Caching and How AWS DynamoDB DAX Works

Introduction to Caching

Imagine you own a coffee shop. Every time a customer orders a cappuccino, you grind fresh beans, steam milk, and prepare the drink from scratch. While this ensures freshness, it also slows down service. Now, imagine pre-steaming milk and grinding beans in advance—customers get their drinks faster, and you reduce workload. This is exactly how caching works in computing.

Caching is a technique used to store frequently accessed data in a fast-access layer, reducing the need to repeatedly fetch the same data from a slower backend like a database. By storing copies of data closer to the user, caching speeds up performance, improves responsiveness, and reduces system load.

Now, let’s see how AWS DynamoDB Accelerator (DAX) takes caching to the next level.

What is AWS DynamoDB DAX?

Amazon DynamoDB Accelerator (DAX) is an in-memory caching layer for DynamoDB that significantly improves read performance for applications. It allows millisecond-level responses for queries that would typically take milliseconds or longer if fetched directly from DynamoDB.

DAX is fully managed, meaning AWS takes care of its deployment, scaling, and maintenance. Unlike traditional caching solutions like Redis or Memcached, which require additional infrastructure setup, DAX is seamlessly integrated with DynamoDB.

How AWS DAX Works

At a high level, DAX sits between your application and DynamoDB as an intelligent caching layer. Instead of your application directly querying DynamoDB, it first checks with DAX.

The Workflow

1. Application Requests Data – When your app makes a GetItem, BatchGetItem, Query, or Scan request, it first goes to DAX.
2. Cache Hit (Data is Found in Cache) – If the requested data is already in DAX, it returns the response instantly without querying DynamoDB.
3. Cache Miss (Data is Not in Cache) – If the data is not in DAX, it retrieves the data from DynamoDB, stores it in the cache, and then returns it to the application.
4. Data Expiry and Updates – Cached data follows a Time to Live (TTL) policy (default is 5 minutes). If data expires or is updated in DynamoDB, DAX ensures consistency by refreshing the cache.

This process drastically reduces the need to query DynamoDB directly, improving response times and reducing costs.

DAX Clusters and Nodes

DAX operates as a cluster of nodes. Each node is an instance of the DAX software that helps store and manage cached data.

Types of Nodes in DAX

1. Primary Node – The main node that handles both read and write requests.
2. Read Replicas – Additional nodes that store copies of cached data, used to distribute read traffic.

DAX automatically synchronizes read replicas with the primary node to ensure consistency.

How DAX Handles Different Types of Requests

Read Operations (Fetching Data)

DAX can process these DynamoDB read operations:

• GetItem
• BatchGetItem
• Query
• Scan

What Happens When You Fetch Data?

1. Eventually Consistent Read (Default Behavior)

   • First, DAX checks if the item exists in cache.
   • If found (cache hit) → Returns data immediately.
   • If not found (cache miss) → Fetches from DynamoDB, stores it in cache, and returns to the app.

2. Strongly Consistent Read

   • DAX bypasses caching and fetches fresh data from DynamoDB.

DAX optimizes for eventually consistent reads, making it ideal for applications that don't need real-time updates.

Write Operations (Updating Data)

DAX supports write-through caching, meaning every write operation updates both DynamoDB and the DAX cache.

Supported write operations:

• PutItem
• UpdateItem
• DeleteItem
• BatchWriteItem

How it works:

1. Data is first written to DynamoDB.
2. Then, it is immediately updated in the DAX cache.
3. This ensures that the latest values are always available.

DAX does not support DynamoDB table management operations (like CreateTable, UpdateTable, or DeleteTable). These must be performed directly on DynamoDB.

DAX Caching Mechanisms

Item Cache (For Individual Items)

• Stores responses from GetItem and BatchGetItem queries.
• Stored by primary key values.
• Default TTL: 5 minutes (configurable).
• Uses Least Recently Used (LRU) eviction, meaning older, unused data gets removed when the cache fills up.

Example:

• You query a user profile: GetItem("user_123")
• DAX checks if it’s in cache:
  • If yes → Returns instantly.
  • If no → Fetches from DynamoDB, stores it, and returns.

Query Cache (For Query and Scan Results)

• Stores result sets from Query and Scan operations.
• Stored by query parameters.
• TTL-based and LRU eviction applies.

Example:

• You run Query to fetch all products in a category.
• If cached → DAX returns it.
• If not → DynamoDB processes it, DAX stores the results, and then returns them.

Handling High Traffic: DAX Auto Scaling & Rate Limiting

If too many requests hit a DAX cluster, it may become overloaded.

Auto Scaling

• DAX can dynamically add more nodes to handle increased load.
• Read replicas help distribute traffic.

Rate Limiting

• If request volume exceeds a node’s capacity, DAX throttles requests by returning a ThrottlingException.
• You can monitor ThrottledRequestCount in Amazon CloudWatch to detect bottlenecks.

Why Use DAX?

DAX is ideal for read-heavy workloads where fast responses are critical. Here’s why you should consider it:

• Ultra-fast reads – Millisecond response times.
• Less load on DynamoDB – Saves on read capacity costs.
• Fully managed – No need to maintain a separate caching infrastructure.
• Seamless integration – Works with existing DynamoDB API calls.
• Automatic scaling – Grows as your traffic increases.

When NOT to Use DAX

• If your app requires strong consistency – DAX caches only eventually consistent data.
• For write-heavy workloads – Since writes still go to DynamoDB, it doesn’t speed up inserts/updates.
• If you need complex queries – DAX only caches individual items and query results, not joins or aggregations.

How to Get Started with DAX

Step 1: Create a DAX Cluster

1. Open the AWS Management Console.
2. Navigate to DAX.
3. Click Create cluster.
4. Configure the instance type, node count, and VPC settings.
5. Click Launch.

Step 2: Connect Your Application

• Install the AWS SDK with DAX Client.
• Change your DynamoDB SDK calls to use the DAX endpoint.

Step 3: Monitor and Optimize

• Use Amazon CloudWatch to track performance.
• Adjust TTL settings and scaling policies as needed.

Conclusion

AWS DynamoDB Accelerator (DAX) is an in-memory caching service that supercharges DynamoDB read performance. It’s perfect for read-intensive applications like recommendation engines, gaming leaderboards, and IoT platforms.

By automatically caching frequent queries, DAX minimizes latency and optimizes costs, making your application much more efficient and scalable.