Data has become the most important commodity of our times. It is being used to drive critical business decisions and create better products that are well-suited to consumer needs. Except for commercial purposes, data is also being used to progress important scientific achievements, like the rapid vaccine development during the current pandemic. Data, to put it simply, is everywhere.
With so much data being constantly harnessed and analyzed, there has also been a significant increase in advanced data tools and technologies. For example, a major part of any data-related process is its storage, and among the many different storage options, databases are the most popular. While relational databases have been the leading choice for the longest time, other options with different approaches have also become popular, like MongoDB.
However, even an advanced database technology like MongoDB can face the inevitable issue of dealing with enormous amounts of data. It handles such a case through techniques called sharding and partitioning. This article discusses and explains both concepts separately and compares them head-on. It also dives into the workings of MongoDB and the features it offers.
The What, How, And Why Of MongoDB
Unlike a traditional relational database, MongoDB is a ‘document-based’ database technology. It is one of the leading ‘No-SQL’ databases, as it doesn’t support SQL to query, create, or modify it. It stores data in JSON-like format inside flexible entities called documents. Not being a relational database enables it not to require a pre-made schema and allows for much more leeway in terms of the amount and form of the data stored.
MongoDB enjoys significant popularity among developers, especially those working on web applications. Its data format allows it to be easily integrated into regular application code in the form of objects. The key-value pairs stored can be directly accessed as object attributes and used within the development. It also offers a fully elastic database-as-a-service for developers to quickly set up and connect to in no time, saving time used to code database functionalities.
Apart from small-time individual use, MongoDB also packs advanced functionality for enterprise-level purposes. Its distributed database storage model helps make features like horizontal scaling and high availability possible for more enormous data storage needs. It also provides features like ad hoc analyses, real-time aggregation, and indexing to help with better data management and analysis.
MongoDB offers its own cloud hosting services, which can host databases as per needs. However, the flexibility of MongoDB also makes it possible to set it up on other popular cloud services like Azure and AWS. There are many resources available to help with, for example, an AWS MongoDB setup.
Understanding Partitioning In Databases
Data today is hardly coming from one source, and it rarely comes in one form. The different levels of data mining promise us tons of data to work with and analyze. Such enormous amounts of data are what are driving the current data revolution. However, with so much data comes challenges like storing it efficiently and operating on it without compromising throughput.
Traditional database technologies handled enormous data proportions by dividing the database into smaller parts, known as partitioning. With smaller data to scan through, queries ran much faster, and retrieval was easier to deal with. Additionally, adding and deleting considerable amounts of data is easier and faster as well because it would only require adding or cutting off partitions rather than separate instances.
With databases essentially being rows and columns, there are two ways to partition them off.
- One would be along the rows, called horizontal partitioning. Such a way of partitioning a database would mean keeping its structure and schema intact while just saving some of the data in a similar table separately. Horizontally partitioning a database helps better divide the data according to a particular attribute, like within a specific date, and access it accordingly.
- The other partitioning method would be on the columns, called vertical partitioning. By this method of partitioning a database, we are saving specific attributes of the data in another table and altering the schema. Vertically partitioning would be helpful if certain attributes are heavier in type than the other and can be partitioned to save on cheaper storage.
The two different ways of partitioning a database are not always exclusive options. There are ways to implement both of them in conjunction to harness both of their pros. With horizontal partitioning, the data can be effectively divided logically, and then the heavier attributes can be stored separately for more efficiency. Popular relational databases like SQL Server and MySQL offer dedicated tools to help with partitioning the database.
Understanding MongoDB Sharding & Difference From Partitioning
While partitioning and sharding are pretty similar in concept, the difference becomes much more apparent regarding No-SQL databases like MongoDB. Such databases don’t have traditional rows and columns, and so it is interesting to learn how they implement partitioning.
Similar to partitioning, MongoDB scales its databases to accommodate data growth in two ways:
- Vertical Scaling: It involves extending the capacities of the existing server by adding additional hardware, like a more powerful CPU, more RAM, or more space. Such a method cannot consistently be implemented as there is always a limit to how much of the above hardware capabilities can be extended.
- Horizontal Scaling: It involves dividing the data onto multiple servers and adding additional servers if required. Each machine shall handle the given dataset as per its computing power and provide overall better efficiency. Adding more servers is often cheaper than expanding the abilities of a single server through high-end hardware.
What is MongoDB Sharding: Step by Step Tutorial
In the case of MongoDB, sharding comes as a way of supporting the horizontal scaling of databases. A sharded ‘cluster’ (collection of multiple documents) essentially contains the subset of the sharded data (called shards). Additionally, it has query routers to help query multiple servers together and communicate between applications and the cluster (called mongos) and storage that saves configuration and metadata information (called config servers).
Like partitioning, sharding is also a method to divide off a database to be saved separately. However, while both are often used interchangeably, partitioning expects the data divided off to be stored on the same computer. Sharding involves saving the partitioned data onto other computers and storage facilities.
In the context of MongoDB, its distributed computing features come in handy to effectively implement its sharding. With sharding, MongoDB promises high availability as each shard is a replicated set of the sharded data. Even if a shard becomes unavailable, the data is still accessible. However, MongoDB has a list of restrictions that you should consult before jumping to sharding. Additionally, sharding adds to the complexity of the infrastructure, and there is no easy way to ‘unsharding’ data.
Sharding and Partitioning In MongoDB Explained
Our growing hunger for data and the wonders it can unlock can safely be considered insatiable. With so much data coming our way, we are going to constantly work and come up with new ways to hold and use it effectively. Techniques like sharding and partitioning are currently some of the best ways out there to handle extensive data. However, they may inspire future techniques that give a much more effective way of handling data.