MongoDB's indexing mechanism is highly flexible. Developers have the option to create indexes in ascending or descending order, specify uniqueness, create sparse indexes to exclude documents that don't have a particular field, and more. Understanding the data access patterns and query requirements helps in designing efficient indexes to support fast queries and maintain good performance.

Indexing in MongoDB is crucial for optimizing query performance. It allows for faster access to the data by creating an index on the desired fields. However, it's important to note that indexes do come with some overhead in terms of storage space and write performance. Therefore, it's important to carefully choose the fields to index based on the types of queries you frequently run on your data.

In MongoDB, indexes are created using the 'createIndex()' method, which takes the collection name and the field(s) you want to index as parameters. MongoDB provides various types of indexes, such as single-field indexes, compound indexes, and multi-key indexes, to cater to different query patterns and optimize performance. It's recommended to analyze query patterns and create indexes accordingly to achieve the best performance.

MongoDB uses a B-tree based index structure called WiredTiger to efficiently handle indexing. Indexes are created on specific fields in a collection to speed up query execution by allowing the database to quickly locate the documents that match specified criteria. By utilizing indexes, MongoDB can avoid scanning the entire collection and significantly improve query performance.