请求提供ElasticSearch分组查询等语句实例（附示例结构与数据）

Hey there! I’ve put together a bunch of practical Elasticsearch query examples based on your sales index mapping and sample data to help you get up to speed quickly. These cover basic searches, filtering, grouping/aggregations, and more common use cases:

1. Basic Match & Term Queries #

These are your go-to for simple lookups:

Match products by title (full-text search) #

GET /sales/_search
{
  "query": {
    "match": {
      "productTitle": "Widget"
    }
  }
}

This will return all documents where productTitle contains "Widget" (works with partial matches if you're using a standard analyzer).

Exact match on product code #

GET /sales/_search
{
  "query": {
    "term": {
      "productCode": "A"
    }
  }
}

Use term when you need an exact match for keyword-like fields (note: if your productCode was mapped as keyword instead of string, this works even better—though in your mapping it's string, which still works for exact matches here).

2. Filtering Queries #

Filter results without affecting relevance scoring:

Find products with quantity greater than 3 #

GET /sales/_search
{
  "query": {
    "bool": {
      "filter": [
        { "range": { "quantity": { "gt": 3 } } }
      ]
    }
  }
}

Filters are cached, so they're great for repeated queries. This returns all docs where quantity is more than 3.

Filter by unit price range ($5 to $6) #

GET /sales/_search
{
  "query": {
    "bool": {
      "filter": [
        { "range": { "unitPrice": { "gte": 5, "lte": 6 } } }
      ]
    }
  }
}

Use gte (greater than or equal) and lte (less than or equal) to define price ranges.

3. Grouping & Aggregation Queries #

This is where you'll get insights from grouped data:

Group by product code, total quantity sold #

GET /sales/_search
{
  "size": 0, // Don't return individual docs, just aggregations
  "aggs": {
    "products_by_code": {
      "terms": { "field": "productCode" },
      "aggs": {
        "total_quantity": { "sum": { "field": "quantity" } }
      }
    }
  }
}

This groups all sales by productCode and calculates the total quantity sold for each product. The size:0 skips returning individual documents since we only care about the aggregation results.

Group by product code, calculate total sales revenue #

GET /sales/_search
{
  "size": 0,
  "aggs": {
    "products_by_code": {
      "terms": { "field": "productCode" },
      "aggs": {
        "total_revenue": {
          "sum": {
            "script": {
              "source": "doc['quantity'].value * doc['unitPrice'].value"
            }
          }
        }
      }
    }
  }
}

Uses a script to multiply quantity by unitPrice for each document, then sums that up per product code to get total revenue.

Group by product code, get average unit price #

GET /sales/_search
{
  "size": 0,
  "aggs": {
    "products_by_code": {
      "terms": { "field": "productCode" },
      "aggs": {
        "avg_unit_price": { "avg": { "field": "unitPrice" } }
      }
    }
  }
}

Calculates the average unitPrice for each product group.

4. Combined Query + Aggregation #

Get filtered results and run aggregations on them:

Filter for quantity > 2, then group by product code to get total revenue #

GET /sales/_search
{
  "size": 0,
  "query": {
    "bool": {
      "filter": [
        { "range": { "quantity": { "gt": 2 } } }
      ]
    }
  },
  "aggs": {
    "products_by_code": {
      "terms": { "field": "productCode" },
      "aggs": {
        "total_revenue": {
          "sum": {
            "script": "doc['quantity'].value * doc['unitPrice'].value"
          }
        }
      }
    }
  }
}

First filters down to sales where quantity is over 2, then runs the revenue aggregation on that subset.

5. Sorting Results #

Sort your search results by a specific field:

Sort sales by quantity (descending) #

GET /sales/_search
{
  "query": { "match_all": {} },
  "sort": [
    { "quantity": { "order": "desc" } }
  ]
}

Returns all sales documents sorted from highest to lowest quantity.

内容的提问来源于stack exchange，提问作者Davout