Indexing Strategies for High Performance in MongoDB
Indexes are unique data structures that store a small portion of the collection’s data in an ordered form that is easy to traverse and search efficiently. They point to the document identity and allow you to look up, access, and update data faster. In MongoDB, indexes are used to improve query performance. Indexes improve query performance by speeding up queries, reducing disc I/O and reducing the required resources. Additionally, they support queries such as equality matches, range-based operations, and return sorted results.
MongoDB's most common index types are single-type indexes and compound indexes. There are also multi-key indexes that operate on an array field.
Follow ESR (Equality, Sort, Range) Rule
When using compound indexes, you should apply the ESR rule to determine the order of fields in the index. Compound indexes refer to indexes with different fields and can improve query response time when used appropriately. Always start by adding the fields against which “Equality” queries are run. In this context, “Equality” refers to an exact match of a single value. As such, the index keys for exact matches must come before any other index fields.
The following fields to be indexed should reflect the Sort order of the query. Since “Sort” determines the order of the results, it’s only logical to have it after equality matches to reduce the number of sorted documents. The last fields represent the “Range” of the data to be accessed. It comes after the equality matches to reduce the number of documents scanned. Therefore, you should narrow the range to the smallest possible interval with the desired documents to improve efficiency.
Leverage covered queries whenever possible
Covered queries can be satisfied fully using an index without needing to access the documents. For an index to cover a query, all fields in the query are part of an index. Also, all the fields returned in the results are in the same index, and no fields in the query are equal to null.
The efficiency of covered queries is because they return results from an index without accessing the source documents. This reduces disk I/O, consequently minimizing the data that needs to be transferred over the network. That way, you can improve your overall MongoDB performance.
Deleting unused or redundant indexes
It goes with no saying that deleting unused or redundant indexes can improve MongoDB query performance. Mingo, the leading GUI for MongoDB, is exceptional at identifying unused or redundant indexes. Indexes come with a write performance cost. During query execution, MongoDB considers all relevant indexes and chooses the most optimal index. There is increased overhead in the selection process when there are unused and redundant indexes. Therefore, deleting unused and redundant narrows down the choice for MongoDB, making it focus on the most efficient indexes. This, in turn, increases the query performance.
Mingo offers a simplified way of finding unused indexes in MongoDB. It also offers a simple way to drop unused indexes with one click. Additionally, the chances of dropping the wrong index are reduced since the indexes are highlighted in the “No. of uses” section. The “No. of uses since” section offers additional information on the frequency with which queries in the database utilize indexes. As such, this Mingo section helps you understand an index's significance in your database. This is because regularly used indexes show that they play a vital role in query execution. Mingo also provides you with zero or low usage indexes that end up slowing down the operations. These are the indexes you need to operate to improve query performance.
Substitute case-insensitive regular expressions for case-insensitive index
Case-insensitive indexes are faster than case-insensitive regex and hence should be used. This applies if the sole reason for using regex is case insensitivity. Case-insensitive indexes allow the searching and sorting of text data without regard for their cases. That way, matching documents can be identified based on the identified values. The cost of data computation when sorting text data can also be expensive when the index is case-sensitive. A case-insensitive index, however, will enable you to perform sorted queries without additional operations. As such performance of the queries is enhanced that, involves sorting by test fields. Overall, it is best practice to use case-insensitive indexes as it impacts other factors that are not performance related.
Visualize Index coverage with Mingo
Are you a developer, database admin, data analyst, DevOps engineer, data scientist or system administrator? Mingo is a powerful tool that you can leverage and will not just solve your indexing issues. It has features beyond index coverage visualization that you can use to optimize your MongoDB deployment.
Once you connect to your MongoDB deployment, you should pick out the database you want to analyze on your left-hand side. The databases are below the “New Connection” section. Then select the specific collection for analysis. Under the indexes tab, multiple selections can help you optimize your MongoDB performance. You can visualize the data distribution and have clear instructions on how the indexes cover your data. The effectiveness of the indexes can be determined with ease. That way, you can identify the areas that need improvements.
Are you satisfied with our recommendations? I bet you are. On that note, we strongly urge you to try out Mingo. We assure you that there is no harm in doing so. We are here to simplify your work and increase efficiency while at it. Mingo allows you to visualize your database and make database relations easy. Not to mention the great user experience without having to worry about the security of your data.
Don’t hesitate to take the first step and see the positive impact of Mingo.