19个很有用的 ElasticSearch 查询语句篇二

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发布于 2018-09-30 10:33:47

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发布于 2018-09-30 10:33:47

13. 词条（Term）查询 - 排序（Sorted）

词条查询 的结果（和其他查询结果一样）可以被轻易排序，多级排序也被允许：

POST /bookdb_index/book/_search
{
    "query": {
        "term" : {
            "publisher": "manning"
        }
    },
    "_source" : ["title","publish_date","publisher"],
    "sort": [
        { "publish_date": {"order":"desc"}},
        { "title": { "order": "desc" }}
    ]
}

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "3",
        "_score": null,
        "_source": {
          "publisher": "manning",
          "title": "Elasticsearch in Action",
          "publish_date": "2015-12-03"
        },
        "sort": [
          1449100800000,
          "in"
        ]
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "4",
        "_score": null,
        "_source": {
          "publisher": "manning",
          "title": "Solr in Action",
          "publish_date": "2014-04-05"
        },
        "sort": [
          1396656000000,
          "solr"
        ]
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "2",
        "_score": null,
        "_source": {
          "publisher": "manning",
          "title": "Taming Text: How to Find, Organize, and Manipulate It",
          "publish_date": "2013-01-24"
        },
        "sort": [
          1358985600000,
          "to"
        ]
      }
    ]

14. 范围查询

另一个结构化查询的例子是 范围查询。在这个例子中，我们要查找 2015 年出版的书。

POST /bookdb_index/book/_search
{
    "query": {
        "range" : {
            "publish_date": {
                "gte": "2015-01-01",
                "lte": "2015-12-31"
            }
        }
    },
    "_source" : ["title","publish_date","publisher"]
}

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 1,
        "_source": {
          "publisher": "oreilly",
          "title": "Elasticsearch: The Definitive Guide",
          "publish_date": "2015-02-07"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "3",
        "_score": 1,
        "_source": {
          "publisher": "manning",
          "title": "Elasticsearch in Action",
          "publish_date": "2015-12-03"
        }
      }
    ]

注：范围查询 用于日期、数字和字符串类型的字段。

15. 过滤(Filtered)查询

过滤查询允许你可以过滤查询结果。对于我们的例子中，要在标题或摘要中检索一些书，查询项为 Elasticsearch，但我们又想筛出那些仅有 20 个以上评论的。

POST /bookdb_index/book/_search
{
    "query": {
        "filtered": {
            "query" : {
                "multi_match": {
                    "query": "elasticsearch",
                    "fields": ["title","summary"]
                }
            },
            "filter": {
                "range" : {
                    "num_reviews": {
                        "gte": 20
                    }
                }
            }
        }
    },
    "_source" : ["title","summary","publisher", "num_reviews"]
}

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.5955761,
        "_source": {
          "summary": "A distibuted real-time search and analytics engine",
          "publisher": "oreilly",
          "num_reviews": 20,
          "title": "Elasticsearch: The Definitive Guide"
        }
      }
    ]

注：过滤查询 并不强制它作用于其上的查询必须存在。如果未指定查询，match_all 基本上会返回索引内的全部文档。实际上，过滤只在第一次运行，以减少所需的查询面积，并且，在第一次使用后过滤会被缓存，大大提高了性能。

更新：过滤查询 将在 ElasticSearch 5 中移除，使用 布尔查询 替代。下面有个例子使用 布尔查询 重写上面的例子：

POST /bookdb_index/book/_search
{
    "query": {
        "bool": {
            "must" : {
                "multi_match": {
                    "query": "elasticsearch",
                    "fields": ["title","summary"]
                }
            },
            "filter": {
                "range" : {
                    "num_reviews": {
                        "gte": 20
                    }
                }
            }
        }
    },
    "_source" : ["title","summary","publisher", "num_reviews"]
}

在后续的例子中，我们将会把它使用在 多重过滤 中。

16. 多重过滤（Multiple Filters）

多重过滤 可以结合 布尔查询 使用，下一个例子中，过滤查询决定只返回那些包含至少20条评论，且必须在 2015 年前出版，且由 O’Reilly 出版的结果。

POST /bookdb_index/book/_search
{
    "query": {
        "filtered": {
            "query" : {
                "multi_match": {
                    "query": "elasticsearch",
                    "fields": ["title","summary"]
                }
            },
            "filter": {
                "bool": {
                    "must": {
                        "range" : { "num_reviews": { "gte": 20 } }
                    },
                    "must_not": {
                        "range" : { "publish_date": { "lte": "2014-12-31" } }
                    },
                    "should": {
                        "term": { "publisher": "oreilly" }
                    }
                }
            }
        }
    },
    "_source" : ["title","summary","publisher", "num_reviews", "publish_date"]
}

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.5955761,
        "_source": {
          "summary": "A distibuted real-time search and analytics engine",
          "publisher": "oreilly",
          "num_reviews": 20,
          "title": "Elasticsearch: The Definitive Guide",
          "publish_date": "2015-02-07"
        }
      }
    ]

17. 作用分值: 域值（Field Value）因子

也许在某种情况下，你想把文档中的某个特定域作为计算相关性分值的一个因素，比较典型的场景是你想根据普及程度来提高一个文档的相关性。在我们的示例中，我们想把最受欢迎的书（基于评论数判断）的权重进行提高，可使用 field_value_factor 用以影响分值。

POST /bookdb_index/book/_search
{
    "query": {
        "function_score": {
            "query": {
                "multi_match" : {
                    "query" : "search engine",
                    "fields": ["title", "summary"]
                }
            },
            "field_value_factor": {
                "field" : "num_reviews",
                "modifier": "log1p",
                "factor" : 2
            }
        }
    },
    "_source": ["title", "summary", "publish_date", "num_reviews"]
}

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.44831306,
        "_source": {
          "summary": "A distibuted real-time search and analytics engine",
          "num_reviews": 20,
          "title": "Elasticsearch: The Definitive Guide",
          "publish_date": "2015-02-07"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "4",
        "_score": 0.3718407,
        "_source": {
          "summary": "Comprehensive guide to implementing a scalable search engine using Apache Solr",
          "num_reviews": 23,
          "title": "Solr in Action",
          "publish_date": "2014-04-05"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "3",
        "_score": 0.046479136,
        "_source": {
          "summary": "build scalable search applications using Elasticsearch without having to do complex low-level programming or understand advanced data science algorithms",
          "num_reviews": 18,
          "title": "Elasticsearch in Action",
          "publish_date": "2015-12-03"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "2",
        "_score": 0.041432835,
        "_source": {
          "summary": "organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization",
          "num_reviews": 12,
          "title": "Taming Text: How to Find, Organize, and Manipulate It",
          "publish_date": "2013-01-24"
        }
      }
    ]

注1: 我们可能刚运行了一个常规的 multi_match (多匹配)查询，并对 num_reviews 域进行了排序，这让我们失去了评估相关性分值的好处。

注2: 有大量的附加参数可用来调整提升原始相关性分值效果的程度，比如 modifier, factor, boost_mode 等等，至于细节可在 Elasticsearch 指南中探索。

18. 作用分值: 衰变（Decay）函数

假设不想使用域值做递增提升，而你有一个理想目标值，并希望用这个加权因子来对这个离你较远的目标值进行衰减。有个典型的用途是基于经纬度、价格或日期等数值域的提升。在如下的例子中，我们查找在2014年6月左右出版的，查询项是 search engines 的书。

POST /bookdb_index/book/_search
{
    "query": {
        "function_score": {
            "query": {
                "multi_match" : {
                    "query" : "search engine",
                    "fields": ["title", "summary"]
                }
            },
            "functions": [
                {
                    "exp": {
                        "publish_date" : {
                            "origin": "2014-06-15",
                            "offset": "7d",
                            "scale" : "30d"
                        }
                    }
                }
            ],
            "boost_mode" : "replace"
        }
    },
    "_source": ["title", "summary", "publish_date", "num_reviews"]
}

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "4",
        "_score": 0.27420625,
        "_source": {
          "summary": "Comprehensive guide to implementing a scalable search engine using Apache Solr",
          "num_reviews": 23,
          "title": "Solr in Action",
          "publish_date": "2014-04-05"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.005920768,
        "_source": {
          "summary": "A distibuted real-time search and analytics engine",
          "num_reviews": 20,
          "title": "Elasticsearch: The Definitive Guide",
          "publish_date": "2015-02-07"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "2",
        "_score": 0.000011564,
        "_source": {
          "summary": "organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization",
          "num_reviews": 12,
          "title": "Taming Text: How to Find, Organize, and Manipulate It",
          "publish_date": "2013-01-24"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "3",
        "_score": 0.0000059171475,
        "_source": {
          "summary": "build scalable search applications using Elasticsearch without having to do complex low-level programming or understand advanced data science algorithms",
          "num_reviews": 18,
          "title": "Elasticsearch in Action",
          "publish_date": "2015-12-03"
        }
      }
    ]

19. 函数分值: 脚本评分

当内置的评分函数无法满足你的需求时，还可以用 Groovy 脚本。在我们的例子中，想要指定一个脚本，能在决定把 num_reviews 的因子计算多少之前，先将 publish_date 考虑在内。因为很新的书也许不会有评论，分值不应该被惩罚。

评分脚本如下：

publish_date = doc['publish_date'].value
num_reviews = doc['num_reviews'].value

if (publish_date > Date.parse('yyyy-MM-dd', threshold).getTime()) {
  my_score = Math.log(2.5 + num_reviews)
} else {
  my_score = Math.log(1 + num_reviews)
}
return my_score

在 script_score 参数内动态调用评分脚本：

POST /bookdb_index/book/_search
{
    "query": {
        "function_score": {
            "query": {
                "multi_match" : {
                    "query" : "search engine",
                    "fields": ["title", "summary"]
                }
            },
            "functions": [
                {
                    "script_score": {
                        "params" : {
                            "threshold": "2015-07-30"
                        },
                        "script": "publish_date = doc['publish_date'].value; num_reviews = doc['num_reviews'].value; if (publish_date > Date.parse('yyyy-MM-dd', threshold).getTime()) { return log(2.5 + num_reviews) }; return log(1 + num_reviews);"
                    }
                }
            ]
        }
    },
    "_source": ["title", "summary", "publish_date", "num_reviews"]
}

[Results]
"hits": {
    "total": 4,
    "max_score": 0.8463001,
    "hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.8463001,
        "_source": {
          "summary": "A distibuted real-time search and analytics engine",
          "num_reviews": 20,
          "title": "Elasticsearch: The Definitive Guide",
          "publish_date": "2015-02-07"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "4",
        "_score": 0.7067348,
        "_source": {
          "summary": "Comprehensive guide to implementing a scalable search engine using Apache Solr",
          "num_reviews": 23,
          "title": "Solr in Action",
          "publish_date": "2014-04-05"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "3",
        "_score": 0.08952084,
        "_source": {
          "summary": "build scalable search applications using Elasticsearch without having to do complex low-level programming or understand advanced data science algorithms",
          "num_reviews": 18,
          "title": "Elasticsearch in Action",
          "publish_date": "2015-12-03"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "2",
        "_score": 0.07602123,
        "_source": {
          "summary": "organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization",
          "num_reviews": 12,
          "title": "Taming Text: How to Find, Organize, and Manipulate It",
          "publish_date": "2013-01-24"
        }
      }
    ]
  }

注1: 要在 Elasticsearch 实例中使用动态脚本，必须在 config/elasticsearch.yaml 文件中启用它；也可以使用存储在 Elasticsearch服务器上的脚本。建议看看 Elasticsearch 指南文档获取更多信息。

注2: 因 JSON 不能包含嵌入式换行符，请使用分号来分割语句。

引用自：23 USEFUL ELASTICSEARCH EXAMPLE QUERIES https://distributedbytes.timojo.com/2016/07/23-useful-elasticsearch-example-queries.html

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