The MongoDB database is one of the most popular NoSQL databases available. MongoDB offers many benefits not offered by relational databases, such as scalability and agility.
However, like all databases, performance issues can depend on your skill level and expertise using the platform.
Here are five essential steps in terms of MongoDB best practices and optimization strategies.
MongoDB is a NoSQL document database that's particularly suited to high-performance apps.
With its JSON-like documents, MongoDB is recognized for horizontal scaling and load balancing, which provides developers an outstanding balance of flexibility and scalability.
Indexing, ad hoc queries, and real-time aggregation offer a wide range of methods to access data.
MongoDB has a distributed architecture by default, allowing for significant horizontal scalability with no application code modifications.
We’ll dive deeper into explaining how to increase performance using MongoDB by treating the below topics (which aren't in any case exhaustive);
The first step in optimizing performance is to understand expected and actual query patterns.
Once you have a firm grasp of your application's query habits, you can develop your data model and choose suitable indices.
MongoDB provides the explain method to show how MongoDB will execute a given query.
Use MongoDB's profiling capabilities to understand what your application is doing with MongoDb and whether it meets expectations.
Develop your application's data model based on MongoDB's capabilities. To do this, you must always understand your schema before starting a project.
If you are unaware of the schema during the development phase, you will often have to redesign things down the road, which can be very costly.
MongoDB is flexible with schemas which is one of the core advantages of the platform.
However, this does not mean you can overlook the data modeling and indexing portion of your project.
You will still need to create indexes that support queries used by the application and monitor accessibility over time.
A great way to get a high-level overview of MongoDB performance is to use MongoDB Manager.
Embed related objects in MongoDB documents when possible.
This will avoid the performance overhead of repeated requests for data stored in separate collections, which can be much slower than embedded fields.
Through embedding, you will prevent application joins which will decrease queries and updates, thus increasing performance.
MongoDB's $lookup command looks up an object referenced by another document and embeds it directly into this document.
Still, it doesn't reference expansion (for example, looking up a collection with hundreds or thousands of records).
A good practice when using MongoDump scripts to export your MongoDB content is to use findAndModify instead of findOneAndUpdate because these two commands are not equivalent.
The first one returns the modified document after changes have been made.
At the same time, the second does not return any result to the application server, even if some result is returned to MongoDB.
You must be aware of the amount of memory and the MongoDB configuration settings.
When the working set of an application fits in RAM, read activity should be minimal.
However, read activity will begin to rise if your working set outstrips the instance size or server's available RAM.
If you detect this is happening, you may fix it by moving to a more notable instance with more memory.
To manage your MongoDB object cache and avoid out-of-memory errors caused by large or complex queries, you can set size limits on each collection with DB.collection. cacheLimit.
It's important to note that this setting doesn't apply locks, so it won't affect read operations for a single update thread (update operation affects only one document), leading to data inconsistency if concurrent updates are executed against the same documents.
Replication and sharding are some of the core advantages when scaling horizontally using MongoDB and are essential when dealing with high volumes of data for performance.
When using MongoDump scripts to export data from MongoDB servers or at any point in time where massive amounts of documents (millions) are exported into a single file, this can generate an I/O bottleneck because each document must have its own JSON object before being written to the output stream.
Developers can use replica sets to replicate data from a primary server or node across several secondaries.
What this accomplishes is to reduce contention and improve load balancing.
Replication allows MongoDB servers to communicate with each other to remain synchronized even if one of them goes down unexpectedly.
Replica sets provide automatic failover by electing a primary replica set member, which handles all operations sent by clients until it fails.
If this happens, an election process begins where another server will be elected as the new primary based on priority settings defined in the replica set configuration file (MongoDB-rs.conf).
Similar to replication, sharding is another way to distribute large sets of data while improving performance.
Sharding is one of the core advantages that accompanies any NoSQL platform, and MongoDB is no exception.
MongoDB sharding is agnostic to your data nodes and can be replicated as many times as necessary for high availability, making it an ideal solution when you need scalability.
Shards distribute data across different database instances according to specific criteria, which lets MongoDB scale horizontally across servers.
When looking to increase storage and optimize performance, shards are valuable for MongoDB best practices.
Performance and optimization are essential when dealing with databases that often have large amounts of data.
Understanding MongoDB best practices can save you the time, money, and frustration that accompanies large NoSQL projects when using MongoDB.
Ensuring you understand your query patterns and profiling, perform and review data modeling and indexing, you are embedding and referencing, optimize your project's storage, and use replication and sharding are critical ways to optimize MongoDB performance.
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