full text search

definition

https://www.techopedia.com/definition/17113/full-text-search

A full-text search is a comprehensive search method that compares every word of the search request against every word within the document or database. Web search engines and document editing software make extensive use of the full-text search technique in functions for searching a text database stored on the Web or on the local drive of a computer; it lets the user find a word or phrase anywhere within the database or document.

Full-text search is the most common technique used in Web search engines and Web pages. Each page is searched and indexed, and if any matches are found, they are displayed via the indexes. Parts of original text are displayed against the user’s query and then the full text. Full-text search reduces the hassle of searching for a word in huge amounts of metadata, such as the World Wide Web and commercial-scale databases. Full-text search became popular in late 1990s, when the Internet began to became a part of everyday life.

特征

非结构化

针对的搜索对象是非结构化， 或者半结构化。 不同于SQL结构中， 按照表头查询的情况。 如果将非结构化数据库赋予全文搜索的能力， 例如 ES和MongoDB， 则叫全文搜索数据库。

https://stackoverflow.com/questions/tagged/full-text-search

Full text search involves searching documents, usually involving unstructured text, as opposed to searching text fields in a structured database.

例如有个js的全文搜索实现， 其可以添加的文档为一个对象， 具有嵌套多层的文档结构。

https://github.com/frankred/node-full-text-search-light

You can also add objects or arrays to the search. Every child value will be added to the search, no matter if it's an array or object.

// Add objects
var obj = {
    name: 'Alexandra',
    age: 27,
    student: true,
    hobbies: ['Tennis', 'Football', 'Party'];
    car: {
        make: 'Volvo',
        year: 2012,
        topspeed: 280
    }
};

search.add(obj);

输入

搜索的对象是 一个或者多个 字或者词组 & 不指定具体的field名称。

返回的是 符合搜索条件的文档集合。

https://www.baeldung.com/elasticsearch-full-text-search-rest-api

Full-text search queries and performs linguistic searches against documents. It includes single or multiple words or phrases and returns documents that match search condition.

ElasticSearch is a search engine based on Apache Lucene, a free and open-source information retrieval software library. It provides a distributed, full-text search engine with an HTTP web interface and schema-free JSON documents.

Compare Full-Text Search queries to the LIKE predicate

https://docs.microsoft.com/en-us/sql/relational-databases/search/full-text-search?view=sql-server-ver15

In contrast to full-text search, the LIKE Transact-SQL predicate works on character patterns only. Also, you cannot use the LIKE predicate to query formatted binary data. Furthermore, a LIKE query against a large amount of unstructured text data is much slower than an equivalent full-text query against the same data. A LIKE query against millions of rows of text data can take minutes to return; whereas a full-text query can take only seconds or less against the same data, depending on the number of rows that are returned.

A full-text index includes one or more character-based columns in a table. These columns can have any of the following data types: char, varchar, nchar, nvarchar, text, ntext, image, xml, or varbinary(max) and FILESTREAM. Each full-text index indexes one or more columns from the table, and each column can use a specific language.

Full-text queries perform linguistic searches against text data in full-text indexes by operating on words and phrases based on the rules of a particular language such as English or Japanese. Full-text queries can include simple words and phrases or multiple forms of a word or phrase. A full-text query returns any documents that contain at least one match (also known as a hit). A match occurs when a target document contains all the terms specified in the full-text query, and meets any other search conditions, such as the distance between the matching terms.

Django and full-text search

https://www.cnblogs.com/lexus/archive/2012/06/08/2541277.html

Django and full-text search

13th February 2009, 11:18 am

Lately I’ve been searching for a simple solution for full-text Model search using Django. Every task up to this point just seemed so easy, so I was a bit surprised to discover there’s no quick, clean and preferred way to go about adding site search functionality in the framework.

So far, the information I read seems to suggest existing solutions are:

• Based on a dedicated full-text search module
  • djangosearch
    • Supposed to become the official search contrib. Rather recent history (during 2008).
    • It’s an framework over existing, dedicated full text indexing engines:
      • Lucene (Java version)
      • Solr (still Java, and also based on Lucene)
      • Xapian (C++)
      • HyperEstraier
  • django-sphinx
    • Wrapper around Sphinx full-text search engine
• Based on a database engine full-text capability (ie. you must create full text indexes with appropriate DB commands)
  • For the MySQL backend, there’s already a “fieldname__search” syntax already supported in the framework, translating into a MATCH AGAINST query in SQL.
    • Supports basic boolean operators
    • Reference (look at the conclusion of the article)
  • For PostgreSQL, depending on the version of the engine, there are solutions, but they seem complex, relative to the MySQL approach
• Most simple, but very inefficient: based on a simple LIKE %keyword% query
  • Uses the “fieldname__icontains” filter syntax
  • That’s what I used temporarily for get the feature going in my prototype

Other approaches are mentioned in this thread on StackOverflow.

ES例子

https://cloud.tencent.com/developer/article/1350622

1. 基本的匹配（Query）查询

有两种方式来执行一个全文匹配查询：

• 使用 Search Lite API，它从 url 中读取所有的查询参数
• 使用完整 JSON 作为请求体，这样你可以使用完整的 Elasticsearch DSL

下面是一个基本的匹配查询，查询任一字段包含 Guide 的记录

GET /bookdb_index/book/_search?q=guide

[Results]
"hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.28168046,
        "_source": {
          "title": "Elasticsearch: The Definitive Guide",
          "authors": ["clinton gormley", "zachary tong"],
          "summary": "A distibuted real-time search and analytics engine",
          "publish_date": "2015-02-07",
          "num_reviews": 20,
          "publisher": "manning"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "4",
        "_score": 0.24144039,
        "_source": {
          "title": "Solr in Action",
          "authors": ["trey grainger", "timothy potter"],
          "summary": "Comprehensive guide to implementing a scalable search engine using Apache Solr",
          "publish_date": "2014-04-05",
          "num_reviews": 23,
          "publisher": "manning"
        }
      }
    ]

下面是完整 Body 版本的查询，生成相同的内容：

{
    "query": {
        "multi_match" : {
            "query" : "guide",
            "fields" : ["_all"]
        }
    }
}

multi_match 是 match 的作为在多个字段运行相同操作的一个速记法。

2. 多字段（Multi-filed）查询

正如我们已经看到来的，为了根据多个字段检索（e.g. 在 title 和 summary 字段都是相同的查询字符串的结果），你可以使用 multi_match 语句

POST /bookdb_index/book/_search
{
    "query": {
        "multi_match" : {
            "query" : "elasticsearch guide",
            "fields": ["title", "summary"]
        }
    }
}

[Results]
"hits": {
    "total": 3,
    "max_score": 0.9448582,
    "hits": [
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "1",
        "_score": 0.9448582,
        "_source": {
          "title": "Elasticsearch: The Definitive Guide",
          "authors": [
            "clinton gormley",
            "zachary tong"
          ],
          "summary": "A distibuted real-time search and analytics engine",
          "publish_date": "2015-02-07",
          "num_reviews": 20,
          "publisher": "manning"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "3",
        "_score": 0.17312013,
        "_source": {
          "title": "Elasticsearch in Action",
          "authors": [
            "radu gheorge",
            "matthew lee hinman",
            "roy russo"
          ],
          "summary": "build scalable search applications using Elasticsearch without having to do complex low-level programming or understand advanced data science algorithms",
          "publish_date": "2015-12-03",
          "num_reviews": 18,
          "publisher": "manning"
        }
      },
      {
        "_index": "bookdb_index",
        "_type": "book",
        "_id": "4",
        "_score": 0.14965448,
        "_source": {
          "title": "Solr in Action",
          "authors": [
            "trey grainger",
            "timothy potter"
          ],
          "summary": "Comprehensive guide to implementing a scalable search engine using Apache Solr",
          "publish_date": "2014-04-05",
          "num_reviews": 23,
          "publisher": "manning"
        }
      }
    ]
  }

注：第三条被匹配，因为 guide 在 summary 字段中被找到。