Optimizing for high performance with Amazon ElastiCache Serverless (DAT327)

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.

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