So, for sake of simplicity, An Index is a binary tree stored in RAM with only the values of the particular column being queried. Now MongoDB only needs to scan this table rather than scanning the whole database. Then MongoDB will load this Index on RAM. Technically Index is not a table but almost similar to Binary Tree. To optimize we will ask MongoDB to create an index, MongoDB will create a table with one column for score and another column for documentID. When no index exists, MongoDB goes through each document to find the queried document (This is called as “Collection scan” / COLLSCAN ) How can we optimize this? Let’s say we want all users who scored 23. Imagine a collection of users, each document containing various fields, one such field is “score”. The index in MongoDB increases the speed of finding documents How do indexes work?įirst, let’s understand how you declare the index in MongoDB //for creating collectionName.createIndex() How does it work under the hood? The idea of indexing in MongoDB is similar to the index of any book, They increase the speed of finding a page. See Create a Multi-Language Text Index in MongoDB for examples of creating text indexes that support multiple languages.Indexing is just a way of taking one or more fields of a table and restructuring it in a binary tree and loading it on the RAM. You can specify a default_language (and language_override) when you create the index. If such a field doesn’t exist in the document, then it will use the default language specified in the default_language field. When you create a document, you can specify the language of that document by using the language field (or some other field defined in the language_override field of the text index). The values in the above index are the default values. These fields assist in dealing with documents in multiple languages. You’ll notice that the above text index includes "default_language" : "english" and "language_override" : "language" in its definition. This means that the body field will have twice the significance of the abstract field, and ten times the significance of the title field. "name" : "title_text_body_text_abstract_text", We can see this when we run getIndexes() again: db.posts.getIndexes() The result is that those two fields will be weighted as specified, and the other field ( title) will have the default weight of 1. When I specified the weights, I specified weights for just two of those fields. When I created the new text index, I specified 3 fields. I started off by dropping the previous index. But you can change this in order to give fields more or less weighting in the search results. By default, each field is given a weight of 1. When creating text indexes, you have the option of specifying a weight on one or more fields. In particular, wildcard text indexes support the $text operator, whereas wildcard indexes don’t. MongoDB also provides us with the ability to create wildcard indexes, however wildcard text indexes and wildcard indexes are two distinct things. Suppose we have a collection called posts, and it contains documents like this: ) To create a text index, use the string literal "text" as the value when creating it. If you have a field that contains a string or an array of strings, you can use a text index on that field. There are various types of indexes that you can create in MongoDB.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |