How to use Elasticsearch for fast full-text search

How to use Elasticsearch for fast full-text search

Introduction:
With the advent of the modern Internet era, a large number of digital documents and data are generated and accumulated at an alarming rate, which makes effective retrieval and Searching for information has become increasingly critical. As an open source distributed search engine, Elasticsearch provides powerful full-text search capabilities, allowing us to quickly and accurately retrieve the required content in massive data. This article will introduce how to use Elasticsearch for fast full-text search, with code examples.

1. Basic concepts of Elasticsearch
2. Installing and configuring Elasticsearch
3. Create an index
4. Add documents to the index
5. Perform full-text search
6. Advanced search skills
7. Performance optimization
8. Basic concepts of Elasticsearch
   Before using Elasticsearch for full-text search, we need to understand some basic concepts.

• Index: Elasticsearch uses indexes to organize and store documents. Each index is similar to a data table in a database and contains multiple documents.
• Document: Document is the basic data unit in Elasticsearch. Each document consists of a set of key-value pairs, where the key is the field name and the value is the field's value.
• Type: Type is a logical classification used to organize documents in the index. After version 6.0, the concept of types in Elasticsearch is deprecated, and an index can only have one type.
• Mapping: Mapping defines the document structure and field types in the index. It tells Elasticsearch how to store and index the data.
• Shards and Replicas: Elasticsearch divides the index into multiple shards for distributed storage and search. Each shard is an independent index and can be replicated on multiple nodes.

2. Installing and configuring Elasticsearch
   First, we need to download and install the appropriate version of Elasticsearch from the Elasticsearch official website. After the installation is complete, we need to perform basic configuration.

In the elasticsearch.yml file, we can configure the cluster name, node name, listening address, number of shards, etc. We can also set different roles for different nodes, such as master node, data node and client node.

3. Create index
   Before indexing, we need to determine the name and mapping of the index. The index name is a string that uniquely identifies the index. The mapping defines the structure of the index.

PUT /index_name
{
  "mappings": {
    "properties": {
      "field1": { "type": "text" },
      "field2": { "type": "keyword" },
      ...
    }
  }
}

4. Add documents to the index
   After the index creation is completed, we can start adding documents.

POST /index_name/_doc/1
{
  "field1": "value1",
  "field2": "value2",
  ...
}

5. Perform a full-text search
   Once we have some documents, we can use Elasticsearch to perform a full-text search. The following is a simple example that searches the index for documents containing a specified keyword.

GET /index_name/_search
{
  "query": {
    "match": {
      "field1": "keyword"
    }
  }
}

6. Advanced Search Tips
   When conducting advanced searches, we can use rich query syntax and filters. Here are some examples of commonly used advanced search techniques.

• Multi-field search: You can specify multiple fields to search.

GET /index_name/_search
{
  "query": {
    "multi_match": {
      "query": "keyword",
      "fields": ["field1", "field2"]
    }
  }
}

• Phrase search: You can use the match_phrase query to search for documents containing a specified phrase.

GET /index_name/_search
{
  "query": {
    "match_phrase": {
      "field1": "keyword"
    }
  }
}

• Range search: You can use range queries to search for values ​​within a specified range.

GET /index_name/_search
{
  "query": {
    "range": {
      "field1": {
        "gte": 10,
        "lte": 100
      }
    }
  }
}

7. Performance Optimization
   In order to improve search performance, you can consider the following points.

• Sharding and replica settings: Make appropriate sharding and replica settings based on data volume and query load.
• Index optimization: Use appropriate data types and field mappings to reduce index size.
• Query optimization: Use appropriate query syntax and paging parameters to reduce query response time.

Conclusion:
This article introduces how to use Elasticsearch for fast full-text search. By using the powerful features and flexible query syntax of Elasticsearch, we can quickly find what we need in massive amounts of data. I hope this article will be helpful for everyone to learn and use Elasticsearch.

Reference link:

• Elasticsearch official document: https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html

The above is the detailed content of How to use Elasticsearch for fast full-text search. For more information, please follow other related articles on the PHP Chinese website!