MongoDB is the most loved NoSQL database, and it can be a great choice for unstructured and semi-structured data that is cumbersome for RDBMS databases to handle. Its schema-free, availability and horizontal scaling properties make NoSQL databases, in particular, well-suited to modern applications. That said, poor performance and poor reliability can occur if the wrong design choices make for inefficient queries, schema design, or indexing.

In this guide, you’ll learn the very best ways to achieve optimum MongoDB performance using our relevant strategies that provide effective ways to operate MongoDB reliably, fast and at scale.

Optimize the Indexing Strategy

Indexes are a key part of improving query speed and reducing load on the database.

Best Practices:

• Compound Indexes: When querying multiple fields, join included fields as a compound index to achieve better multi-condition filtering.
• Always Index Frequently Queried Fields: Always index any field used in any filter, sort, or join expression.
• Never Over-index: Too many indexes may affect write performance.
• Use covered queries: Queries that are fully satisfied in the index, and therefore do not require” reading” the document in its entirety.

Pro Tip: Check regularly on index utilization with MongoDB’s explain() or Atlas Performance Advisor to uncover potential unused indexes or index candidates.

2. Schema Design Guidelines

MongoDB’s flexibility allows us to embed or reference data; however, it is imperative to plan the schema carefully.

Guidelines:

• Embed Related Data: Utilize embedded documents whenever possible for a one-to-few relationship in order to minimize the need for joins from relational databases and to boost read performance.
• Reference for Data that can be Large and/or can Change: You can use reference documents when dealing with datasets that are large or change quickly.
• Don’t Create Large Documents: individual documents should remain below 16MB in size, for the sake of performance (querying getting slow, memory).
• Be Mindful of Field Names: The shorter the field names, the less storage they will take, and the less memory it will take.

Example: A possible e-commerce application may embed product specifications in one document and reference user reviews in another, which nicely balances reading and writing performance.

3. Query optimization

Good user design to eliminate resources from the processes with optimized queries may give better times to response.

Strategies:

• Use Projection: Return only the fields you need, instead of fetching the entire document(s) would be better for performance.
• Filter Early: Use filtering before applying an aggregation or sorting.
• Use Native Operators over JavaScript: JavaScript can be terribly slow; use the native operators as much as possible.
• Use the Aggregation Pipeline: You can optimize your stages of the aggregation pipeline to perform complex transformations.

Pro Tip: You can understand the execution of a query by using explain(“executionStats”). you will see execution time, the number of documents examined and returned, and how the stages execute time to help you determine indexes more appropriately.

4. Use Sharding for Scalability

Sharding is the practice of distributing data across multiple servers, which allows for horizontal scaling for very large datasets.

Some best practices include:

• Choosing the Right Shard Key: Make sure you shard your data properly so you have an even data distribution; hotspots can limit the scalability due to constant high demand.
• Monitoring Chunk Balance: Ensure chunks are constantly balanced — unbalanced chunks can require additional processing when queries are issued to a Never Balanced Partitions.
• Using Sharding with Indexing: ensuring you are using your shard keys with an index is the best approach to get the most out of the query.

As an example, social media organizations shard posts by user ID to replicate the load evenly across multiple servers while also keeping query performance in the fast range.

5. Replication and High Availability

Replication improves upon the concept of redundancy and fault tolerance. It is a critical consideration for any enterprise application.

Some best practices are:

• Replica Sets: Replication is usually implemented through “replica sets,” which includes three copies of your data, providing high availability.
• Optimizing Read Preferences: Off-load reporting or analytic processing by using the read preference of secondary nodes, which will reduce primary balances of reads.
• Automatic Failover: automatic failover is a best practice with data redundancy, so you can maintain uptime should a data needs replacement.

As an example, financial applications can configure replica sets across multiple data centers to keep data live during domain outages, such in the event of power or internet outages.

6. Monitoring and Performance Evaluation

Monitoring performance can highlight bottlenecks in databases to address them proactively through maintenance.

Tools and techniques include:

• Cloud – MongoDB would provide tools such as MongoDB Atlas or Ops Manager application to monitor CPU, memory usage, disk usage, or replication lag.
• Database Profiler– you can monitor the execution of queries, or identify slow queries or previously frequently access/previously utilized operations.
• Logs and Alerts, – monitoring tool can comprise alert notifications for high latency, lock contention or replication issues.

7. Hardware and Storage Considerations

The performance of a system is not just limited to the software but rather the complete hardware and storage configuration.
Recommendations:

• Use SSD: Flash drives present the ability, for read/write, to work far faster than traditional hard drives.
• Allocate adequate RAM: The data that is often reused should be able to reside in memory for improved performance.
• Minimize disk I/O: Utilizing RAID technology or storing your data offsite in the cloud helps maintain minimal latency with disk space.
• Separate data and logs: It is always a good approach to keep the journal and log files on separate disks so that disk I/O contention does not occur.

8. Maintenance Best Practices

MongoDB requires periodic maintenance to ensure good performance continually.
Periodic maintenance tasks:

• Compact and Repair Database: Compact and repair databases will lower storage fragmentation.
• Update Statistics: Regularly keeping all the statistics up to date will improve the effect of the database execution plans made by the query planner.
• Monitor index fragmentation: Indexes should be rebuilt after regularly monitoring index fragmentation.
• Archive or purge old data: If outdated data is archived or purged, this will improve the speed of your queries, as it reduces the size of the database.

Pro Tip: There are repeatable tasks users of MongoDB can automate using MongoDB or its set up tools or third-party scripts to reduce manual overhead.

9. Advanced Optimization Techniques

• Caching: Employ an in-memory caching solution like Redis or Memcached to prevent the need to read or reread data from the database.
• Batch Writes: When feasible, group write operations will improve throughput and reduce network overhead on your batches.
• Connection pooling: Reduce latency by efficiently managing your database connections.
• Data compression: Built-in MongoDB compression means you’ve gone to bat using storage appropriately.

10. How Empirical Edge Can Help

At Empirical Edge, we are a MongoDB Performance Optimization Company. We work with companies to help them become fast, reliable, and scalable with their database. We offer:

• Schema design and optimization
• Indexing and query performance tuning
• Sharding and replication set up
• Monitoring and proactive performance management.
• Cloud database migration and scaling.

More about our MongoDB and database management services to ensure optimized performance and reliability.

Conclusion

There are a number of ways — plumbing or tools — that encourage high-performance, working within MongoDB’s architecture, including:

• Efficient indexing
• Schema design
• Query optimization
• Sharding and replication
• Monitoring and maintaining

Following these specific best practices and calling in for additional services can help organizations be high-scale fast, reliable! Some of the benefits can be: allowing for growth, supporting real-time analytics, embedding into modern applications.