How to Optimize PHP Elasticsearch Search Result Sorting Algorithms? Boost Efficiency and Accuracy

Sorting with Boost Values

In Elasticsearch, the default sorting mechanism is based on the relevance score (i.e., the score of documents). However, sometimes we need to sort results based on specific business needs, such as price, sales, etc. To achieve this, you can use boost values to adjust the priority of the results. A higher boost value means the result ranks higher.

For instance, if you have a product index containing the fields `name` (product name) and `price`, and you want to sort the results by price in descending order, you can use the following code:

GET /products/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "price": {
        "order": "desc"
      }
    }
  ]
}

This code returns all products and sorts them in descending order by price. You can adjust the sorting field based on your actual needs.

Defining Complex Sorting Rules

In some cases, you may need to sort based on multiple criteria. This can be done using function scripts to define complex sorting rules. For example, if you want to sort products based on both rating and sales, with a weight of 0.7 for rating and 0.3 for sales, you can use the following code:

First, when creating the index, you need to include fields for `rating` and `sales`:

PUT /products
{
  "mappings": {
    "properties": {
      "name": {
        "type": "text"
      },
      "rating": {
        "type": "float"
      },
      "sales": {
        "type": "integer"
      }
    }
  }
}

Next, you can use a function script to define the sorting rule:

GET /products/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "_script": {
        "type": "number",
        "script": {
          "source": "(0.7 * doc['rating'].value) + (0.3 * doc['sales'].value)"
        },
        "order": "desc"
      }
    }
  ]
}

This code will sort the products based on the weighted average of the rating and sales. You can adjust the weight and calculation method as needed.

Optimizing Field Mappings and Settings

Elasticsearch offers several mapping types and settings that can help optimize search result sorting. Key options include:

• "index": "not_analyzed": If you want to ensure that a field's sorting result remains consistent with its exact text content, you can set its mapping type to "not_analyzed". This prevents tokenization during sorting and ensures accurate results.
• "fielddata": true: If you need to sort by a particular field frequently, enabling `fielddata` can load field values into memory, improving sorting performance.

For example, if you want to sort users by age, you first need to define the field mapping when creating the index:

PUT /users
{
  "mappings": {
    "properties": {
      "name": {
        "type": "text"
      },
      "age": {
        "type": "keyword",
        "index": "not_analyzed",
        "fielddata": true
      }
    }
  }
}

Then, you can use the following code to sort by age in ascending order:

GET /users/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "age": {
        "order": "asc"
      }
    }
  ]
}

This code will sort the users by age in ascending order.

Conclusion

By using boost values for sorting, defining complex sorting rules, and optimizing field mappings and settings, you can significantly improve the efficiency and accuracy of search result sorting in Elasticsearch. Depending on your business needs, these methods can be flexibly adjusted to achieve a better search experience.