MongoDB: a NoSQL Database 3 Ways it Works for Today’s Data Generation
MongoDB is an open-source document database that provides high performance, high availability and automatic scaling. It’s ideal for running modern applications that rely on structured and unstructured data — and to support rapidly changing data.
When compared to relational databases, MongoDB databases are more scalable and provide superior performance. Their data models also address several issues that relational database models cannot, including:
- Large volumes of structured, semi-structured and unstructured data
- Agile sprints, quick iterations and frequent code pushes
- Efficient, scale-out architecture instead of expensive, scale-up architecture
This blog will explore the three major features of MongoDB that you won’t find in a traditional relational database system for rapid, agile development.
1.Dynamic schema for more development speed
In a relational database world, one needs to define a schema in order to store data. For example, if we want to store details of a customer like “name”, “address”, “phone” or “email”, we need to define the schema and table structure first. The relational database needs to know what we are storing in advance.
This approach works so long as your requirements are defined and there won’t be many future changes. But if you need to add additional columns for example, “customer’s favorite products”, you will have to modify the schema and migrate the changes. With agile development approach, these changes could be quite frequent. With every iteration you have new requirements and new changes coming in. If we use a relational database, we need to change the schema every time. This means frequent migrations which leads to recurrent downtime. If the database is large, this process could be very slow, thus increasing downtime even more – and it doesn’t address unstructured data that’s unknown in advance.
Because MongoDB is a document-oriented database, it stores data in JSON documents. Collections in JSON documents are equivalent to tables in relational databases. The benefit is that it’s self-describing and easy to understand. It’s nothing but a key-value pair of data. Each document can be treated on its own terms. This makes MongoDB’s schema design dynamic. One can represent, for example, “customer data” in different ways within a collection. When you need to add a new column, you can do so easily and anytime using the existing schema. This fits well with agile development and it can be used with both semi-structured and unstructured data types.
2.Auto-sharding for more scalability
Sharding is a technique to distribute and store data across multiple servers. With the advent of social media, it is becoming increasingly difficult to store data in traditional database systems. High query rates are putting a strain on CPUs and big data is giving rise to data sets too large to store. More capacity is needed every day. What’s more, processing large data sets which are greater than the systems’ RAM puts stress on I/O capacity.
In order to resolve these issues, relational databases usually take a scale-up approach. This means a single server hosting the database needs to be upgraded with more capacity and performance in terms of storage and CPUs. But this has a major drawback, though, as it introduces a single point of system failure when it comes to reliability and resilience. Moreover, upgrading the servers vertically is an expensive activity.
MongoDB, on the other hand, uses the scale-out approach, which means it scales horizontally. Whenever additional scalability or performance is needed, one simply adds commodity servers. It like an assembly line in a manufacturing industry. When you need to increase production, you add additional resources to perform the work as needed to drive efficiency.
MongoDB has built-in support for auto-sharding, which means it distributes the data natively and automatically across various servers. If you have four commodity servers storing 10 terabytes of data, MongoDB distributes it into four chunks of two-and-a-half terabytes each. The data and query is load balanced automatically across the servers. If one server goes down, it can be replaced quickly and transparently with no application disruption or downtime.
3.Automatic replication for more availability
MongoDB databases support automatic replication that synchronize data across multiple servers. It addresses two major issues in any database systems; data redundancy and high availability. You can configure your MongoDB system for replication in different ways, but ideally you should have a replication factor of three in order to provide good redundancy and high availability at any point in time. This replication set is nothing but a group of mongod processes; one acts a primary whereas the other two act as secondary. The primary mongod receives all the write operations which is then replicated by secondary ones. If one instance goes down, there are two other instances to provide the same data set. If the primary goes down, MongoDB automatically switches to one of the secondary ones so that write operations are not affected.
This flexibility makes it possible to configure these replica sets for dedicated operations like database writes, reporting and backup. It also suits today’s application development because it’s flexible and dynamic data modeling, ideal for agile development, scalability, high availability and redundancy.