If you’re familiar with SQL, you might know about the UNION clause, which concatenates the results of two queries into a single result set. In particular, UNION ALL includes duplicates.
In MongoDB, we can use the $unionWith aggregation pipeline stage to achieve the same effect that UNION ALL produces. The $unionWith stage performs a union of two collections – it combines pipeline results from two collections into a single result set. And it includes duplicates.
In MongoDB, when you create an index on a field that holds an array, it’s automatically created as a multikey index.
Multikey indexes support efficient queries against array fields.
Multikey indexes can be created for arrays that hold scalar data (e.g. strings, numbers, etc) and nested documents.
Continue readingMongoDB provides the following geospatial index types that support geospatial queries.
2dsphere indexes support queries that calculate geometries on an earth-like sphere.In this article, I create a 2dsphere index.
When you create an index in MongoDB, if you don’t specify a name for it, MongoDB will assign one.
The default name consists of each indexed field name concatenated with the key value for that field. For example, the sort order, or if it’s a text index, the string _text, or if it’s a 2dsphere index, the string _2dsphere, etc.
But you can assign your own name when creating indexes in MongoDB.
Continue readingWhen you create a wildcard index in MongoDB, you have the option of specifying a single field, all fields, or just some.
You also have the option of excluding certain fields. In other words, you can specify all fields except for one or more specific fields.
You can use the wildcardProjection parameter to include or exclude specific field paths from the wildcard index. This article presents an example of excluding specific fields in the wildcard index.
When you create a wildcard index in MongoDB, you have the option of specifying a single field, all fields, or just some.
You can use the wildcardProjection parameter to include or exclude specific field paths from the wildcard index. This article presents an example of including specific fields in the wildcard index.
When you create a text index in MongoDB, the index uses a default language of english.
The default language determines the rules to parse word roots (i.e. stemming) and ignore stop words.
However, you can change the default language if required.
You can also specify a language at the document level, and even at the subdocument level. The default language will only be used if a language hasn’t been specified at the document or subdocument level.
This article presents examples of specifying a language for a text index.
There are various types of indexes that you can create in MongoDB. If you have a field that contains a string or an array of strings, you can use a text index on that field.
When you create a text index in MongoDB, you have the option of applying different weights to each indexed field.
These weights denote the relative significance of the indexed fields to each other. A field with a higher weight will have more impact in the search results than a field with a lower weight.
This provides you with a certain amount of control over how the search results are calculated.
The default weight is 1, so if you don’t specify a weight for field, it will be assigned a weight of 1.
Continue readingMongoDB provides us with the ability to create wildcard text indexes.
Wildcard text indexes are similar to wildcard indexes, except that wildcard text indexes support the $text operator, whereas wildcard indexes don’t.
That said, creating each index type is very similar in the sense that they both share the wildcard $** field pattern.