DynamoDB Vs MongoDB: Most Important Comparison (2023)

MongoDB Vs DynamoDB

Here's a quick comparison table between DynamoDB vs MongoDB:

DynamoDB Vs MongoDB: Operational Maturity

Operational maturity is an important consideration when comparing DynamoDB Vs MongoDB.

DynamoDB is a fully managed service provided by AWS, which means that AWS takes care of the underlying infrastructure and provides automatic scaling and backup services. This makes DynamoDB a highly available and durable service that requires minimal operational maintenance. However, DynamoDB's limited query capabilities, complex management of indexes, and high cost can make it difficult to manage and optimize for certain use cases. DynamoDB abstracts away underlying partitioning and replication schemes. Provisioning is easy, but compared to MongoDB, it lacks some key operational tasks.

AWS Cloudwatch reports fewer than 35 database metrics, providing limited visibility into real-time database behavior. AWS CloudTrail creates audit trails for a small subset of administrative actions to the database, not all users have access to individual tables or records.

DynamoDB lacks adequate tools for improving performance through visualizing schema or profiling query performance graphically. Although DynamoDB supports cross-region replication, multi-primary global tables add complexity, cost, eventual consistency, risks of data loss due to write conflicts between regions, and no automatic client failover.

On the other hand, MongoDB provides a more flexible and scalable platform that can be deployed on a variety of cloud services, on-premises, or through a database-as-a-service provider like MongoDB Atlas. MongoDB offers a more advanced query language and indexing capabilities that make it easier to handle complex data structures and optimize queries. However, running MongoDB requires more operational expertise, as administrators must manage the underlying infrastructure, including scaling, backup, and monitoring services. MongoDB Atlas is highly flexible, allowing for elastic scaling with no downtime, and provides detailed monitoring and alerting. MongoDB also offers advanced operational tooling for running the database on-premises with comprehensive monitoring and continuous backup. MongoDB supports global deployments with single clusters spanning across regions and replica sets supporting up to 50 members. Atlas Global Clusters allow for fully managed active-active clusters with localized data, eliminating risks of data loss and eventual consistency. Finally, multi-cloud clusters enable users to provision clusters across different cloud providers for maximum resilience and flexibility.

Overall, while DynamoDB may be easier to manage for simple use cases, in the race of MongoDB Vs DynamoDB, MongoDB's flexibility and advanced capabilities make it a more mature and scalable option for complex and evolving applications.

DynamoDB Vs MongoDB: Indexes and Queries

Indexes and Queries are key features and form an important segment in the topic DynamoDB Vs MongoDB. DynamoDB offers limited indexing options and only supports single-key queries, which limits query flexibility. Additionally, global secondary indexes are inconsistent with underlying data, causing stale data issues. DynamoDB is limited to key-value queries only and cannot support more complex queries without copying data to additional AWS technologies. It offers two types of indexes, but they have limitations, such as the inability to index sub-documents or arrays. During runtime, issues can arise because DynamoDB's indexes are provisioned independently from its tables. While it does support multi-record transactions, each transaction is limited to 100 write operations and may require multiple transactions for complex business logic, which could result in more conflicts and failures. Additionally, secondary indexes, backups, and streams may not be transactionally consistent with the base tables, leading to potential data loss or incorrect query results.

On the other hand, MongoDB supports a wide range of indexing options, including compound, unique, geospatial, and text indexes, which support any query pattern. Its indexes are strongly consistent, always returning the latest results. MongoDB also offers on-demand materialized views for fast analytic queries. MongoDB offers a flexible API that allows developers to build applications with a variety of query options, from simple key lookups to complex aggregations and geospatial queries. The database also supports secondary indexes on any field, including individual values in arrays, for fast data access. MongoDB's multi-document transactions combine the ACID guarantees of relational databases with the scalability and flexibility of a document model and are easy to integrate into any application. Hence based on the need and flexibility requirements of your application, you have to carefully evaluate which one to choose while comparing MongoDB Vs DynamoDB.

DynamoDB Vs MongoDB: Concepts and Terminologies

MongoDB uses a document model to organize data. A document is a set of key-value pairs, where the keys are strings and the values can be of various types, including strings, numbers, dates, arrays, and other documents. Documents are organized into collections, which are similar to tables in relational databases. Flexible data modeling is possible because each document can have a unique structure. MongoDB also supports nested documents and arrays.

DynamoDB uses a key-value model to organize data. Each item in the database has a primary key, which can be a single attribute or a combination of attributes. Other attributes can be added to the item as needed. Items are organized into tables, which are similar to collections in MongoDB. DynamoDB also supports secondary indexes, which can be used to query data by attributes other than the primary key.

There are several similarities between the concepts and terminology used in DynamoDB and MongoDB. The following table provides an overview of some of the common concepts shared by both databases.

These concepts form a core difference in explaining DynamoDB Vs MongoDB.

DynamoDB Vs MongoDB: Consistency

By default, MongoDB ensures strong consistency by directing all read and write operations to the primary server in a replica set, which is then scaled across multiple partitions. However, users can opt for less strict consistency requirements for read operations through secondary consistency controls. In contrast, DynamoDB is eventually consistent by default, but users can configure read operations to be strongly consistent at an additional cost and latency. However, this feature is not available when querying against DynamoDB's global secondary indexes (GSIs), which can return potentially stale or deleted data, adding complexity to the application. Consistency thus is an important aspect when we evaluate DynamoDB Vs MongoDB.

DynamoDB Vs MongoDB: Deployment Environments

Let's talk about deployment environments and the key role they place in the topic DynamoDB Vs MongoDB.

DynamoDB is an Amazon Web Services (AWS) product and is primarily deployed on the AWS cloud infrastructure. It can be accessed through the AWS Management Console or via an API, and is highly scalable and elastic, with automatic sharding and replication across multiple availability zones.

MongoDB can be deployed in multiple environments, including on-premise, in the cloud, or a hybrid environment. It can be installed and managed on a single server, as well as on clusters of servers, providing scalability and high availability. MongoDB can be accessed via a shell, drivers, or the MongoDB Compass graphical user interface. Additionally, MongoDB offers a fully managed cloud service called MongoDB Atlas, which provides features such as automated backups, monitoring, and elastic scaling.

DynamoDB Vs MongoDB: Pricing and Commercial Considerations

Pricing model is a very useful and need-dependent topic. Thus the key differences in this form an important aspect of MongoDB Vs DynamoDB. DynamoDB pricing is based on throughput, and AWS throttles any reads or writes that exceed the capacity of a given table. However, correctly estimating pricing is complicated due to factors such as

1. The size of the data set,
2. Number of reads/writes per second,
3. Use of transactions
4. Replication across multiple regions,
5. Backup/restore costs, among others.
6. Strongly consistent reads are twice as expensive as eventually consistent reads, and indexes can also affect pricing. Designing a data model that supports provisioned throughput is essential. AWS recently introduced Adaptive Capacity and DynamoDB On-Demand to help with capacity planning.
7. Factors that can impact the cost of running DynamoDB include data transfers through Dynamo streams and in/out of the database, cross-regional data transfers, the need for additional AWS services to supplement DynamoDB's query capabilities, and the use of on-demand or reserved instances.
8. Additionally, the number of metrics pushed into CloudWatch for monitoring and the number of events pushed into CloudTrail for auditing can also affect the cost.

In contrast to DynamoDB, the pricing model for MongoDB Atlas is more straightforward. Users only need to select the appropriate instance size with enough RAM to handle the portion of data (including indexes) that clients access most often. They also need to choose the number of replicas and shards that will form the cluster, whether to include fully managed backups and the regions where the cluster needs to operate. Users can modify any of these parameters as needed, and the only additional cost is for data transfer expenses.

MongoDB Vs DynamoDB: Data Model

DynamoDB and MongoDB have different approaches to data modeling.

DynamoDB is a NoSQL database that uses a key-value data model, which means that data is stored as a collection of items, each with a unique primary key. Each item consists of one or more attributes, which are essentially key-value pairs. DynamoDB allows for nested attributes and supports different data types, including strings, numbers, binary data, and sets. The data is stored in partitions, which can be scaled horizontally by adding more nodes.

MongoDB, on the other hand, is a document database that uses a document data model, where data is stored in documents. A document is a JSON-like structure that can contain nested fields, arrays, and other data types. Each document has a unique identifier, called the "id" field, and documents with different structurescan be stored in the same collection. MongoDB allows forflexible schema design, with support for dynamic schema changes and partial updates.

DynamoDB's key-value data model provides fast and efficient access to data by using primary keys to locate items. However, this model can be limiting for more complex data structures, and querying data based on non-primary keys can be more difficult. MongoDB's document data model provides more flexibility for data modeling and querying, but may not be as efficient for certain use cases as it requires more complex queries to locate data. The size limit for an item or record is 400KB in DynamoDB. Compared to MongoDB, DynamoDB has less extensive support for different data types. For example, it only supports one type of numeric value and does not support dates. This means that developers must manage data types on the client side, which can make applications more complex and limit the reusability of data across different applications. Additionally, DynamoDB lacks built-in data validation capabilities.

DynamoDB Vs MongoDB: Cloud Provider Availability

Comparing DynamoDB vs MongoDB, one factor to consider is their availability across cloud platforms. DynamoDB is a fully managed service provided by Amazon Web Services (AWS), which means it is only available on AWS. On the other hand, MongoDB is available on multiple cloud providers including AWS, Microsoft Azure, and Google Cloud Platform (GCP),` as well as through its cloud service called MongoDB Atlas. This makes MongoDB a more versatile option for companies that use different cloud providers or who want to switch cloud providers in the future.

When to Use, Which One to Prefer?

MongoDB Vs DynamoDB, which one of these should you when? Let's understand their use cases.

DynamoDB may be suitable for organizations with simple key-value workloads that are already heavily invested in AWS with no intention of changing their deployment environment. For organizations seeking deployment flexibility and a database that can accommodate a broader range of use cases, MongoDB may be a more suitable option than DynamoDB.

DynamoDB is a better fit for users who are currently using the AWS echo system to deploy and manage applications. It offers compatibility, ease of use, and integrations. However, its major drawback is vendor locking, which restricts users from easily changing the deployment environment.

On the other hand, MongoDB Atlas allows users to create MongoDB database clusters on any supported cloud provider and even move to an on-premise MongoDB database with minimal configurations. It provides a simple platform to provision and manage MongoDB clusters across multi-cloud environments but lacks the tight integration of an inbuilt product like DynamoDB.

As a mature platform, MongoDB has an edge in its available feature set for managing the underlying data set with native schema validations, multiple index type support, etc. Moreover, it can be configured to cater to most database needs.

Hence one should think about their specific use cases when choosing between MongoDB Vs DynamoDB.