Introduction to Elasticsearch
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…
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 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: bridgeYou 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 (...) 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. npm install @nestjs/elasticsearch @elastic/elasticsearchDue 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 (...) ELASTICSEARCH_NODE=http://localhost:9200 ELASTICSEARCH_USERNAME=elastic ELASTICSEARCH_PASSWORD=adminNow we can create our module that uses the above configuration. /src/search/search.module.ts 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 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 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 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