- 1. API with NestJS #1. Controllers, routing and the module structure
- 2. API with NestJS #2. Setting up a PostgreSQL database with TypeORM
- 3. API with NestJS #3. Authenticating users with bcrypt, Passport, JWT, and cookies
- 4. API with NestJS #4. Error handling and data validation
- 5. API with NestJS #5. Serializing the response with interceptors
- 6. API with NestJS #6. Looking into dependency injection and modules
- 7. API with NestJS #7. Creating relationships with Postgres and TypeORM
- 8. API with NestJS #8. Writing unit tests
- 9. API with NestJS #9. Testing services and controllers with integration tests
- 10. API with NestJS #10. Uploading public files to Amazon S3
- 11. API with NestJS #11. Managing private files with Amazon S3
- 12. API with NestJS #12. Introduction to Elasticsearch
- 13. API with NestJS #13. Implementing refresh tokens using JWT
- 14. API with NestJS #14. Improving performance of our Postgres database with indexes
- 15. API with NestJS #15. Defining transactions with PostgreSQL and TypeORM
- 16. API with NestJS #16. Using the array data type with PostgreSQL and TypeORM
- 17. API with NestJS #17. Offset and keyset pagination with PostgreSQL and TypeORM
- 18. API with NestJS #18. Exploring the idea of microservices
- 19. API with NestJS #19. Using RabbitMQ to communicate with microservices
- 20. API with NestJS #20. Communicating with microservices using the gRPC framework
- 21. API with NestJS #21. An introduction to CQRS
- 22. API with NestJS #22. Storing JSON with PostgreSQL and TypeORM
- 23. API with NestJS #23. Implementing in-memory cache to increase the performance
- 24. API with NestJS #24. Cache with Redis. Running the app in a Node.js cluster
- 25. API with NestJS #25. Sending scheduled emails with cron and Nodemailer
- 26. API with NestJS #26. Real-time chat with WebSockets
- 27. API with NestJS #27. Introduction to GraphQL. Queries, mutations, and authentication
- 28. API with NestJS #28. Dealing in the N + 1 problem in GraphQL
- 29. API with NestJS #29. Real-time updates with GraphQL subscriptions
- 30. API with NestJS #30. Scalar types in GraphQL
- 31. API with NestJS #31. Two-factor authentication
- 32. API with NestJS #32. Introduction to Prisma with PostgreSQL
- 33. API with NestJS #33. Managing PostgreSQL relationships with Prisma
- 34. API with NestJS #34. Handling CPU-intensive tasks with queues
- 35. API with NestJS #35. Using server-side sessions instead of JSON Web Tokens
- 36. API with NestJS #36. Introduction to Stripe with React
- 37. API with NestJS #37. Using Stripe to save credit cards for future use
- 38. API with NestJS #38. Setting up recurring payments via subscriptions with Stripe
- 39. API with NestJS #39. Reacting to Stripe events with webhooks
- 40. API with NestJS #40. Confirming the email address
- 41. API with NestJS #41. Verifying phone numbers and sending SMS messages with Twilio
- 42. API with NestJS #42. Authenticating users with Google
- 43. API with NestJS #43. Introduction to MongoDB
- 44. API with NestJS #44. Implementing relationships with MongoDB
- 45. API with NestJS #45. Virtual properties with MongoDB and Mongoose
- 46. API with NestJS #46. Managing transactions with MongoDB and Mongoose
- 47. API with NestJS #47. Implementing pagination with MongoDB and Mongoose
- 48. API with NestJS #48. Definining indexes with MongoDB and Mongoose
- 49. API with NestJS #49. Updating with PUT and PATCH with MongoDB and Mongoose
- 50. API with NestJS #50. Introduction to logging with the built-in logger and TypeORM
- 51. API with NestJS #51. Health checks with Terminus and Datadog
We can find some searching functionalities in a lot of web applications. While we might be fine when iterating through a small data set, the performance for more extensive databases can become an issue. Relational databases might prove to be relatively slow when searching through a lot of data.
A solution to the above problem might be Elasticsearch. It is a search engine that highly focuses on performance. When using it, we maintain a separate document-oriented database.
If you are familiar with MongoDB, document-oriented databases will ring a bell for you. In theory, we might use Elasticsearch as a general-purpose database. It wasn’t designed for this purpose, though. If you would like to read more about it, check out this question on Stackoverflow.
Running Elasticsearch
Running Elasticsearch includes maintaining a separate, search-optimized database. Because of that, we need to choose one of the ways to fire it up.
In the second part of this series, we’ve started using Docker Compose. Therefore, a fitting way to start using Elasticsearch would be to do so through Docker. When we go to the official Elasticsearch documentation, we can see an example using Docker Compose. It includes three nodes.
An Elasticsearch cluster is a group of one or more Elasticsearch nodes connected. Each node is an instance of Elasticsearch.
Let’s add the above official configuration to our existing file.
docker-compose.yml
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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | version: "3" services: postgres: container_name: postgres image: postgres:latest ports: - "5432:5432" volumes: - /data/postgres:/data/postgres env_file: - docker.env networks: - postgres pgadmin: links: - postgres:postgres container_name: pgadmin image: dpage/pgadmin4 ports: - "8080:80" volumes: - /data/pgadmin:/root/.pgadmin env_file: - docker.env networks: - postgres es01: image: docker.elastic.co/elasticsearch/elasticsearch:7.9.1 container_name: es01 environment: - node.name=es01 - cluster.name=es-docker-cluster - discovery.seed_hosts=es02,es03 - cluster.initial_master_nodes=es01,es02,es03 - bootstrap.memory_lock=true - "ES_JAVA_OPTS=-Xms512m -Xmx512m" ulimits: memlock: soft: -1 hard: -1 volumes: - data01:/usr/share/elasticsearch/data ports: - 9200:9200 networks: - elastic es02: image: docker.elastic.co/elasticsearch/elasticsearch:7.9.1 container_name: es02 environment: - node.name=es02 - cluster.name=es-docker-cluster - discovery.seed_hosts=es01,es03 - cluster.initial_master_nodes=es01,es02,es03 - bootstrap.memory_lock=true - "ES_JAVA_OPTS=-Xms512m -Xmx512m" ulimits: memlock: soft: -1 hard: -1 volumes: - data02:/usr/share/elasticsearch/data networks: - elastic es03: image: docker.elastic.co/elasticsearch/elasticsearch:7.9.1 container_name: es03 environment: - node.name=es03 - cluster.name=es-docker-cluster - discovery.seed_hosts=es01,es02 - cluster.initial_master_nodes=es01,es02,es03 - bootstrap.memory_lock=true - "ES_JAVA_OPTS=-Xms512m -Xmx512m" ulimits: memlock: soft: -1 hard: -1 volumes: - data03:/usr/share/elasticsearch/data networks: - elastic volumes: data01: driver: local data02: driver: local data03: driver: local networks: postgres: driver: bridge elastic: driver: bridge |
You might run into an issue when doing the above: es01 exited with code 78. There is a high chance that increasing the vm.max_map_count will help, as described here.
By default, the password for Elasticsearch is changeme. To set up a password, we can add it to our docker.env file:
docker-compose.yml
1 2 | (...) ELASTIC_PASSWORD=admin |
The default username is “elastic“
Connecting to Elasticsearch in NestJS
To use Elasticsearch within our NestJS project, we can use the official @nestjs/elasticsearch library.
It wraps the @elastic/elasticsearch client. Since it is a peer dependency of @nestjs/elasticsearch, we need to install it.
Don’t confuse it with the “elasticsearch” client that will soon be deprecated.
1 | npm install @nestjs/elasticsearch @elastic/elasticsearch |
Due to how we did set up Elesticsearch, our cluster is available at http://localhost:9200. Our username is elastic, and the password is admin. We need to add all of the above to our environment variables.
.env
1 2 3 4 | (...) ELASTICSEARCH_NODE=http://localhost:9200 ELASTICSEARCH_USERNAME=elastic ELASTICSEARCH_PASSWORD=admin |
Now we can create our module that uses the above configuration.
/src/search/search.module.ts
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | import { Module } from '@nestjs/common'; import { ConfigModule, ConfigService } from '@nestjs/config'; import { ElasticsearchModule } from '@nestjs/elasticsearch'; @Module({ imports: [ ConfigModule, ElasticsearchModule.registerAsync({ imports: [ConfigModule], useFactory: async (configService: ConfigService) => ({ node: configService.get('ELASTICSEARCH_NODE'), auth: { username: configService.get('ELASTICSEARCH_USERNAME'), password: configService.get('ELASTICSEARCH_PASSWORD'), } }), inject: [ConfigService], }), ], exports: [ElasticsearchModule] }) export class SearchModule {} |
We export the ElasticsearchModule above so that we are able to use some of its features when importing SearchModule as suggested here
Populating Elasticsearch with data
The first thing to consider when populating Elasticsearch with data is the concept of the index. In the context of Elasticsearch, we group similar documents by assigning them with the same index.
In the previous versions of Elasticsearch we also used types to group documents, but this concept is being abandoned
When populating the Elasticsearch database with data, we throw in only the parts that we later use when searching. Let’s create an interface for that purpose.
/src/posts/types/postSearchBody.interface.ts
1 2 3 4 5 6 | interface PostSearchBody { id: number, title: string, content: string, authorId: number } |
The TypeScript support with Elasticsearch is not that good, unfortunately. Following the official documentation, we can create a search response type for our posts.
/src/posts/types/postSearchBody.interface.ts
1 2 3 4 5 6 7 8 9 10 | import PostSearchBody from './postSearchBody.interface'; interface PostSearchResult { hits: { total: number; hits: Array<{ _source: PostSearchBody; }>; }; } |
When we’re done with the above, we can create a service that takes care of interacting with our Elasticsearch cluster.
/src/posts/postsSearch.service.ts
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 | import { Injectable } from '@nestjs/common'; import { ElasticsearchService } from '@nestjs/elasticsearch'; import Post from './post.entity'; import PostSearchResult from './types/postSearchResponse.interface'; import PostSearchBody from './types/postSearchBody.interface'; @Injectable() export default class PostsSearchService { index = 'posts' constructor( private readonly elasticsearchService: ElasticsearchService ) {} async indexPost(post: Post) { return this.elasticsearchService.index<PostSearchResult, PostSearchBody>({ index: this.index, body: { id: post.id, title: post.title, content: post.content, authorId: post.author.id } }) } async search(text: string) { const { body } = await this.elasticsearchService.search<PostSearchResult>({ index: this.index, body: { query: { multi_match: { query: text, fields: ['title', 'content'] } } } }) const hits = body.hits.hits; return hits.map((item) => item._source); } } |
Above we use multi_match becase we want to search both through the title and the content of the posts
The crucial thing to acknowledge about elasticsearchService.search is that it returns just the properties that we’ve put into the Elasticsearch database. Since we save the ids of the posts, we can now get the whole documents from our Postgres database. Let’s put this logic into PostsService.
/src/posts/posts.service.ts
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 | import { Injectable } from '@nestjs/common'; import CreatePostDto from './dto/createPost.dto'; import Post from './post.entity'; import { InjectRepository } from '@nestjs/typeorm'; import { Repository, In } from 'typeorm'; import User from '../users/user.entity'; import PostsSearchService from './postsSearch.service'; @Injectable() export default class PostsService { constructor( @InjectRepository(Post) private postsRepository: Repository<Post>, private postsSearchService: PostsSearchService ) {} // (...) async createPost(post: CreatePostDto, user: User) { const newPost = await this.postsRepository.create({ ...post, author: user }); await this.postsRepository.save(newPost); this.postsSearchService.indexPost(newPost); return newPost; } async searchForPosts(text: string) { const results = await this.postsSearchService.search(text); const ids = results.map(result => result.id); if (!ids.length) { return []; } return this.postsRepository .find({ where: { id: In(ids) } }); } } |
The last thing to do is to modify the controller so that it accepts a query parameter.
/src/posts/posts.controller.ts
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 | import { Controller, Get, UseInterceptors, ClassSerializerInterceptor, Query, } from '@nestjs/common'; import PostsService from './posts.service'; @Controller('posts') @UseInterceptors(ClassSerializerInterceptor) export default class PostsController { constructor( private readonly postsService: PostsService ) {} @Get() async getPosts(@Query('search') search: string) { if (search) { return this.postsService.searchForPosts(search); } return this.postsService.getAllPosts(); } // (...) } |
Don’t forget to import the SearchModule in the PostsModule.
Keeping Elasticsearch consistent with our database
Through our API, we can also edit and delete posts. Therefore, we need to put some effort into keeping the Elasticsearch database consistent with our Postgres instance.
Deleting documents
Since we save the id of the post in our Elasticsearch database, we can use it to find it and delete it. To do so, we can use the deleteByQuery function.
/src/posts/postsSearch.service.ts
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 | import { Injectable } from '@nestjs/common'; import { ElasticsearchService } from '@nestjs/elasticsearch'; @Injectable() export default class PostsSearchService { index = 'posts' constructor( private readonly elasticsearchService: ElasticsearchService ) {} // (...) async remove(postId: number) { this.elasticsearchService.deleteByQuery({ index: this.index, body: { query: { match: { id: postId, } } } }) } } |
Let’s call the above method in PostsService every time we delete a post.
/src/posts/posts.service.ts
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 | import { Injectable } from '@nestjs/common'; import Post from './post.entity'; import { InjectRepository } from '@nestjs/typeorm'; import { Repository, In } from 'typeorm'; import PostNotFoundException from './exceptions/postNotFound.exception'; import PostsSearchService from './postsSearch.service'; @Injectable() export default class PostsService { constructor( @InjectRepository(Post) private postsRepository: Repository<Post>, private postsSearchService: PostsSearchService ) {} // (...) async deletePost(id: number) { const deleteResponse = await this.postsRepository.delete(id); if (!deleteResponse.affected) { throw new PostNotFoundException(id); } await this.postsSearchService.remove(id); } } |
Modifying documents
The other thing to make sure that the Elasticsearch database is consistent with our main database is to modify existing documents. To do that, we can use the updateByQuery function.
Unfortunately, we need to write a script that updates all of the necessary fields. For example, to update the title and the content, we need:
1 | ctx._source.title='New title'; ctx._source.content= 'New content'; |
We can create the above script dynamically.
/src/posts/postsSearch.service.ts
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 | import { Injectable } from '@nestjs/common'; import { ElasticsearchService } from '@nestjs/elasticsearch'; import Post from './post.entity'; import PostSearchBody from './types/postSearchBody.interface'; @Injectable() export default class PostsSearchService { index = 'posts' constructor( private readonly elasticsearchService: ElasticsearchService ) {} // (...) async update(post: Post) { const newBody: PostSearchBody = { id: post.id, title: post.title, content: post.content, authorId: post.author.id } const script = Object.entries(newBody).reduce((result, [key, value]) => { return `${result} ctx._source.${key}='${value}';`; }, ''); return this.elasticsearchService.updateByQuery({ index: this.index, body: { query: { match: { id: post.id, } }, script: { inline: script } } }) } } |
Now we need to use the above method whenever we modify existing posts.
/src/posts/posts.service.ts
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 | import { Injectable } from '@nestjs/common'; import Post from './post.entity'; import UpdatePostDto from './dto/updatePost.dto'; import { InjectRepository } from '@nestjs/typeorm'; import { Repository, In } from 'typeorm'; import PostNotFoundException from './exceptions/postNotFound.exception'; import PostsSearchService from './postsSearch.service'; @Injectable() export default class PostsService { constructor( @InjectRepository(Post) private postsRepository: Repository<Post>, private postsSearchService: PostsSearchService ) {} async updatePost(id: number, post: UpdatePostDto) { await this.postsRepository.update(id, post); const updatedPost = await this.postsRepository.findOne(id, { relations: ['author'] }); if (updatedPost) { await this.postsSearchService.update(updatedPost); return updatedPost; } throw new PostNotFoundException(id); } } |
The Elasticsearch documents also have ids. An alternative to the above deletes and updates would be to store the Elasticsearch id in our Postgres database and use it when deleting and updating.
Summary
Today we’ve learned the very basics of Elasticsearch. When doing so, we’ve added it to our NestJS API. We’ve also created our documents and searched through them. All of that is the tip of the Elasticsearch iceberg. There is a lot more to learn here, so stay tuned!