MongoDB vs DynamoDB

Organizations are switching to the cloud environment to ease business operations and management. One of the crucial decisions that an enterprise has to make while moving to the cloud is to select the right type of database that supports their business. The growth of big data has spurred the need for NoSQL databases to deliver better cloud performance.

NoSQL databases manage volumes of structured, semi-structured, and unstructured data and require no predefined schema. They also facilitate real-time application modifications, faster application development and require less administrative overhead. Here, you would learn about the two most popular NoSQL databases - MongoDB and Amazon DynamoDB. So, let's get started.

In this article, we have enlightened the essential aspects of MongoDB vs DynamoDB which will help you understand both MongoDB and DynamoDB.

MongoDB vs DynamoDB - 6 Basic Comparisons 

The following topics will be covered in this MongoDB vs DynamoDB:

1. What is MongoDB?

2. What is DynamoDB?

3. MongoDB vs DynamoDB - Which is Better?

4. When to use DynamoDB vs. MongoDB?

What is MongoDB?

MongoDB is a document-oriented NoSQL database used for high volume data storage. It makes use of collections and documents, unlike traditional relational databases, which make use of tables and rows. Collections contain sets of documents and functions, which is the equivalent of relational database tables. A document is a basic unit of data in MongoDB and consists of key-value pairs. 

What is DynamoDB?

Amazon DynamoDB is a NoSQL databases service that offers seamless scalability with faster performance. It allows you to offload the administrative burdens of operating a distributed database without worrying about hardware provisioning, replication, cluster scaling along with setup and configuration.

MongoDB vs DynamoDB - Which is Better? 

#1. Deployment Environments

MongoDB can run anywhere from a laptop to an on-premise data center to a public cloud environment. In contrast to this, DynamoDB is available only on Amazon Web Services (AWS). 

#2. Data Model

MongoDB stores the data in the JSON-like format called BSON and supports multiple data types, including dates, timestamps, 64-bit integers, & Decimal128. A MongoDB document can go up to a size of 16 MB; with GridFS, more massive data sets can be natively stored within the database.

MongoDB also provides built-in schema validation. Users can check the document structure, data types, presence of mandatory fields, and data ranges. As a result, Database administrators can apply data governance standards, and developers can easily maintain a flexible document model's benefits.

DynamoDB stores data in a key-value format and supports JSON to provide document-like data structures to match objects in application code. Also, a record/item cannot exceed 400KB in size. DynamoDB supports limited data types, resulting in application complexity and minimizes the scope of data re-use across different applications. Also, DynamoDB does not have native data validation capabilities.

#3. Queries and Indexes

MongoDB's query language allows developers to build applications whose data can be analyzed and queried in several ways, such as single keys, faceted search, ranges, graph travels, geospatial queries, and JOINs without using any additional tools. As a result, users can avoid the latency due to syncing data between the analytical and operational engine. It also supports multi-document transactions and combines the ACID guarantees of traditional relational databases. MongoDB can take up to 1000 operations in a single transaction. 

DynamoDB supports key-value only. For queries requiring graph traversals, aggregations, or search, additional tools such as Redshift or Elastic MapReduce are used, which increases latency, cost, and developer work. It supports two types of indexes: local secondary indexes (LSIs) and Global secondary indexes (GSIs). Users can define up to 5 LSIs and 20 GSIs per table. DynamoDB indexes are provisioned individually from the underlying tables, which leads to some unforeseen issues at runtime. It supports multi-record ACID transactions and allows just 25 write operations.

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#4. Consistent Operational Maturity

By default, MongoDB is strongly consistent because all the read/write operations are present in primary in a MongoDB replica set, scaled across several partitions (shards). If needed, read operations consistency can be relaxed. Read queries are routed to secondary replicas falling under the allowed limits of the primary server with the help of secondary consistency controls.

DynamoDB is eventually consistent by default. Read operations are configured to return strongly consistent data, which adds latency and doubles the cost of the read. No guarantee is given for reading consistency while querying against DynamoDB's global secondary indexes. An operation performed against a GSI is consistent, returns stale or deleted data, leading to more application complexity.

#5. Pricing and Commercial Considerations

DynamoDB charges users for reading, writing, and storing data in tables. It offers two capacity modes having specific billing options to process reads and writes on tables: on-demand and provisioned.  

Pricing for on-demand capacity mode

DynamoDB on-demand capacity mode charges for the data reads and writes performed by the application on the tables. Users do not need to specify the expected read and write throughput that the application can perform because DynamoDB instantly accommodates workloads as they ramp up or down.

The on-demand capacity mode might be best if you:

• Have unpredictable application traffic.
• Create new tables with unknown workloads.
• Prefer the ease of paying for only what you use.

Pricing for provisioned capacity mode

In provisioned capacity mode, the user has to specify the expected number of reads and writes per second needed by the application. Autoscaling automatically adjusts the table's capacity as per the specified utilization rate for better application performance while reducing costs.

The provisioned capacity mode might be best for:

• Predictable application traffic.
• Applications having consistent traffic or reduce gradually.
• Forecasts capacity requirements to maintain costs.

Pricing/cost for MongoDB Atlas is quite straightforward where the user has to select:

• The instance size with sufficient RAM to cover the portion of data (including indexes) that clients access most often
• Count of replicas and shards making up a cluster
• Regions the cluster needs to run in
• Whether to include fully managed backups

A user can choose any of these parameters as per requirement and demand. The only additional charge will be for data transfer costs.

#6. Operational Maturity

Users can deploy, scale, and manage clusters using MongoDB built-in operational and security best practices, such as role-based access control, end-to-end encryption, network isolation, VPC peering, and more. Atlas deployments are guaranteed and durable with distributed and auto-healing replica set members. There is no need to worry about data loss due to continuous backups with a point in time recovery. It has zero downtime configuration changes and offers auto-scaling for both storage and compute capacity. Also, you get to see comprehensive dashboards to gain business insights to track real-time performance and customizable alerting.

In DynamoDB, below listed operational tasks are lacking if compared to MongoDB:

• Less than 20 database metrics are reported by AWS Cloudwatch and put a constraint in the visibility of real-time database behavior
• AWS CloudTrail creates audit trails but can only track a small subset of DDL actions to the database.
• Limited tooling to allow developers/DBAs to optimize performance through schema visualization or graphically profiling query performance
• DynamoDB allows cross-region replication in multi-primary global tables; thus, application complexity and cost add up, with eventual consistency, data corruption due to writing conflicts between regions, and no automatic client failover.

When to use DynamoDB vs. MongoDB?

DynamoDB might work for organizations that are:

• Seeking a database that supports key-value workloads
• Invested in AWS and not planning to switch the deployment environment in the future

MongoDB might work for organizations that are:

• Seeking a database to support a broader range of use cases
• Deployment flexibility and no platform lock-in

Conclusion

That was all about MongoDB Vs. DynamoDB database. We hope you might have understood the difference between MongoDB and DynamoDB very clearly. You can also avail our MongoDB training and AWS training to understand each concept in a better way. In case you have doubt, please feel free to drop your comments in the comment section below.

Thanks for reading!