In my previous articles I have given the architecture of MongoDB and DynamoDB too. In this article I would like to brief you on MongoDB vs DynamoDB with diagrammatic representation. In short I would like to give you idea about difference between MongoDB and DynamoDB with real examples. Everyone knows about MongoDB and DynamoDB but MongoDB vs DynamoDB will give you idea to choose the accurate database.
What is MongoDB?
MongoDB is a popular NoSQL database that is used on mainframes, in-house, in hybrid clouds, and as an AWS service. Due to its distributed scalability, strict data validation policies, and extensive monitoring capabilities, MongoDB is best suited for internet-scale applications.
What is DynamoDB?
A key-value and document database called DynamoDB is specifically designed for internet-scale mobile, online, and gaming applications that need fast data access. DynamoDB is used by more than 100,000 AWS customers because it makes setup and management simple. No servers or software has to be installed or maintained for DynamoDB.
Data Modeling – MongoDB vs DynamoDB :
Relational databases were not built to handle large-scale applications, whereas NoSQL databases can. In order to do this, NoSQL databases eliminated several relational databases’ slowing joins and aggregations. As your data grows, they also leverage the concept of partitions to help spread data over numerous storage nodes, enabling consistently quick response times.
The principle behind DynamoDB is that it won’t permit you to construct a poor query. The term “poor query” refers to a query that won’t scale. You won’t be able to use DynamoDB to perform operations like joins or aggregations, even if your data is smaller than 10 GB. As a result, the data model becomes stiff. On the bright side, DynamoDB gives you complete transparency. Even if your application grows to be 1000 times larger than it was originally, your performance won’t suffer over time.
MongoDB vs DynamoDB Tabular format :
| Function | MongoDB | DynamoDB |
| Data Model | BSON(JSON-like) document | Key-value and document data with JSON suppor |
| Index | Data is consistent with indexes. Compound, unique, array, partial, TTL, geographic, sparse, hash, and text are some indexing techniques. | Indexes are created independently of data. Global secondary indexes (GSIs) are defined whenever a new partition key and new sort key are used. When a table is formed, it needs to have local secondary indexes (LSIs) specified. It needs to have the same partition key as the sort key |
| Data Type | More types of data than DynamoDB. such as decimal128, integer, timestamp, double, string, object, array, binary data, undefined, object ID, Boolean, date, null, regular expression, DBPointer, javascript, symbol, and javascript with scope | Number, String, Binary, Boolean, Null, and Scalar data types. Data types for documents: List and Map. Set data types include binary, number, and string sets. |
| Monitoring | Tracks more than 100 metrics that can impact performance. | Having little access to real-time database behavior. |
| Backup | Querable backup | No backup is required. |
| Charge | The fixed pricing model used by MongoDB requires upfront payment for supplied services. For MongoDB Atlas, pricing is determined by RAM, I/O, and storage, as well as server and sysadmin time if you’re hosting MongoDB yourself. Spending is dependable, but it might not be the best solution for varying workloads. | With DynamoDB, you pay only for the services you really use. This variable pricing model is based primarily on a throughput design with additional fees for services like backup and restoration, on-demand from customers potential, streams, better information capture (CDC), and others. Your spending could become less predictable as a result. |
| Replication | Replica sets, which are several copies of data dispersed among servers, racks, and data centers, are automatically maintained by MongoDB. | Data is synchronously replicated using DynamoDB between three locations within an AWS Region. The Global Table option for DynamoDB Streams is offered by DynamoDB. |
| When to Use? | When trying to find a database that can handle key-value workloads. When with a commitment to AWS and no future plans to change the deployment environment | When trying to find a database that can handle key-value workloads. When with a commitment to AWS and no future plans to change the deployment environment. |
Consistent Operational Maturity :
Because every read/write activity is present in the primary MongoDB replica set and is scaled across several partitions, MongoDB is by default strongly consistent (shards). Read operation consistency can be loosened if necessary. Secondary consistency controls are used to direct read queries to secondary replicas that are within the primary server’s permitted limits.
By design, DynamoDB is eventually consistent. Strongly consistent data is returned by read operations, which increases latency and doubles the cost of the read. When running queries on DynamoDB’s global secondary indexes, there is no assurance that the reading consistency will hold. A GSI operation is consistent, returns outdated or destroyed data, and increases program complexity.
Operational Maturity :
Using built-in operational and security best practices from MongoDB, including as role-based access control, end-to-end encryption, network isolation, VPC peering, and more, users can create, scale, and manage clusters. Because the replica set members are distributed and self-healing, Atlas deployments are assured to be reliable. Due to continuous backups and point-in-time recovery, data loss is not a concern. It supports auto-scaling for both storage and computing capacity with zero downtime during configuration changes. Additionally, you get to see thorough dashboards that provide business insights for monitoring performance in real time and customizable notifications.
I hope I have covered all the points of MongoDB vs DynamoDB with examples. If you like this article or if you have any issues with the same kindly comment in comments section.