渗透器在弹性搜索中的含义/作用是什么?
尽管我阅读了 Elasticsearch 的文档以了解什么是过滤器.我仍然很难理解它的含义以及它的简单用途.谁能提供更多细节?
Even though I read the documentation for Elasticsearch to understand what a percolator is. I still have difficulty understanding what it means and where it is used in simple terms. Can anyone provide me with more details?
推荐答案
你通常做的是索引文档并通过查询将它们取回.简而言之，渗透器允许您做的就是索引您的查询并根据索引查询过滤文档以了解它们匹配哪些查询.它也被称为反向搜索，因为你所做的与你习惯的相反.
What you usually do is index documents and get them back by querying. What the percolator allows you to do in a nutshell is index your queries and percolate documents against the indexed queries to know which queries they match. It's also called reversed search, as what you do is the opposite to what you are used to.
渗透器有不同的用例，第一个是存储用户兴趣的任何平台，以便在它进入时立即将正确的内容发送给正确的用户.
There are different usecases for the percolator, the first one being any platform that stores users interests in order to send the right content to the right users as soon as it comes in.
例如，一个用户订阅了一个特定的主题，一旦该主题的新文章出现，就会向感兴趣的用户发送通知.您可以使用 将用户兴趣表达为弹性搜索查询查询 DSL，你可以在 elasticsearch 中注册它，因为它是一个文档.每次发布新文章，无需对其进行索引，您就可以对其进行渗透，以了解哪些用户对它感兴趣.此时您知道谁需要接收包含文章链接的通知(但发送通知不是由 elasticsearch 完成的).另一个步骤是索引内容本身，但这不是必需的.
For instance a user subscribes to a specific topic, and as soon as a new article for that topic comes in, a notification will be sent to the interested users. You can express the users interests as an elasticsearch query, using the query DSL, and you can register it in elasticsearch as it was a document. Every time a new article is issued, without needing to index it, you can percolate it to know which users are interested in it. At this point in time you know who needs to receive a notification containing the article link (sending the notification is not done by elasticsearch though). An additional step would also be to index the content itself but that is not required.
查看 此演示文稿 了解其他几个用例和其他从 elasticsearch 1.0 开始，可与 percolator 结合使用的功能.
Have a look at this presentation to see other couple of usecases and other features available in combination with the percolator starting from elasticsearch 1.0.
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