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深入学习 Elasticsearch 查询语法与实战

In-depth study of Elasticsearch query syntax and practice

Introduction:
Elasticsearch is an open source search engine based on Lucene, mainly used for distributed search and analysis. It is widely used in scenarios such as full-text search, log analysis, and recommendation systems for large-scale data. When using Elasticsearch for data query, flexible use of query syntax is the key to improving query efficiency. This article will delve into the Elasticsearch query syntax and give detailed code examples based on actual cases.

1. Overview
The query syntax of Elasticsearch uses JSON format, which mainly includes query statements, filter conditions, sorting, paging and other functions. By flexibly combining these syntaxes, various complex data queries can be implemented.

2. Query statement

  1. Match query:
    Match query is the most basic full-text query, which matches query results in specified fields based on keywords. The sample code is as follows:

    GET /index/_search
    {
      "query": {
     "match": {
       "field": "keyword"
     }
      }
    }
  2. Term query:
    Term query is used to accurately match the value of the specified field. The sample code is as follows:

    GET /index/_search
    {
      "query": {
     "term": {
       "field": "value"
     }
      }
    }
  3. Range query:
    Range query is used to query the values ​​within the range of the specified field. The sample code is as follows:

    GET /index/_search
    {
      "query": {
     "range": {
       "field": {
         "gte": "start value",
         "lte": "end value"
       }
     }
      }
    }
  4. Bool query:
    Bool query is used to combine multiple query conditions and supports logical relationships such as must, must_not, should, etc. The sample code is as follows:

    GET /index/_search
    {
      "query": {
     "bool": {
       "must": [
         { "match": { "field1": "value1" } },
         { "match": { "field2": "value2" } }
       ],
       "must_not": { "term": { "field3": "value3" } },
       "should": { "term": { "field4": "value4" } }
     }
      }
    }

3. Filter conditions
Filter conditions are used to limit the range of query results and reduce unnecessary calculations. Commonly used filtering conditions are:

  1. Term filter: Filter based on the precise value of the field.
  2. Range filter: Filter based on the range of the field.
  3. Exists filter: Filter based on whether the field exists.
  4. Bool filter: combine multiple filter conditions.

4. Sorting
In the query results, we can sort according to the value of the specified field. Commonly used sorting methods are:

  1. Field sorting: Sort according to the value of the specified field.
  2. Score sorting: Sort documents according to their relevance.

5. Paging
In order to avoid returning too much data at one time, we can paginate the query results. Commonly used paging methods are:

  1. From/Size paging: specify the starting position and quantity of the returned results through the from and size parameters.
  2. Scroll paging: Use scroll API for paging.

6. Practical Case
The following is a practical case to show how to use Elasticsearch's query syntax for data query.

Case: Search for product keywords on e-commerce websites and sort them based on sales volume and price.

GET /products/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "name": "手机" } }
      ]
    }
  },
  "sort": [
    { "sales": "desc" },
    { "price": "asc" }
  ]
}

In the above query, we use the match statement in the bool query to search for products containing "mobile phone" in the product name, and use the sort parameter to sort by sales volume in descending order and price in ascending order.

Conclusion:
This article provides an in-depth study of the query syntax of Elasticsearch and provides detailed code examples through actual cases. Flexible use of these query syntax can improve the efficiency and accuracy of data query. In actual projects, we can use different query syntaxes in combination according to specific needs to meet different data query scenarios.

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