12 docs tagged with "schemas"

Background indexes

Capped collections are a special type of MongoDB collection that have a fixed size and support high-throughput operations. They automatically remove the oldest documents to make space for new ones when they reach their maximum size. Capped collections are ideal for use-cases like logging, caching, and real-time analytics where you need a FIFO (First-In, First-Out) data structure.

In MongoDB, a compound index is an index that involves multiple fields within a collection's documents. Compound indexes can greatly improve query performance by allowing the database engine to filter on multiple criteria simultaneously. They are particularly useful for queries that involve sorting or filtering on more than one field.

The explain() method in MongoDB provides detailed information about how a query is executed, allowing you to analyze its performance characteristics. By using explain(), you can understand the query execution plan, which helps in optimizing queries for better performance.

Geospatial indexing in MongoDB allows you to perform queries on geometric data types like points, lines, and polygons. These indexes are particularly useful for location-based services such as mapping, routing, and geofencing. MongoDB supports two main types of geospatial indexes: 2d indexes and 2dsphere indexes.

A hashed index in MongoDB is a type of index that stores the hash of the field value instead of the value itself. Hashed indexes are particularly useful for sharding collections, as they provide a more even distribution of data across shards. They are also useful for equality-based queries but are not suitable for range queries or sorting.

Indexing is a critical feature in MongoDB that allows you to perform queries more efficiently. Without indexes, MongoDB would have to perform a collection scan, i.e., scan every document in a collection, to find the documents that match the query statement. With indexes, however, MongoDB can limit the number of documents it needs to examine, thereby improving query performance.

Multi-Key indexes in MongoDB allow you to index fields that contain an array value. When you create a Multi-Key index on an array field, MongoDB creates separate index entries for each element in the array. This enables efficient queries on individual elements within arrays.

In MongoDB, the concept of a schema is more flexible than in traditional relational databases. Unlike SQL databases, where you must define the structure of the data before inserting it, MongoDB collections do not enforce a fixed schema. This means that documents within a single collection can have different fields, and the data types of these fields can vary across documents.

In MongoDB, single-field indexing refers to creating an index on a single field of the documents in a collection. Indexing improves the performance of search queries by allowing the database engine to look up values more efficiently. The most basic type of index is the single-field index, and MongoDB automatically creates one on the _id field when you create a new collection.

In MongoDB, a text index is a special type of index that allows for text search queries on string content. Text indexes can include any field whose value is a string or an array of string elements. These indexes are particularly useful for implementing features like search engines, where you need to locate documents based on text content.

Updating schema validation rules in MongoDB is a common operation, especially as your application evolves and requires more complex data structures or validation logic. You can update the schema validation rules for an existing collection using the collMod command or its equivalent in various MongoDB drivers.