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英文标题

英文标题

What is DynamoDB?

DynamoDB is a fully managed NoSQL database service offered by Amazon Web Services (AWS). It is designed to deliver consistent, single-digit millisecond latency at any scale. DynamoDB stores data in tables, where each table contains items, and each item has attributes. Unlike traditional relational databases, DynamoDB does not require a fixed schema for attributes beyond the primary key, which makes it well suited for flexible, evolving data models. When people ask, what is dynamodb, the core idea to communicate is that it provides a highly scalable data store with minimal operational overhead, along with built‑in security, backup and restore, and performance features that cater to modern applications.

Core concepts you should know

At the heart of DynamoDB are a few simple ideas. A table groups related items. An item is a collection of attributes, and an item is uniquely identified by its primary key. There are two kinds of primary keys:

  • Partition key only (also called a simple primary key)
  • Partition key plus sort key (composite primary key)

In addition to the primary key, each item can have any number of attributes, and DynamoDB supports a variety of data types such as strings, numbers, binary, booleans, sets, and more complex structures like maps and lists. This flexibility makes it easy to model diverse data—user profiles, product catalogs, session data, and telemetry—as your application evolves.

Data model and access patterns

Unlike relational databases, DynamoDB emphasizes how you access data rather than how you store it. Your access patterns determine the table design. The primary key enables constant-time lookups, while secondary indexes expand query capabilities:

  • Global Secondary Indexes (GSI) enable queries on non-key attributes across the entire table.
  • Local Secondary Indexes (LSI) provide alternate sort keys for a given partition key within the same partition.

Queries and scans are the primary ways to retrieve data. A query operation finds items based on the primary key and optional sort key conditions, while a scan reads across an entire table. For performance and cost reasons, you should prefer queries with well-chosen keys and indexes over broad scans. Understanding what is dynamodb in terms of access patterns helps you design efficient data models that scale with your workload.

Performance, consistency, and scaling

DynamoDB offers two throughput modes: provisioned and on-demand. In provisioned mode, you specify how many read and write capacity units you expect, which helps you predict costs and ensure predictable performance for steady workloads. In on-demand mode, DynamoDB automatically scales to meet high request traffic, which is ideal for unpredictable or spiky workloads. Reads can be configured as strongly consistent or eventually consistent; strongly consistent reads return the most up‑to-date data but may have higher latency, while eventually consistent reads can be faster but require replication time.

To handle rapid growth, DynamoDB can autoscale provisioned capacity and supports DynamoDB Streams for near-real-time data capture. For very large-scale applications, you can use DynamoDB Global Tables to replicate data across multiple regions, providing low-latency access for international users and improved resilience.

Notable features and capabilities

  • Fully managed: AWS handles software updates, scalability, and fault tolerance.
  • Single-digit millisecond latency at any scale, for both reads and writes.
  • ACID transactions: ability to perform coordinated operations across multiple items and tables.
  • DynamoDB Streams: capture item-level changes for event-driven architectures.
  • Point-in-time recovery and on-demand backups for data protection.
  • Encryption at rest and in transit, along with fine-grained access control via IAM.
  • TTL (Time to Live) for automatic item expiration and cost control.
  • Serverless operation with automatic scaling and no servers to manage.
  • Integration with other AWS services and SDKs for various languages.

Common use cases

What is dynamodb in practice? It excels in workloads that require low latency at scale and flexible data models. Typical use cases include:

  • E-commerce product catalogs and shopping carts with rapidly changing data.
  • User profiles and session management for web and mobile apps.
  • Content management and metadata stores for media platforms.
  • Gaming leaderboards and real-time game state tracking.
  • IoT telemetry ingestion and fast querying of recent events.
  • Event-driven architectures using streams to trigger downstream processing.

Getting started: a quick path to DynamoDB

New developers can begin with DynamoDB by following a simple workflow. First, determine your access patterns and choose a primary key design that minimizes the need for scans. Next, decide whether to start in on-demand mode or provisioned mode based on your expected traffic. Then, create a table in the AWS Management Console or via an SDK, define the primary key (and, if needed, a GSI), and populate a few items to test your queries. As you build, you’ll likely add secondary indexes to support additional read patterns and refine your throughput settings.

For developers integrating from applications, start with the AWS SDK in your preferred language. Basic operations include put_item, get_item, update_item, delete_item, query, and scan. To protect data integrity and consistency, consider enabling ACID transactions if your workflow involves multiple related items. To observe changes in real time, enable DynamoDB Streams and connect it to a Lambda function or another consumer.

Design considerations and best practices

Designing for DynamoDB requires thinking about access patterns early. Long-term success depends on choosing a primary key that distributes traffic evenly to avoid hot partitions, and using GSIs to support additional query scenarios without resorting to scans. Some practical tips:

  • Avoid large, composite attributes that exceed 400 KB per item; keep item size efficient.
  • Model data around access patterns rather than trying to fit a relational schema.
  • Use on-demand mode for unpredictable workloads, but review cost implications for sustained traffic.
  • Leverage GSIs for alternate query keys and keep your write paths as simple as possible.
  • Enable automatic backups and consider point-in-time recovery for data safety.
  • Monitor performance with CloudWatch metrics and set alarms on throttling or latency spikes.

Pricing and cost considerations

Pricing in DynamoDB is based on the throughput you provision (read and write capacity units) or on-demand capacity, plus storage and optional features like streams and backups. In on-demand mode you pay per request, which is convenient for variable workloads, while provisioned mode offers cost predictability for steady usage. Additional costs may come from DynamoDB Streams, global tables replication, and data transfer to other AWS regions. A practical approach is to start with on-demand mode during development and transition to provisioned or autoscaled provisioning as traffic patterns stabilize.

Common pitfalls to avoid

  • Underestimating the cost of heavy write workloads due to hot partitions; design your keys to distribute traffic.
  • Relying heavily on scans for large datasets; favor queries with appropriate keys and indexes.
  • Overusing large attributes or nested data structures without considering item size limits.
  • Neglecting data lifecycle management and TTL settings, leading to higher storage costs.

How DynamoDB compares with alternatives

Compared with traditional relational databases, DynamoDB offers managed scalability, lower operational overhead, and a flexible data model focused on access patterns. Compared to document stores like MongoDB, DynamoDB provides tighter integration within the AWS ecosystem, built-in scaling, and strong consistency options. When evaluating, consider your deployment model, required latency, operational overhead, and the need for cross-region replication or ACID transactions. If your primary goal is to deliver fast, predictable performance at scale with minimal database administration, DynamoDB is a strong candidate.

Conclusion: why DynamoDB fits modern applications

In the evolving landscape of cloud-native architectures, DynamoDB stands out as a purpose-built solution for scalable, low-latency data storage. By focusing on data access patterns, offering flexible indexing, and providing features like Global Tables and Streams, it supports both complex workflows and simple, fast operations. If you are considering a serverless or microservices-driven architecture, understanding what is dynamodb helps you design resilient, cost-efficient data stores that grow with your application. With thoughtful modeling, appropriate throughput strategies, and the right use of indexes, DynamoDB can power a wide range of workloads—from mobile apps to enterprise systems—while keeping operational complexity to a minimum.