FQL v4 will be decommissioned on June 30, 2025. Ensure that you complete your migration from FQL v4 to FQL v10 by that date. Fauna accounts created after August 21, 2024 must use FQL v10. These accounts will not be able to run FQL v4 queries or access the v4 Dashboard. For more details, see the v4 EOL announcement and migration guide. Contact support@fauna.com with any questions. |
How to perform CRUD operations in Fauna
Fauna allows you to store documents and query them in a relational fashion. This section walks you through a basic example of creating, retrieving, updating, and deleting (CRUD) documents in Fauna, including working with collections. If you are new to Fauna, make sure to check out our Glossary for definitions.
Introduction
To demonstrate how to perform CRUD operations in Fauna, we are going to use the example of blog posts: creating blog posts, updating them with additional attributes, and querying for specific posts.
The steps are:
-
Make sure that the Requirements are met.
We have set up this example so you can follow along from start to finish. Feel free to skip straight to Create a post if you are just looking for examples of the create, retrieve, update, and delete process.
Requirements
This section walks you through setting up your environment, installing a driver, importing the driver, obtaining an admin key, and instantiating the client.
Supported runtimes
Before you install the driver, it’s important to ensure you’re running a compatible version of the language runtime and have satisfied other dependencies.
The JavaScript driver is supported on:
-
Node.js
-
LTS (v12)
-
Stable (v10+)
-
-
Chrome
-
Firefox
-
Safari
-
Internet Explorer 11
Currently, the driver is tested on Go versions:
-
1.11
-
1.12
-
1.13
-
1.14
Compatible with:
-
.NET SDK
-
Mono (on macOS or Linux)
-
Jackson for JSON parsing.
-
Java 8
-
Jackson for JSON parsing.
-
Scala 2.11.x
-
Scala 2.12.x
The following versions of Python are supported:
-
Python 2.7
-
Python 3.3
-
Python 3.4
-
Python 3.5
-
Python 3.6
-
Python 3.7
-
Python 3.8
Install the driver
To install the JavaScript driver, run this command in the terminal:
npm install --save faunadb
See faunadb
on NPM for more
information. Note that not all Node.js/serverless environments are
supported.
The JavaScript driver can be included via CDN:
<script src="//cdn.jsdelivr.net/npm/faunadb@latest/dist/faunadb.js"></script>
The minified version of the driver can also be used via CDN:
<script src="//cdn.jsdelivr.net/npm/faunadb@latest/dist/faunadb-min.js"></script>
See the driver’s repository for more details: fauna/fauna-js
To install the Go driver, run this in the terminal:
go get github.com/fauna/faunadb-go/{driver-go-get)/faunadb
First install the Nuget package by adding the package reference to your MSBuild project:
<PackageReference Include="FaunaDB.Client" Version="0.3.0-beta" />
or by using your IDE and searching for FaunaDB.Client
.
Download from the Maven central repository:
faunadb-java/pom.xml
<dependencies>
...
<dependency>
<groupId>com.faunadb</groupId>
<artifactId>faunadb-java</artifactId>
<version>0.2.0-B1</version>
<scope>compile</scope>
</dependency>
...
</dependencies>
faunadb-scala/sbt
libraryDependencies += ("com.faunadb" %% "faunadb-scala" % "0.2.0-B1")
pip install faunadb
Import the driver
Import the client and the query language helpers.
using FaunaDB.Client;
using FaunaDB.Types;
using static FaunaDB.Query.Language;
We recommend that you import this driver with an alias import such as:
import (
"fmt"
f "github.com/fauna/faunadb-go/v4/faunadb"
)
import com.faunadb.client.*;
import com.faunadb.client.types.*;
import com.faunadb.client.types.Value.*;
var faunadb = require('faunadb')
var q = faunadb.query;
This is the recommended require stanza. The faunadb.query
module
contains all of the functions to create Fauna query expressions.
Similarly with ES6 modules:
import faunadb, { query as q } from "faunadb"
The CDN package exposes a global faunadb
variable.
from faunadb import query as q
from faunadb.objects import Ref
from faunadb.client import FaunaClient
import faunadb.FaunaClient
import faunadb.query._
import faunadb.values._
Obtain an admin key
To follow along, you need an admin key, which you can create using the Fauna Dashboard.
Instantiate an admin client
var adminClient = new FaunaClient(secret: YOUR_FAUNADB_ADMIN_SECRET);
adminClient := f.NewFaunaClient("YOUR_FAUNADB_ADMIN_SECRET")
FaunaClient adminClient = FaunaClient.builder()
.withSecret("YOUR_FAUNADB_ADMIN_SECRET")
.build();
var adminClient = new faunadb.Client({ secret: 'YOUR_FAUNADB_ADMIN_SECRET' });
adminClient = FaunaClient(secret="YOUR_FAUNADB_ADMIN_SECRET")
val adminClient = FaunaClient(secret = "YOUR_FAUNADB_ADMIN_SECRET")
Create a database
Create a database called my_app
in Fauna:
{
ref: Database("my_app"),
ts: 1622574499020000,
name: 'my_app',
global_id: 'yoijzam91ybyy'
}
{'ref': Ref(id=my_app, collection=Ref(id=databases)), 'ts': 1622574506710000, 'name': 'my_app', 'global_id': 'yoijza49hydyy'}
map[global_id:yoijzpsy6ybyy name:my_app ref:{my_app 0xc0001037a0 0xc0001037a0 <nil>} ts:1622574323940000]
ObjectV(ref: RefV(id = "my_app", collection = RefV(id = "databases")),ts: LongV(1622574227070000),name: StringV(my_app),global_id: StringV(yoijz8a4sybyy))
{ref: ref(id = "my_app", collection = ref(id = "databases")), ts: 1622574355920000, name: "my_app", global_id: "yoijzxwxsybyy"}
{
ref: Database("my_app"),
ts: 1624310594390000,
name: 'my_app',
global_id: 'yoat8d8erydyy'
}
Create a server key to access the my_app
database
Create a server key. The server key has unrestricted access to a single
database; in this case, the server key allows access only to the
my_app
database that we just created.
{
ref: Ref(Keys(), "300223718348554752"),
ts: 1622574499380000,
database: Database("my_app"),
role: 'server',
secret: 'fnAEKpvhlVACAGnxyrjFa-zJiNatObJjyXFc3V8L',
hashed_secret: '$2a$05$mCeHKqZYZHN5.MGC6RIIRO3M7JSt6UHmoziYWRzezBHrpuUiEdRFi'
}
{'ref': Ref(id=300223726396375552, collection=Ref(id=keys)), 'ts': 1622574507050000, 'database': Ref(id=my_app, collection=Ref(id=databases)), 'role': 'server', 'secret': 'fnAEKpvjdQACAFruhIE6q_-k9h8y_CJLLwFmzXNd', 'hashed_secret': '$2a$05$bpLlW.51p54oU.F9HRTxXOyBZ7G8T8ATiMxGPPq7elGG667ThDLtG'}
map[database:{my_app 0xc0001805a0 0xc0001805a0 <nil>} hashed_secret:$2a$05$EnvOSmQkFDzdY1Uv5GcE3uAQOm/sowh63w18On0RZs8HYKB/GhET6 ref:{300223535791473152 0xc0001804b0 0xc0001804b0 <nil>} role:server secret:fnAEKpu3FBACAO5Y68g1aIGP4Bj-9-3XuAvGvlnq ts:1622574325280000]
ObjectV(ref: RefV(id = "300223437306069504", collection = RefV(id = "keys")),ts: LongV(1622574231350000),database: RefV(id = "my_app", collection = RefV(id = "databases")),role: StringV(server),secret: StringV(fnAEKpugJeACABxgtt0QWzlpfy0hJevB3yaiT4FZ),hashed_secret: StringV($2a$05$.DPcB6qo3ZYP3AKzaqH/r.gBDsvf2l0arylIrL0d02Rgf0EG5BIpm))
{ref: ref(id = "300223578563936768", collection = ref(id = "keys")), ts: 1622574366070000, database: ref(id = "my_app", collection = ref(id = "databases")), role: "server", secret: "fnAEKpvBCYACAGh1X1AByGzUVqd6-BajTEdOdtCO", hashed_secret: "$2a$05$.mcEBa9OANfdF.6iYsquuue0di.W8erLc6ki6pPeRzlcRnq0u6xra"}
{
ref: Ref(Keys(), "302044146566169088"),
ts: 1624310595010000,
database: Database("my_app"),
role: 'server',
secret: 'fnAEMRONDpACAMrMtkE9PJk2CjjqPBk71xnFs44W',
hashed_secret: '$2a$05$8J9xjhMmc3LJSrS5bng95u1WSC2vYeuDRanwGOe1v5uqcAxaIYm4W'
}
Save this key’s secret; it is only displayed once, and if you lose it, you would have to generate a new one. The key is used to perform all of the remaining database setup steps.
Your key will be different than the key you see displayed in the command output in this documentation. The key you saved just above will be different. |
Instantiate a client that has server key privileges
Instantiate a client that uses the server key that we just set up, to
perform the rest of the tasks in this tutorial. Be sure to copy the
secret returned in the previous step and replace
YOUR_FAUNADB_SERVER_SECRET
with that value.
var serverClient = new FaunaClient(secret: YOUR_FAUNADB_SERVER_SECRET);
serverClient := f.NewFaunaClient("YOUR_FAUNADB_SERVER_SECRET")
FaunaClient serverClient = FaunaClient.builder()
.withSecret("YOUR_FAUNADB_SERVER_SECRET")
.build();
var serverClient = new faunadb.Client({ secret: 'YOUR_FAUNADB_SERVER_SECRET' });
serverClient = FaunaClient(secret="YOUR_FAUNADB_SERVER_SECRET")
val serverClient = FaunaClient(secret = "YOUR_FAUNADB_SERVER_SECRET")
Create a collection
Fauna stores documents in the form of nested containers. A database contains collections, and collections contain documents. Each document belongs to a specific collection. So in order to create a document for a post, we need to first create a collection for posts.
Create a collection using the CreateCollection
function with a
param_object
containing the name
of the collection. We shall name
our collection "Posts":
{
ref: Collection("Posts"),
ts: 1622574499730000,
history_days: 30,
name: 'Posts'
}
{'ref': Ref(id=Posts, collection=Ref(id=collections)), 'ts': 1622574507400000, 'history_days': 30, 'name': 'Posts'}
map[history_days:30 name:Posts ref:{Posts 0xc00009b7a0 0xc00009b7a0 <nil>} ts:1622574326590000]
ObjectV(ref: RefV(id = "Posts", collection = RefV(id = "collections")),ts: LongV(1622574236210000),history_days: LongV(30),name: StringV(Posts))
{ref: ref(id = "Posts", collection = ref(id = "collections")), ts: 1622574376120000, history_days: 30, name: "Posts"}
{
ref: Collection("Posts"),
ts: 1624310595640000,
history_days: 30,
name: 'Posts'
}
Create an index
Before we create any document, let’s ensure that we can easily access
them. We do this by creating an index using the CreateIndex
function.
We create this index now to help make the examples clear; in production,
you can create an index at any time.
The customary way to access documents within a collection is by specifying a criteria for one of the fields. To enable criteria-based searches, we need to first create an index using the path of the field within the document.
Create an index on the post’s title:
{
ref: Index("posts_by_title"),
ts: 1622574500130000,
active: true,
serialized: true,
name: 'posts_by_title',
source: Collection("Posts"),
terms: [ { field: [ 'data', 'title' ] } ],
partitions: 1
}
{'ref': Ref(id=posts_by_title, collection=Ref(id=indexes)), 'ts': 1622574507770000, 'active': True, 'serialized': True, 'name': 'posts_by_title', 'source': Ref(id=Posts, collection=Ref(id=collections)), 'terms': [{'field': ['data', 'title']}], 'partitions': 1}
map[active:true name:posts_by_title partitions:1 ref:{posts_by_title 0xc0000924b0 0xc0000924b0 <nil>} serialized:true source:{Posts 0xc0000925a0 0xc0000925a0 <nil>} terms:[map[field:[data title]]] ts:1622574327960000]
ObjectV(ref: RefV(id = "posts_by_title", collection = RefV(id = "indexes")),ts: LongV(1622574241570000),active: BooleanV(True),serialized: BooleanV(True),name: StringV(posts_by_title),source: RefV(id = "Posts", collection = RefV(id = "collections")),terms: Arr(ObjectV(field: Arr(StringV(data), StringV(title)))),partitions: LongV(1))
{ref: ref(id = "posts_by_title", collection = ref(id = "indexes")), ts: 1622574386080000, active: true, serialized: true, name: "posts_by_title", source: ref(id = "Posts", collection = ref(id = "collections")), terms: [{field: ["data", "title"]}], partitions: 1}
{
ref: Index("posts_by_title"),
ts: 1624310596280000,
active: true,
serialized: true,
name: 'posts_by_title',
source: Collection("Posts"),
terms: [ { field: [ 'data', 'title' ] } ],
partitions: 1
}
It is also possible to specify the values
of the document that should
be returned when querying the index. We also create an index for a
post’s tags:
{
ref: Index("posts_by_tags_with_title"),
ts: 1622574500460000,
active: true,
serialized: true,
name: 'posts_by_tags_with_title',
source: Collection("Posts"),
terms: [ { field: [ 'data', 'tags' ] } ],
values: [ { field: [ 'data', 'title' ] } ],
partitions: 1
}
{'ref': Ref(id=posts_by_tags_with_title, collection=Ref(id=indexes)), 'ts': 1622574508140000, 'active': True, 'serialized': True, 'name': 'posts_by_tags_with_title', 'source': Ref(id=Posts, collection=Ref(id=collections)), 'terms': [{'field': ['data', 'tags']}], 'values': [{'field': ['data', 'title']}], 'partitions': 1}
map[active:true name:posts_by_tags_with_title partitions:1 ref:{posts_by_tags_with_title 0xc000282240 0xc000282240 <nil>} serialized:true source:{Posts 0xc000282330 0xc000282330 <nil>} terms:[map[field:[data tags]]] ts:1622574329310000 values:[map[field:[data title]]]]
ObjectV(ref: RefV(id = "posts_by_tags_with_title", collection = RefV(id = "indexes")),ts: LongV(1622574247700000),active: BooleanV(True),serialized: BooleanV(True),name: StringV(posts_by_tags_with_title),source: RefV(id = "Posts", collection = RefV(id = "collections")),terms: Arr(ObjectV(field: Arr(StringV(data), StringV(tags)))),values: Arr(ObjectV(field: Arr(StringV(data), StringV(title)))),partitions: LongV(1))
{ref: ref(id = "posts_by_tags_with_title", collection = ref(id = "indexes")), ts: 1622574396410000, active: true, serialized: true, name: "posts_by_tags_with_title", source: ref(id = "Posts", collection = ref(id = "collections")), terms: [{field: ["data", "tags"]}], values: [{field: ["data", "title"]}], partitions: 1}
{
ref: Index("posts_by_tags_with_title"),
ts: 1624310596920000,
active: true,
serialized: true,
name: 'posts_by_tags_with_title',
source: Collection("Posts"),
terms: [ { field: [ 'data', 'tags' ] } ],
values: [ { field: [ 'data', 'title' ] } ],
partitions: 1
}
Create a post
Posts are created by calling the Create
function with the ref
of the
posts collection and a param_object
that specifies the structure of
the document to be created, as well as permissions for the document. The
post’s data is included in the param_object
's data
field.
{
ref: Ref(Collection("Posts"), "300223719783006720"),
ts: 1622574500780000,
data: { title: 'What I had for breakfast ..' }
}
{'ref': Ref(id=300223727893742080, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574508510000, 'data': {'title': 'What I had for breakfast ..'}}
map[data:map[title:What I had for breakfast ..] ref:{300223541417083392 0xc00011dad0 0xc00011dad0 <nil>} ts:1622574330710000]
ObjectV(ref: RefV(id = "300223461515592192", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574254500000),data: ObjectV(title: StringV(What I had for breakfast ..)))
{ref: ref(id = "300223621377294848", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574406990000, data: {title: "What I had for breakfast .."}}
{
ref: Ref(Collection("Posts"), "302044149229552128"),
ts: 1624310597570000,
data: { title: 'What I had for breakfast ..' }
}
The Create
function returns the post document just created. As you can
see in the output, the ref
of the document is an
automatically-generated identifier that is unique to the document within
its database.
Using the Ref
function, you can specify a document ID to use
instead of the auto-generated number, but it must be a unique
string-encoded 64-bit integer. For example:
{
ref: Ref(Collection("Posts"), "1"),
ts: 1622574501060000,
data: { title: 'The first post' }
}
{'ref': Ref(id=1, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574508850000, 'data': {'title': 'The first post'}}
map[data:map[title:The first post] ref:{1 0xc00009dbc0 0xc00009dbc0 <nil>} ts:1622574332010000]
ObjectV(ref: RefV(id = "1", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574261840000),data: ObjectV(title: StringV(The first post)))
{ref: ref(id = "1", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574416990000, data: {title: "The first post"}}
{
ref: Ref(Collection("Posts"), "1"),
ts: 1624310598180000,
data: { title: 'The first post' }
}
Create several posts
It can quickly become tedious to repeat the Create
function for
multiple posts.
This is where Fauna’s transaction language really shines. Let’s use a
Map
function to create several posts at once:
[
{
ref: Ref(Collection("Posts"), "300223720476115456"),
ts: 1622574501410000,
data: { title: 'My cat and other marvels' }
},
{
ref: Ref(Collection("Posts"), "300223720476116480"),
ts: 1622574501410000,
data: { title: 'Pondering during a commute' }
},
{
ref: Ref(Collection("Posts"), "300223720476117504"),
ts: 1622574501410000,
data: { title: 'Deep meanings in a latte' }
}
]
[{'ref': Ref(id=300223728646619648, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574509200000, 'data': {'title': 'My cat and other marvels'}}, {'ref': Ref(id=300223728646620672, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574509200000, 'data': {'title': 'Pondering during a commute'}}, {'ref': Ref(id=300223728646621696, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574509200000, 'data': {'title': 'Deep meanings in a latte'}}]
[map[data:map[title:My cat and other marvels] ref:{300223544222024192 0xc000113dd0 0xc000113dd0 <nil>} ts:1622574333320000] map[data:map[title:Pondering during a commute] ref:{300223544222025216 0xc0001a4000 0xc0001a4000 <nil>} ts:1622574333320000] map[data:map[title:Deep meanings in a latte] ref:{300223544222026240 0xc0001a4210 0xc0001a4210 <nil>} ts:1622574333320000]]
Arr(ObjectV(ref: RefV(id = "300223477212774912", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574269420000),data: ObjectV(title: StringV(My cat and other marvels))), ObjectV(ref: RefV(id = "300223477212775936", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574269420000),data: ObjectV(title: StringV(Pondering during a commute))), ObjectV(ref: RefV(id = "300223477212776960", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574269420000),data: ObjectV(title: StringV(Deep meanings in a latte))))
[{ref: ref(id = "300223642454721024", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574427010000, data: {title: "My cat and other marvels"}}, {ref: ref(id = "300223642454722048", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574427010000, data: {title: "Pondering during a commute"}}, {ref: ref(id = "300223642454723072", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574427010000, data: {title: "Deep meanings in a latte"}}]
[
{
ref: Ref(Collection("Posts"), "302044150538174976"),
ts: 1624310598800000,
data: { title: 'My cat and other marvels' }
},
{
ref: Ref(Collection("Posts"), "302044150538176000"),
ts: 1624310598800000,
data: { title: 'Pondering during a commute' }
},
{
ref: Ref(Collection("Posts"), "302044150538177024"),
ts: 1624310598800000,
data: { title: 'Deep meanings in a latte' }
}
]
Using the Map
function, we can restructure the data as an array and
wrap the Create
in a lambda function, which then runs over each
document in the collection. The anonymous lambda function specifies a
variable post_title
which is used as a placeholder in the parameters
sent to the create
function. This way, multiple documents in a
collection can be created using a single query.
Retrieve posts
The easiest way to retrieve a document is by using its reference value:
{
ref: Ref(Collection("Posts"), "1"),
ts: 1622574501060000,
data: { title: 'The first post' }
}
{'ref': Ref(id=1, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574508850000, 'data': {'title': 'The first post'}}
map[data:map[title:The first post] ref:{1 0xc000092570 0xc000092570 <nil>} ts:1622574332010000]
ObjectV(ref: RefV(id = "1", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574261840000),data: ObjectV(title: StringV(The first post)))
{ref: ref(id = "1", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574416990000, data: {title: "The first post"}}
{
ref: Ref(Collection("Posts"), "1"),
ts: 1624310598180000,
data: { title: 'The first post' }
}
You can query for posts with a specific title using the match
function
and the index we created earlier:
{
ref: Ref(Collection("Posts"), "300223720476115456"),
ts: 1622574501410000,
data: { title: 'My cat and other marvels' }
}
{'ref': Ref(id=300223728646619648, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574509200000, 'data': {'title': 'My cat and other marvels'}}
map[data:map[title:My cat and other marvels] ref:{300223544222024192 0xc000184570 0xc000184570 <nil>} ts:1622574333320000]
ObjectV(ref: RefV(id = "300223477212774912", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574269420000),data: ObjectV(title: StringV(My cat and other marvels)))
{ref: ref(id = "300223642454721024", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574427010000, data: {title: "My cat and other marvels"}}
{
ref: Ref(Collection("Posts"), "302044150538174976"),
ts: 1624310598800000,
data: { title: 'My cat and other marvels' }
}
The match
function returns a logical set of elements, which can be
combined with other sets with set-operations like join
, intersect
,
subtract
, etc.
Update posts
You can easily modify documents by supplying the new data along with the reference to the document. For example, we want to add tags to each of our blog posts:
{
ref: Ref(Collection("Posts"), "1"),
ts: 1622574502370000,
data: { title: 'The first post', tags: [ 'welcome', 'short' ] }
}
{'ref': Ref(id=1, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574510200000, 'data': {'title': 'The first post', 'tags': ['welcome', 'short']}}
map[data:map[tags:[welcome short] title:The first post] ref:{1 0xc000109bf0 0xc000109bf0 <nil>} ts:1622574337210000]
ObjectV(ref: RefV(id = "1", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574293510000),data: ObjectV(title: StringV(The first post),tags: Arr(StringV(welcome), StringV(short))))
{ref: ref(id = "1", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574459880000, data: {title: "The first post", tags: ["welcome", "short"]}}
{
ref: Ref(Collection("Posts"), "1"),
ts: 1624310600630000,
data: { title: 'The first post', tags: [ 'welcome', 'short' ] }
}
The Update
function updates specific fields in a document. It
preserves the old fields if they are not specified in params
. In the
case of nested values (known as objects, due to the JSON data format),
the old and the new values are merged. If null
is specified as a value
for a field, it is removed.
Replace posts
The Replace
function replaces the document’s data with the fields
provided in params
. Old fields not mentioned in params
are removed.
{
ref: Ref(Collection("Posts"), "1"),
ts: 1622574502680000,
data: { title: 'The replacement first post' }
}
{'ref': Ref(id=1, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574510530000, 'data': {'title': 'The replacement first post'}}
map[data:map[title:The replacement first post] ref:{1 0xc000096570 0xc000096570 <nil>} ts:1622574338510000]
ObjectV(ref: RefV(id = "1", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574301140000),data: ObjectV(title: StringV(The replacement first post)))
{ref: ref(id = "1", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574471430000, data: {title: "The replacement first post"}}
{
ref: Ref(Collection("Posts"), "1"),
ts: 1624310601250000,
data: { title: 'The replacement first post' }
}
Note that the title
has been updated, but tags
has been deleted.
Delete a post
Lastly, a post can be removed using the Delete
function:
{
ref: Ref(Collection("Posts"), "1"),
ts: 1622574502680000,
data: { title: 'The replacement first post' }
}
{'ref': Ref(id=1, collection=Ref(id=Posts, collection=Ref(id=collections))), 'ts': 1622574510530000, 'data': {'title': 'The replacement first post'}}
map[data:map[title:The replacement first post] ref:{1 0xc000109980 0xc000109980 <nil>} ts:1622574338510000]
ObjectV(ref: RefV(id = "1", collection = RefV(id = "Posts", collection = RefV(id = "collections"))),ts: LongV(1622574301140000),data: ObjectV(title: StringV(The replacement first post)))
{ref: ref(id = "1", collection = ref(id = "Posts", collection = ref(id = "collections"))), ts: 1622574471430000, data: {title: "The replacement first post"}}
{
ref: Ref(Collection("Posts"), "1"),
ts: 1624310601250000,
data: { title: 'The replacement first post' }
}
Once the post is deleted, attempting to retrieve the post results in an error:
Error: [NotFound] instance not found: Document not found.
Error: <class 'faunadb.errors.NotFound'> ErrorData(code='instance not found', description='Document not found.', position=[], failures=None)
Response error 404. Errors: [](instance not found): Document not found., details: []
ERROR: instance not found: Document not found.
ERROR com.faunadb.client.errors.NotFoundException: instance not found: Document not found.
{
errors: [
{
position: [],
code: 'instance not found',
description: 'Document not found.'
}
]
}
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