Choosing the right AWS NoSQL database feels daunting, I know! When you're crafting scalable applications that demand performance, cloud-native reliability tips the scales, so getting the database choice right is critical. In the AWS ecosystem, my go-tos are Amazon DynamoDB, the serverless key-value and document store, and Amazon DocumentDB, the managed MongoDB-compatible document store. Each shines in different scenarios and packs its own features, pricing, and strengths worth investigating side-by-side.
That’s why I’m putting together this laid-back, hands-on comparison between DocumentDB vs DynamoDB, so you can figure out easily which one is the perfect fit for your project.
What is Amazon DocumentDB?
Amazon DocumentDB is a serverless, fully managed document database service built on AWS, crafted for effortless compatibility with MongoDB. It natively stores JSON, permitting evolving schemas while simplifying migrations from any MongoDB-based application. Whether conducting intricate queries, performing analytics, or consolidating an existing MongoDB cluster, DocumentDB supplies the following advantages:
MongoDB Compatibility: Migrate with minimal changes to application logic, reaping the benefits of standard MongoDB libraries and drivers.
Flexible Document Model: Query richly nested documents without predefined constraints to simplify diverse data structures.
Managed Infrastructure: Automated scaling, continuous backup, and integrated security protocols reduce operational overhead.
High Availability: Scale out to as many as 15 read replicas, distributing workloads for sustained low latency and high throughput.
What is Amazon DynamoDB?
Amazon DynamoDB is a serverless and fully managed NoSQL database service on the AWS Cloud, engineered for applications demanding reliably high throughput regardless of load predictability. This service scales horizontally without the complexity of cluster management and maintains single-digit millisecond latency for both read and write operations. It suits workloads including real-time analytics, IoT streams, mobile backends, and multiplayer gaming, offering:
Managed Scalability: Scale automatically from gigabytes of data to multiple petabytes without manual intervention.
High Performance: Process tens of millions of reads and writes per second, with the Dynamo Accelerator option for microsecond latency reads.
Flexible Pricing: Choose between on-demand scaling for burst workloads or provisioned capacity for predictably steady workloads.
Backup and Restore: Automatically retrieved point-in-time backups and full data export options assure data durability and recovery speed.
The Basics of AWS NoSQL Databases.
(Image Source: AWS Documentation)
AWS NoSQL databases present adaptable choices for data workloads that exceed relational SQL approaches. Rather than constrain data to static tables, these services embrace multiple data models, key-value, document, graph, and wide-column, surpassing rigid schema limits. Their design excels at ingesting and processing sheer volumes of unstructured or semi-structured data at impressive velocities. Take DocumentDB, for example: it comfortably handles nested and hierarchical data, letting developers query and index rich JSON grammars. Conversely, DynamoDB, as a key-value system, sharpens its focus on immediate key lookups with deterministic latency.
Key benefits of AWS NoSQL databases include:
Horizontal Scalability: Automatically distribute data and workloads over growing fleets, confidently absorbing surging concurrent traffic.
Flexible Data Models: Schemas evolve on-the-fly, speeding iterative development and enabling responsive corrections without prolonged downtime.
High Performance: Purpose-built optimizations for the most common access patterns yield single-digit millisecond read and write latencies.
Feature Comparison: DynamoDB vs DocumentDB
Feature | DocumentDB | DynamoDB |
Data Model | Document (JSON, BSON), MongoDB compatible | Key-value, JSON document |
Scalability | Up to 64TiB, vertical & read replica scaling | Petabytes, horizontal scaling |
Latency | Single-digit ms, minimal replica lag | Single-digit ms, DAX for microsecond reads |
Backups | S3 backups, AWS Backup integration | Point-in-time, on-demand |
Pricing | Instance-based, backup/IO charges | On-demand, provisioned, pay-per-request |
Security | KMS encryption, VPC support | KMS encryption, VPC support |
Transaction Support | ACID transactions, multi-document transactions | ACID transactions with transactional APIs |
Maintenance | Managed, but instance-based, requiring planning | Serverless, fully managed, no server patching |
When to Choose DocumentDB or DynamoDB
Choosing between AWS managed databases comes down to the unique traits your app demands:
Choose DocumentDB when:
You need rich queries over flexible schemas, like in a content management system or a catalog, and want to analyze semi-structured data without a rigid model.
You’re migrating an existing MongoDB workload to AWS and want to minimize changes to application code. DocumentDB’s MongoDB-compatible APIs ease the lift and reduce risk.
Your data is complex or hierarchical, as in deeply nested JSON. DocumentDB’s JSON-based model efficiently stores and queries nested structures, keeping the retrieval simple and fast.
Choose DynamoDB when:
Your app demands sustained high throughput and single-digit millisecond latency, as seen in real-time bidding, gaming leaderboards, or streaming IoT data, so millions of operations are executed with virtually no delay.
You’re building on an event-driven, serverless architecture. The on-demand capacity and automatic gradients for workload spikes and valleys fit naturally with AWS Lambda, requiring no base layer to manage.
Your access pattern is simple, querying on a single partition key, like in session stores, user profiles, or shopping carts, where the pattern is predictable and the overhead of complex indexing is unwarranted.
Pricing Comparison
Understanding pricing models is key to cost optimization. Here's how DocumentDB and DynamoDB stack up:
DocumentDB Pricing
Pay-for-what-you-use: Charges apply for compute instances (per second, 10-minute minimum), storage (per GB/month), backup storage (free up to database size, excess charged), and database I/O (per million I/Os unless using I/O-Optimized mode).
Instance Pricing: Example: db.r5.large $0.23/hour, db.r5.8xlarge $4.43/hour (region-dependent).
Storage: $0.10/GB/month for database storage, $0.21/GB/month for excess backup storage, and $0.30/GB/month for Elastic Clusters.
I/O Costs: Standard mode charges per million I/Os. I/O-Optimized clusters are ideal for high-activity workloads and don’t charge for I/O.
Reserved Instances: Save up to 45% by committing to 1-year or 3-year terms for predictable workloads.
DynamoDB Pricing
Pay-as-you-go: Charges apply for reading, writing, and storing data, plus optional features like backups, streams, and data transfer.
On-Demand Capacity Mode: No need to specify capacity; charges are per request. Write requests cost $1.25 per million, read requests $0.25 per million, ideal for unpredictable workloads.
Provisioned Capacity Mode: Specify read (RCUs) and write (WCUs) capacity. Each WCU costs $0.00065/hour, RCU $0.00013/hour (us-east-1 pricing). This is better for predictable traffic.
Table Storage: $0.25/GB/month for Standard tables; $0.10/GB/month for Standard-IA (infrequent access) tables.
Optional Features: Streams, backups, global tables, and cross-region replication incur extra charges.
Free Tiers
Both DocumentDB and DynamoDB provide free tiers for new users. The DocumentDB Free Tier offers a 1-month free trial, which includes up to 750 hours of db.t3.medium usage, 30 million I/Os, 5 GB of database storage, and 5 GB of backup storage. On the other hand, the DynamoDB Free Tier includes 25 GB of storage, 25 RCUs, and 25 WCUs free each month for up to 12 months.
Conclusion
Choosing the final choice between Amazon DocumentDB and Amazon DynamoDB requires you to weigh demands on schema flexibility, query complexity, throughput, and budget. For applications anchored to key-value access, capable of phenomenal growth with almost zero operational overhead, DynamoDB often emerges as the leader. Its seamless scalability, serverless provisioning backend, and consistent low latency make it attractive for scenarios such as online games and IoT telemetry.
Meanwhile, DocumentDB becomes a compelling contender in the face of complex query needs, a progressively evolving schema, or a forthcoming move from a MongoDB-based workload. DocumentDB’s Mongo-compatible APIs and rich document model perfectly match content repositories and analytic pipelines. Cost factors may very well tip the balance, with DocumentDB offering significant savings, sometimes up to 45%, when its Reserved Instances line up with predictable usage patterns. Using solutions such as Pump to monitor consumption and reap savings helps to lock in those efficiencies.
Still uncertain? The most dependable path is to prototype. Both database options are available on the free tier, allowing you to validate assumptions and performance expectations before going live.
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