Here is a detailed summary of the video transcription in markdown format with the key takeaways:
Caching in Serverless Architectures
Overview
- The session covers how to implement caching in a serverless architecture using Elastic Cache Serverless.
- The speakers will dive deep into the details of how Elastic Cache Serverless was built and the improvements made over the last year.
Caching Strategies
- Cache Aside/Look-aside Caching: Simple approach where the compute system reads from the cache first and falls back to the database if the data is not found in the cache.
- Write-through Caching: Data is written to both the cache and the database simultaneously, ensuring data consistency but slower write latency.
- Write-behind Caching: Data is written to the cache first and then asynchronously written to the database, providing faster write latency but potential for data loss and inconsistency.
Cache Invalidation and Consistency
- Time-based invalidation: Data is evicted from the cache based on a pre-defined Time-to-Live (TTL).
- Event-based invalidation: A separate system monitors the database for changes and invalidates the corresponding cache entries.
- Version-based invalidation: Each cache entry is versioned, and reads/writes ensure the latest version is accessed.
Caching Use Cases
- Session management: Store user session data in hash or JSON structures.
- Rate limiting: Use cache to track and limit access to resources.
- Caching expensive computations: Store the results of expensive database queries in the cache.
- Leaderboards and rankings: Use sorted sets in the cache to store and retrieve leaderboard data.
Elastic Cache Serverless Architecture
- Proxy layer that abstracts the underlying cache topology changes and provides a single endpoint for clients.
- Automatic scaling, both vertically (burst capacity) and horizontally (doubling capacity every 2 minutes).
- Multi-AZ data replication and local-AZ read optimization for low-latency access.
- No capacity management required, true pay-per-use pricing model.
Elastic Cache Serverless Implementation Details
- Utilizes a platform technology with a flexible memory and CPU footprint to enable instant scaling.
- Employs heat management to balance the load across the physical hosts running the cache nodes.
- Provides instant vertical scaling (5 seconds) and horizontal scaling (30 minutes) to handle spiky workloads.
- Leverages a proxy layer to abstract topology changes and provide a single logical entry point for clients.
- Supports microSecond read latency by leveraging local-AZ read replicas.
- Introduces a new multi-threaded architecture in Valkyrie (the open-source cache engine) to achieve 1 million requests per second on a single instance.
Best Practices
- Use long-lived connections for better performance.
- Leverage read-from-replica option for low-latency reads.
- Avoid expensive operations like
SCAN
on the cache.
- Limit large objects to manage network and CPU usage.
- Use Time-to-Live (TTL) to manage cache size, with random jitter for eviction.