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Comparing Amazon DocumentDB and MongoDB

It may seem that there are multiple options for deploying MongoDB in the cloud. Amazon DocumentDB, for example, claims to be an AWS-native database with full support for the MongoDB API. While they support some MongoDB features, it is important to note that DocumentDB is not fully compatible. The only place to access fully-featured MongoDB as a service on AWS is through MongoDB Atlas.

In this comparison we aim to explain the differences between Amazon DocumentDB and MongoDB Atlas, focusing around the following key areas:

  • Compatibility: Amazon claims that migrating an application from MongoDB to DocumentDB is “as easy as changing the database endpoint to the new Amazon DocumentDB cluster”. We have debunked this by running a suite of compatibility tests, which show that DocumentDB is only ~35% compatible with the features and functionality available in MongoDB Atlas.
  • Architecture: Amazon DocumentDB is built on top of AWS’s Aurora platform, which has historically been used to host relational databases. This has implications on scalability and the potential for adding more native MongoDB features in the future. DocumentDB can be deployed using Elastic Clusters, which impose additional operational constraints.
  • Deployment: Amazon’s DocumentDB relies on AWS’s proprietary technology, and there is no way to run the database outside of AWS.
  • Developer Productivity: DocumentDB lacks native integration with features or tools to support mobile, time series, search, and analytical use cases. Users must take on the operational burden of moving data out of DocumentDB and into other services in order to access features to support those workloads.
  • Operational Maturity: Database management and maintenance in DocumentDB is held back by limited tooling, a complex upgrade process, few backup options, and gaps in security features.

What is DocumentDB?

Amazon DocumentDB is a NoSQL JSON document database service with a limited degree of compatibility with MongoDB.

DocumentDB is not based on the MongoDB server. Rather it emulates the MongoDB API, and runs on top of Amazon’s Aurora backend platform. This creates significant architectural constraints, functionality limitations and broken compatibility.

DocumentDB claims to support the MongoDB 4.0 API, which implies that it is at parity with MongoDB v4.0, released back in June 2018. In actual fact, the DocumentDB 4.0 feature set more closely resembles MongoDB 3.0 and 3.2, released in 2015. In addition, compatibility testing reveals it fails over 64% of the MongoDB API correctness tests. Applications written for MongoDB will need to be re-written to work with Amazon DocumentDB.

Interested in up-to-date results on DocumentDB's compatibility with the MongoDB API? Get the latest results at Is DocumentDB Really MongoDB?

The key differences between DocumentDB and MongoDB’s on-demand, elastic, and fully managed Atlas service are summarized below.

Amazon DocumentDBMongoDB Atlas
Fully compatible with MongoDB

No, incomplete
Imitation API fails 64% of correctness tests

Support for latest MongoDB version

No
Feature set resembles MongoDB 3.0/3.2, released in 2015.

Scale writes and partition data beyond a single node / Sharding support

Limited
Sharding available within an Elastic Cluster deployment.

Only hash-sharding. Prone to develop hot partitions. Elastic Clusters impose additional functionality limitations.

Replicate and scale beyond a single region / Comply with data locality regulations and survive regional outages

No
Limited to 20 clusters per region, with 32 shards per cluster and 2 instances per shard. All constrained to a single region.

High resilience, rapid failure recovery, fast failover, retryable writes, multi-region

No
~120 second failover, no retryable writes, no multi-region within a single or elastic cluster

Multi-statement distributed ACID transactions

Limited
Ambiguous commits, poor error handling, small data sizes. Transactions not supported across shards in Elastic Clusters.

Integrated text search, geospatial processing, graph traversals

Limited
Data replicated to multiple prerequisite bolt-on AWS services for text search, and only basic geospatial operators, adding cost and complexity

Native support for time series dataNo

Hedged Reads

Queries submitted to multiple replicas for consistent low latency

No

Online Archive

Automatically tier data out from database to cloud object storage (Amazon S3)

No
Integrated querying of data in Amazon S3

No
Data must be replicated to multiple adjacent AWS services, driving up cost and complexity

On-demand Materialized ViewsNo
Schema governance

No
Schema controls must be enforced in the app

Rich data types

Limited
DocumentDB supports storing decimal128 values, but none of the powerful aggregation features for working with them.

Reactive, event-driven data pipelines

Limited
Change streams run against primary only & incur additional cost.

Support for role-based access control and authentication restrictions

Limited
Coarse-grained roles only

Client-side field level encryption for fine-grained separation of duties in the cloudNo
Availability of advanced developer and analysis toolsLimited
Fine-grained monitoring telemetry & prescriptive performance recommendations

No
<50 metrics

Freedom from vendor lock-in

No
AWS only

Develop & run anywhere
On your mobile device, on your laptop, in your own data center, and in hybrid configurations

No
AWS only

Mobile & Edge Sync

No

Data must be replicated to multiple adjacent AWS services, driving up cost and complexity

Access to MongoDB expertiseNo
Amazon DocumentDB
Fully compatible with MongoDB

No, incomplete
Imitation API fails 64% of correctness tests

Support for latest MongoDB version

No
Feature set resembles MongoDB 3.0/3.2, released in 2015.

Scale writes and partition data beyond a single node / Sharding support

Limited
Sharding available within an Elastic Cluster deployment.

Only hash-sharding. Prone to develop hot partitions. Elastic Clusters impose additional functionality limitations.

Replicate and scale beyond a single region / Comply with data locality regulations and survive regional outages

No
Limited to 20 clusters per region, with 32 shards per cluster and 2 instances per shard. All constrained to a single region.

High resilience, rapid failure recovery, fast failover, retryable writes, multi-region

No
~120 second failover, no retryable writes, no multi-region within a single or elastic cluster

Multi-statement distributed ACID transactions

Limited
Ambiguous commits, poor error handling, small data sizes. Transactions not supported across shards in Elastic Clusters.

Integrated text search, geospatial processing, graph traversals

Limited
Data replicated to multiple prerequisite bolt-on AWS services for text search, and only basic geospatial operators, adding cost and complexity

Native support for time series dataNo

Hedged Reads

Queries submitted to multiple replicas for consistent low latency

No

Online Archive

Automatically tier data out from database to cloud object storage (Amazon S3)

No
Integrated querying of data in Amazon S3

No
Data must be replicated to multiple adjacent AWS services, driving up cost and complexity

On-demand Materialized ViewsNo
Schema governance

No
Schema controls must be enforced in the app

Rich data types

Limited
DocumentDB supports storing decimal128 values, but none of the powerful aggregation features for working with them.

Reactive, event-driven data pipelines

Limited
Change streams run against primary only & incur additional cost.

Support for role-based access control and authentication restrictions

Limited
Coarse-grained roles only

Client-side field level encryption for fine-grained separation of duties in the cloudNo
Availability of advanced developer and analysis toolsLimited
Fine-grained monitoring telemetry & prescriptive performance recommendations

No
<50 metrics

Freedom from vendor lock-in

No
AWS only

Develop & run anywhere
On your mobile device, on your laptop, in your own data center, and in hybrid configurations

No
AWS only

Mobile & Edge Sync

No

Data must be replicated to multiple adjacent AWS services, driving up cost and complexity

Access to MongoDB expertiseNo
MongoDB Atlas
Fully compatible with MongoDBYes
Support for latest MongoDB version

Yes
MongoDB 6.0+

Scale writes and partition data beyond a single node / Sharding support

Yes
Full support for multiple sharding methodologies, including hash, range, and geo-zone.

Replicate and scale beyond a single region / Comply with data locality regulations and survive regional outages

Yes
Global clusters, with up to 50 replicas per shard across multiple regions

High resilience, rapid failure recovery, fast failover, retryable writes, multi-region

Yes
Typical failover sub-5 seconds, retryable reads and writes, multi-region & multi-cloud clusters

Multi-statement distributed ACID transactionsYes
Integrated text search, geospatial processing, graph traversals

Yes
All available from a single API and platform

Native support for time series dataYes

Hedged Reads

Queries submitted to multiple replicas for consistent low latency

Yes

Online Archive

Automatically tier data out from database to cloud object storage (Amazon S3)

Yes
Online Archive

Integrated querying of data in Amazon S3

Yes
Atlas Query Federation

On-demand Materialized Views

Yes
$merge aggregation stage

Schema governance

Yes
JSON schema

Rich data typesYes
Reactive, event-driven data pipelines

Yes
MongoDB Change Streams & Atlas Triggers

Support for role-based access control and authentication restrictionsYes
Client-side field level encryption for fine-grained separation of duties in the cloudYes
Availability of advanced developer and analysis tools

Yes
MongoDB Compass, Charts, SQL Connector, Tableau Connector, Spark Connector

Fine-grained monitoring telemetry & prescriptive performance recommendations

Yes
100+ metrics. Performance Advisor for index and schema recommendations

Freedom from vendor lock-in

Yes
AWS, Azure, and Google Cloud. Available on ~100 regions

Develop & run anywhere
On your mobile device, on your laptop, in your own data center, and in hybrid configurations

Yes
Atlas App Services / MongoDB Server / multi-cloud clusters in Atlas

Mobile & Edge Sync
Access to MongoDB expertise

Yes
Hundreds of engineers with multi-year MongoDB development, support, and consulting experience.

What is MongoDB Atlas?

MongoDB offers the fully managed, on-demand and global MongoDB Atlas service, in the public cloud. Atlas enables customers to deploy, operate, and scale MongoDB databases on AWS, Azure, or Google Cloud. MongoDB Atlas is available through a pay-as-you-go model and billed on an hourly basis. It’s easy to get started – use a simple GUI or programmatic API calls to select the public cloud provider, region, instance size, and features you need. MongoDB Atlas provides:

  • Automated database and infrastructure provisioning along with auto-scaling so teams can get the database resources they need, when they need them, and can elastically scale in response to application demands.

  • Security features to protect your data, with network isolation, fine-grained access control, auditing, and end-to-end encryption down to the level of individual fields. enabling you to comply with regulations such as ISO, HIPAA and GDPR. Built in replication both within and across regions for always-on availability.

  • Global Clusters for a fully managed, globally distributed database that provides low latency, responsive reads and writes to users anywhere, with strong data placement controls for regulatory compliance.

  • Combined transactional and analytical capabilities with Atlas Analytics Nodes to isolate analytics queries from operational workloads while providing real-time insight. Native MongoDB analytics tools, such as MongoDB Charts and MongoDB Connectors (for SQL, Tableau and Spark) are configured to utilize Analytics Nodes by default. MongoDB's rich aggregation pipeline engine allows users to run expressive queries against their data.

  • Fully integrated native MongoDB data visualization tools – MongoDB Charts, which supports the full richness of the document model, including nested, hierarchical and geospatial data and provide embedding and sharing capabilities. Quickly build visualizations of your data without needing to deploy or manage any software or infrastructure.

  • Fully integrated MongoDB Atlas Data Lake which allows users to quickly and easily query data in any format on Amazon S3 using the MongoDB Query Language (MQL) and tools and get value from data faster.

  • Fully managed backups with point in time recovery to protect against data corruption, and the ability to query backups in-place without full restores.

  • Automatic data tiering which helps lower costs by moving data to lower cost storage such as Amazon S3.

  • Fine-grained monitoring and customizable alerts for comprehensive performance visibility. Automated patching and single-click upgrades for new major versions of the database, enabling you to take advantage of the latest MongoDB features.

  • Access to the Atlas App Services, with Data Access Controls, Functions, and Triggers delivered in a completely serverless model.

  • Native time-series support optimized for both highly performant data ingestion and querying, along with reduced I/O and storage overhead

  • The full text Atlas Search service providing rich search capabilities against your fully managed databases with no additional infrastructure or systems to provision, manage, or scale.

  • Live migration to move your self-managed MongoDB clusters into the Atlas service or to move Atlas clusters between cloud providers.

  • Stable API to make upgrades risk-free, future-proofing your development

  • Widespread coverage on the major cloud platforms with availability in ~100 cloud regions across Amazon Web Services, Microsoft Azure, and Google Cloud. MongoDB Atlas delivers a consistent experience across each of the cloud platforms, ensuring developers can deploy wherever they need to, without compromising critical functionality or risking lock-in.

MongoDB Atlas is serving a vast range of workloads for startups, Fortune 500 companies, and government agencies, including mission-critical applications handling highly sensitive data in regulated industries. The developer experience across MongoDB Atlas and self-managed MongoDB is consistent, ensuring that you easily move from on-premises to the public cloud, and between providers as your needs evolve.

Beyond the database, the MongoDB Atlas Data Lake allows you to quickly and easily query data in any format on Amazon S3 using the MongoDB Query Language (MQL) and tools. You can spin up your data lake right alongside your Atlas OLTP clusters from a common UI. With Atlas Data Lake you can realize the value of your data lake faster: you don't have to move data anywhere, you can work with complex data immediately in its native form, and with its fully-managed, serverless architecture, you control costs and remove the operational burden. DocumentDB offers no equivalent capability, and so users must spin up an entirely separate service with a different query language to access S3 data.

Built and run by the same team that develops the data platform, MongoDB Atlas is the best way to run MongoDB apps.

Is DocumentDB Compatible with MongoDB?

Amazon DocumentDB claims to support the MongoDB 4.0 API, which implies that it is at parity with MongoDB v4.0, released back in June 2018. In actual fact, the DocumentDB 4.0 feature set still closely resembles early MongoDB 3.0 and 3.2, released in 2015, and compatibility testing reveals it fails 64% of the MongoDB API correctness tests. Applications written for MongoDB will need to be rewritten to work with Amazon DocumentDB. However, since DocumentDB emulates a MongoDB API, applications written for DocumentDB can be easily migrated into MongoDB Atlas.

Want to learn more?

Interested in migrating from DocumentDB to MongoDB Atlas? Please refer to our migration guide.

Interested in up-to-date results on DocumentDB's compatibility with the MongoDB API? Get the latest results at Is DocumentDB Really MongoDB?

Try MongoDB Atlas for free for a real MongoDB experience.

FAQs

When should I use a document database?

Document databases store data in JSON-like documents which allows them to be inherently easy for developers to work with. MongoDB is a modern document database which contains many of the powerful features found in popular relational databases. These include ACID-compliant transactions, schema governance, enterprise-grade security, and more.

Can MongoDB read data from Amazon S3?

MongoDB offers the Atlas Data Federation engine, which allows users to quickly and easily query data in any format on Amazon S3 using the MongoDB Query API.

What are the key differences between MongoDB and DocumentDB?

DocumentDB does not contain any MongoDB code, supports a limited subset of MongoDB features, and is more costly and difficult to manage.

Is MongoDB a good fit for mobile and web applications?

As part of its developer data platform, MongoDB Atlas Application Services offers a suite of tools for building mobile and web applications. This includes the Realm database.