为什么此Firestore查询需要索引? [英] Why does this firestore query require an index?

问题描述

我有一个查询，该查询使用带有等于运算符的where()方法，然后是一个orderBy()方法，但我不知道为什么它需要索引. where方法检查对象(地图)中的值，并且by的顺序是带数字的.

I have a query with a where() method with an equality operator and then an orderBy() method and I can't figure out why it requires an index. The where method checks for a value in an object (a map) and the order by is with a number.

文档说

如果您的过滤器具有范围比较(<，< =，>，> =)，则您的第一次订购必须在同一字段上

If you have a filter with a range comparison (<, <=, >, >=), your first ordering must be on the same field

所以我本以为相等过滤器就可以了.

So I would have thought that an equality filter would be fine.

这是我的查询代码:

this.afs.collection('posts').ref
.where('tags.' + this.courseID,'==',true)
.orderBy("votes")
.limit(5)
.get().then(snap => {
  snap.forEach(doc => {
    console.log(doc.data());
  });
});

这里是数据库结构的一个例子

Here is an example of the database structure

推荐答案

为什么此Firestore查询需要索引?

Why does this firestore query require an index?

您可能已经注意到，Cloud Firestore中的查询非常快，这是因为Firestore会自动为文档中的任何字段创建索引.因此，当您仅使用范围比较进行过滤时，Firestore会自动创建所需的索引.如果您还尝试订购结果，则需要另一个索引.不会自动不创建这种索引.您应该自己创建它.可以通过在 Firebase控制台中手动创建它来完成，或者在日志中找到一个听起来像这样的消息:

As you probably noticed, queries in Cloud Firestore are very fast and this is because Firestore automatically creates an indexes for any fields you have in your document. So when you simply filter with a range comparison, Firestore creates the required index automatically. If you also try to order your results, another index is required. This kind of index is not created automatically. You should create it yourself. This can be done, by creating it manually in your Firebase Console or you'll find in your logs a message that sounds like this:

FAILED_PRECONDITION: The query requires an index. You can create it here: ...

您只需单击该链接或将URL复制并粘贴到网络浏览器中，您的索引就会自动创建.

You can simply click on that link or copy and paste the url into a web broswer and you index will be created automatically.

因此Firestore需要索引，这样您才能进行非常快速的查询.

So Firestore require an index so you can have very fast queries.

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