DAX (DynamoDB Accelerator) is a fully managed, highly available, in-memory caching service designed specifically for Amazon DynamoDB. It delivers up to 10x performance improvement, reducing response times from milliseconds to microseconds for read-heavy and bursty workloads.
DAX operates as a writ…DAX (DynamoDB Accelerator) is a fully managed, highly available, in-memory caching service designed specifically for Amazon DynamoDB. It delivers up to 10x performance improvement, reducing response times from milliseconds to microseconds for read-heavy and bursty workloads.
DAX operates as a write-through cache, meaning when you write data to DynamoDB through DAX, the data is written to both the cache and the underlying DynamoDB table simultaneously. For read operations, DAX first checks if the requested item exists in the cache. If found (cache hit), it returns the data from memory. If not found (cache miss), DAX retrieves the data from DynamoDB, caches it, and returns it to the application.
Key features of DAX include:
1. **API Compatibility**: DAX is compatible with existing DynamoDB API calls. Applications using the DynamoDB SDK can switch to the DAX SDK with minimal code changes, making migration straightforward.
2. **Cluster Architecture**: DAX runs as a cluster with a primary node and optional read replicas. The cluster handles failover automatically, ensuring high availability.
3. **Two Cache Types**: DAX maintains an item cache for individual items retrieved via GetItem and BatchGetItem operations, and a query cache for results from Query and Scan operations.
4. **TTL Configuration**: You can configure Time-to-Live settings to control how long items remain in the cache before expiration.
5. **VPC Integration**: DAX clusters run within your VPC, providing network isolation and security.
DAX is ideal for applications requiring microsecond read latency, read-intensive workloads, and scenarios where reducing DynamoDB read capacity costs is beneficial. However, DAX is not suitable for write-heavy applications, applications requiring strongly consistent reads (DAX supports eventual consistency), or applications that perform infrequent reads on large datasets.
DAX is a fully managed, highly available, in-memory caching service specifically designed for Amazon DynamoDB. It delivers up to a 10x performance improvement - from milliseconds to microseconds - even at millions of requests per second. DAX is a write-through caching service, meaning data is written to the cache and the backend store simultaneously.
Why is DAX Important?
• Microsecond Response Times: DAX provides response times in microseconds for cached reads, making it ideal for read-intensive workloads • Reduced DynamoDB Costs: By caching frequently accessed data, DAX reduces the number of read requests to DynamoDB, lowering your provisioned read capacity needs • API Compatible: DAX is compatible with existing DynamoDB API calls, requiring minimal code changes • Highly Available: DAX runs within a VPC and supports Multi-AZ deployments for fault tolerance • Ideal for Read-Heavy Workloads: Perfect for applications requiring repeated reads of the same data
How DAX Works
Architecture: • DAX cluster consists of one or more nodes running within your VPC • A DAX cluster has a primary node and zero or more read replica nodes • Minimum of 3 nodes recommended for production (1 primary + 2 read replicas)
Caching Mechanism: • Item Cache: Caches individual items retrieved via GetItem and BatchGetItem operations with a default TTL of 5 minutes • Query Cache: Caches results of Query and Scan operations based on parameters used
Write-Through Caching: • When you write data, DAX writes to DynamoDB first, then updates the cache • This ensures data consistency between cache and database
Cache Misses: • When requested data is not in cache, DAX retrieves it from DynamoDB • The data is then stored in the cache for subsequent requests
When to Use DAX
• Applications requiring the fastest possible response times for reads • Read-intensive workloads with repeated reads of the same items • Applications that can tolerate eventually consistent reads • Reducing costs on read-heavy DynamoDB tables
When NOT to Use DAX
• Applications requiring strongly consistent reads - DAX only supports eventually consistent reads • Write-intensive applications that do not benefit from caching • Applications that do not perform repeated reads of individual items • Applications that require response times that are already acceptable
Key DAX Features
• Encryption at Rest: DAX supports encryption using AWS KMS • Encryption in Transit: TLS encryption for data between application and DAX • IAM Integration: Fine-grained access control using IAM policies • VPC Only: DAX clusters run exclusively within a VPC for security • CloudWatch Integration: Monitor DAX performance metrics
DAX vs ElastiCache
• DAX: Purpose-built for DynamoDB, API-compatible, minimal code changes required • ElastiCache: General-purpose caching, supports Redis and Memcached, requires more application logic changes • Choose DAX when working with DynamoDB and needing seamless integration
Exam Tips: Answering Questions on DAX
• Microsecond latency: If a question mentions needing microsecond response times for DynamoDB reads, DAX is the answer • Read-heavy workloads: DAX is the solution for read-intensive DynamoDB applications • Eventually consistent only: Remember that DAX does NOT support strongly consistent reads - if the scenario requires strong consistency, DAX is not appropriate • Write-through cache: DAX writes data to both the cache and DynamoDB simultaneously • VPC requirement: DAX must be deployed within a VPC - this is a common exam point • Minimal code changes: DAX uses the same API calls as DynamoDB, so existing applications need minimal modifications • Item Cache vs Query Cache: Know the difference - Item Cache for GetItem/BatchGetItem, Query Cache for Query/Scan operations • Cost optimization: If a question asks about reducing DynamoDB read costs for frequently accessed data, DAX is likely the answer • Multi-AZ: For high availability scenarios with DAX, remember it supports Multi-AZ deployments • Not for writes: DAX does not improve write performance - it is a read caching solution