Hey there! Let's break down your questions about caching for Cosmos DB, focusing on Reliable Dictionary and Redis Cache.

• Ideal scenarios for Reliable Dictionary:

  • You're already running a Service Fabric cluster—since Reliable Dictionary is a core component of Service Fabric, it integrates seamlessly without needing to deploy a separate caching service. This cuts down on operational overhead.
  • You need strong consistency for cached data. Unlike many distributed caches, Reliable Dictionary supports distributed transactions, making it a good fit if your cached data requires strict consistency with your Cosmos DB state changes.
  • Your workload is tightly coupled with Service Fabric stateful services. It’s great for extending the state storage of your stateful services into a distributed, persistent cache that’s managed alongside your service.

• TTL (Time-To-Live):
  Reliable Dictionary doesn’t have a built-in expiration mechanism. To handle TTL, you’ll need to implement it yourself—for example, storing a timestamp alongside each cached item, then checking that timestamp on read operations to evict expired entries, or setting up a background task to periodically clean up stale data.

• Capacity:
  The capacity is tied directly to your Service Fabric cluster resources. Each partition of a Reliable Dictionary has a default maximum size of 10GB, but this can be adjusted via configuration. Since it supports automatic partitioning and scaling, you can expand the total capacity linearly by adding more nodes to your cluster or increasing the partition count. It also persists data to disk (depending on your Service Fabric configuration), so you’re not limited just by node memory—disk space plays a role too.

Redis is the go-to for most Cosmos DB caching scenarios because of its flexibility, performance, and wide ecosystem support. Here’s the breakdown:

• Capacity:
  If you’re using Azure Redis Cache (the most common pairing with Cosmos DB), capacity varies by pricing tier:

  • Basic/Standard tiers: Range from 250MB up to 53GB per instance.
  • Premium tier: Up to 12TB per node, and you can cluster up to 10 nodes, giving you a total maximum capacity of 120TB.
    You can scale up/down tiers or scale out clusters as your caching needs grow, so it’s highly flexible.

• TTL (Available Duration):
  Redis gives you full control over item expiration:

  • You can set a TTL for individual keys using commands like EXPIRE key seconds or SET key value EX 3600 (sets a 1-hour TTL). TTL values can be as short as 1 second, or you can omit TTL entirely to keep keys permanently (until manually deleted or evicted).
  • When Redis reaches its memory limit, it uses configurable eviction policies (like LRU - Least Recently Used) to automatically remove keys to free up space. So even without a set TTL, keys might be evicted if memory is constrained—you can tweak this policy based on your priorities (e.g., evict expired keys first, or least used keys).

内容的提问来源于stack exchange，提问作者RKh