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python - elasticsearch mapping 字段 [name] 上的属性 [fields] 的预期映射，但有一个类 java.lang.String

问题描述

这是我的数据，它基于模式，我需要生成要在 ES 上索引的映射。我在 ES 方面的背景并不多，但我以为我得到了它，直到我尝试它并失败并且无法在网上找到正确的答案..

{
  "@context": {
    "schema": "http://schema.org/",
    "outbreak": "https://discovery.biothings.io/view/outbreak/"
  },
  "@type": "outbreak:Publication",
  "keywords": [
    "COVID-19",
    "City lockdown",
    "Epidemic",
    "Governmental action",
    "Individual reaction",
    "Mathematical modelling"
  ],
  "author": [
    {
      "@type": "outbreak:Person",
      "affiliation": [
        {
          "@type": "outbreak:Organization",
          "name": "Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China. Electronic address: daihai.he@polyu.edu.hk."
        }
      ],
      "familyName": "He",
      "givenName": "Daihai",
      "name": "Daihai He"
    }
  ],
  "publicationType": [
    "Journal Article"
  ],
  "_id": "pmid32145465",
  "curatedBy": {
    "@type": "schema:WebSite",
    "name": "litcovid",
    "url": "https://www.ncbi.nlm.nih.gov/research/coronavirus/publication/32145465"
  },
  "name": "A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action.",
  "identifier": "32145465",
  "pmid": "32145465",
  "abstract": "The ongoing coronavirus disease 2019 (COVID-19) outbreak, emerged in Wuhan, China in the end of 2019, has claimed more than 2600 lives as of 24 February 2020 and posed a huge threat to global public health. The Chinese government has implemented control measures including setting up special hospitals and travel restriction to mitigate the spread. We propose conceptual models for the COVID-19 outbreak in Wuhan with the consideration of individual behavioural reaction and governmental actions, e.g., holiday extension, travel restriction, hospitalisation and quarantine. We employe the estimates of these two key components from the 1918 influenza pandemic in London, United Kingdom, incorporated zoonotic introductions and the emigration, and then compute future trends and the reporting ratio. The model is concise in structure, and it successfully captures the course of the COVID-19 outbreak, and thus sheds light on understanding the trends of the outbreak.",
  "license": "Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.",
  "journalName": "International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases",
  "journalAbbreviation": "Int. J. Infect. Dis.",
  "issueNumber": "1878-3511",
  "doi": "S1201-9712(20)30117-X",
  "url": "https://www.doi.org/S1201-9712(20)30117-X",
  "datePublished": "2020-03-04",
  "dateModified": "2020-02-26"
}

到目前为止，这是我的映射：

{
                'fields':{
                    'type': 'string'
                },
                'abstract': {
                    'type': 'text'
                },
                'pmid': {
                    'type': 'integer'
                },
                'author': {
                    'type': 'nested',
                    'properties': {
                        'name':{
                            'type': 'text'
                        },
                        'givenName':{
                            'type': 'text'
                        },
                        'familyName':{
                            'type': 'text'
                        },
                        'affiliation':{
                            'type': 'nested',
                            'properties': {
                                'name':{
                                    'type': 'text'
                                }
                            }
                        }
                    }
                },
                'isBasedOn': {
                    'type': 'text'
                },
                'funding': {
                    'type': 'nested',
                    'properties': {
                        'funder':{
                            'type': 'nested',
                            'properties':{
                                'name': 'text'
                            }
                        },
                        'identifier':{
                            'type': 'text'
                        }
                    }
                },
                'license': {
                    'type': 'text'
                },
                'keywords': {
                    'normalizer': 'keyword_lowercase_normalizer',
                    'type': 'keyword',
                    'copy_to': ['all']
                },
                'publicationType': {
                    'normalizer': 'keyword_lowercase_normalizer',
                    'type': 'keyword',
                    'copy_to': ['all']
                },
                'name': {
                    'type': 'text'
                },
                'journalName': {
                    'type': 'text'
                },
                'identifier': {
                    'type': 'text'
                },
                'doi': {
                    'type': 'text'
                },
                'datePublished': {
                    'type': 'date'
                },
                'dateModified': {
                    'type': 'date'
                },
                'issueNumber': {
                    'type': 'text'
                }
         }

我的数据中没有字段“字段”，所以我不确定这意味着什么，“名称”是一个简单的字符串

我已经尝试过了，还包括 "mappings" :{"properties":{...}} 但这也失败了。任何指针？

标签： pythonelasticsearchmapping

解决方案

尝试使用文本或关键字而不是string字段fields

string 在 Elasticsearch 中不是有效的数据类型。您可以使用关键字或文字

  • 文本数据类型

    用于索引全文值的字段，例如电子邮件正文或产品描述。对这些字段进行分析，也就是说，它们在被索引之前通过分析器将字符串转换为单个术语的列表。分析过程允许 Elasticsearch 在每个全文字段中搜索单个单词。文本字段不用于排序，也很少用于聚合（尽管重要的文本聚合是一个明显的例外）。

  • 关键字数据类型

    用于索引结构化内容的字段，例如 ID、电子邮件地址、主机名、状态代码、邮政编码或标签。

来源：https ://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html

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