如何在预先存在的SQL数据库之上使用弹性搜索？ [英] How to use Elastic Search on top of a pre-existing SQL Database?

问题描述

非常好

I've been reading through a lot of good documentation about how to implement Elastic Search on a website with javascript or PHP.

我已经阅读了很多很好的文档，了解如何使用javascript或PHP在网站上实现弹性搜索。 http://www.elasticsearchtutorial.com/basic-elasticsearch-concepts.html\">引入ES 。

Very good introduction to ES.

非常完整的文档此处和这里。

整个 CRUD 。

使用PHP进行弹性搜索：这里，这里和此处。

Elastic search with PHP: here, here, and here.

所以原因为什么我给你这些URL是下面的在使用一个预先存在的SQL DB的时候，应该如何使用这些很好的文档中的一个或多个。

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.

我在这里没有提到这一点：正如他们所说的，Elasticsearch将创建它自己的索引和数据库与MongoDB，我不明白如何使用我的（巨人）数据库使用SQL？假设我有一个MySQL数据库，我想使用Elasticsearch更快地进行研究，并提出用户预先做的查询，我该如何做？ MySQL如何在MySQL上运行？如何将这个巨大的数据集（超过8GB）转移到ES DB中，以便在开始时完全有效？

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?

非常感谢

推荐答案

我正在使用 jdbc-river w / mysql。这是非常快的您可以将其配置为持续轮询数据，或使用一次性（一次性策略）导入。

I am using jdbc-river w/ mysql. It is very fast. You can configure them to continually poll data, or use one-time (one-shot strategy) imports.

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
    }
}'

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