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https://github.com/artemadams/atlas-search-mongoflix
Atlas Search Workshop: MongoFlix - integrate MongoDB Atlas Search, Realm into your apps
https://github.com/artemadams/atlas-search-mongoflix
graphql mongodb mongodb-atlas mongodb-database next nextjs react realm search tailwind
Last synced: 2 months ago
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Atlas Search Workshop: MongoFlix - integrate MongoDB Atlas Search, Realm into your apps
- Host: GitHub
- URL: https://github.com/artemadams/atlas-search-mongoflix
- Owner: artemadams
- License: mit
- Created: 2022-02-01T10:41:02.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-01-24T12:23:35.000Z (almost 2 years ago)
- Last Synced: 2024-09-26T17:21:31.389Z (3 months ago)
- Topics: graphql, mongodb, mongodb-atlas, mongodb-database, next, nextjs, react, realm, search, tailwind
- Language: JavaScript
- Homepage: https://atlas-search-mongoflix.vercel.app
- Size: 5.97 MB
- Stars: 46
- Watchers: 3
- Forks: 57
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Atlas Search Workshop
MongoFlix - interactive demo for [MongoDB Atlas Search](https://www.mongodb.com/atlas/search), [MongoDB App Services](https://www.mongodb.com/realm), [GraphQL](https://docs.mongodb.com/realm/graphql/) and so much more.
## Live Demo
[This is what we will build!](https://atlas-search-mongoflix-git-prod-node-artemadams.vercel.app/)
[Also available on App Services as a static site!](https://application-0-kjasg.mongodbstitch.com/)
## Code with us
You can of course clone the repo and run the project locally `npm install && npm run build`.
Alternatively, you can open the project in your browser without any installation required on your machine.
Open the project live on [StackBlitz](http://stackblitz.com/):
[](https://stackblitz.com/github/artemadams/atlas-search-mongoflix)
Duplicate the file `.env.local.example-add-app-id-here` and name it: `.env.local`.
You will need to change the `` value to the app id of your MongoDB App Services app, which will be created at a later step.
You have also to update the `NEXT_PUBLIC_REALM_BASE_URL` value if you have a different base URL for your MongoDB App Services app.
This value will depend on the deployment region of your MongoDB App Services app.
# Agenda
All Steps
1. [Atlas Cluster](#AtlasCluster)
1. [Load Sample Data](#LoadSampleData)
1. [Atlas Search Index Creation](#AtlasSearchIndexCreation)
1. [Create App Services App](#CreateAppServicesApp)
1. [App Services Activate Anonymous Authentication](#AppServicesActivateAnonymousAuthentication)
1. [App Services Configure Access Rules](#AppServicesConfigureAccessRules)
1. [App Services Generate Schema](#AppServicesGenerateSchema)
1. [Feature 1: Autocomplete](#Feature1Autocomplete)
1. [Create Autocomplete Function](#CreateAutocompleteFunction)
1. [Implement Autocomplete Function](#ImplementAutocompleteFunction)
1. [Create Autocomplete Custom Resolver](#CreateAutocompleteCustomResolver)
1. [Feature 2: Highlights and Scoring](#Feature2HighlightsAndScoring)
1. [Create Highlights Function](#CreateHighlightsFunction)
1. [Implement Highlights Function](#ImplementHighlightsFunction)
1. [Create Highlights Custom Resolver](#CreateHighlightsCustomResolver)
1. [Feature 3: Facets](#Feature3Facets)
1. [Facets Search Index Creation](#FacetsSearchIndexCreation)
1. [Create Facets Function](#CreateFacetsFunction)
1. [Implement Facets Function](#ImplementFacetsFunction)
1. [Create Facets Custom Resolver](#CreateFacetsCustomResolver)
1. [App Services Static Site Hosting](#AppServicesStaticSiteHosting)
## Atlas Cluster
To follow along with the demo, you will need to create a MongoDB Atlas cluster and load the sample data set into your cluster.
Please create an account on [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) and follow the instructions.
If it is the first time you use Atlas you will need to create an organization and a project. After you complete the account setup, you will see the **Atlas UI**. If you don not have any cluster click the **Build a Database** button.
In the following dialog select **Shared** and click **Create**.
The following screen will provide an interface to configure the cluster.
If you choose a region other than Frankfurt you will need to update the endpoint in the app in `.env.local` to match the region.
Here are the settings for the cluster:
- **Cloud Provider & Region**: `AWS, Frankfurt (eu-central-1)`
- **Cluster Tier**: `MO Sandbox (Shared RAM, 512 MB Storage)`
- **Cluster Name**: `Cluster0`
### Load Sample Data
After your cluster was deployed in the region of your choice, you will need to load the sample data set into your cluster.
Click the three dots menu in the top headline of the cluster card.
Click **Load Sample Dataset**. Click the **Load Sample Dataset** button in the overlay to start the process. (It should take about 5-10 minutes. ☕️ 🍵)
## Atlas Search Index Creation
Click the Cluster name to open it. In your cluster on **Atlas** click the **Search** tab. Click the **Create Search Index** button to create an index.
1. Select the JSON editor and click **Next**.
1. In the **Database and Collection** sidebar select `sample_mflix` and select `movies`.
1. For the name leave it as `default` and paste the following JSON.
1. Click **Next**.
1. After reviewing it, click **Create Search Index**.
```json
{
"mappings": {
"dynamic": true,
"fields": {
"title": [
{
"dynamic": true,
"type": "document"
},
{
"type": "autocomplete"
}
]
}
}
}
```
The index creation should take less tha a minute.
Lets test it, to verify that it works.
Still in the **Search** tab, click the **Query** button besides the newly created index.
Enter the following querry to find all movies containing the text `time` in any text values.
```json
{ "$search": { "text": "time travel" } }
```
## Create App Services App
In the **Atlas** UI click the **App Services** tab at the top. If you are using App Services for the first time, you will see a dialog with additonal instructions. You can safely select **Build your own App** it and click the **Next**.
The information should be populated automatically. Make sure to use the same name for simplicity.
In the following dialog, setup the name of the App Services App, connect it to your newly created cluster and select a local (single region) deployment model. It should be preferable to use the region closest to your cluster region.
- Name: Application-0
- Cluster: Cluster0
- Deployment Model: Local
To create the app click **Create App Services Application**.
_Hint:_ Now with the app created you can update the `.env.local` file to include the **App ID** value from your App Services app.
### App Services Activate Anonymous Authentication
On the left side bar of the Atlas UI, within **Data Access**, click **Authentication**. As you see **App Services** provides many authentication methods, we will use **Anonymous** for this demo. Click on the **Edit** button and set the checkbox to **ON** for this authentication method.
### App Services Configure Access Rules
On the left side bar of the Atlas UI, within **Data Access**, click **Rules**. **Rules** provide you many ways to limit and configure data access per collection and user role, deep down to the document level. For this demo we will allow all users to only `read` all documents in the movies colelction. **App Services** provides templates for many scenarios and we will use the **Users can only read all data** template.
### App Services Generate Schema
On the left side bar of the Atlas UI, within **Data Access**, click **Schema**. **Schema** defines the data structures and types for documents in each collection in the databases. Select the **movies** collection within the **sample_mflix** database. Click the generate schema button.
Select just the **movies** collection, leave the samling size as default and click the **Generate Schema** button.
This will also generate all the neccessary types and queries for a **GraphQL** schema. Which can be used immediately to access the data through the GraphQL endpoint managed by App Services.
Click the **Review Draft & Deploy** button at the top of the page and **Deploy** your changes.
_Hint:_ Now with the schema generated you can update the `.env.local` file to include the following base URL from your App Services app.
Lets test how GraphQL actually works.
In the **GraphQL** tab, within the GraphQL editor paste in the following code snippet to test the generated scheme.
```graphql
query {
movie(query: { title: "The Godfather" }) {
_id
title
metacritic
num_mflix_comments
fullplot
}
}
```
---
## Feature 1: Autocomplete
Now with the correct rules and schema in place we can start creating functions for the app.
For the first feature we will create a function that will return a list of movies that match the search term by the title. It will use our dynamic index created in the previous step with the autocomplete functionality. This enables us to provide autocomplete and fuzzy search for movie tiltes in the search bar of the frontend app.
### Create Autocomplete Function
On the left side bar of the Atlas UI, within **Build**, click **Functions**. **Functions** provide a way to execute serverside logic on **App Services** integrating data from the connected cluster. With the **Aggregation Framework** at your disposal you can create very powerful aggregations, even without a driver.
Click the **Create New Function** button and enter `autocompleteTitle` as the name for the function.
### Implement Autocomplete Function
Now click the **Function Editor** tab.
Paste the following code into the **Function Editor**:
```js
exports = async (title) => {
const collection = context.services.get("mongodb-atlas").db("sample_mflix").collection("movies");
return await collection
.aggregate([
{
$search: {
autocomplete: {
path: "title",
query: title,
fuzzy: { maxEdits: 1 },
},
},
},
{
$project: {
title: 1,
},
},
{
$limit: 10,
},
])
.toArray();
};
```
Click the **Save Draft** button to save the function.
### Create Autocomplete Custom Resolver
We want to use the autocomplete function in our GraphQL schema. To do this we need to create a custom resolver. Custom resolvers allow us to define custom queries and mutations for our GraphQL schema, backed by **Functions** created on App Services.
On the left side bar of the Atlas UI, within **Build**, click **GraphQL**. Click the **Custom Resolvers** tab and click the **Add a Custom Resolver** button. For the **GraphQL Field Name** enter `autocompleteTitle`, for the **Parent Type** select **Query** and for the **Function Name** select the newly created function `autocompleteTitle`.
The input type defines the data type of what will be send to the GraphQL API as input for this resolver.
The return type defines the data type of what will be returned by the API.
We will send a string as input and expect a list of movie objects as output.
#### Input Type
- Input Type: `Scalar Type`, `String`
#### Return Type
- Return Type: `Existing Type (List)`, `[Movie]`
Click the **Save Draft** button to save the custom resolver.
Click the **Review Draft & Deploy** button at the top of the page and **Deploy** your changes.
Now with the first feature setup take the time to test the app, enter some movie titles into the search bar and see the autocomplete results.
---
## Feature 2: Highlights and Scoring
Now with the autocomplete function in place we can create a new function for highlights and scoring. This function will return a list of movies that match the search term by the title, the genres selected and the country where a certain movie was produced.
Additionally, it will return highlights and search scores for the results. The highlights contain the exact substring within the title and the plot strings, containing the matched search term.
This will allow us to highlight the found search terms within the frontend UI.
### Create Highlights Function
Similar to the previous function we will create a new function for highlights and scoring.
On the left side bar of the Atlas UI, within **Build**, click **Functions**.
Click the **Create New Function** button and enter `filteredMovies` as the name for the function.
### Implement Highlights Function
Now click the **Function Editor** tab.
Paste the following code into the **Function Editor**:
```js
exports = async (input) => {
const collection = context.services.get("mongodb-atlas").db("sample_mflix").collection("movies");
const { term, genres, countries } = input;
const searchShould = [];
const searchMust = [];
if (term.length > 0) {
const termStage = {
autocomplete: {
path: "title",
query: term,
fuzzy: { maxEdits: 1.0 },
score: {
boost: {
path: "imdb.rating",
undefined: 1,
},
},
},
};
searchMust.push(termStage);
const plotStage = {
text: {
query: term,
path: "plot",
},
};
searchShould.push(plotStage);
}
if (genres.length > 0) {
const genresStage = {
text: {
query: genres,
path: "genres",
},
};
searchMust.push(genresStage);
}
if (countries.length > 0) {
const countryStage = {
text: {
query: countries,
path: "countries",
},
};
searchMust.push(countryStage);
}
const searchQuery = [
{
$search: {
compound: {
should: searchShould,
must: searchMust,
},
highlight: { path: ["title", "genres", "countries", "plot"] },
},
},
{
$project: {
_id: 1,
title: 1,
poster: 1,
cast: 1,
directors: 1,
plot: 1,
fullplot: 1,
year: 1,
genres: 1,
countries: 1,
imdb: 1,
score: { $meta: "searchScore" },
highlights: { $meta: "searchHighlights" },
},
},
{ $limit: 20 },
];
return await collection.aggregate(searchQuery).toArray();
};
```
### Create Highlights Custom Resolver
On the left side bar of the Atlas UI, within **Build**, click **GraphQL**. Click the **Custom Resolvers** tab and click the **Add a Custom Resolver** button. For the **GraphQL Field Name** enter `filteredMovies`, for the **Parent Type** select **Query** and for the **Function Name** select the newly created function `filteredMovies`.
We will send a string as input and expect a list of custom movie objects, containing the scores and highlights for each movie as output.
#### Input Type
- Input Type: `Custom Type`
```json
{
"type": "object",
"title": "FilteredMoviesInput",
"properties": {
"term": {
"bsonType": "string"
},
"genres": {
"bsonType": "array",
"items": {
"bsonType": "string"
}
},
"countries": {
"bsonType": "array",
"items": {
"bsonType": "string"
}
}
}
}
```
#### Return Type
- Return Type: `Custom Type`
```json
{
"items": {
"bsonType": "object",
"properties": {
"_id": {
"bsonType": "objectId"
},
"cast": {
"bsonType": "array",
"items": {
"bsonType": "string"
}
},
"countries": {
"bsonType": "array",
"items": {
"bsonType": "string"
}
},
"directors": {
"bsonType": "array",
"items": {
"bsonType": "string"
}
},
"fullplot": {
"bsonType": "string"
},
"genres": {
"bsonType": "array",
"items": {
"bsonType": "string"
}
},
"highlights": {
"bsonType": "array",
"items": {
"bsonType": "object",
"properties": {
"path": {
"bsonType": "string"
},
"score": {
"bsonType": "double"
},
"texts": {
"bsonType": "array",
"items": {
"bsonType": "object",
"properties": {
"type": {
"bsonType": "string"
},
"value": {
"bsonType": "string"
}
}
}
}
}
}
},
"imdb": {
"bsonType": "object",
"properties": {
"id": {
"bsonType": "int"
},
"rating": {
"bsonType": "double"
},
"votes": {
"bsonType": "int"
}
}
},
"plot": {
"bsonType": "string"
},
"poster": {
"bsonType": "string"
},
"score": {
"bsonType": "double"
},
"title": {
"bsonType": "string"
},
"year": {
"bsonType": "int"
}
}
},
"title": "FilteredMovies",
"type": "array"
}
```
Click the **Save Draft** button to save the custom resolver.
Click the **Review Draft & Deploy** button at the top of the page and **Deploy** your changes.
Now with the highlights feature setup take the time to test the app, enter some movie titles into the search bar scroll in the results list and verify that the fuzzy matched search term is highlighted within the movie title and the short plot when there is a match.
---
## Feature 3: Facets
Facets open many powerful use cases for grouping your search results. The following feature shows how to run an Atlas Search query to get results grouped by values for genres of each movie in the **movies** collection, including the count for each of those groups.
### Facets Search Index Creation
In your cluster on **Atlas** in the **Search** tab, create a new index with the name `facets` and the following JSON for the **movies** collection.
```json
{
"mappings": {
"dynamic": false,
"fields": {
"genres": [
{
"dynamic": true,
"type": "document"
},
{
"type": "stringFacet"
}
],
"year": [
{
"dynamic": true,
"type": "document"
},
{
"representation": "int64",
"type": "number"
}
]
}
}
}
```
### Create Facets Function
Now with the index created, in the **Atlas** UI click the **App Services** tab. Click **Application-0** in the UI. On the left side bar of the Atlas UI, within **Build**, click **Functions**.
Click the **Create New Function** button and enter `facetsGenres` as the name for the function.
### Implement Facets Function
Now click the **Function Editor** tab.
Paste the following code into the **Function Editor**:
```js
exports = async (arg) => {
const collection = context.services.get("mongodb-atlas").db("sample_mflix").collection("movies");
return await collection
.aggregate([
{
$searchMeta: {
index: "facets",
facet: {
operator: {
range: {
path: "year",
gte: 1900,
},
},
facets: {
genresFacet: {
type: "string",
path: "genres",
},
},
},
},
},
])
.toArray();
};
```
### Create Facets Custom Resolver
On the left side bar of the Atlas UI, within **Build**, click **GraphQL**. Click the **Custom Resolvers** tab and click the **Add a Custom Resolver** button. For the **GraphQL Field Name** enter `facetsGenres`, for the **Parent Type** select **Query** and for the **Function Name** select the newly created function `facetsGenres`.
We won't send input to this query and expect a list of custom objects representing the facets for each genre, containing the number of movies for each genre.
#### Input Type
- Input Type: `None`
#### Return Type
- Return Type: `Custom Type`
```json
{
"title": "GenresMeta",
"type": "array",
"items": {
"bsonType": "object",
"properties": {
"count": {
"bsonType": "double"
},
"facet": {
"bsonType": "object",
"properties": {
"genresFacet": {
"bsonType": "object",
"properties": {
"buckets": {
"bsonType": "array",
"items": {
"bsonType": "object",
"properties": {
"_id": {
"bsonType": "string"
},
"count": {
"bsonType": "double"
}
}
}
}
}
}
}
}
}
}
}
```
Click the **Save Draft** button to save the custom resolver.
Click the **Review Draft & Deploy** button at the top of the page and **Deploy** your changes.
Now with the facets setup test the app and open the dropdown for **Genres**. Notice that there is now a number besides each genre representing the total number of movies for that genre.
---
## App Services Static Site Hosting
**MongoDB App Services Hosting** allows you to host, manage, and serve your application's static media and document files. You can use Hosting to store individual pieces of content or to upload and serve your entire client application.
Our frontend app contains all the necessary calls to the GraphQL API on App Services. We can export the whole frontend app as a static site and host it on MongoDB App Services.
For this you need to execute the follwing code in the root folfder of the project.
Make sure that you have the dependencies installed with.
```bash
npm install
```
and then build and export the site with an npm script using nextjs.
```bash
npm run build && npm run export
```
This will create a folder called `out` in the root folder of the project.
On the MongoDB Atlas UI on the **App Services** tab. On the left side bar of the Atlas UI, within **Manage**, click **Hosting**. Click the _Enable Hosting_ button. Drag and drop the contents of the folder `out` into the **Hosting** tab to upload all files.
Click the **Review Draft & Deploy** button at the top of the page and **Deploy** your changes.
Click now the **Settings** tab copy the **App Services Domain** paste it in a browser of your choice and press enter to view the site. 🎉