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Document JSON Format

This document describes the JSON format used for sending document operations to Vespa. Field types are defined in the schema reference. This is a reference for:

  • JSON representation of field types in Vespa documents
  • JSON representation of document operations (put, get, remove, update)
  • JSON representation of addressing fields for update, and update operations

Also refer to encoding troubleshooting.

Field types

string 

"name": "Polly"

Feeding in an empty string ("") for a field will have the same effect as not feeding a value for that field, and the field will not be rendered in the document API and in document summaries.

int 

"age": 42
long 

"age": 42
bool

true or false:

"alive": false
byte 

"tinynumber": 128
float 

"weight": 123.4567
double 

"weight": 123.4567
position

A position is encoded as a lat/lng object:

"mypos": {
    "lat": 37.4181488,
    "lng": -122.0256157
}

See Geo Search for details.
predicate

A predicate is a string:

"predicate_field": "gender in [Female] and age in [20..30] and pos in [1..4]"
raw

The content of a raw field is represented as a base64-encoded string: 

"raw_field": "VW5rbm93biBhcnRpc3QgZnJvbSB0aGUgbW9vbg=="

When used as summary field it will be rendered as a base64-encoded string.

uri

A URI is a string:

"url": "https://www.yahoo.com/"
array

Arrays are represented as JSON arrays.

"int_array_field": [
    123,
    456,
    789
]

"string_array_field": [
    "item 1",
    "item 2",
    "item 3"
]

Feeding in an empty array ([]) for a field will have the same effect as not feeding a value for that field, and the field will not be rendered in the document API and in document summaries.

weightedset

Weighted sets are represented as maps where the value is the weight. Note, even if the key is not a string as such, it will be represented as a string in the JSON format. 

"int_weighted_set": {
    "123": 2,
    "456": 78
}

"string_weighted_set": {
    "item 1": 143,
    "item 2": 6
}

Feeding in an empty weightedset ({}) for a field will have the same effect as not feeding a value for that field, and the field will not be rendered in the document API and in document summaries.

tensor

Tensor fields may be represented as an array of cells: 

"tensorfield": [
        { "address": { "x": "a", "y": "0" }, "value": 2.0 },
        { "address": { "x": "a", "y": "1" }, "value": 3.0 },
        { "address": { "x": "b", "y": "0" }, "value": 4.0 },
        { "address": { "x": "b", "y": "1" }, "value": 5.0 }
]

This works for any tensor but is verbose, so shorter forms specific to various tensor types are also supported. Use the shortest form applicable to your tensor type for the best possible performance.

The cells array can optionally be nested in an object under the key "cells". This is how tensor values are returned by default, along with another key "type" containing the tensor type.

Indexed tensors short form: An array where the values are ordered in the standard value order, where indexes of dimensions to the right are incremented before indexes to the left, where dimensions are ordered alphabetically (such that, e.g. with a tensor with dimensions x,y the "y" values for each value of "x" are adjacent): 

"tensorfield": [ 2.0, 3.0, 5.0, 7.0 ]

The cells array can optionally be nested in an object under the key "values". This is how tensor values are returned by default, along with another key "type" containing the tensor type.

Mixed tensors short form: If the tensor has a single sparse dimension: A map where the key is the value of that dimension and the value is a nested array containing the values of the dense subspace within that key.
If the tensor has multiple sparse dimensions: An array where the elements consist of a map with the keys "address" and "values", where "address" is a map with the sparse dimensions and their values (as in cells), and "values" is a nested array containing the values of the dense subspace within that address.

Example - single sparse dimension:

"tensorfield": {
       "x1":[2.0,3.0],
       "x2":[4.0,5.0]
}

Example - multiple sparse dimensions:

"tensorfield": [
       {"address":{"x":"x1","y":"y2"},"values":[2.0,3.0]},
       {"address":{"x":"x2","y":"y2"},"values":[4.0,5.0]}
]

The values can optionally be nested in an object under the key "blocks". This is how tensor values are returned by default, along with another key "type" containing the tensor type.

Short form for tensors with a single mapped dimension: A map with the dimension key as key and the value as value. 

"tensorfield": {
      "a": 2.0,
      "b": 3.0
}

The cells object can optionally be nested in an object under the key "cells". This is how tensor values are returned by default, along with another key "type" containing the tensor type.

Short form for indexed tensors representing binary data (with int8 cell value type): May use a string with a hex dump of the cell values: 

"tensorfield": "FF00118022FE"

can be used to represent the value tensor<int8>(x[6]):[-1,0,17,-128,34,-2].

The string object can optionally be nested in an object under the key "values".

struct 

"mystruct": {
    "intfield": 123,
    "stringfield": "foo"
}
map

The JSON dictionary key must be a string, even if the map key type in the schema is not a string: 

"int_to_string_map": {
    "123": "foo",
    "456": "bar",
    "789": "foobar"
}

Feeding in an empty map ({}) for a field will have the same effect as not feeding a value for that field, and the field will not be rendered in the document API and in document summaries.

annotationreference

Annotation references do not have a JSON representation

reference

String with document ID referring to a parent document:

"artist_ref": "id:mynamespace:artists::artist-1"

Empty fields

In general, fields that have not received a value during feeding will be ignored when rendering the document. They are considered as empty fields. However, certain field types have some values which causes them to be considered empty. For instance, the empty string ("") is considered empty, as well as the empty array ([]). See the above table for more information for each type.reads-and-writes.html

Document operations

Refer to reads and writes for details. There are two methods for document operations:

  • Vespa feed client: Java APIs / command line tool to feed document operations asynchronously to Vespa, over HTTP. Input is JSON with one or more document operations (for high throughput, batch operations), where the document IDs are in the JSON feed—one for each operation. The Vespa feed client requires HTTP/2 to be enabled.
  • /document/v1/: This API accepts one operation per request, with the document ID encoded in the URL. This is the API used by the Vespa feed client, and is the recommendation for new feed clients using HTTP/2.

PUT

Vespa HTTP/feed client: 
{
    "put": "id:music:music::123",
    "fields": {
        "title": "Best of Bob Dylan"
    }
}
/document/v1/: 
http POST /document/v1/music/music/docid/123
{
    "fields": {
        "title": "Best of Bob Dylan"
    }
}

GET

Vespa HTTP/feed client: 
# not supported - use vespa-get
/document/v1/: 
http GET /document/v1/music/music/docid/123

REMOVE

Vespa HTTP/feed client: 
{
    "remove": "id:music:music::123"
}
/document/v1/: 
http DELETE /document/v1/music/music/docid/123

UPDATE

Vespa HTTP/feed client: 
{
    "update": "id:music:music::123",
    "fields": {
        "title": {
            "assign": "The best of Bob Dylan"
        }
    }
}
/document/v1/: 
http PUT /document/v1/music/music/docid/123
{
    "fields": {
        "title": {
            "assign": "The best of Bob Dylan"
        }
    }
}

Test and set

An optional condition can be added to operations to specify a test and set condition - see conditional writes. The value of the condition is a document selection, encoded as a string. Example: Increment the sales field only if it is already equal to 999:

Vespa HTTP/feed client: 
{
    "update": "id:music:music::bob/BestOf",
        "condition": "music.sales==999",
        "fields": {
            "sales": {
                "increment": 1
            }
    }
}
Document API: 
http PUT /document/v1/music/music/docid/bob%2FBestOf?condition=music.sales%3D%3D%27999%27
{
   "fields": {
       "sales": {
           "increment": 1
        }
    }
}

If the condition is not met, an error response (code 412) is returned.

create (create if nonexistent)

Updates to nonexistent documents are supported using create. Refer to the guide.

Vespa HTTP/feed client: 
{
    "update": "id:mynamespace:music::bob/BestOf",
    "create": true,
    "fields": {
        "title": {
            "assign": "The best of Bob Dylan"
        }
    }
}
Document API: 
http PUT /document/v1/mynamespace/music/docid/bob%2FBestOf?create=true
{
    "fields": {
        "title": {
            "assign": "The best of Bob Dylan"
        }
    }
}

assign

assign is used to replace the value of a field (or an element of a collection) with a new value. When assigning, one can generally use the same syntax and structure as when feeding that field's value in a put operation.

Single value field

field title type string {
    indexing: summary
}

{
    "update": "id:mynamespace:music::example",
    "fields": {
        "title": {
            "assign": "The best of Bob Dylan"
        }
    }
}

Tensor field

field tensorfield type tensor(x{},y{}) {
    indexing: attribute | summary
}

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "assign": {
                "cells": [
                    { "address": { "x": "a", "y": "b" }, "value": 2.0 },
                    { "address": { "x": "c", "y": "d" }, "value": 3.0 }
                ]
            }
        }
    }
}

Struct field

Replacing all fields in a struct

A full struct is replaced by assigning an object of struct key/value pairs. 

struct person {
    field first_name type string {}
    field last_name type string {}
}
field contact type person {
    indexing: summary
}

{
    "update": "id:mynamespace:workers::example",
    "fields": {
        "contact": {
            "assign": {
                "first_name": "Bob",
                "last_name": "The Plumber"
            }
        }
    }
}

Individual struct fields

Individual struct fields are updated using field path syntax. Refer to the reference for restrictions using structs. 

{
    "update": "id:mynamespace:workers::example",
    "fields": {
        "contact.first_name": { "assign": "Bob" },
        "contact.last_name":  { "assign": "The Plumber" }
    }
}

Map field

Individual map entries can be updated using field path syntax. The following declaration defines a map where the key is an Integer and the value is a person struct. 

struct person {
    field first_name type string {}
    field last_name type string {}
}
field contact type map<int, person> {
    indexing: summary
}

Example updating part of an entry in the contact map:

  • contact is the name of the map field to be updated
  • {0} is the key that is going to be updated
  • first_name is the struct field to be updated inside the person struct
{
    "update": "id:mynamespace:workers::example",
    "fields": {
       "contact{0}.first_name": { "assign": "John" }
    }
}

Assigning an element to a key in a map will insert the key/value mapping if it does not already exist, or overwrite it with the new value if it does exist. Refer to the reference for restrictions using maps.

Map to primitive value

field my_food_scores type map<string, string> {
    indexing: summary
}

{
    "update": "id:mynamespace:food::example",
    "fields": {
        "my_food_scores{Strawberries}": {
            "assign": "Delicious!"
        }
    }
}

Map to struct

struct contact_info {
    field phone_number type string {}
    field email type string {}
}
field contacts type map<string, contact_info> {
    indexing: summary
}

{
    "update": "id:mynamespace:people::d_duck",
    "fields": {
        "contacts{\"Uncle Scrooge\"}": {
            "assign": {
                "phone_number": "555-123-4567",
                "email": "number_one_dime_luvr1877@example.com"
            }
        }
    }
}

Array field

Array of primitive values

field ingredients type array<string> {
    indexing: summary
}

Assign full array:

{
    "update": "id:mynamespace:cakes:tasty_chocolate_cake",
    "fields": {
        "ingredients": {
            "assign": [ "sugar", "butter", "vanilla", "flour" ]
        }
    }
}

Assign existing elements in array:

{
    "update": "id:mynamespace:cakes:tasty_chocolate_cake",
    "fields": {
        "ingredients[3]": {
            "assign": "2 cups of flour (editor's update: NOT asbestos!)"
        }
    }
}

Note that the index element 3 needs to exist. Alternative using match:

{
    "update": "id:mynamespace:cakes:tasty_chocolate_cake",
    "fields": {
        "ingredients": {
            "match" {
                "element": 3,
                "assign": "2 cups of flour (editor's update: NOT asbestos!)"
            }
        }
    }
}

Individual array elements may be updated using field path or match syntax.

Array of struct

Refer to the reference for restrictions using array of structs. 

struct person {
    field first_name type string {}
    field last_name type string {}
}
field people type array<person> {
    indexing: summary
}

{
    "update": "id:mynamespace:students:example",
    "fields": {
        "people[34]": {
            "assign": {
                "first_name": "Bobby",
                "last_name": "Tables"
            }
        }
    }
}

Note that the element index needs to exist. Use add to add a new element. Alternative syntax using match:

{
    "update": "id:mynamespace:students:example",
    "fields": {
        "people": {
            "match": {
                "element": 34,
                "assign": {
                     "first_name": "Bobby",
                     "last_name": "Tables"
                }
            }
        }
    }
}

Weighted set field

Adding new elements to a weighted set can be done using add, or by assigning with field{key} syntax. Example of the latter: 

field int_weighted_set type weightedset<int> {
    indexing: summary
}
field string_weighted_set type weightedset<string> {
    indexing: summary
}

{
    "update":"id:weightedsetdoctype:weightedsetdoctype::example1",
    "fields": {
        "int_weighted_set{123}": {
            "assign": 123
        },
        "int_weighted_set{456}": {
            "assign": 100
        },
        "string_weighted_set{\"item 1\"}": {
            "assign": 144
        },
        "string_weighted_set{\"item 2\"}": {
            "assign": 7
        }
    }
}

Note that using the field{key} syntax for weighted sets may be less efficient than using add.

Clearing a field

To clear a field, assign a null value to it. 

{
    "update": "id:mynamespace:music::example",
    "fields": {
        "title": {
            "assign": null
        }
    }
}

add

add is used to add entries to arrays, weighted sets or to the mapped dimensions of tensors.

Adding array elements

The added entries are appended to the end of the array in the order specified. 

field tracks type array<string> {
    indexing: summary
}

{
    "update": "id:mynamespace:music::https://music.yahoo.com/bobdylan/BestOf",
    "fields": {
       "tracks": {
            "add": [
                "Lay Lady Lay",
                "Every Grain of Sand"
            ]
        }
    }
}

Add weighted set entries

Add weighted set elements by using a JSON key/value syntax, where the value is the weight of the element.

Adding a key/weight mapping that already exists will overwrite the existing weight with the new one. 

field int_weighted_set type weightedset<int> {
    indexing: summary
}
field string_weighted_set type weightedset<string> {
    indexing: summary
}

{
    "update":"id:weightedsetdoctype:weightedsetdoctype::example1",
    "fields": {
        "int_weighted_set":  {
            "add": {
                "123": 123,
                "456": 100
            }
        },
        "string_weighted_set": {
            "add": {
                "item 1": 144,
                "item 2": 7
            }
        }
    }
}

Add tensor cells

Add cells to mapped or mixed tensors. Invalid for tensors with only indexed dimensions. Adding a cell that already exists will overwrite the cell value with the new value. The address must be fully specified, but cells with bound indexed dimensions not specified will receive the default value of 0.0. See system test tensor add update for more examples. 

field tensorfield type tensor(x{},y[3]) {
    indexing: attribute | summary
}

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "add": {
                "cells": [
                    { "address": { "x": "b", "y": "0" }, "value": 2.0 },
                    { "address": { "x": "b", "y": "1" }, "value": 3.0 }
                ]
            }
        }
    }
}

In this example, cell {"x":"b","y":"2"} will implicitly be set to 0.0.

Prefer the block short form for mixed tensors instead. This also avoids the problem where cells with indexed dimensions are not specified: 

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "add": {
                "blocks": [
                    { "address": { "x": "b" }, "values": [2.0, 3.0, 5.0] }
                ]
            }
        }
    }
}

remove

Remove elements from weighted sets, maps and tensors with remove.

Weighted set field

field string_weighted_set type weightedset<string> {
    indexing: summary
}

{
    "update":"id:mynamespace:weightedsetdoctype::example1",
    "fields":  {
        "string_weighted_set": {
            "remove": {
                "item 2": 0
            }
        }
    }
}

Map field

field string_map type map<string, string> {
    indexing: summary
}

{
    "update":"id:mynamespace:mapdoctype::example1",
    "fields":  {
        "string_map{item 2}": {
            "remove": 0
        }
    }
}

Tensor field

Removes cells from mapped or mixed tensors. Invalid for tensors with only indexed dimensions. Only mapped dimensions should be specified for tensors with both mapped and indexed dimensions, as all indexed cells the mapped dimensions point to will be removed implicitly. See system test tensor remove update for more examples. 

field tensorfield type tensor(x{},y[2]) {
    indexing: attribute | summary
}

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "remove": {
                "addresses": [
                    {"x": "b"},
                    {"x": "c"}
                ]
            }
        }
    }
}

In this example, cells {x:b,y:0},{x:b,y:1},{x:c,y:0},{x:c,y:1} will be removed.

It is also supported to specify only a subset of the mapped dimensions in the addresses. In that case, all cells that match the label values of the specified dimensions are removed. In the given example, all cells having label b for dimension x are removed. 

field tensorfield type tensor(x{},y{},z[2]) {
    indexing: attribute | summary
}

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "remove": {
                "addresses": [
                    {"x": "b"}
                ]
            }
        }
    }
}

Arithmetic

The four arithmetic operators increment, decrement, multiply and divide are used to modify single value numeric values without having to look up the current value before applying the update. Example: 

field sales type int {
    indexing: summary | attribute
}

{
    "update": "id:music:music::https://music.yahoo.com/bobdylan/BestOf",
    "fields": {
        "sales": {
            "increment": 1
        }
    }
}

match

If an arithmetic operation is to be done for a specific key in a weighted set or array, use the match operation: 

field track_popularity type weightedset<string> {
    indexing: summary | attribute
}

{
    "update": "id:music:music::https://music.yahoo.com/bobdylan/BestOf",
    "fields": {
        "track_popularity": {
            "match": {
                "element": "Lay Lady Lay",
                "increment": 1
            }
        }
    }
}

In other words, for the weighted set "track_popularity", match the element "Lay Lady Lay", then increment its weight by 1. See the reference for how to auto-create keys.

If the updated field is an array, the element value would be a positive integer.

Note: only one element can be matched per operation.

Modify tensors

Individual cells in tensors can be modified using the modify update. The cells are modified according to the given operation:

  • replace - replaces cell values
  • add - adds a value to the cell
  • multiply - multiples a value with the cell

The addresses of cells must be fully specified. If the cell does not exist, the update for that cell will be ignored. See system test tensor modify update for more examples. 

field tensorfield type tensor(x[3]) {
    indexing: attribute | summary
}

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "modify": {
                "operation": "replace",
                "addresses": [
                    { "address": { "x": "1" }, "value": 7.0 },
                    { "address": { "x": "2" }, "value": 8.0 }
                ]
            }
        }
    }
}

In this example, cell {"x":"1"} is replaced with value 7.0 and {"x":"2"} with value 8.0. If operation add or multiply was used instead, 7.0 and 8.0 would be added or multiplied to the current values of cells {"x":"1"} and {"x":"2"}.

For mixed tensors the block short form can also be used to modify entire dense subspaces:

field tensorfield type tensor(x{},y[3]) {
    indexing: attribute | summary
}

{
    "update": "id:mynamespace:tensordoctype::example",
    "fields": {
        "tensorfield": {
            "modify": {
                "operation": "replace",
                "blocks": { "a": [1,2,3], "b": [4,5,6] }
            }
        }
    }
}

fieldpath

Fieldpath is for accessing fields within composite structures - for structures that are not part of index or attribute, it is possible to access elements directly using fieldpaths. This is done by adding more information to the field value. For map structures, specify the key (see example). 

mymap{mykey}

and then do operation on the element which is keyed by "mykey". Arrays can be accessed as well (see details). 

myarray[3]

And this is also true for structs (see details). Note: Struct updates do not work for index mode: 

mystruct.value1

This also works for nested structures, e.g. a map of map to array of struct: 

{
    "update": "id:mynamespace:complexdoctype::foo",
    "fields": {
        "nested_structure{firstMapKey}{secondMapKey}[4].title": {
            "assign": "Look at me, mom! I'm hiding deep in a nested type!"
        }
    }
}