Skip to content

API

In our API, each SQL table is reflected as a set of GraphQL types. At a high level, tables become types and columns/foreign keys become fields on those types.

By default, PostgreSQL table and column names are not inflected when reflecting GraphQL names. For example, an account_holder table has GraphQL type name account_holder. In cases where SQL entities are named using snake_case, enable inflection to match GraphQL/Javascript conventions e.g. account_holder -> AccountHolder.

Individual table, column, and relationship names may also be manually overridden.

Primary Keys (Required)

Every table must have a primary key for it to be exposed in the GraphQL schema. For example, the following Blog table will be available in the GraphQL schema as blogCollection since it has a primary key named id:

1 2 3 4
create table "Blog"(  id serial primary key,  name varchar(255) not null, );

But the following table will not be exposed because it doesn't have a primary key:

1 2 3 4
create table "Blog"(  id int,  name varchar(255) not null, );

QueryType

The Query type is the entrypoint for all read access into the graph.

Node

The node interface allows for retrieving records that are uniquely identifiable by a globally unique nodeId: ID! field. For more information about nodeId, see nodeId.

SQL Setup 

1 2 3 4 5 6 7
create table "Blog"(  id serial primary key,  name varchar(255) not null,  description varchar(255),  "createdAt" timestamp not null,  "updatedAt" timestamp not null );

GraphQL Types

1 2 3 4 5 6 7
"""The root type for querying data""" type Query { """Retrieve a record by its `ID`""" node(nodeId: ID!): Node }

To query the node interface effectively, use inline fragments to specify which fields to return for each type.

Example

 1  2  3  4  5  6  7  8  9 10 11 12
{ node( nodeId: "WyJwdWJsaWMiLCAiYmxvZyIsIDFd" ) { nodeId # Inline fragment for `Blog` type ... on Blog { name description } } } 
1 2 3 4 5 6 7 8 9
{  "data": {  "node": {  "name": "Some Blog",  "nodeId": "WyJwdWJsaWMiLCAiYmxvZyIsIDFd",  "description": "Description of Some Blog"  }  } }

Collections

Each table has top level entry in the Query type for selecting records from that table. Collections return a connection type and can be paginated, filtered, and sorted using the available arguments.

SQL Setup

1 2 3 4 5 6 7
create table "Blog"(  id serial primary key,  name varchar(255) not null,  description varchar(255),  "createdAt" timestamp not null,  "updatedAt" timestamp not null );

GraphQL Types

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
"""The root type for querying data""" type Query { """A pagable collection of type `Blog`""" blogCollection( """Query the first `n` records in the collection""" first: Int """Query the last `n` records in the collection""" last: Int """Query values in the collection before the provided cursor""" before: Cursor """Query values in the collection after the provided cursor""" after: Cursor """  Skip n values from the after cursor. Alternative to cursor pagination. Backward pagination not supported.  """ offset: Int """Filters to apply to the results set when querying from the collection""" filter: BlogFilter """Sort order to apply to the collection""" orderBy: [BlogOrderBy!] ): BlogConnection }

Connection types are the primary interface to returning records from a collection.

Connections wrap a result set with some additional metadata.

 1  2  3  4  5  6  7  8  9 10 11 12
type BlogConnection { # Count of all records matching the *filter* criteria totalCount: Int! # Pagination metadata pageInfo: PageInfo! # Result set edges: [BlogEdge!]! } 
1 2 3 4 5 6 7 8 9
type BlogEdge { # Unique identifier of the record within the query cursor: String! # Contents of a record/row in the results set node: Blog } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14
type PageInfo { # unique identifier of the first record within the query startCursor: String # unique identifier of the last record within the query endCursor: String # is another page of content available hasNextPage: Boolean! # is another page of content available hasPreviousPage: Boolean! } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
# A record from the `blog` table type Blog { # globally unique identifier nodeId: ID! # Value from `id` column id: Int! # Value from `name` column name: String! # Value from `description` column description: String # Value from `createdAt` column createdAt: Datetime! # Value from `updatedAt` column updatedAt: Datetime! } 
1 2 3 4 5 6 7
input BlogOrderBy { id: OrderByDirection name: OrderByDirection description: OrderByDirection createdAt: OrderByDirection updatedAt: OrderByDirection } 
 1  2  3  4  5  6  7  8  9 10 11
input BlogFilter { nodeId: IDFilter id: IntFilter name: StringFilter description: StringFilter createdAt: DatetimeFilter updatedAt: DatetimeFilter and: [BlogFilter!] or: [BlogFilter!] not: BlogFilter }

Note

The totalCount field is disabled by default because it can be expensive on large tables. To enable it use a comment directive

Pagination

Keyset Pagination

Paginating forwards and backwards through collections is handled using the first, last, before, and after parameters, following the relay spec.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
type Query { blogCollection( """Query the first `n` records in the collection""" first: Int """Query the last `n` records in the collection""" last: Int """Query values in the collection before the provided cursor""" before: Cursor """Query values in the collection after the provided cursor""" after: Cursor ...truncated... ): BlogConnection }

Metadata relating to the current page of a result set is available on the pageInfo field of the connection type returned from a collection.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14
type PageInfo { # unique identifier of the first record within the query startCursor: String # unique identifier of the last record within the query endCursor: String # is another page of content available hasNextPage: Boolean! # is another page of content available hasPreviousPage: Boolean! } 
1 2 3 4 5 6 7 8 9
type BlogConnection { # Pagination metadata pageInfo: PageInfo! # Result set edges: [BlogEdge!]! }

To paginate forward in the collection, use the first and after arguments. To retrieve the first page, the after argument should be null or absent.

Example

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19
{ blogCollection( first: 2, after: null ) { pageInfo { startCursor endCursor hasPreviousPage hasNextPage } edges { cursor node { id } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1  },  "cursor": "WzFd"  },  {  "node": {  "id": 2  },  "cursor": "WzJd"  }  ],  "pageInfo": {  "startCursor": "WzFd",  "endCursor": "WzJd",  "hasNextPage": true,  "hasPreviousPage": false  }  }  } }

To retrieve the next page, provide the cursor value from data.blogCollection.pageInfo.endCursor to the after argument of another query.

1 2 3 4 5 6 7
{ blogCollection( first: 2, after: "WzJd" ) { ...truncated... } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 3  },  "cursor": "WzNd"  },  {  "node": {  "id": 4  },  "cursor": "WzRd"  }  ],  "pageInfo": {  "startCursor": "WzNd",  "endCursor": "WzRd",  "hasNextPage": false,  "hasPreviousPage": true  }  }  } }

once the collection has been fully enumerated, data.blogConnection.pageInfo.hasNextPage returns false.

To paginate backwards through a collection, repeat the process substituting first -> last, after -> before, hasNextPage -> hasPreviousPage

Offset Pagination

In addition to keyset pagination, collections may also be paged using first and offset, which operates like SQL's limit and offset to skip offset number of records in the results.

Note

offset based pagination becomes inefficient the offset value increases. For this reason, prefer cursor based pagination where possible.

1 2 3 4 5 6 7
{ blogCollection( first: 2, offset: 2 ) { ...truncated... } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 3  },  "cursor": "WzNd"  },  {  "node": {  "id": 4  },  "cursor": "WzRd"  }  ],  "pageInfo": {  "startCursor": "WzNd",  "endCursor": "WzRd",  "hasNextPage": false,  "hasPreviousPage": true  }  }  } }

Filtering

To filter the result set, use the filter argument.

 1  2  3  4  5  6  7  8  9 10 11
type Query { blogCollection( """Filters to apply to the results set when querying from the collection""" filter: BlogFilter ...truncated... ): BlogConnection }

Where the <Table>Filter type enumerates filterable fields and their associated <Type>Filter.

 1  2  3  4  5  6  7  8  9 10 11 12
input BlogFilter { nodeId: IDFilter id: IntFilter name: StringFilter description: StringFilter tags: StringListFilter createdAt: DatetimeFilter updatedAt: DatetimeFilter and: [BlogFilter!] or: [BlogFilter!] not: BlogFilter } 
 1  2  3  4  5  6  7  8  9 10 11 12 13
""" Boolean expression comparing fields on type "Int" """ input IntFilter { eq: Int gt: Int gte: Int in: [Int!] lt: Int lte: Int neq: Int is: FilterIs } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
""" Boolean expression comparing fields on type "String" """ input StringFilter { eq: String gt: String gte: String in: [String!] lt: String lte: String neq: String is: FilterIs startsWith: String like: String ilike: String regex: String iregex: String } 
 1  2  3  4  5  6  7  8  9 10
""" Boolean expression comparing fields on type "StringList" """ input StringListFilter { contains: [String!] containedBy: [String!] eq: [String!] overlaps: [String!] is: FilterIs } 
1 2 3 4
enum FilterIs { NULL NOT_NULL }

The following list shows the operators that may be available on <Type>Filter types.

Operator Description
eq Equal To
neq Not Equal To
gt Greater Than
gte Greater Than Or Equal To
in Contained by Value List
lt Less Than
lte Less Than Or Equal To
is Null or Not Null
startsWith Starts with prefix
like Pattern Match. '%' as wildcard
ilike Pattern Match. '%' as wildcard. Case Insensitive
regex POSIX Regular Expression Match
iregex POSIX Regular Expression Match. Case Insensitive
contains Contains. Applies to array columns only.
containedBy Contained in. Applies to array columns only.
overlaps Overlap (have points in common). Applies to array columns only.

Not all operators are available on every <Type>Filter type. For example, UUIDFilter only supports eq and neq because UUIDs are not ordered.

Example: simple

 1  2  3  4  5  6  7  8  9 10 11 12
{ blogCollection( filter: {id: {lt: 3}}, ) { edges { cursor node { id } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1  },  "cursor": "WzFd"  },  {  "node": {  "id": 2  },  "cursor": "WzJd"  }  ]  }  } }

Example: array column

The contains filter is used to return results where all the elements in the input array appear in the array column.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
{ blogCollection( filter: {tags: {contains: ["tech", "innovation"]}}, ) { edges { cursor node { id name tags createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation"]  },  "cursor": "WzFd"  },  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation", "entrepreneurship"]  },  "cursor": "WzJd"  }  ]  }  } }

The contains filter can also accept a single scalar.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
{ blogCollection( filter: {tags: {contains: "tech"}}, ) { edges { cursor node { id name tags createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation"]  },  "cursor": "WzFd"  },  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation", "entrepreneurship"]  },  "cursor": "WzJd"  }  ]  }  } }

The containedBy filter is used to return results where every element of the array column appears in the input array.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
{ blogCollection( filter: {tags: {containedBy: ["entrepreneurship", "innovation", "tech"]}}, ) { edges { cursor node { id name tags createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation"]  },  "cursor": "WzFd"  },  {  "node": {  "id": 3,  "name": "A: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["innovation", "entrepreneurship"]  },  "cursor": "WzNd"  }  ]  }  } }

The containedBy filter can also accept a single scalar. In this case, only results where the only element in the array column is the input scalar are returned.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
{ blogCollection( filter: {tags: {containedBy: "travel"}}, ) { edges { cursor node { id name tags createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 4,  "name": "A: Blog 4",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["travel"]  },  "cursor": "WzPd"  }  ]  }  } }

The overlaps filter is used to return results where the array column and the input array have at least one element in common.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
{ blogCollection( filter: {tags: {overlaps: ["tech", "travel"]}}, ) { edges { cursor node { id name tags createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation"]  },  "cursor": "WzFd"  },  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["tech", "innovation", "entrepreneurship"]  },  "cursor": "WzJd"  },  {  "node": {  "id": 4,  "name": "A: Blog 4",  "createdAt": "2023-07-24T04:01:09.882781",  "tags": ["travel"]  },  "cursor": "WzPd"  }  ]  }  } }

Example: and/or

Multiple filters can be combined with and, or and not operators. The and and or operators accept a list of <Type>Filter.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
{ blogCollection( filter: { and: [ {id: {eq: 1}} {name: {eq: "A: Blog 1"}} ] } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc1"  },  "cursor": "WzFd"  }  ]  }  } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
{ blogCollection( filter: { or: [ {id: {eq: 1}} {name: {eq: "A: Blog 2"}} ] } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc1"  },  "cursor": "WzFd"  },  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc2"  },  "cursor": "WzJd"  }  ]  }  } }

Example: not

not accepts a single <Type>Filter.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
{ blogCollection( filter: { not: {id: {eq: 1}} } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc2"  },  "cursor": "WzJd"  },  {  "node": {  "id": 3,  "name": "A: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc3"  },  "cursor": "WzNd"  },  {  "node": {  "id": 4,  "name": "B: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "b desc1"  },  "cursor": "WzRd"  }  ]  }  } }

Example: nested composition

The and, or and not operators can be arbitrarily nested inside each other.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
{ blogCollection( filter: { or: [ { id: { eq: 1 } } { id: { eq: 2 } } { and: [{ id: { eq: 3 }, not: { name: { eq: "A: Blog 2" } } }] } ] } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc1"  },  "cursor": "WzFd"  },  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc2"  },  "cursor": "WzJd"  },  {  "node": {  "id": 3,  "name": "A: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc3"  },  "cursor": "WzNd"  }  ]  }  } }

Example: empty

Empty filters are ignored, i.e. they behave as if the operator was not specified at all.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
{ blogCollection( filter: { and: [], or: [], not: {} } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 1,  "name": "A: Blog 1",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc1"  },  "cursor": "WzFd"  },  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc2"  },  "cursor": "WzJd"  },  {  "node": {  "id": 3,  "name": "A: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc3"  },  "cursor": "WzNd"  },  {  "node": {  "id": 4,  "name": "B: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "b desc1"  },  "cursor": "WzRd"  }  ]  }  } }

Example: implicit and

Multiple column filters at the same level will be implicitly combined with boolean and. In the following example the id: {eq: 1} and name: {eq: "A: Blog 1"} will be anded.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
{ blogCollection( filter: { # Equivalent to not: { and: [{id: {eq: 1}}, {name: {eq: "A: Blog 1"}}]} not: { id: {eq: 1} name: {eq: "A: Blog 1"} } } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc2"  },  "cursor": "WzJd"  },  {  "node": {  "id": 3,  "name": "A: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc3"  },  "cursor": "WzNd"  },  {  "node": {  "id": 4,  "name": "B: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "b desc1"  },  "cursor": "WzRd"  }  ]  }  } }

This means that an and filter can be often be simplified. In the following example all queries are equivalent and produce the same result.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
{ blogCollection( filter: { and: [ {id: {gt: 0}} {id: {lt: 2}} {name: {eq: "A: Blog 1"}} ] } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19
{ blogCollection( filter: { id: {gt: 0} id: {lt: 2} name: {eq: "A: Blog 1"} } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
{ blogCollection( filter: { id: {gt: 0, lt: 2} name: {eq: "A: Blog 1"} } ) { edges { cursor node { id name description createdAt } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 2,  "name": "A: Blog 2",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc2"  },  "cursor": "WzJd"  },  {  "node": {  "id": 3,  "name": "A: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "a desc3"  },  "cursor": "WzNd"  },  {  "node": {  "id": 4,  "name": "B: Blog 3",  "createdAt": "2023-07-24T04:01:09.882781",  "description": "b desc1"  },  "cursor": "WzRd"  }  ]  }  } }

Be aware that the above simplification only works for the and operator. If you try it with an or operator it will behave like an and.

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
{ blogCollection( filter: { # This is really an `and` in `or`'s clothing or: { id: {eq: 1} name: {eq: "A: Blog 2"} } } ) { edges { cursor node { id name description createdAt } } } } 
1 2 3 4 5 6 7
{  "data": {  "blogCollection": {  "edges": []  }  } }

This is because according to the rules of GraphQL list input coercion, if a value passed to an input of list type is not a list, then it is coerced to a list of a single item. So in the above example or: {id: {eq: 1}, name: {eq: "A: Blog 2}} will be coerced into or: [{id: {eq: 1}, name: {eq: "A: Blog 2}}] which is equivalent to or: [and: [{id: {eq: 1}}, {name: {eq: "A: Blog 2}}}] due to implicit anding.

Note

Avoid naming your columns and, or or not. If you do, the corresponding filter operator will not be available for use.

The and, or and not operators also work with update and delete mutations.

Ordering

The default order of results is defined by the underlying table's primary key column in ascending order. That default can be overridden by passing an array of <Table>OrderBy to the collection's orderBy argument.

 1  2  3  4  5  6  7  8  9 10 11
type Query { blogCollection( """Sort order to apply to the collection""" orderBy: [BlogOrderBy!] ...truncated... ): BlogConnection } 
1 2 3 4 5 6 7
input BlogOrderBy { id: OrderByDirection name: OrderByDirection description: OrderByDirection createdAt: OrderByDirection updatedAt: OrderByDirection } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14
"""Defines a per-field sorting order""" enum OrderByDirection { """Ascending order, nulls first""" AscNullsFirst """Ascending order, nulls last""" AscNullsLast """Descending order, nulls first""" DescNullsFirst """Descending order, nulls last""" DescNullsLast }

Example

 1  2  3  4  5  6  7  8  9 10 11
{ blogCollection( orderBy: [{id: DescNullsLast}] ) { edges { node { id } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "id": 4  }  },  {  "node": {  "id": 3  }  },  {  "node": {  "id": 2  }  },  {  "node": {  "id": 1  }  }  ]  }  } }

Note, only one key value pair may be provided to each element of the input array. For example, [{name: AscNullsLast}, {id: AscNullFirst}] is valid. Passing multiple key value pairs in a single element of the input array e.g. [{name: AscNullsLast, id: AscNullFirst}], is invalid.

MutationType

The Mutation type is the entrypoint for mutations/edits.

Each table has top level entry in the Mutation type for inserting insertInto<Table>Collection, updating update<Table>Collection and deleting deleteFrom<Table>Collection.

SQL Setup 

1 2 3 4 5 6 7
create table "Blog"(  id serial primary key,  name varchar(255) not null,  description varchar(255),  "createdAt" timestamp not null default now(),  "updatedAt" timestamp ); 

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
"""The root type for creating and mutating data""" type Mutation { """Adds one or more `BlogInsertResponse` records to the collection""" insertIntoBlogCollection( """Records to add to the Blog collection""" objects: [BlogInsertInput!]! ): BlogInsertResponse """Updates zero or more records in the collection""" updateBlogCollection( """  Fields that are set will be updated for all records matching the `filter`  """ set: BlogUpdateInput! """Restricts the mutation's impact to records matching the critera""" filter: BlogFilter """  The maximum number of records in the collection permitted to be affected  """ atMost: Int! = 1 ): BlogUpdateResponse! """Deletes zero or more records from the collection""" deleteFromBlogCollection( """Restricts the mutation's impact to records matching the critera""" filter: BlogFilter """  The maximum number of records in the collection permitted to be affected  """ atMost: Int! = 1 ): BlogDeleteResponse! }

Insert

To add records to a collection, use the insertInto<Table>Collection field on the Mutation type.

SQL Setup 

1 2 3 4 5 6 7
create table "Blog"(  id serial primary key,  name varchar(255) not null,  description varchar(255),  "createdAt" timestamp not null default now(),  "updatedAt" timestamp );

GraphQL Types

 1  2  3  4  5  6  7  8  9 10 11 12
"""The root type for creating and mutating data""" type Mutation { """Adds one or more `BlogInsertResponse` records to the collection""" insertIntoBlogCollection( """Records to add to the Blog collection""" objects: [BlogInsertInput!]! ): BlogInsertResponse } 
1 2 3 4 5 6
input BlogInsertInput { name: String description: String createdAt: Datetime updatedAt: Datetime } 
1 2 3 4 5 6 7
type BlogInsertResponse { """Count of the records impacted by the mutation""" affectedCount: Int! """Array of records impacted by the mutation""" records: [Blog!]! }

Where elements in the objects array are inserted into the underlying table.

Example

 1  2  3  4  5  6  7  8  9 10 11 12 13 14
mutation { insertIntoBlogCollection( objects: [ {name: "foo"}, {name: "bar"}, ] ) { affectedCount records { id name } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
{  "data": {  "insertIntoBlogCollection": {  "records": [  {  "id": 1,  "name": "foo"  },  {  "id": 2,  "name": "bar"  }  ],  "affectedCount": 2  }  } }

Update

To update records in a collection, use the update<Table>Collection field on the Mutation type.

SQL Setup 

1 2 3 4 5 6 7
create table "Blog"(  id serial primary key,  name varchar(255) not null,  description varchar(255),  "createdAt" timestamp not null default now(),  "updatedAt" timestamp );

GraphQL Types

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
"""The root type for creating and mutating data""" type Mutation { """Updates zero or more records in the collection""" updateBlogCollection( """  Fields that are set will be updated for all records matching the `filter`  """ set: BlogUpdateInput! """Restricts the mutation's impact to records matching the critera""" filter: BlogFilter """  The maximum number of records in the collection permitted to be affected  """ atMost: Int! = 1 ): BlogUpdateResponse! } 
1 2 3 4 5 6
input BlogUpdateInput { name: String description: String createdAt: Datetime updatedAt: Datetime } 
1 2 3 4 5 6 7 8 9
type BlogUpdateResponse { """Count of the records impacted by the mutation""" affectedCount: Int! """Array of records impacted by the mutation""" records: [Blog!]! }

Where the set argument is a key value pair describing the values to update, filter controls which records should be updated, and atMost restricts the maximum number of records that may be impacted. If the number of records impacted by the mutation exceeds the atMost parameter the operation will return an error.

Example

 1  2  3  4  5  6  7  8  9 10 11 12
mutation { updateBlogCollection( set: {name: "baz"} filter: {id: {eq: 1}} ) { affectedCount records { id name } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13
{  "data": {  "updateBlogCollection": {  "records": [  {  "id": 1,  "name": "baz"  }  ],  "affectedCount": 1  }  } }

Delete

To remove records from a collection, use the deleteFrom<Table>Collection field on the Mutation type.

SQL Setup 

1 2 3 4 5 6 7
create table "Blog"(  id serial primary key,  name varchar(255) not null,  description varchar(255),  "createdAt" timestamp not null default now(),  "updatedAt" timestamp );

GraphQL Types

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
"""The root type for creating and mutating data""" type Mutation { """Deletes zero or more records from the collection""" deleteFromBlogCollection( """Restricts the mutation's impact to records matching the critera""" filter: BlogFilter """  The maximum number of records in the collection permitted to be affected  """ atMost: Int! = 1 ): BlogDeleteResponse! } 
 1  2  3  4  5  6  7  8  9 10
input BlogFilter { id: IntFilter name: StringFilter description: StringFilter createdAt: DatetimeFilter updatedAt: DatetimeFilter and: [BlogFilter!] or: [BlogFilter!] not: BlogFilter } 
1 2 3 4 5 6 7
type BlogDeleteResponse { """Count of the records impacted by the mutation""" affectedCount: Int! """Array of records impacted by the mutation""" records: [Blog!]! }

Where filter controls which records should be deleted and atMost restricts the maximum number of records that may be deleted. If the number of records impacted by the mutation exceeds the atMost parameter the operation will return an error.

Example

 1  2  3  4  5  6  7  8  9 10 11
mutation { deleteFromBlogCollection( filter: {id: {eq: 1}} ) { affectedCount records { id name } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13
{  "data": {  "deleteFromBlogCollection": {  "records": [  {  "id": 1,  "name": "baz"  }  ],  "affectedCount": 1  }  } }

Concepts

nodeId

The base GraphQL type for every table with a primary key is automatically assigned a nodeId: ID! field. That value, can be passed to the node entrypoint of the Query type to retrieve its other fields. nodeId may also be used as a caching key.

relay support

By default relay expects the ID field for types to have the name id. pg_graphql uses nodeId by default to avoid conflicting with user defined id columns. You can configure relay to work with pg_graphql's nodeId field with relay's nodeInterfaceIdField option. More info available here.

SQL Setup 

1 2 3 4
create table "Blog"(  id serial primary key,  name varchar(255) not null );

GraphQL Types

1 2 3 4 5
type Blog {  nodeId: ID! # this field  id: Int!  name: String! }

Relationships

Relationships between collections in the Graph are derived from foreign keys.

One-to-Many

A foreign key on table A referencing table B defines a one-to-many relationship from table A to table B.

SQL Setup 

 1  2  3  4  5  6  7  8  9 10 11
create table "Blog"(  id serial primary key,  name varchar(255) not null ); create table "BlogPost"(  id serial primary key,  "blogId" integer not null references "Blog"(id),  title varchar(255) not null,  body varchar(10000) );

GraphQL Types

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
type Blog {  # globally unique identifier  nodeId: ID!  id: Int!  name: String!  description: String  blogPostCollection(  """Query the first `n` records in the collection"""  first: Int  """Query the last `n` records in the collection"""  last: Int  """Query values in the collection before the provided cursor"""  before: Cursor  """Query values in the collection after the provided cursor"""  after: Cursor  """  Skip n values from the after cursor. Alternative to cursor pagination. Backward pagination not supported.  """  offset: Int  """Filters to apply to the results set when querying from the collection"""  filter: BlogPostFilter  """Sort order to apply to the collection"""  orderBy: [BlogPostOrderBy!]  ): BlogPostConnection }

Where blogPostCollection exposes the full Query interface to BlogPosts.

Example

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
{ blogCollection { edges { node { name blogPostCollection { edges { node { id title } } } } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
{  "data": {  "blogCollection": {  "edges": [  {  "node": {  "name": "pg_graphql blog",  "blogPostCollection": {  "edges": [  {  "node": {  "id": 2,  "title": "fIr3t p0sT"  }  },  {  "node": {  "id": 3,  "title": "graphql with postgres"  }  }  ]  }  }  }  ]  }  } }

Many-to-One

A foreign key on table A referencing table B defines a many-to-one relationship from table B to table A.

SQL Setup 

 1  2  3  4  5  6  7  8  9 10 11
create table "Blog"(  id serial primary key,  name varchar(255) not null ); create table "BlogPost"(  id serial primary key,  "blogId" integer not null references "Blog"(id),  title varchar(255) not null,  body varchar(10000) );

GraphQL Types

1 2 3 4 5 6 7 8 9
type BlogPost {  nodeId: ID!  id: Int!  blogId: Int!  title: String!  body: String  blog: Blog }

Where blog exposes the Blog record associated with the BlogPost.

 1  2  3  4  5  6  7  8  9 10 11 12
{ blogPostCollection { edges { node { title blog { name } } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
{  "data": {  "blogPostCollection": {  "edges": [  {  "node": {  "blog": {  "name": "pg_graphql blog"  },  "title": "fIr3t p0sT"  }  },  {  "node": {  "blog": {  "name": "pg_graphql blog"  },  "title": "graphql with postgres"  }  }  ]  }  } }

One-to-One

A one-to-one relationship is defined by a foreign key on table A referencing table B where the columns making up the foreign key on table A are unique.

SQL Setup 

 1  2  3  4  5  6  7  8  9 10
create table "EmailAddress"(  id serial primary key,  address text unique not null ); create table "Employee"(  id serial primary key,  name text not null,  email_address_id int unique references "EmailAddress"(id) );

GraphQL Types

1 2 3 4 5 6 7
type Employee {  nodeId: ID!  id: Int!  name: String!  emailAddressId: Int  emailAddress: EmailAddress } 
1 2 3 4 5 6
type EmailAddress {  nodeId: ID!  id: Int!  address: String!  employee: Employee }

Example

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
{ employeeCollection { edges { node { name emailAddress { address employee { name } } } } } } 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19
{  "data": {  "employeeCollection": {  "edges": [  {  "node": {  "name": "Foo Barington",  "emailAddress": {  "address": "foo@bar.com",  "employee": {  "name": "Foo Barington"  }  }  }  }  ]  }  } }

Custom Scalars

Due to differences among the types supported by PostgreSQL, JSON, and GraphQL, pg_graphql adds several new Scalar types to handle PostgreSQL builtins that require special handling.

JSON

pg_graphql serializes json and jsonb data types as String under the custom scalar name JSON.

1
scalar JSON

Example

Given the setup

1 2 3 4 5 6 7
create table "User"(  id bigserial primary key,  config jsonb ); insert into "User"(config) values (jsonb_build_object('palette', 'dark-mode')); 
1 2 3 4 5
type User {  nodeId: ID!  id: BigInt!  config: JSON }

The query

1 2 3 4 5 6 7 8 9
{ userCollection { edges { node { config } } } }

The returns the following data. Note that config is serialized as a string

 1  2  3  4  5  6  7  8  9 10 11 12 13
{  "data": {  "userCollection": {  "edges": [  {  "node": {  "config": "{\"palette\": \"dark-mode\"}"  }  }  ]  }  } }

Use serialized JSON strings when updating or inserting JSON fields via the GraphQL API.

JSON does not currently support filtering.

BigInt

PostgreSQL bigint and bigserial types are 64 bit integers. In contrast, JSON supports 32 bit integers.

Since PostgreSQL bigint values may be outside the min/max range allowed by JSON, they are represented in the GraphQL schema as BigInts and values are serialized as strings.

 1  2  3  4  5  6  7  8  9 10 11 12
scalar BigInt input BigIntFilter { eq: BigInt gt: BigInt gte: BigInt in: [BigInt!] lt: BigInt lte: BigInt neq: BigInt is: FilterIs }

Example

Given the setup

1 2 3 4 5 6 7
create table "Person"(  id bigserial primary key,  name text ); insert into "Person"(name) values ('J. Bazworth'); 
1 2 3 4 5
type Person {  nodeId: ID!  id: BigInt!  name: String }

The query

 1  2  3  4  5  6  7  8  9 10
{ personCollection { edges { node { id name } } } }

The returns the following data. Note that id is serialized as a string

 1  2  3  4  5  6  7  8  9 10 11 12 13 14
{  "data": {  "personCollection": {  "edges": [  {  "node": {  "id": "1",  "name": "Foo Barington",  }  }  ]  }  } }

BigFloat

PostgreSQL's numeric type supports arbitrary precision floating point values. JSON's float is limited to 64-bit precision.

Since a PostgreSQL numeric may require more precision than can be handled by JSON, numeric types are represented in the GraphQL schema as BigFloat and values are serialized as strings.

 1  2  3  4  5  6  7  8  9 10 11 12
scalar BigFloat input BigFloatFilter { eq: BigFloat gt: BigFloat gte: BigFloat in: [BigFloat!] lt: BigFloat lte: BigFloat neq: BigFloat is: FilterIs }

Example

Given the SQL setup

1 2 3 4 5 6 7
create table "GeneralLedger"(  id serial primary key,  amount numeric(10,2) ); insert into "GeneralLedger"(amount) values (22.15);

The query

 1  2  3  4  5  6  7  8  9 10
{ generalLedgerCollection { edges { node { id amount } } } }

The returns the following data. Note that amount is serialized as a string

 1  2  3  4  5  6  7  8  9 10 11 12 13 14
{  "data": {  "generalLedgerCollection": {  "edges": [  {  "node": {  "id": 1,  "amount": "22.15",  }  }  ]  }  } }

Opaque

PostgreSQL's type system is extensible and not all types handle all operations e.g. filtering with like. To account for these, pg_graphql introduces a scalar Opaque type. The Opaque type uses PostgreSQL's to_json method to serialize values. That allows complex or unknown types to be included in the schema by delegating handling to the client.

1 2 3 4 5 6
scalar Opaque input OpaqueFilter { eq: Opaque is: FilterIs }