Choosing the right database is challenging when multiple options provide unique offerings. Sometimes it becomes hectic when you’re figuring out the suitability of your application data structure, analyzing the use case, and checking out factors for relational or NoSQL databases. However, today we’ve got you covered with all the details needed to make this decision as easy as pie.
As the number of Snapchat users increased to millions, the company required a database solution that could scale and adapt to changing daily needs. Eventually, Snap’s wise selection of databases resulted in lower latency, desired scalability, and infrastructure cost savings.
“Migrating to Amazon DynamoDB has helped Snap optimize annual infrastructure costs significantly—in addition to providing low latency and operational reliability for core use cases like messaging and our friend graph,” says Jain.
Snap saw the migration of Snapchat datasets as an opportunity rather than a challenge. Accordingly, the application was refactored into a microservices architecture, and a live migration was undertaken so as not to disrupt the user experience. And the decisions were made about how to structure datasets for cloud migration.
What did Snapchat accomplish by selecting the right database?
- Snaps were sent with a 20% reduction in median latency.
- The system could process over 10 million searches per second on busy days.
- Consistent, low-latency performance for users worldwide while lowering annual infrastructure costs.
To answer your question, “How to choose the right database on AWS,” read the rest of this blog post. Then, let us know how we can assist you in developing a database strategy for your project.
What is the relationship between scalability, database, and application performance?
Slow database performance leads to slow application performance. This is due to your server’s inability to achieve its requirements due to high CPU utilization. Disk usage consumption and a lack of memory are two other bottlenecks. Scaling up your database is the ideal long-term solution when your application demands more disc space for maximum performance. And you can achieve it with modern, AWS purpose-built databases. When the server’s physical memory runs exhausted, the databases begin to use the hard drive to store mission-critical processes that would normally be saved in RAM. The issue here is that the RAM is 100 times faster than the hard disc, resulting in poor performance.
Do you need structured or unstructured data modeling for your application?
Another important element to consider while selecting the correct database is data modeling. It helps map your application’s characteristics to the appropriate data structure. Entities, associated properties, and required relationships can all be identified via data modeling. Therefore, choosing the proper database for your application is a fundamental decision.
Determine whether your application requires structured or unstructured data. For example, suppose you’re creating a social media platform like Facebook. The users will have organized information such as their name, birth date, gender, and so on. However, this is not the case if your application offers environmental or geospatial data with no set structure. No-SQL databases can be used in such applications. This is also true in some cases with social networking platforms like Facebook, LinkedIn, and Instagram, where users post on the news feed. As this type of data is unstructured, it must be kept in No-SQL databases.
For a long time, relational databases have dominated the market. However, in today’s application market, NoSQL databases have become an essential option.
Relational databases, like spreadsheets, organize data in tables with rows and columns. The primary key identifies numerous rows in each table. Keys establish the relationship between these tables, and tables require fixed schemas.
NoSQL or Document databases are more versatile because they don’t require tables to store data in a specific format. Unlike SQL tables, which require predefined schemas, each document stored may contain various fields. Embedded documents are used by several document repositories to handle complicated data structures.
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