One of the myriad decisions that an organization considering moving to the Cloud needs to make is selecting databases to support their applications. The growth of big data and the proliferation of modern web applications have spurred the adoption of NoSQL databases to deliver scalable, distributed, cloud performance.
NoSQL or “non SQL” databases provide a method for data storage and retrieval that involves a variety of data structures—graphs, key-values, wide columns, and documents—to manage volumes of structured, semi-structured, and unstructured data. NoSQL databases require no predefined schema and facilitate faster application development, real-time application modifications, and less administrative overhead.
One of the lauded advantages of NoSQL is faster performance as a result of non-relational data structures. The ability to scale, thereby taking advantage of commodity storage hardware in cloud data centers, makes NoSQL the preferred database technology for media and entertainment, retail, IoT, financial services, and healthcare applications.
DynamoDB vs MongoDB Performance Considerations
Two of the most popular options for NoSQL databases are MongoDB and Amazon DynamoDB. Both are widely deployed and deliver highly-scalable, cloud-level performance. Below, we’ve highlighted some of the key performance capabilities that support mission-critical applications. Each database has advantages, but your long-term cloud strategy and specific application needs will likely be the most influential factor in determining which NoSQL database you select.
MongoDB is widely implemented and can be deployed on mainframes, on-premise, hybrid cloud, as well as a service on AWS. Its distributed scalability, data validation rules, and extensive monitoring capabilities address the needs of Internet-scale applications in a cloud computing environment. MongoDB is used by global enterprises such as eBay, Expedia, Telefonica, and Cisco.
Key capabilities that underpin MongoDB performance, allowing cloud architects to optimally configure resources and system administrators to continually fine-tune operational parameters, include:
- Distributed architecture that supports automated sharding, scales horizontally and allows for optimal location of data to support low-latency writes from any geography.
- High-availability clusters support uptime SLA of 99.995% across all cloud providers.
- Minimum of three data nodes per replica set are automatically deployed across AWS availability zones for continuous application uptime.
- Document-oriented database stores data in a binary-encoded JSON format that increases JSON speed and flexibility.
- Complex queries are executed natively in the database to minimize latency. There’s no need for additional analytics frameworks or tools.
- Extensive monitoring dashboard, a real-time performance panel, and customizable alerting provide metrics on database performance.
Amazon DynamoDB Performance
Amazon DynamoDB is a key-value and document database designed for internet-scale mobile, web, gaming, IoT, retail, media, and entertainment applications that require low-latency data access. DynamoDB is used by more than 100,000 AWS customers such as Nike, Netflix, and Lyft. As part of the AWS environment, DynamoDB simplifies implementation and management. There are no servers to provision or manage and no software to install or maintain.
DynamoDB performance is supported by various native AWS capabilities and automated management and enhanced by easy integration with other AWS services. Key performance capabilities and metrics include:
- Tables automatically replicate data across AWS regions allowing globally distributed applications to access data locally in the selected regions for millisecond read and write performance.
- Automatically scales tables up and down to adjust for capacity and maintain performance.
- Monthly uptime of 99.999% SLA for each AWS region.
- Fully managed in-memory caching reduces read time to microseconds to supporting 10+ trillion requests per day and peaks of 20+ million requests per second.
- Backups of hundreds of terabytes do not affect application performance or availability.
- Application performance monitoring automatically scales throughput and storage, scaling up when application traffic grows and down when traffic shrinks.
DynamoDB vs MongoDB Performance and Other Considerations
Both MongoDB and DynamoDB are capable NoSQL products that offer the performance needed for mission-critical applications. MongoDB runs on multiple cloud platforms, scales horizontally across geographies, and has extensive performance monitoring capabilities. Its ability to run across various environments such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) make it the logical choice for businesses that develop and manage applications in multi-cloud environments.
For businesses planning to adopt AWS as their cloud standard or business already invested in AWS and looking for a NoSQL solution, however, DynamoDB is the logical choice. Ease of deployment, automated management, and integration with other AWS services enhance the value and versatility of DynamoDB.
Although the long-term cloud strategy greatly influences the selection of a NoSQL database, businesses should holistically evaluate their application needs. Both MongoDB and DynamoDB deliver mission-critical performance capabilities. However, databases are only one aspect of an overall cloud environment. Businesses venturing into the Cloud for the first time will achieve better results with the help of solution architects who have worked with dozens of customers to successfully transition to the Cloud. Whether you decide to run MongoDB or DynamoDB, a solution architect will help you design and implement the optimal cloud environment to maximize database and application performance.
One More Thing: The MongoDB to DynamoDB Migration Option
For businesses that are currently running MongoDB but planning to develop applications on AWS or move existing applications to AWS, Amazon offers AWS Data Migration Service (AWS DMS). Every migration process is unique and needs to consider design and data differences between source and target databases, for example, sharded MongoDB cluster data moving to DynamoDB tables. Seeking the experience and expertise of an AWS Consulting Partner is a wise investment to ensure migration success, as well as minimum downtime in making the transition.
Architecting for the Future
For a long time, technical decision makers avoided moving Windows Workloads to AWS, out of concern that the cloud was unfit for these applications. The data, however, reveals that times are changing: more companies than ever before are moving their Microsoft Workloads to AWS, reducing costs and increasing agility in the process.