php 如何在预先存在的 SQL 数据库之上使用弹性搜索？
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How to use Elastic Search on top of a pre-existing SQL Database?
提问by Miles M.
I've been reading through a lot of good documentation about how to implement Elastic Search on a website with javascript or PHP.
我已经阅读了很多关于如何使用 javascript 或 PHP 在网站上实现弹性搜索的优秀文档。
Very good introduction to ES.
很好的ES 介绍。
Very complete documentation hereand here.
A whole CRUD.
整个CRUD。
Elastic search with PHP: here, here, and here.
So the reason why I'm giving you those URLs is to understand how to use one or many of those great documentations when having a pre-existing SQL DB.
所以我给你这些 URL 的原因是为了了解在拥有一个预先存在的 SQL 数据库时如何使用这些伟大的文档中的一个或多个。
I'm missing the point somewhere: As they said Elasticsearch will create its own indexes and DB with MongoDB, I don't understand how can I use my (gigantic) database using SQL? Let say I have a MySQL DB, and I would like to use Elasticsearch to make my research faster and to propose the user pre-made queries, how do I do that? How does ES works over/along MySQL? How to transfer this gigantic set of Datas (over 8GB) into ES DB in order to be fully efficient at the beginning?
我在某处遗漏了一点：正如他们所说的 Elasticsearch 将使用 MongoDB 创建自己的索引和数据库，我不明白如何使用 SQL 使用我的（巨大的）数据库？假设我有一个 MySQL 数据库，我想使用 Elasticsearch 使我的研究更快，并向用户提出预制查询，我该怎么做？ES 如何在 MySQL 上/与 MySQL 一起工作？如何将这组庞大的数据（超过 8GB）传输到 ES DB 中才能在开始时完全高效？
Many Thanks
非常感谢
采纳答案by Tim
I am using jdbc-riverw/ mysql. It is very fast. You can configure them to continually poll data, or use one-time (one-shot strategy) imports.
我正在使用带有 mysql 的jdbc-river。它非常快。您可以将它们配置为持续轮询数据，或使用一次性（一次性策略）导入。
e.g.
例如
curl -xPUT http://es-server:9200/_river/my_river/_meta -d '
{
"type" : "jdbc",
"jdbc" : {
"strategy" : "simple",
"poll" : "5s",
"scale" : 0,
"autocommit" : false,
"fetchsize" : 10,
"max_rows" : 0,
"max_retries" : 3,
"max_retries_wait" : "10s",
"driver" : "com.mysql.jdbc.Driver",
"url" : "jdbc:mysql://mysql-server:3306/mydb",
"user" : "root",
"password" : "password*",
"sql" : "select c.id, c.brandCode, c.companyCode from category c"
},
"index" : {
"index" : "mainIndex",
"type" : "category",
"bulk_size" : 30,
"max_bulk_requests" : 100,
"index_settings" : null,
"type_mapping" : null,
"versioning" : false,
"acknowledge" : false
}
}'
回答by Tony O'Hagan
If you need a more performant and scalable solution to the polling offered by jdbc-river, I recommend that you watch this presentation that explains how to perform incremental syncing from SQL Server into Elastic Search:
如果您需要针对 jdbc-river 提供的轮询的更高性能和可扩展性的解决方案，我建议您观看此演示文稿，该演示文稿解释了如何执行从 SQL Server 到 Elastic Search 的增量同步：
The principles discussed in the video also apply for other RDBMS -> NoSQL replication applications.
视频中讨论的原则也适用于其他 RDBMS -> NoSQL 复制应用程序。
