Einführung in DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service provided by AWS. It's designed for applications that require high performance at any scale, making it perfect for modern web, mobile, gaming, ad tech, and IoT applications.

As a NoSQL database, it offers flexibility with its schema-less design, meaning you don't need to define a rigid table structure upfront.

Unlike traditional relational (SQL) databases that use fixed schemas and tables with rows/columns, DynamoDB is a NoSQL (Not Only SQL) database. It stores data in a flexible, document-like format.

• Flexibility: No fixed schema, easy to evolve your data model.
• Scalability: Designed for massive scale with consistent performance.
• Performance: Delivers single-digit millisecond performance at any scale.
• Fully Managed: AWS handles patching, backups, and scaling, so you don't have to.

In DynamoDB, your data is organized into tables. A table is a collection of items, similar to how a table in a relational database is a collection of rows.

For example, you might have a Users table, a Products table, or an Orders table, each holding related data.

Each table contains items. An item is a group of attributes that is uniquely identifiable among all other items. Think of an item as a row in a relational database.

An attribute is a fundamental data element, similar to a column. Unlike SQL, attributes can be added or removed from items dynamically, and different items in the same table can have different attributes.

Every item in a DynamoDB table must have a primary key, which uniquely identifies it. There are two types of primary keys:

• Partition Key: A simple primary key, like a user ID. DynamoDB uses its value as input to an internal hash function to determine where the item is stored.
• Composite Primary Key: Composed of a Partition Key and a Sort Key. This allows for more complex queries and ensures uniqueness within a partition.

Let's say you have an Orders table. You could use OrderID as a simple Partition Key.

For a ForumPosts table, you might use ThreadID as the Partition Key and PostDateTime as the Sort Key. This allows you to find all posts in a thread, ordered by time.

DynamoDB offers different consistency models. By default, reads are eventually consistent. This means that if you write data to DynamoDB and then immediately try to read it, you might not see the latest update.

The data will eventually become consistent, usually within milliseconds. This model is suitable for applications where immediate data consistency isn't critical, like social media feeds or user activity logs.

For situations where you need to be absolutely sure you're reading the latest data, DynamoDB offers strongly consistent reads. When you request a strongly consistent read, DynamoDB returns the most up-to-date data, reflecting all prior successful write operations.

This comes with a slight trade-off: strongly consistent reads might have higher latency and can consume more read capacity units.

DynamoDB offers two capacity modes to manage throughput and costs:

• On-Demand: DynamoDB automatically scales read/write capacity and you pay per request. Great for unpredictable workloads.
• Provisioned: You specify the number of reads and writes per second (Read/Write Capacity Units). Best for predictable workloads and often more cost-effective.

You've learned about DynamoDB's core concepts and consistency models. Let's test your understanding.

You've completed your introduction to Amazon DynamoDB! Here's a quick recap:

• DynamoDB is a fully managed, high-performance NoSQL database.
• It uses tables, composed of items and flexible attributes.
• Each item requires a primary key (Partition Key or Partition Key + Sort Key).
• It offers eventually consistent reads (default) and strongly consistent reads for different needs.
• You can choose between On-Demand and Provisioned capacity modes.

Next, we'll dive into DynamoDB Data Modeling!