Elasticsearch使用：Scripting API（二）

HLee

修改于 2021-02-04 16:34:10

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修改于 2021-02-04 16:34:10

文章被收录于专栏：房东的猫

简介

我们之前看见了在 Elasticsearch 里的 ingest node 里，我们可以通过以下 processor 的处理帮我们处理我们的一些数据。它们的功能是非常具体而明确的。那么在 Elasticsearch 里，有没有一种更加灵活的方式可供我们来进行编程处理呢？如果有，它使用的语言是什么呢？

在 Elasticsearch 中，它使用了一个叫做 Painless 的语言。它是专门为 Elasticsearch 而建立的。Painless 是一种简单，安全的脚本语言，专为与 Elasticsearch 一起使用而设计。它是 Elasticsearch 的默认脚本语言，可以安全地用于 inline 和 stored 脚本。它具有像 Groovy 那样的语法。自 Elasticsearch 6.0 以后的版本不再支持 Groovy，Javascript 及 Python 语言。

使用脚本，你可以在 Elasticsearch 中评估自定义表达式。例如，您可以使用脚本来返回 “script fields” 作为搜索请求的一部分，或者评估查询的自定义分数。

脚本

脚本的语法为:

"script": {
    "lang":   "...",  
    "source" | "id": "...",  
    "params": { ... }  
  }

这里 lang 默认的值为 "painless"。在实际的使用中可以不设置，除非有第二种语言供使用
source 可以为 inline 脚本，或者是一个 id，那么这个 id 对应于一个 stored 脚本
任何有名字的参数，可以被用于脚本的输入参数

Scripting应用

1.inline 脚本

首先我们来创建一个简单的文档：

PUT twitter/_doc/1
{
  "user": "双榆树-张三",
  "message": "今儿天气不错啊，出去转转去",
  "uid": 2,
  "age": 20,
  "city": "北京",
  "province": "北京",
  "country": "中国",
  "address": "中国北京市海淀区",
  "location": {
    "lat": "39.970718",
    "lon": "116.325747"
  }
}

在这个文档里，我们现在想把 age 修改为 30，那么一种办法就是把所有的文档内容都读出来，让修改其中的 age 想为30，再重新用同样的方法写进去。首先这里需要有几个动作：先读出数据，然后修改，再次写入数据。显然这样比较麻烦。在这里我们可以直接使用 Painless 语言直接进行修改：

POST twitter/_update/1
{
  "script": {
    "source": "ctx._source.age = 30"
  }
}

这里的 source 表明是我们的 Painless 代码。这里我们只写了很少的代码在 DSL 之中。这种代码称之为 inline。在这里我们直接通过 ctx._source.age 来访问 _souce 里的 age。这样我们通过编程的办法直接对年龄进行了修改。运行的结果是：

{
    "_index":"twitter",
    "_type":"_doc",
    "_id":"1",
    "_version":16,
    "_seq_no":20,
    "_primary_term":1,
    "found":true,
    "_source":{
        "user":"双榆树-张三",
        "message":"今儿天气不错啊，出去转转去",
        "uid":2,
        "age":30,
        "city":"北京",
        "province":"北京",
        "country":"中国",
        "address":"中国北京市海淀区",
        "location":{
            "lat":"39.970718",
            "lon":"116.325747"
        }
    }
}

显然这个 age 已经改变为 30。上面的方法固然好，但是每次执行 scripts 都是需要重新进行编译的。编译好的 script 可以缓存并供以后使用。上面的 script 如果是改变年龄的话，需要重新进行编译。一种更好的方法是改为这样的：

POST twitter/_update/1
{
  "script": {
    "source": "ctx._source.age = params.value",
    "params": {
      "value": 34
    }
  }
}

这样，我们的 script 的 source 是不用改变的，只需要编译一次。下次调用的时候，只需要修改 params 里的参数即可。

在 Elasticsearch 里，以下两个被视为两个不同的脚本，需要分别进行编译，所以最好的办法是使用 params 来传入参数。

"script": {  "source": "ctx._source.num_of_views += 2"}

"script": {  "source": "ctx._source.num_of_views += 3"}

除了上面的 update 之外，我们也可以使用 script query 来对我们的文档来继续搜索：

GET twitter/_search
{
  "query": {
    "script": {
      "script": {
        "source": "doc['city'].contains(params.name)",
        "lang": "painless",
        "params": {
          "name": "北京"
        }
      }
    }
  }
}

在上面的脚本中，查询在 city 字段中含有 “北京” 的所有文档。

2.存储的脚本 (stored script)

在这种情况下，scripts 可以被存放于一个集群的状态中。它之后可以通过 ID 进行调用：

PUT _scripts/add_age
{
  "script": {
    "lang": "painless",
    "source": "ctx._source.age += params.value"
  }
}

在这里，我们定义了一个叫做 add_age 的 script。它的作用就是帮我们把 source 里的 age 加上一个数值。我们可以在之后调用它：

POST twitter/_update/1
{
  "script": {
    "id": "add_age",
    "params": {
      "value": 2
    }
  }
}

通过上面的执行，我们可以看到，age 将会被加上 2。

3.访问source里的字段

Painless 中用于访问字段值的语法取决于上下文。在 Elasticsearch 中，有许多不同的 Plainless上下文。就像那个链接显示的那样，Plainless 上下文包括：ingest processor, update, update by query, sort，filter等等。

Context	访问字段
Ingest node: 访问字段使用ctx	ctx.field_name
Updates: 使用_source 字段	ctx._source.field_name

这里的 updates 包括 _update，_reindex 以及 update_by_query。这里，我们对于 context（上下文的理解）非常重要。它的意思是针对不同的 API，在使用中 ctx 所包含的字段是不一样的。在下面的例子中，我们针对一些情况来做具体的分析。

首先我们创建一个叫做 add_field_c 的 pipeline。

例子1

PUT _ingest/pipeline/add_field_c
{
  "processors": [
    {
      "script": {
        "lang": "painless",
        "source": "ctx.field_c = (ctx.field_a + ctx.field_b) * params.value",
        "params": {
          "value": 2
        }
      }
    }
  ]
}

这个 pipepline 的作用是创建一个新的field：field_c。它的结果是 field_a 及 field_b 的和，并乘以 2。那么我们创建一个如下的文档：

PUT test_script/_doc/1?pipeline=add_field_c
{
  "field_a": 10,
  "field_b": 20
}

在这里，我们使用了pipleline add_field_c。执行后的结果是：

POST test_script/_search
{
  "query": {
    "match_all": {}
  }
}

结果：
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "test_script",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "field_c" : 60,
          "field_a" : 10,
          "field_b" : 20
        }
      }
    ]
  }
}

显然，我们可以看到 field_c 被成功创建了。

例子2

在 ingest 过程中，可以使用脚本处理器来处理 metadata，如 _index 和 _type。下面是一个Ingest Pipeline 的示例，无论原始索引请求中提供了什么，它都会将索引和类型重命名为 my_index：

PUT _ingest/pipeline/my_index
{
  "description": "use index:my_index and type:_doc",
  "processors": [
    {
      "script": {
        "source": "ctx._index = 'my_index'; ctx._type = '_doc';"
      }
    }
  ]
}

使用上面的 pipeline，我们可以尝试 index 一个文档到 any_index：

PUT any_index/_doc/1?pipeline=my_index
{
  "message": "text"
}

结果：
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "message" : "text"
        }
      }
    ]
  }
}

也就是说真正的文档时存到 my_index 之中，而不是 any_index。

例子3

PUT _ingest/pipeline/blogs_pipeline
{
  "processors": [
    {
      "script": {
        "source": """ if (ctx.category == "") { ctx.category = "None"} """
      }
    }
  ]
}

我们上面定义了一个 pipeline，它可以帮我们检查如果 category 字段是否为空，如果是，就修改为 “None”。还是以之前的那个 test_script 索引为例：

PUT test_script/_doc/2?pipeline=blogs_pipeline
{
  "field_a": 5,
  "field_b": 10,
  "category": ""
} 

GET test_script/_doc/2

结果：
{
  "_index" : "test_script",
  "_type" : "_doc",
  "_id" : "2",
  "_version" : 1,
  "_seq_no" : 1,
  "_primary_term" : 1,
  "found" : true,
  "_source" : {
    "field_a" : 5,
    "field_b" : 10,
    "category" : "None"
  }
}

显然，它把 category 为 “” 的字段变为 “None” 了。

例子4

POST _reindex
{
  "source": {
    "index": "blogs"
  },
  "dest": {
    "index": "blogs_fixed"
  },
  "script": {
    "source": """ if (ctx._source.category == "") { ctx._source.category = "None" }"""
  }
}

上面的这个例子在 reindex 时，如果 category 为空时，写入“None”。我们可以从上面的两个例子中看出来，针对 pipeline，我们可以直接对 cxt.field 进行操作，而针对 update 来说，我们可以对 cxt._source 下的字段进行操作。这也是之前提到的上下文的区别。

例子5

PUT test/_doc/1
{
  "counter": 1,
  "tags": [
    "red"
  ]
}

您可以使用和 update 脚本将 tag 添加到 tags 列表（这只是一个列表，因此即使存在标记也会添加）：

POST test/_update/1
{
  "script": {
    "source": "ctx._source.tags.add(params.tag)",
    "lang": "painless",
    "params": {
      "tag": "blue"
    }
  }
}

GET test/_doc/1

结果：
{
  "_index" : "test",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 2,
  "_seq_no" : 1,
  "_primary_term" : 1,
  "found" : true,
  "_source" : {
    "counter" : 1,
    "tags" : [
      "red",
      "blue"
    ]
  }
}

显示 “blue”，已经被成功加入到 tags 列表之中了。

您还可以从 tags 列表中删除 tag。删除 tag 的 Painless 函数采用要删除的元素的数组索引。为避免可能的运行时错误，首先需要确保 tag 存在。如果列表包含tag的重复项，则此脚本只删除一个匹配项。

POST test/_update/1
{
  "script": {
    "source": "if (ctx._source.tags.contains(params.tag)) { ctx._source.tags.remove(ctx._source.tags.indexOf(params.tag)) }",
    "lang": "painless",
    "params": {
      "tag": "blue"
    }
  }
} 

GET test/_doc/1

结果：
{
  "_index" : "test",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 3,
  "_seq_no" : 2,
  "_primary_term" : 1,
  "found" : true,
  "_source" : {
    "counter" : 1,
    "tags" : [
      "red"
    ]
  }
}

“blue” 显然已经被删除了。

4.使用 Painless 访问 Doc 里的值

为了说明 Painless 的工作原理，让我们将一些曲棍球统计数据加载到 Elasticsearch 索引中：

PUT hockey/_bulk?refresh
{"index":{"_id":1}}
{"first":"johnny","last":"gaudreau","goals":[9,27,1],"assists":[17,46,0],"gp":[26,82,1],"born":"1993/08/13"}
{"index":{"_id":2}}
{"first":"sean","last":"monohan","goals":[7,54,26],"assists":[11,26,13],"gp":[26,82,82],"born":"1994/10/12"}
{"index":{"_id":3}}
{"first":"jiri","last":"hudler","goals":[5,34,36],"assists":[11,62,42],"gp":[24,80,79],"born":"1984/01/04"}
{"index":{"_id":4}}
{"first":"micheal","last":"frolik","goals":[4,6,15],"assists":[8,23,15],"gp":[26,82,82],"born":"1988/02/17"}
{"index":{"_id":5}}
{"first":"sam","last":"bennett","goals":[5,0,0],"assists":[8,1,0],"gp":[26,1,0],"born":"1996/06/20"}
{"index":{"_id":6}}
{"first":"dennis","last":"wideman","goals":[0,26,15],"assists":[11,30,24],"gp":[26,81,82],"born":"1983/03/20"}
{"index":{"_id":7}}
{"first":"david","last":"jones","goals":[7,19,5],"assists":[3,17,4],"gp":[26,45,34],"born":"1984/08/10"}
{"index":{"_id":8}}
{"first":"tj","last":"brodie","goals":[2,14,7],"assists":[8,42,30],"gp":[26,82,82],"born":"1990/06/07"}
{"index":{"_id":39}}
{"first":"mark","last":"giordano","goals":[6,30,15],"assists":[3,30,24],"gp":[26,60,63],"born":"1983/10/03"}
{"index":{"_id":10}}
{"first":"mikael","last":"backlund","goals":[3,15,13],"assists":[6,24,18],"gp":[26,82,82],"born":"1989/03/17"}
{"index":{"_id":11}}
{"first":"joe","last":"colborne","goals":[3,18,13],"assists":[6,20,24],"gp":[26,67,82],"born":"1990/01/30"}

文档里的值可以通过一个叫做 doc 的 Map 值来访问。例如，以下脚本计算玩家的总进球数。此示例使用类型 int 和 fo r循环。

GET hockey/_search
{
  "query": {
    "function_score": {
      "script_score": {
        "script": {
          "lang": "painless",
          "source": " int total = 0; for (int i = 0; i < doc['goals'].length; ++i) {              total += doc['goals'][i]; } return total; "
        }
      }
    }
  }
}

这里我们通过 script 来计算每个文档的 _score。通过 script 把每个运动员的 goal 都加起来，并形成最终的 _score。这里我们通过doc['goals'] 这个 Map 类型来访问我们的字段值。显示的结果为：

{
  "took" : 12,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 87.0,
    "hits" : [
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 87.0,
        "_source" : {
          "first" : "sean",
          "last" : "monohan",
          "goals" : [
            7,
            54,
            26
          ],
          "assists" : [
            11,
            26,
            13
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1994/10/12"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 75.0,
        "_source" : {
          "first" : "jiri",
          "last" : "hudler",
          "goals" : [
            5,
            34,
            36
          ],
          "assists" : [
            11,
            62,
            42
          ],
          "gp" : [
            24,
            80,
            79
          ],
          "born" : "1984/01/04"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "39",
        "_score" : 51.0,
        "_source" : {
          "first" : "mark",
          "last" : "giordano",
          "goals" : [
            6,
            30,
            15
          ],
          "assists" : [
            3,
            30,
            24
          ],
          "gp" : [
            26,
            60,
            63
          ],
          "born" : "1983/10/03"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : 41.0,
        "_source" : {
          "first" : "dennis",
          "last" : "wideman",
          "goals" : [
            0,
            26,
            15
          ],
          "assists" : [
            11,
            30,
            24
          ],
          "gp" : [
            26,
            81,
            82
          ],
          "born" : "1983/03/20"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 37.0,
        "_source" : {
          "first" : "johnny",
          "last" : "gaudreau",
          "goals" : [
            9,
            27,
            1
          ],
          "assists" : [
            17,
            46,
            0
          ],
          "gp" : [
            26,
            82,
            1
          ],
          "born" : "1993/08/13"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "7",
        "_score" : 31.0,
        "_source" : {
          "first" : "david",
          "last" : "jones",
          "goals" : [
            7,
            19,
            5
          ],
          "assists" : [
            3,
            17,
            4
          ],
          "gp" : [
            26,
            45,
            34
          ],
          "born" : "1984/08/10"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "10",
        "_score" : 31.0,
        "_source" : {
          "first" : "mikael",
          "last" : "backlund",
          "goals" : [
            3,
            15,
            13
          ],
          "assists" : [
            6,
            24,
            18
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1989/03/17"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "4",
        "_score" : 25.0,
        "_source" : {
          "first" : "micheal",
          "last" : "frolik",
          "goals" : [
            4,
            6,
            15
          ],
          "assists" : [
            8,
            23,
            15
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1988/02/17"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "8",
        "_score" : 23.0,
        "_source" : {
          "first" : "tj",
          "last" : "brodie",
          "goals" : [
            2,
            14,
            7
          ],
          "assists" : [
            8,
            42,
            30
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1990/06/07"
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : 5.0,
        "_source" : {
          "first" : "sam",
          "last" : "bennett",
          "goals" : [
            5,
            0,
            0
          ],
          "assists" : [
            8,
            1,
            0
          ],
          "gp" : [
            26,
            1,
            0
          ],
          "born" : "1996/06/20"
        }
      }
    ]
  }
}

或者，您可以使用 script_fields 而不是 function_score 执行相同的操作：

GET hockey/_search
{
  "query": {
    "match_all": {}
  },
  "script_fields": {
    "total_goals": {
      "script": {
        "lang": "painless",
        "source": " int total = 0; for (int i = 0; i < doc['goals'].length; ++i) {            total += doc['goals'][i]; } return total;        "
      }
    }
  }
}

结果：
{
  "took" : 7,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            37
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            87
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            75
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "4",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            25
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            5
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            41
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "7",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            31
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "8",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            23
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "39",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            51
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "10",
        "_score" : 1.0,
        "fields" : {
          "total_goals" : [
            31
          ]
        }
      }
    ]
  }
}

以下示例使用 Painless 脚本按其组合的名字和姓氏对玩家进行排序。使用 doc ['first'].value 和 doc ['last'].value 访问名称。

GET hockey/_search
{
  "query": {
    "match_all": {}
  },
  "sort": {
    "_script": {
      "type": "string",
      "order": "asc",
      "script": {
        "lang": "painless",
        "source": "doc['first.keyword'].value + ' ' + doc['last.keyword'].value"
      }
    }
  }
}

结果：
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "7",
        "_score" : null,
        "_source" : {
          "first" : "david",
          "last" : "jones",
          "goals" : [
            7,
            19,
            5
          ],
          "assists" : [
            3,
            17,
            4
          ],
          "gp" : [
            26,
            45,
            34
          ],
          "born" : "1984/08/10"
        },
        "sort" : [
          "david jones"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : null,
        "_source" : {
          "first" : "dennis",
          "last" : "wideman",
          "goals" : [
            0,
            26,
            15
          ],
          "assists" : [
            11,
            30,
            24
          ],
          "gp" : [
            26,
            81,
            82
          ],
          "born" : "1983/03/20"
        },
        "sort" : [
          "dennis wideman"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : null,
        "_source" : {
          "first" : "jiri",
          "last" : "hudler",
          "goals" : [
            5,
            34,
            36
          ],
          "assists" : [
            11,
            62,
            42
          ],
          "gp" : [
            24,
            80,
            79
          ],
          "born" : "1984/01/04"
        },
        "sort" : [
          "jiri hudler"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : null,
        "_source" : {
          "first" : "johnny",
          "last" : "gaudreau",
          "goals" : [
            9,
            27,
            1
          ],
          "assists" : [
            17,
            46,
            0
          ],
          "gp" : [
            26,
            82,
            1
          ],
          "born" : "1993/08/13"
        },
        "sort" : [
          "johnny gaudreau"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "39",
        "_score" : null,
        "_source" : {
          "first" : "mark",
          "last" : "giordano",
          "goals" : [
            6,
            30,
            15
          ],
          "assists" : [
            3,
            30,
            24
          ],
          "gp" : [
            26,
            60,
            63
          ],
          "born" : "1983/10/03"
        },
        "sort" : [
          "mark giordano"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "4",
        "_score" : null,
        "_source" : {
          "first" : "micheal",
          "last" : "frolik",
          "goals" : [
            4,
            6,
            15
          ],
          "assists" : [
            8,
            23,
            15
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1988/02/17"
        },
        "sort" : [
          "micheal frolik"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "10",
        "_score" : null,
        "_source" : {
          "first" : "mikael",
          "last" : "backlund",
          "goals" : [
            3,
            15,
            13
          ],
          "assists" : [
            6,
            24,
            18
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1989/03/17"
        },
        "sort" : [
          "mikael backlund"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : null,
        "_source" : {
          "first" : "sam",
          "last" : "bennett",
          "goals" : [
            5,
            0,
            0
          ],
          "assists" : [
            8,
            1,
            0
          ],
          "gp" : [
            26,
            1,
            0
          ],
          "born" : "1996/06/20"
        },
        "sort" : [
          "sam bennett"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : null,
        "_source" : {
          "first" : "sean",
          "last" : "monohan",
          "goals" : [
            7,
            54,
            26
          ],
          "assists" : [
            11,
            26,
            13
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1994/10/12"
        },
        "sort" : [
          "sean monohan"
        ]
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "8",
        "_score" : null,
        "_source" : {
          "first" : "tj",
          "last" : "brodie",
          "goals" : [
            2,
            14,
            7
          ],
          "assists" : [
            8,
            42,
            30
          ],
          "gp" : [
            26,
            82,
            82
          ],
          "born" : "1990/06/07"
        },
        "sort" : [
          "tj brodie"
        ]
      }
    ]
  }
}

5.检查缺失项

doc ['field'].value。如果文档中缺少该字段，则抛出异常。要检查文档是否缺少值，可以调用 doc ['field'] .size() == 0。

使用Painless更新字段

您还可以轻松更新字段。您可以使用 ctx._source.<field-name> 访问字段的原始源。首先，让我们通过提交以下请求来查看玩家的源数据：

GET hockey/_search
{
  "stored_fields": [
    "_id",
    "_source"
  ],
  "query": {
    "term": {
      "_id": 1
    }
  }
}

结果：
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "first" : "johnny",
          "last" : "gaudreau",
          "goals" : [
            9,
            27,
            1
          ],
          "assists" : [
            17,
            46,
            0
          ],
          "gp" : [
            26,
            82,
            1
          ],
          "born" : "1993/08/13"
        }
      }
    ]
  }
}

要将玩家1的姓氏更改为 hockey，只需将 ctx._source.last 设置为新值：

POST hockey/_update/1
{
  "script": {
    "lang": "painless",
    "source": "ctx._source.last = params.last",
    "params": {
      "last": "hockey"
    }
  }
}

您还可以向文档添加字段。例如，此脚本添加一个包含玩家 nickname 为 hockey的新字段。

POST hockey/_update/1
{
  "script": {
    "lang": "painless",
    "source": "ctx._source.last = params.last; ctx._source.nick = params.nick",
    "params": {
      "last": "gaudreau",
      "nick": "hockey"
    }
  }
}

GET hockey/_doc/1

结果：
{
  "_index" : "hockey",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 4,
  "_seq_no" : 12,
  "_primary_term" : 1,
  "found" : true,
  "_source" : {
    "first" : "johnny",
    "last" : "hockey",
    "goals" : [
      9,
      27,
      1
    ],
    "assists" : [
      17,
      46,
      0
    ],
    "gp" : [
      26,
      82,
      1
    ],
    "born" : "1993/08/13",
    "nick" : "hockey"
  }
}

有一个叫做 “nick” 的新字段被加入了。

我们甚至可以对日期类型来进行操作从而得到年月等信息：

GET hockey/_search
{
  "script_fields": {
    "birth_year": {
      "script": {
        "source": "doc.born.value.year"
      }
    }
  }
}

结果：
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1994
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1984
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "4",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1988
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1996
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1983
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "7",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1984
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "8",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1990
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "39",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1983
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "10",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1989
          ]
        }
      },
      {
        "_index" : "hockey",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "fields" : {
          "birth_year" : [
            1993
          ]
        }
      }
    ]
  }
}

6.Script Caching

Elasticsearch第一次看到一个新脚本，它会编译它并将编译后的版本存储在缓存中。无论是 inline 或是 stored 脚本都存储在缓存中。新脚本可以驱逐缓存的脚本。默认的情况下是可以存储100个脚本。我们可以通过设置 script.cache.max_size 来改变其大小，或者通过 script.cache.expire 来设置过期的时间。这些设置需要在 config/elasticsearch.yml 里设置。

7.Script 调试

不能调试的脚本是非常难的。有一个好的调试手段无疑对我们的脚本编程是非常有用的。

Debug.explain

Painless 没有 REPL，虽然有一天它很好，但它不会告诉你关于调试 Elasticsearch 中嵌入的 Painless 脚本的全部故事，因为脚本可以访问的数据或 “上下文” 是如此重要。目前，调试嵌入式脚本的最佳方法是在选择位置抛出异常。虽然你可以抛出自己的异常（throw new exception('whatever'），但 Painless 的沙箱会阻止你访问有用的信息，如对象的类型。所以 Painless 有一个实用工具方法 Debug.explain，它会为你抛出异常。例如，你可以使用 _explain 来探索 script query 可用的上下文。

PUT /hockey/_doc/1?refresh
{
  "first": "johnny",
  "last": "gaudreau",
  "goals": [
    9,
    27,
    1
  ],
  "assists": [
    17,
    46,
    0
  ],
  "gp": [
    26,
    82,
    1
  ]
}

POST /hockey/_explain/1
{
  "query": {
    "script": {
      "script": "Debug.explain(doc.goals)"
    }
  }
}

这表明doc.goals类是通过 org.elasticsearch.index.fielddata.ScriptDocValues.Long 来响应的：

{
  "error" : {
    "root_cause" : [
      {
        "type" : "script_exception",
        "reason" : "runtime error",
        "painless_class" : "org.elasticsearch.index.fielddata.ScriptDocValues.Longs",
        "to_string" : "[1, 9, 27]",
        "java_class" : "org.elasticsearch.index.fielddata.ScriptDocValues$Longs",
        "script_stack" : [
          "Debug.explain(doc.goals)",
          "                 ^---- HERE"
        ],
        "script" : "Debug.explain(doc.goals)",
        "lang" : "painless",
        "position" : {
          "offset" : 17,
          "start" : 0,
          "end" : 24
        }
      }
    ],
    "type" : "script_exception",
    "reason" : "runtime error",
    "painless_class" : "org.elasticsearch.index.fielddata.ScriptDocValues.Longs",
    "to_string" : "[1, 9, 27]",
    "java_class" : "org.elasticsearch.index.fielddata.ScriptDocValues$Longs",
    "script_stack" : [
      "Debug.explain(doc.goals)",
      "                 ^---- HERE"
    ],
    "script" : "Debug.explain(doc.goals)",
    "lang" : "painless",
    "position" : {
      "offset" : 17,
      "start" : 0,
      "end" : 24
    },
    "caused_by" : {
      "type" : "painless_explain_error",
      "reason" : null
    }
  },
  "status" : 400
}

您可以使用相同的技巧来查看 _source 是 _update API 中的 LinkedHashMap：

POST /hockey/_update/1
{
  "script": "Debug.explain(ctx._source)"
}

{
  "error" : {
    "root_cause" : [
      {
        "type" : "illegal_argument_exception",
        "reason" : "failed to execute script"
      }
    ],
    "type" : "illegal_argument_exception",
    "reason" : "failed to execute script",
    "caused_by" : {
      "type" : "script_exception",
      "reason" : "runtime error",
      "painless_class" : "java.util.LinkedHashMap",
      "to_string" : "{first=johnny, last=gaudreau, goals=[9, 27, 1], assists=[17, 46, 0], gp=[26, 82, 1]}",
      "java_class" : "java.util.LinkedHashMap",
      "script_stack" : [
        "Debug.explain(ctx._source)",
        "                 ^---- HERE"
      ],
      "script" : "Debug.explain(ctx._source)",
      "lang" : "painless",
      "position" : {
        "offset" : 17,
        "start" : 0,
        "end" : 26
      },
      "caused_by" : {
        "type" : "painless_explain_error",
        "reason" : null
      }
    }
  },
  "status" : 400
}

参考：

【1】https://www.elastic.co/guide/en/elasticsearch/painless/current/painless-walkthrough.html

【2】https://www.elastic.co/guide/en/elasticsearch/painless/current/painless-debugging.html

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如有侵权，请联系 cloudcommunity@tencent.com 删除。

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