ElasticSearch Nested类型全文检索、聚合查询

ElasticSearch Nested类型全文检索、聚合查询

Nested类型全文检索

1. 创建索引

PUT /products1
{"mappings":{"properties":{"fulltext":{"type":"text"},
      "name":{"type":"text",
        "fields":{"keyword":{"type":"keyword",
            "ignore_above":256}}},
      "reviews":{"type":"nested",
        "properties":{"rating":{"type":"integer"},
          "author":{"type":"text",
            "copy_to":"fulltext"},
          "date":{"type":"date"}}}}}}

以上创建索引语句中实现全文检索重点为

"fulltext": { "type": "text" }

和

"copy_to": "fulltext"

，nested类型中哪个text类型的字段需要全文检索，就在字段上加

"copy_to": "fulltext"

1. 添加数据

PUT /products1/_doc/1
{"name":"Product A",
  "reviews":[{"rating":5,
      "author":"Alice",
      "date":"2021-01-01"},
    {"rating":4,
      "author":"Bob",
      "date":"2021-01-02"}]}

PUT /products1/_doc/2
{"name":"Product B",
  "reviews":[{"rating":1,
      "author":"John",
      "date":"2021-01-03"},
    {"rating":2,
      "author":"Mary",
      "date":"2021-01-04"},
    {"rating":3,
      "author":"James",
      "date":"2021-01-05"},
    {"rating":4,
      "author":"Elisabeth",
      "date":"2021-01-06"},
    {"rating":5,
      "author":"Richard",
      "date":"2021-01-07"}]}

PUT /products1/_doc/3
{"name":"Product C",
  "reviews":[{"rating":1,
      "author":"Alex",
      "date":"2021-01-03"},
    {"rating":2,
      "author":"Alice",
      "date":"2021-01-04"}]}

1. 执行查询

POST products1/_search
{"query":{"simple_query_string":{"query":"Alice"}}}

1. 结果如下，可以看到nested类型中包含Alice的数据也被检索出来了

{"took":2,
  "timed_out": false,
  "_shards":{"total":1,
    "successful":1,
    "skipped":0,
    "failed":0},
  "hits":{"total":{"value":2,
      "relation":"eq"},
    "max_score":0.5442147,
    "hits":[{"_index":"products1",
        "_type":"_doc",
        "_id":"1",
        "_score":0.5442147,
        "_source":{"name":"Product A",
          "reviews":[{"rating":5,
              "author":"Alice",
              "date":"2021-01-01"},
            {"rating":4,
              "author":"Bob",
              "date":"2021-01-02"}]}},
      {"_index":"products1",
        "_type":"_doc",
        "_id":"3",
        "_score":0.5442147,
        "_source":{"name":"Product C",
          "reviews":[{"rating":1,
              "author":"Alex",
              "date":"2021-01-03"},
            {"rating":2,
              "author":"Alice",
              "date":"2021-01-04"}]}}]}}

以上可以看到实现nested类型全文检索

nested类型聚合查询

还是在上面product1索引中测试

1. 现在，您可以对嵌套文档执行嵌套聚合。例如，让我们计算每个产品的平均评分：

GET /products1/_search
{"size":0,
  "aggs":{"聚合名称":{"terms":{"field":"name.keyword"},
      "aggs":{"reviews":{"nested":{"path":"reviews"},
          "aggs":{"average_rating":{"avg":{"field":"reviews.rating"}}}}}}}}

1. 我们首先使用术语聚合为每个产品创建存储桶。然后，对于每个产品，我们运行嵌套聚合，以便我们可以访问嵌套文档的集合。最后，我们可以计算这些嵌套文档的指标聚合，在我们的示例中是平均评分

{"took":3,
  "timed_out": false,
  "_shards":{"total":1,
    "successful":1,
    "skipped":0,
    "failed":0},
  "hits":{"total":{"value":3,
      "relation":"eq"},
    "max_score": null,
    "hits":[]},
  "aggregations":{"products":{"doc_count_error_upper_bound":0,
      "sum_other_doc_count":0,
      "buckets":[{"key":"Product A",
          "doc_count":1,
          "reviews":{"doc_count":2,
            "average_rating":{"value":4.5}}},
        {"key":"Product B",
          "doc_count":1,
          "reviews":{"doc_count":5,
            "average_rating":{"value":3.0}}},
        {"key":"Product C",
          "doc_count":1,
          "reviews":{"doc_count":2,
            "average_rating":{"value":1.5}}}]}}}