extension with no arguments:
ExtensionLeaf:EmptySource[_ => ]§Custom Extension Types
To use custom relation types with protobuf detail payloads, register them with an ExtensionRegistry. See the API documentation for details on implementing the Explainable trait.
§Advanced Extensions
Advanced extensions allow attaching enhancement and optimization metadata to any standard relation via the Substrait AdvancedExtension protobuf field.
§Overview
Each relation can carry:
- At most one enhancement (
+ Enh:) — extra semantic metadata attached to a relation - Zero or more optimizations (
+ Opt:) — hints for the query planner
§Syntax
addendum := "+" addendum_type ":" name "[" (empty | extension_args)? "]"
addendum_type := "Enh" | "Opt" | "Ext"Where:
addendum_type—Enhfor an enhancement,Optfor an optimization, orExtfor anExtensionTabledetail onRead:Extensionname— the registered type name (e.g.PartitionHint)extension_args— positional and/or named arguments; use_for empty
Addendum lines are indented one level deeper than the relation they annotate, just like child relations. They MUST appear before any child relations. + Enh: and + Opt: lines attach advanced extensions to standard relations. + Ext: lines are only valid under Read:Extension; extension relations (ExtensionLeaf, ExtensionSingle, and ExtensionMulti) do not support addenda.
§Argument Syntax
Extension arguments follow the same rules as extension-relation arguments. Enum values are written with a & prefix:
enum_value := "&" identifier§Examples
&HASH,&RANGE,&BROADCAST—PartitionStrategyvariants forPartitionHint&AscNullsFirst— sort direction enum in a relation argument
§Example: Enhancement on a Read Relation
=== Plan
Root[result]
Read[data => col:i64]
+ Enh:PartitionHint[&HASH, count=8]§Example: Enhancement and Multiple Optimizations
=== Plan
Root[result]
Read[data => col:i64]
+ Enh:PartitionHint[&HASH, count=4]
+ Opt:PlanHint[hint='use_index']
+ Opt:PlanHint[hint='parallel']§Custom Extension Types
To parse or textify advanced extensions with custom protobuf payloads, register them with an ExtensionRegistry:
- Enhancements:
registry.register_enhancement::<MyEnhancement>() - Optimizations:
registry.register_optimization::<MyOptimization>()
Both require implementing the Explainable trait, which provides from_args / to_args for text-format conversion, and prost::Message + prost::Name for protobuf serialization.
§Parse failure behaviour
If a + Enh: or + Opt: name is not registered in the registry at parse time, the parser returns a hard error.
If the registry does not know the type URL at textify time (e.g. when formatting a plan received from an external source), the line is still emitted with a failure token and a FormatError is collected — the rest of the plan is unaffected.
For example, if a plan contains an enhancement whose type URL is not registered, the textified output replaces the name and arguments with !{extension}:
=== Plan
Root[result]
Read[my.table => col:i64]
+ Enh[!{extension}]The collected FormatError carries the full detail:
FormatError::Extension(ExtensionError::NotFound {
name: "type.googleapis.com/acme.PartitionHint".to_string(),
})The Read line and everything else in the plan are textified normally; only the unrecognized enhancement line degrades to the failure token.
§Complete Example
A complete query that joins users and orders tables, calculates total order value, filters for high-value orders, and groups by user to show total revenue per customer:
=== Extensions
URNs:
@ 1: https://github.com/substrait-io/substrait/blob/main/extensions/functions_comparison.yaml
@ 2: https://github.com/substrait-io/substrait/blob/main/extensions/functions_arithmetic.yaml
@ 3: https://github.com/substrait-io/substrait/blob/main/extensions/functions_aggregate.yaml
Functions:
# 10 @ 1: eq
# 11 @ 1: gt
# 12 @ 2: multiply
# 13 @ 3: sum
=== Plan
Root[customer_revenue]
Aggregate[$0, $1 => $0, $1, sum($3):i64]
Filter[gt($3, 100):boolean => $0, $1, $2, $3]
Project[$0, $1, $2, multiply($4, $5):i64]
Join[&Inner, eq($0, $3):boolean => $0, $1, $2, $3, $4, $5]
Read[users => id:i64, name:string, region:string]
Read[orders => user_id:i64, quantity:i32, price:i64]