How to Implement a Search Engine Using Elasticsearch with TypeScript

Elasticsearch is a powerful, distributed search and analytics engine. It’s widely used for full-text search, log and metrics analysis, and complex data visualization. In this blog, we'll walk through how to implement a basic search engine using Elasticsearch with TypeScript.

Why Elasticsearch?

Elasticsearch is renowned for its:

• Speed: Near real-time search and analytics.
• Scalability: Handles petabytes of data across multiple nodes.
• Flexibility: Powerful query DSL and support for structured and unstructured data.

By combining it with TypeScript, we can leverage strong typing for better maintainability and developer experience.

---

Prerequisites

Before we start, make sure you have:

1. Node.js installed.

2. Elasticsearch up and running. Use Docker to set it up quickly:

   bash
   
   docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:8.10.2
   
   
   

3. TypeScript installed in your Node.js project:

   bash
   
   npm install typescript --save-dev
   
   
   

4. Elasticsearch Client for Node.js:

   bash
   
   npm install @elastic/elasticsearch
   
   
   

---

Step 1: Setting Up Elasticsearch Client

First, configure the Elasticsearch client in your TypeScript project.

typescript

import { Client } from '@elastic/elasticsearch';

const client = new Client({
  node: 'http://localhost:9200', // Replace with your Elasticsearch instance
});

export default client;

---

Step 2: Creating an Index

An index is like a database in Elasticsearch. Let’s create a simple index for storing articles.

typescript

const createIndex = async () => {
  const indexName = 'articles';

  const exists = await client.indices.exists({ index: indexName });

  if (!exists) {
    await client.indices.create({
      index: indexName,
      body: {
        mappings: {
          properties: {
            title: { type: 'text' },
            content: { type: 'text' },
            tags: { type: 'keyword' },
            createdAt: { type: 'date' },
          },
        },
      },
    });
    console.log(`Index '${indexName}' created.`);
  } else {
    console.log(`Index '${indexName}' already exists.`);
  }
};

createIndex();

---

Step 3: Indexing Documents

Now, let’s add some data to the articles index.

typescript

const indexDocument = async (doc: any) => {
  await client.index({
    index: 'articles',
    document: doc,
  });
  console.log('Document indexed:', doc);
};

// Example
indexDocument({
  title: 'Getting Started with Elasticsearch',
  content: 'Elasticsearch is a distributed search engine...',
  tags: ['search', 'elasticsearch'],
  createdAt: new Date().toISOString(),
});

---

Step 4: Searching Documents

Elasticsearch offers powerful query capabilities. Here’s how you can perform a basic full-text search.

typescript

const searchArticles = async (query: string) => {
  const result = await client.search({
    index: 'articles',
    body: {
      query: {
        match: {
          content: query,
        },
      },
    },
  });

  console.log(
    'Search Results:',
    result.hits.hits.map((hit) => hit._source)
  );
};

// Example
searchArticles('Elasticsearch');

---

Step 5: Advanced Search Features

Elasticsearch supports filters, sorting, and pagination. Here’s an example of a filtered search.

typescript

const searchWithFilters = async (query: string, tags: string[], size: number = 10, page: number = 1) => {
  const result = await client.search({
    index: 'articles',
    body: {
      query: {
        bool: {
          must: {
            match: { content: query },
          },
          filter: {
            terms: { tags },
          },
        },
      },
      from: (page - 1) * size,
      size,
      sort: [{ createdAt: { order: 'desc' } }],
    },
  });

  console.log(
    'Filtered Search Results:',
    result.hits.hits.map((hit) => hit._source)
  );
};

// Example
searchWithFilters('Elasticsearch', ['search']);

---

Step 6: Handling Errors Gracefully

Always handle errors when interacting with Elasticsearch.

typescript

const safeSearch = async (query: string) => {
  try {
    const result = await client.search({
      index: 'articles',
      body: {
        query: {
          match: { content: query },
        },
      },
    });
    console.log(result.hits.hits.map((hit) => hit._source));
  } catch (error) {
    console.error('Search failed:', error.meta.body.error);
  }
};

safeSearch('Invalid query');

---

Happy coding!