Using the Stargate GraphQL API
Stargate is a data gateway deployed between client applications and a database. The GraphQL API plugin that exposes CRUD access to data stored in Cassandra tables.
|
For more information about the GraphQL API, see the blog post on the GraphQL API. |
Prerequisites
To use Stargate you need:
-
Docker installed and running, if using Docker
-
cURLto run REST queries
|
If you are looking to just get started, DataStax Astra Database-as-a-Service can get you started with no install steps. |
This image contains the Cassandra Query Language (CQL), REST, Document, GraphQL APIs, and GraphQL Playground, along with an Apache Cassandra™ 3.11 backend.
docker pull stargateio/stargate-3_11:v1.0.7Start the Stargate container in developer mode. Developer mode removes the need to set up a separate Cassandra instance and is meant for development and testing only.
docker run --name stargate \
-p 8080:8080 \
-p 8081:8081 \
-p 8082:8082 \
-p 127.0.0.1:9042:9042 \
-d \
-e CLUSTER_NAME=stargate \
-e CLUSTER_VERSION=3.11 \
-e DEVELOPER_MODE=true \
stargateio/stargate-3_11:v1.0.7Using the Auth API to generate an auth token
In order to use the Stargate Document API, an authorization token must be generated to access the interface. A REST API token is used for this purpose.
The step below uses cURL to access the REST interface to generate the needed
token.
Generate an auth token
First generate an auth token that is required in each subsequent request
in the X-Cassandra-Token header. Note the port for the auth service is 8081.
curl -L -X POST 'http://localhost:8081/v1/auth' \
-H 'Content-Type: application/json' \
--data-raw '{
"username": "cassandra",
"password": "cassandra"
}'You should receive a token in the response.
{"authToken":"{auth-token}"}You will need to add this token to the GraphQL Playground in order to authorize
your GraphQL requests. Copy the value after "authToken" to use later.
Using the GraphQL Playground
The easiest way to get started is to use the built-in GraphQL playground that is
included in the stargate docker container. It can be accessed at
http://localhost:8080/playground using a local
browser after the docker container is started.
Once in the playground, you can create new schema and interact with the GraphQL APIs. The server paths are structured to provide access to creating and querying schema, as well as querying and modifying Cassandra data:
-
/graphql-schema-
An API for exploring and creating schema, or Data Definition Language (DDL). For example, Cassandra has queries to create, modify, drop keyspaces and tables, such as
CREATE KEYSPACE,CREATE TABLE1, orDROP TABLE.
-
-
/graphql/<keyspace>-
An API for querying and modifying your Cassandra tables using GraphQL fields.
-
We’ll start the playground with /graphql-schema to create some schema.
Creating or dropping schema
In order to use the GraphQL API, you must create schema that defines the keyspace and
tables that will store the data. A keyspace is a container for which a
replication factor defines the number of data replicas the database will store.
Tables consist of columns that have a defined data type. Multiple tables are contained
in a keyspace, but a table cannot be contained in multiple keyspaces.
Creating a keyspace
Before you can start using the GraphQL API, you must first create a Cassandra keyspace and at least one table in your database. If you are connecting to a Cassandra database with existing schema, you can skip this step.
Inside the GraphQL playground, navigate to http://localhost:8080/graphql-schema and create a keyspace by executing the following mutation:
# create a keyspace called library
mutation createKsLibrary {
createKeyspace(name:"library", replicas: 1)
}For each keyspace created in your Cassandra schema, a new path is created under
the graphql-path root (default is: /graphql). For example, the mutation just
executed creates a path /graphql/library for the library keyspace when
Cassandra creates the keyspace.
Add the auth token to the HTTP Headers box in the lower lefthand corner:
{
"X-Cassandra-Token":"bff43799-4682-4375-99e8-23c8a9d0f304"
}|
Notice that the key for this JSON token is different than the value that the
generate token has. It is |
Now run the mutation to create the keyspace. You should see a return value of:
{
"data": {
"createKeyspace": true
}
}Creating a user-defined type (UDT)
User-defined types (UDTs) can be created and used in table definitions.
This example creates a UDT called address_type that includes a street, city, state, and zipcode.
# create a user-defined type (UDT)
mutation createAddressUDT {
createType(
keyspaceName: "library"
typeName: "address_type"
fields: [
{ name: "street", type: { basic: TEXT } }
{ name: "city", type: { basic: TEXT } }
{ name: "state", type: { basic: TEXT } }
{ name: "zip", type: { basic: TEXT } }
]
)
}{
"data": {
"createType": true
}
}Creating a table
After the keyspace exists, you can create two tables by executing the following mutation:
# create two tables (book, reader) in library with a single mutation
# DATA TYPES: TEXT, UUID, SET(TEXT), TUPLE(TEXT, INT, DATE), LIST(UDT)
mutation createTables {
book: createTable(
keyspaceName:"library",
tableName:"book",
partitionKeys: [ # The keys required to access your data
{ name: "title", type: {basic: TEXT} }
]
clusteringKeys: [
{ name: "author", type: {basic: TEXT} }
]
)
reader: createTable(
keyspaceName:"library",
tableName:"reader",
partitionKeys: [
{ name: "name", type: {basic: TEXT} }
]
clusteringKeys: [ # Secondary key used to access values within the partition
{ name: "user_id", type: {basic: UUID}, order: "ASC" }
]
values: [
{ name: "birthdate", type: {basic: DATE} }
{ name: "email", type: {basic: SET, info:{ subTypes: [ { basic: TEXT } ] } } }
{ name: "reviews", type: {basic: TUPLE, info: { subTypes: [ { basic: TEXT }, { basic: INT }, { basic: DATE } ] } } }
{ name: "addresses", type: { basic: LIST, info: { subTypes: [ { basic: UDT, info: { name: "address_type", frozen: true } } ] } } }
]
)
} "data": {
"book": true,
"reader": true
}
}It is worth noting that one mutation is used to create two tables. Information about partition keys and clustering keys can be found in the CQL reference.
The second table, reader, also defines a column using a user-defined type (UDT).
|
The GraphQL API uses specific naming conventions to preserve capitalization and special characters.
|
Data types
Tables can be created with an option ifNotExists.
They can also be created with collections (set, list, map), tuples, and UDTs.
IF NOT EXISTS option
A table can be created with an option ifNotExists that will only create the
table if it does not already exist:
# create two tables, magazine and article, IF THEY DON'T EXIST
# DATA TYPES: TEXT, INT, LIST(TEXT)
mutation createTableIfNotExists {
magazine: createTable(
keyspaceName:"library",
tableName:"magazine",
partitionKeys: [ # The keys required to access your data
{ name: "title", type: {basic: TEXT} }
]
clusteringKeys: [ # Secondary key used to access values within the partition
{ name: "pub_yr", type: {basic: INT}, order: "ASC" }
{ name: "pub_mon", type: {basic: INT} }
{ name: "mag_id", type: {basic: INT} }
],
ifNotExists: true,
values: [ # The values associated with the keys
{ name: "editor", type: {basic: TEXT} }
]
)
article: createTable(
keyspaceName:"library",
tableName:"article",
partitionKeys: [ # The keys required to access your data
{ name: "title", type: {basic: TEXT} }
]
clusteringKeys: [ # Secondary key used to access values within the partition
{ name: "mtitle", type: {basic: TEXT} }
],
ifNotExists: true,
values: [ # The values associated with the keys
{ name: "authors", type: {basic:LIST, info:{ subTypes: [ { basic: TEXT } ] } } }
]
)
}{
"data": {
"magazine": true,
"article": true
}
}One of these tables includes creating a column with the data type LIST, an ordered
collection of text values.
Collection (set, list, map) columns
Including a collection in a table has a couple of extra parts:
# create a table with a MAP
# DATA TYPE: TEXT, INT, MAP(TEXT, DATE)
mutation createMapTable {
badge: createTable (
keyspaceName:"library",
tableName: "badge",
partitionKeys: [
{name: "badge_type", type: {basic:TEXT}}
]
clusteringKeys: [
{ name: "badge_id", type: { basic: INT} }
],
ifNotExists:true,
values: [
{name: "earned", type:{basic:MAP, info:{ subTypes: [ { basic: TEXT }, {basic: DATE}]}}}
]
)
}{
"data": {
"badge": true
}
}This example shows a map. A previous example shows a list. In the next example, a set will be used.
Adding columns to table schema
If you need to add more attributes to something you are storing in a table, you can add one or more columns:
# alter a table and add columns
# DATA TYPES: TEXT, INT, SET(TEXT)
mutation alterTableAddCols {
alterTableAdd(
keyspaceName:"library",
tableName:"book",
toAdd:[
{ name: "isbn", type: { basic: TEXT } }
{ name: "language", type: {basic: TEXT} }
{ name: "pub_year", type: {basic: INT} }
{ name: "genre", type: {basic:SET, info:{ subTypes: [ { basic: TEXT } ] } } }
{ name: "format", type: {basic:SET, info:{ subTypes: [ { basic: TEXT } ] } } }
]
)
}{
"data": {
"alterTableAdd": true
}
}Checking that keyspaces and tables exist
To check if a keyspace, tables, or particular table columns exist, execute a GraphQL query:
For keyspaces and tables:
query GetKeyspace {
keyspace(name: "library") {
name
dcs {
name
replicas
}
tables {
name
columns {
name
kind
type {
basic
info {
name
}
}
}
}
}
}{
"data": {
"keyspace": {
"name": "library",
"dcs": [],
"tables": [
{
"name": "book",
"columns": [
{
"name": "title",
"kind": "PARTITION",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "author",
"kind": "CLUSTERING",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "format",
"kind": "REGULAR",
"type": {
"basic": "SET",
"info": {
"name": null
}
}
},
{
"name": "genre",
"kind": "REGULAR",
"type": {
"basic": "SET",
"info": {
"name": null
}
}
},
{
"name": "isbn",
"kind": "REGULAR",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "language",
"kind": "REGULAR",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "pub_year",
"kind": "REGULAR",
"type": {
"basic": "INT",
"info": null
}
}
]
},
{
"name": "reader",
"columns": [
{
"name": "name",
"kind": "PARTITION",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "user_id",
"kind": "CLUSTERING",
"type": {
"basic": "UUID",
"info": null
}
},
{
"name": "addresses",
"kind": "REGULAR",
"type": {
"basic": "LIST",
"info": {
"name": null
}
}
},
{
"name": "birthdate",
"kind": "REGULAR",
"type": {
"basic": "DATE",
"info": null
}
},
{
"name": "email",
"kind": "REGULAR",
"type": {
"basic": "SET",
"info": {
"name": null
}
}
},
{
"name": "reviews",
"kind": "REGULAR",
"type": {
"basic": "TUPLE",
"info": {
"name": null
}
}
}
]
}
}
}
}And for tables:
query GetTables {
keyspace(name: "library") {
name
tables {
name
columns {
name
kind
type {
basic
info {
name
}
}
}
}
}
}{
"data": {
"keyspace": {
"name": "library",
"tables": [
{
"name": "reader",
"columns": [
{
"name": "name",
"kind": "PARTITION",
"type": {
"basic": "VARCHAR",
"info": null
}
},
]
},
{
"name": "book",
"columns": [
{
"name": "title",
"kind": "PARTITION",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "author",
"kind": "REGULAR",
"type": {
"basic": "VARCHAR",
"info": null
}
},
{
"name": "isbn",
"kind": "REGULAR",
"type": {
"basic": "VARCHAR",
"info": null
}
}
]
}
]
}
}
}Because these queries are named, the GraphQL playground will allow you to select
which query to run. The first query will return information about the keyspace
library and the tables within it. The second query will return just information
about the tables in that keyspace.
Drop keyspaces, user-defined types (UDTs), tables or columns
Dropping a keyspace
You can delete a keyspace. All tables and table data will be deleted along with the keyspace schema.
mutation dropKsLibrary {
dropKeyspace(name:"library", ifExists: true)
}Dropping a type
You can delete a type. All tables that use the UDT must first be deleted.
# drop a UDT
mutation dropType {
dropType(keyspaceName:"library", typeName:"address_type", ifExists:true)
}Dropping a table
You can delete a table. All data will be deleted along with the table schema.
# drop a table
mutation dropTableBook {
dropTable(keyspaceName:"library",
tableName:"article")
}{
"data": {
"dropTable": true
}
}IF EXISTS option
You can delete a table after checking that it exists with the ifExists option.
All data will be deleted along with the table schema.
Dropping columns from table schema
If you find an attribute is no longer required in a table, you can remove a column. All column data will be deleted along with the column schema.
Interacting with data stored in tables
API generation
Once schema is created, the GraphQL API generates mutations and queries can be used. In the GraphQL playground, expand the tabs on the righthand side labelled "DOCS" or "SCHEMA", to discover the items available and the syntax to use.
For each table in the Cassandra schema that we just created, several GraphQL
fields are created for
handling queries and mutations. For example, the GraphQL API generated for the
books table is:
schema {
query: Query
mutation: Mutation
}
type Query {
book(value: bookInput, filter: bookFilterInput, orderBy: [bookOrder], options: QueryOptions): bookResult
bookFilter(filter: bookFilterInput!, orderBy: [bookOrder], options: QueryOptions): bookResult
}
type Mutation {
insertbook(value: bookInput!, ifNotExists: Boolean, options: UpdateOptions): bookMutationResult
updatebook(value: bookInput!, ifExists: Boolean, ifCondition: bookFilterInput, options: UpdateOptions): bookMutationResult
deletebook(value: bookInput!, ifExists: Boolean, ifCondition: bookFilterInput, options: UpdateOptions): bookMutationResult
}The query books() can query book values by equality. If no value argument is
provided, then the first hundred (default pagesize) values are returned.
Several mutations are created that you can use to insert, update, or delete books. Some important facts about these mutations are:
-
insertBooks()is an upsert operation if a book with the same information exist, unless theifNotExistsis set to true. -
updateBooks()is also an upsert operation, and will create a new book if it doesn’t exist, unlessifNotExistsis set to true. -
Using the
ifNotExistsorifConditionoptions affects the performance of operations because of the compare-and-set execution path in Cassandra. Under the hood these operations are using a feature in Cassandra called lightweight transactions (LWTs).
As more tables are added to a keyspace, additional GraphQL fields will add query and mutation types that can be used to interact with the table data.
Write data
Any of the created APIs can be used to interact with the GraphQL data, to write or read data.
First, let’s navigate to your new keyspace library inside the playground.
Change the location to
http://localhost:8080/graphql/library
and add a couple of books to the book table:
# insert 2 books in one mutation
mutation insert2Books {
moby: insertbook(value: {title:"Moby Dick", author:"Herman Melville"}) {
value {
title
}
}
catch22: insertbook(value: {title:"Catch-22", author:"Joseph Heller"}) {
value {
title
}
}
}{
"data": {
"moby": {
"value": {
"title": "Moby Dick"
}
},
"catch22": {
"value": {
"title": "Catch-22"
}
}
}
}Note that the keyword value is used twice in the mutation.
The first use defines the value that the record is set to, for instance, the title
to Moby Dick and the author to Herman Melville.
The second use defines the values that will be displayed after the success
of the mutation, so that proper insertion can be verified.
This same method is valid for updates and read queries.
Insertion options
Three insertion options are configurable during data insertion or updating:
An example insertion that sets the consistency level and TTL:
# insert a book and set the option for consistency level
mutation insertBookWithOption {
nativeson: insertbook(value: {title:"Native Son", author:"Richard Wright"}, options: {consistency: LOCAL_QUORUM, ttl:86400}) {
value {
title
}
}
}{
"data": {
"moby": {
"value": {
"title": "Moby Dick"
}
}
}
}The serial consistency can also be set with serialConsistency in the options,
if needed.
Insert collections (set, list, map)
Inserting a collection is simple. An example of inserting a list:
# insert an article USING A LIST (authors)
mutation insertArticle {
magarticle: insertarticle(value: {title:"How to use GraphQL", authors: ["First author", "Second author"], mtitle:"Database Magazine"}) {
value {
title
mtitle
authors
}
}
}{
"data": {
"magarticle": {
"value": {
"title": "How to use GraphQL",
"mtitle": "Database Magazine",
"authors": [
"First author",
"Second author"
]
}
}
}
}A map is slightly more complex:
mutation insertOneBadge {
gold: insertBadges(value: { btype:"Gold", earned: "2020-11-20", category: ["Editor", "Writer"] } ) {
value {
btype
earned
category
}
}
}{
"data": {
"gold": {
"value": {
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
}
}
}Insert a tuple
Inserting a tuple involves inserting an object; note the use of item0, item`1,
and so on, to insert the parts of the tuple
# insert a reader record that uses a TUPLE
mutation insertJaneWithTuple{
jane: insertreader(
value: {
user_id: "b5b5666b-2a37-4d0b-a5eb-053e54fc242b"
name: "Jane Doe"
birthdate: "2000-01-01"
email: ["janedoe@gmail.com", "janedoe@yahoo.com"]
reviews: { item0: "Moby Dick", item1: 5, item2: "2020-12-01" }
}
) {
value {
user_id
name
birthdate
reviews {
item0
item1
item2
}
}
}
}{
"data": {
"jane": {
"value": {
"user_id": "b5b5666b-2a37-4d0b-a5eb-053e54fc242b",
"name": "Jane Doe",
"birthdate": "2000-01-01",
"reviews": {
"item0": "Moby Dick",
"item1": 5,
"item2": "2020-12-01"
}
}
}
}
}Insert a user-defined type (UDT)
Inserting a UDT requires taking careful note of the brackets used:
# insert a reader record that uses a UDT
mutation insertReaderWithUDT{
ag: insertreader(
value: {
user_id: "e0ed81c3-0826-473e-be05-7de4b4592f64"
name: "Allen Ginsberg"
birthdate: "1926-06-03"
addresses: [{ street: "Haight St", city: "San Francisco", zip: "94016" }]
}
) {
value {
user_id
name
birthdate
addresses {
street
city
zip
}
}
}
}{
"data": {
"ag": {
"value": {
"user_id": "e0ed81c3-0826-473e-be05-7de4b4592f64",
"name": "Allen Ginsberg",
"birthdate": "1926-06-03",
"addresses": [
{
"street": "Haight St",
"city": "San Francisco",
"zip": "94016"
}
]
}
}
}
}Read data
Let’s check that the data was inserted.
Now let’s search for a particular record using a WHERE clause. The primary
key of the table can be used in the WHERE clause, but non-primary key columns
cannot be used.
The following query, looking at the location
http://localhost:8080/graphql/library
will get both the title and the author for the specified book WHERE
title:"Moby Dick":
# get one book using the primary key title with a value
query oneBook {
book (value: {title:"Moby Dick"}) {
values {
title
author
}
}
}{
"data": {
"books": {
"values": [
{
"title": "Moby Dick",
"author": "Herman Melville"
}
]
}
}
}To find multiple books, an addition to the WHERE clause is required, to denote that
the list of titles desired is IN a group:
# get 3 books using the primary keys with an "in" filter clause of the primary key title
query ThreeBooks {
book(filter: { title: { in: ["Native Son", "Moby Dick", "Catch-22"] } } ) {
values {
title
author
}
}
}{
"data": {
"book": {
"values": [
{
"title": "Catch-22",
"author": "Joseph Heller"
},
{
"title": "Moby Dick",
"author": "Herman Melville"
}
]
}
}
}To display the contents of a UDT, notice the inclusion of addresses in the values displayed for this read query:
# query the author to see the UDT
query getReaderWithUDT{
reader(value: { name:"Allen Ginsberg" user_id: "e0ed81c3-0826-473e-be05-7de4b4592f64" }) {
values {
name
birthdate
addresses {
street
city
zip
}
}
}
}{
"data": {
"reader": {
"values": [
{
"name": "Allen Ginsberg",
"birthdate": "1926-06-03",
"addresses": [
{
"street": "Haight St",
"city": "San Francisco",
"zip": "94016"
}
]
}
]
}
}
}To display the contents of a map collection, notice the inclusion of earned in the values displayed for this read query:
# query a badge record that has a MAP (earned) with only the partition key
query oneGoldBadge {
badge(value: { badge_type: "Gold" } ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": [
{
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
]
}
}
}Filter options for reading
The filters available are:
-
eq (equal)
-
notEq (not equal)
-
gt (greater than)
-
gte (greater than or equal to)
-
lt (less than)
-
lte (less than or equal to)
-
in (within)
-
contains (a map contains the specified value)
-
containsKey (a map contains the specified key)
-
containsEntry (a map contains the specified key:value pair)
Note that these can only be used with primary key columns, just like in Cassandra, unless indexing is created.
The next examples will query the same table, badge, using a variety of filters to illustrate
the versatility of such filters.
The first example finds the record that has the partition key badge_type equal to Gold, and
the badge_id equal to 100:
# query a badge record that has a MAP (earned) with the partition key and the clustering key
query oneGold100Badge {
badge(filter: { badge_type: {eq:"Gold"} badge_id: {eq:100}} ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": [
{
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
]
}
}
}Now if we use a different operator gt with the same query, notice that the query will fail,
because no badge_id greater than a value of 100 is found:
# query a badge record that has a MAP (earned) with the partition key and the clustering key
# filter badge_id: {gt:100 will fail}
query oneGold100BadgeFail {
badge(filter: { badge_type: {eq:"Gold"} badge_id: {gt:100}} ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": []
}
}
}In order to use filters for any columns that are not part of the primary key, currently you need to use CQL to create a secondary index using the CQL shell. The next three examples show the CQL creation of an index in order to query a column that is a map collection.
In this example, an index is created on the keys of the map earned, so the containsKey
filter can be used to query in GraphQL.
# query a badge record that has a MAP (earned) with the partition key, clustering key, and a MAP key
# Requires: CREATE INDEX badge_idx ON library.badge(KEYS(earned));
query oneWriterBadge {
badge(filter: { badge_type: {eq:"Gold"} badge_id: {eq:100} earned: { containsKey: "Writer"} } ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": [
{
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
]
}
}
}Because the index now exists, it is also possible to just filter based on the map key itself:
# query a badge record that has a MAP (earned) with only a MAP key
# CREATE INDEX badge_idx ON library.badge(KEYS(earned));
query oneWriterKeyBadge {
badge(filter: { earned: { containsKey: "Writer"} } ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": [
{
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
]
}
}
}In this next example, an index is created on the values of the map earned, so the contains
filter can be used to query in GraphQL.
# query a badge record that has a MAP (earned) with only a MAP value
# Requires: CREATE INDEX badge2_idx ON library.badge(VALUES(earned));
query oneWriterValueBadge {
badge(filter: { earned: { contains: "2020-11-20"} } ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": [
{
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
]
}
}
}To make a complete set of filters, an index is created on the entries of the map earned, so the containsEntry
filter can be used to query in GraphQL.
# query a badge record that has a MAP (earned) with only a MAP entry
# Requires: CREATE INDEX badge3_idx ON library.badge(ENTRIES(earned));
query oneWriterEntryBadge {
badge(filter: { earned: { containsEntry: {key:"Writer", value:"2020-11-20"}} } ) {
values {
badge_type
badge_id
earned {
key
value
}
}
}
}{
"data": {
"badge": {
"values": [
{
"badge_type": "Gold",
"badge_id": 100,
"earned": [
{
"key": "Writer",
"value": "2020-11-20"
}
]
}
]
}
}
}Update data
Using the column that we added earlier, the data for a book is updated with the
ISBN value:
mutation updateOneBook {
moby: updatebook(value: {title:"Moby Dick", author:"Herman Melville", isbn: "9780140861723"}, ifExists: true ) {
value {
title
author
isbn
}
}
}{
"data": {
"moby": {
"value": {
"title": "Moby Dick",
"author": "Herman Melville",
"isbn": "9780140861723"
}
}
}
}|
Updates are upserts. If the row doesn’t exist, it will be created. If it does exist, it will be updated with the new row data. |
It is also possible to update other types of data, such as a set:
# update one book, adding a SET (genre)
mutation updateOneBookAgain {
moby: updatebook(value: {title:"Moby Dick", author:"Herman Melville", genre: ["Drama", "Classic lit"]}, ifExists: true ) {
value {
title
author
genre
}
}
}{
"data": {
"moby": {
"value": {
"title": "Moby Dick",
"author": "Herman Melville",
"genre": [
"Drama",
"Classic lit"
]
}
}
}
}Delete data
After adding the book "Pride and Prejudice" with an insertBooks(), you can delete
the book using deleteBooks() to illustrate deleting data:
mutation deleteOneBook {
PaP: deletebook(value: {title:"Pride and Prejudice", author: "Jane Austen"}, ifExists: true ) {
value {
title
}
}
}{
"data": {
"PaP": {
"value": {
"title": "Pride and Prejudice"
}
}
}
}Note the use of ifExists to validate that the book exists before deleting it.
Deletion options
Similar to the option ifExists, you can delete a book using consistency,
serialConsistency, or ttl, similar to insertions: