Assessing the performance of two completely different database systems is very difficult, since both management systems approach the task for data storage and retrieval in completely different ways. For example: MySQL is optimized for high performance joins across multiple tables that have been appropriately indexed. MongoDB is also optimized for write performance, and features a specific insertMany API for rapidly inserting data, prioritizing speed over transaction safety wherein MySQL data needs to be inserted row by row.
Observing some of the high-level query behaviors of the two systems, we can see that MySQL is faster at selecting a large number of records, while MongoDB is significantly faster at inserting or updating a large number of records.
This is an easy one, and a hands down win for MongoDB. The schemaless design of MongoDB documents makes it extremely easy to build and enhance applications over time, without needing to run complex and expensive schema migration processes as you would with a relational database.
With MongoDB, there are more dynamic options for updating the schema of a collection, such as creating new fields based on an aggregation pipeline or updating nested array fields. This benefit is particularly important as databases grow in size. In contrast, larger MySQL databases are slower to migrate schemas and stored procedures that can be dependent on the updated schemas. Both are free to get started with, both are easy to install on Linux and Windows, and both have wide programming language support for popular languages like Java, node.
In addition, MongoDB offers MongoDB Atlas , a managed cloud solution which is also forever free to use for exploratory purposes, while for a MySQL managed cloud version, you would need to have an account with one of the major public cloud providers and fall within their free tier terms in order to not pay. MongoDB leverages the popular role-based access control model with a flexible set of permissions. Users are assigned to a role, and that role grants them specific permissions over data sets and database operations.
Users can be granted roles but also privileges, giving them permissions over particular database operations and against particular data sets. In this article, we have talked about the main differences between MongoDB and MySQL, a schemaless non-relational database system and a relational database system, respectively.
We have explained when it is better to use one over the other. We have discussed the scalability, performance, and user-friendliness for each system. Finally, we have also explained the flexibility and security features for both database systems from a comparison point of view. To get started for free, try MongoDB Atlas. Another option to consider is a hybrid deployment approach - giving you the benefit of both worlds, and the flexibility to choose the tool that works for you.
Check out this hybrid deployment guide for more details. Organizations of all sizes are adopting MongoDB, especially as a cloud database , because it enables them to build applications faster, handle highly diverse data types, and manage applications more efficiently at scale.
Development is simplified as MongoDB documents map naturally to modern, object-oriented programming languages. Using MongoDB removes the complex object-relational mapping ORM layer that translates objects in code to relational tables. MySQL's rigid relational structure adds overhead to applications and slows developers down as they must adapt objects in code to a relational structure. MongoDB can also be scaled within and across multiple distributed data centers, providing new levels of availability and scalability previously unachievable with relational databases like MySQL.
As your deployments grow in terms of data volume and throughput, MongoDB scales easily with no downtime, and without changing your application. In contrast, achieving scale with MySQL often requires significant custom engineering work. This happens during restart or has to be done manually. Sometimes, the ugly can be worse than the bad.
It's important to know the ugly part before using the technology. It does not stop you from using the product, but it can make your life very tough. If you have a data model where an object can have recursive children i. Indexing, searching, and sorting these recursive embedded documents can be very hard.
Joining two documents is also not simple in MongoDB. Though MongoDB 3. If your applications require pulling data from multiple collections in a single query, it might not be possible. Hence you have to make multiple queries, which might make your code look a bit messy. Though speed is advertised as a big plus of MongoDB, it is achievable only if you have the right indexes. If you end up having poorly implemented indexes or composite indexes in an incorrect order, MongoDB can be one of the slowest databases.
If you have a lot of filter by and sort by fields, you may end up having a lot of indexes on a collection, which, of course, is not good. You may end up having a lot of duplicate data, as MongoDB does not support well-defined relationships. Updating this duplicate data can be hard and, also due to lack of ACID compliance, we might end up having corrupted data. Overall, MongoDB is a good database, provided it suits your use case. If it does not, it can get very ugly.
Try using it in the wrong place and you will get burned. Analyze it well and consult an expert. You will definitely enjoy using it when it's right.
As for the bad and the ugly parts, you can work around a few of them using the design patterns which I have explained in my article MongoDB Design Patterns. If you enjoyed this article and want to learn more about MongoDB, check out this collection of tutorials and articles on all things MongoDB.
Thanks for visiting DZone today,. Edit Profile. Sign Out View Profile. Over 2 million developers have joined DZone. This cheatsheet gives a quick overview of what's good about MongoDB, what's not so good, and what might send you running for the hills. Like Join the DZone community and get the full member experience. Join For Free. The Good Since MongoDB is as popular as it is today, there should be more good than the bad and the ugly. Flexible Data Model In today's dynamic use cases and ever-changing applications, having a flexible data model is a boon.
Easy to Learn MongoDB is easy to learn and quick to start with. You should be able to use the MongoDB database with ease in your project. Performance Query performance is one of the strong points of MongoDB.
Scalable and Reliable MongoDB is highly scalable, using shards. Async Drivers Nonblocking IO using async drivers are essential in all modern applications that are built for speed.
Documentation Having a good documentation can make developers' lives a lot easier, especially when the developer is new to the technology. Nesting arrays and subdocuments allow you to model complex relationships between data using simple text documents.
Data modeling is a vast and sprawling topic that you could spend months learning about. MongoDB documentation includes a great section on data modeling , starting from planning out your document data model and going into detail on specifics such as embedding and referencing.
MongoDB University offers a free training course on data modeling. This is a great way for beginners to get started with schema design and document data models. A natural extension of data modelling, embedding allows you to avoid application joins, which minimizes queries and updates. Notably, data with a relationship should be embedded within a single document. Data with a 1:many relationship in which "many" objects appear with or are viewed alongside their parent documents are also great candidates for embedding.
Because these types of data are always accessed together, storing them together in the same document just makes sense. Embedding generally provides better performance for read operations due to this kind of data locality. Embedded data models also allow developers to update related data in a single write operation because single document writes are transactional.
However, not all and 1:many relationships are good candidates for embedding in a single document. Referencing makes much more sense when modeling many:many relationships. However, whenever referencing, your application must issue follow-up queries to resolve any references.
This, in turn, requires more round-trips to the server. A document is frequently accessed but contains data that is rarely used. Embedding would only increase in-memory requirements, so referencing may make more sense. A part of a document is frequently updated and keeps getting longer, while the remainder of the document is relatively static. This could occur when modeling many:1 relationships, such as product reviews:product, for example.
While other factors play a part in performance, RAM size is obviously the most important consideration for instance sizing. But if your working set exceeds the RAM of the instance size or server, read activity will begin to shoot up.
If you notice this happening, you may be able to solve the problem by moving over to a larger instance with more memory.
Or you could partition shard your database across multiple servers more on this later. You can opt into cluster tier auto-scaling , for example, which automatically adjusts compute capacity in response to real-time changes in application demands. No discussion of increased performance or horizontal scaling is complete without mentioning replication, which increases data availability via horizontal scaling.
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