Amazon DocumentDB features

Amazon DocumentDB Elastic Clusters enables customers to handle millions of writes and reads per second, allowing customers to scale their document databases in minutes with little to no downtime or impact to performance. Customers can also store petabytes of data and only pay for the capacity they consume with zero management of underlying infrastructure. With Amazon DocumentDB Elastic Clusters customers can now meet the scaling needs of virtually any application

Amazon DocumentDB has a flexible JSON document model, data types, and efficient indexing. The service uses a scale-up, in-memory optimized architecture to allow for fast query evaluation over large documents sets.

With a few clicks in the AWS Management Console, customers can scale the compute and memory resources up or down by creating new replica instances of the desired size or by removing instances. Compute scaling operations typically complete in a few minutes.

Amazon DocumentDB will automatically grow the size of the storage volume as your cluster storage needs grow. The storage volume will grow in increments of 10 GB up to a maximum of 4 PiB. Customers don't need to provision excess storage for your document database to handle future growth.

Increase read throughput to support high volume application requests by creating up to 15 database read replicas. Amazon DocumentDB replicas share the same underlying storage as the source instance, lowering costs and avoiding the need to perform writes at the replica nodes. This frees up more processing power to serve read requests and reduces the replica lag time–often down to single digit milliseconds. Amazon DocumentDB also provides a single endpoint for read queries, so the application can connect without the need to keep track of replicas as they are added and removed.

Amazon DocumentDB is compatible with MongoDB 3.6, 4.0, and 5.0 drivers and tools. A vast majority of the applications, drivers, and tools that customers already use today with their open-source MongoDB non-relational database can be used with Amazon DocumentDB. Amazon DocumentDB emulates the responses that a client expects from a MongoDB server by implementing the Apache 2.0 open source MongoDB 3.6, 4.0, and 5.0 APIs on a purpose-built, distributed, fault-tolerant, and self-healing storage system that gives customers the performance, scalability, and availability they need when operating mission-critical MongoDB workloads at scale. Learn more about supported MongoDB APIs.

The launch of Geospatial query capabilities enables customers to use Amazon DocumentDB to support storing, querying and indexing Geospatial data. Customers can create 2dsphere indexes and use popular MongoDB geospatial APIs such as $nearSphere, $geoNear, $minDistance, $maxDistance to perform queries stored on data stored DocumentDB.

ACID (atomicity, consistency, isolation, durability) is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps. With the launch of support for MongoDB 4.0 compatibility, Amazon DocumentDB supports the ability to perform ACID transactions across multiple documents, statements, collections, and databases.

Customers can easily migrate their MongoDB databases on-premises or on Amazon Elastic Compute Cloud (EC2) to Amazon DocumentDB with virtually no downtime using the AWS Database Migration Service (DMS). With DMS, customers can migrate from a MongoDB replica set or from a sharded cluster to Amazon DocumentDB. For more information about migrating both relational and non-relational databases to Amazon DocumentDB, see Migrating to Amazon DocumentDB.

There is no upfront commitment with Amazon DocumentDB. You pay an hourly charge for each instance that you launch, and when you’re finished with an Amazon DocumentDB instance, you can delete or pause it. You do not need to overprovision storage as a safety margin, and you only pay for the storage you actually consume. To see more details, visit the Amazon DocumentDB pricing page.

Amazon DocumentDB offers I/O-Optimized storage configuration for those seeking price predictability. Amazon DocumentDB I/O-Optimized offers up to 40% cost savings for I/O-intensive applications where I/O charges exceed 25% of the total Amazon DocumentDB I/O-Optimized database spend. With Amazon DocumentDB I/O-Optimized, you can effectively eliminate the uncertainty of variable I/O charges from your billing structure. Instead, you are billed only for compute, storage, and backup charges, ensuring price predictability and transparency.

Amazon DocumentDB enables you to choose between Standard and I/O-Optimized storage configurations for your database cluster. The flexibility enables you to maximize price-performance by choosing the appropriate configuration based on your needs. If your application requires low to moderate I/O consumption, you should choose Standard configuration. If your use case demands I/O intensive workloads then you can opt for I/O-Optimized storage configuration.

Getting started with Amazon DocumentDB is easy. Just launch a new Amazon DocumentDB cluster using the AWS Management Console. Amazon DocumentDB instances are pre-configured with parameters and settings appropriate for the instance class selected. Customers can launch a cluster and connect the application within minutes without additional configuration.

Amazon DocumentDB provides Amazon CloudWatch metrics for the cloud database instances. Customers can use the AWS Management Console to view over 40 key operational metrics for the cluster, including compute, memory, storage, query throughput, MongoDB opcounters, and active connections.

Amazon DocumentDB will keep customers database up-to-date with the latest patches. Customers can control if and when the cluster is patched via Database Engine Version Management.

Amazon DocumentDB runs in Amazon Virtual Private Cloud (VPC), which allows customers to isolate the cluster in the virtual network and connect to on-premises IT infrastructure using industry- standard encrypted IPsec virtual private networks (VPNs). In addition, using Amazon DocumentDB’s VPC configuration, customers can configure firewall settings and control network access to the cluster.

Amazon DocumentDB supports role-based access control (RBAC) with built-in roles and defined roles. RBAC enables customers to enforce least privilege as a best practice by restricting the actions that users are authorized to perform. Amazon DocumentDB is integrated with AWS Identity and Access Management (IAM) and provides customers the ability to control the actions that AWS IAM users and groups can take on specific Amazon DocumentDB resources, including clusters, instances, snapshots, and parameter groups. In addition, tag the Amazon DocumentDB resources, and control the actions that the IAM users and groups can take on groups of resources that have the same tag (and tag value).

Amazon DocumentDB allows customers to encrypt databases using keys created and controlled through AWS Key Management Service (KMS). On a cluster running with Amazon DocumentDB encryption, data stored at rest in the underlying storage is encrypted, as are the automated backups, snapshots, and replicas in the same cluster. By default, connections between a client and Amazon DocumentDB are encrypted-in-transit with TLS.

Amazon DocumentDB was designed to meet the highest security standards and to make it easy for customers to verify our security and meet regulatory and compliance obligations. Amazon DocumentDB has been assessed to comply with PCI DSS, ISO 9001, 27001, 27017, and 27018, SOC 1, 2 and 3, and Health Information Trust Alliance Common Security Framework certification (HITRUST CSF), in addition to being HIPAA eligible.

Amazon DocumentDB Global Clusters provides disaster recovery from region-wide outages and enables low-latency global reads. Amazon DocumentDB Global Clusters replicates your data to clusters in up to 5 AWS regions with little to no impact on performance, with a typical lag of less than one second. Learn more about setting up Global Clusters in the Amazon DocumentDB user guide.

The health of your Amazon DocumentDB cluster and its instances are continuously monitored. If the instance powering your database fails, the instance and associated processes are automatically restarted. Amazon DocumentDB recovery does not require the potentially lengthy replay of database redo logs, so your instance restart times are typically 30 seconds or less. It also isolates the database cache from database processes, allowing the cache to survive a database restart.

If there is instance failure, Amazon DocumentDB automates failover to one of up to 15 Amazon DocumentDB replicas customers have created in any of three Availability Zones. If no Amazon DocumentDB replicas have been provisioned, in the case of a failure, Amazon DocumentDB will attempt to create a new instance for customers automatically.

Each 10 GB portion of your storage volume is replicated six ways, across three Availability Zones (AZs). Amazon DocumentDB uses fault-tolerant storage that transparently handles the loss of up to two copies of data without affecting database write availability and up to three copies without affecting read availability. Amazon DocumentDB’s storage is also self-healing; data blocks and disks are continuously scanned for errors and replaced automatically.

Amazon DocumentDB's simple database backup capability enables point-in-time recovery for clusters. Customers can restore their cluster to any second during the retention period, up until the last five minutes. The automatic backup retention period can be configured up to thirty-five days. Automated backups are stored in Amazon Simple Storage Service (Amazon S3), which is designed for 99.999999999% durability. Amazon DocumentDB backups are automatic, incremental, and continuous and have no impact on cluster performance.

Cluster snapshots are user-initiated backups of cluster stored in Amazon S3 that will be kept until explicitly deleted. They leverage the automated incremental snapshots to reduce the time and storage required. Customers can create a new cluster from a Cluster Snapshot whenever desired.

Amazon DocumentDB offers capabilities to enable machine learning (ML) and generative artificial intelligence (AI) models to work with data stored in Amazon DocumentDB in real time. Customers no longer have to spend time managing separate infrastructure, writing code to connect with another service, and duplicating data from their primary database.  

With vector search for Amazon DocumentDB, you can store, index, and search millions of vectors with millisecond response times. A vector is a numerical representation that represents the semantic meaning of unstructured data such as text, images, and video. You can store vectors from Amazon Bedrock, Amazon SageMaker, and other third party or propriety models. Learn how to get started by visiting our vector search for Amazon DocumentDB documentation.  

Amazon DocumentDB integrates with Amazon SageMaker Canvas, making it easy to build generative applications using data stored in Amazon DocumentDB. The in-console integration removes the undifferentiated heavy lifting to connect and access Amazon DocumentDB to accelerate your AI/ML development with a low code no code (LCNC) experience. You can build AI/ML models for classic use cases such as regression and forecasting, or create generative AI solutions such as content generation, text extraction, and text summarization within SageMaker Canvas. Read our Amazon DocumentDB generative AI documentation to learn more.

Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service provides advanced search capabilities (such as fuzzy search, semantic search, and more) on their Amazon DocumentDB documents using the OpenSearch API. With this integration, you can also uniquely search across collections and other non-English languages. With a few clicks in the AWS Console, you can now seamlessly synchronize their data from Amazon DocumentDB to Amazon OpenSearch Service, eliminating the need to write any custom code to extract, transform, and load the data.

This zero-ETL integration uses Amazon OpenSearch Ingestion which seamlessly moves document data from Amazon DocumentDB to Amazon OpenSearch Service. It automatically understands the format of the data in Amazon DocumentDB collections and maps the data to Amazon OpenSearch Service to yield the most performant search results. This zero-ETL integration enables consolidation from multiple Amazon DocumentDB collections into one Amazon OpenSearch managed cluster or serverless collection. You can read our documentation on working with Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service.