Redis Cache Keys & TTLs: A Guide

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Hey folks! Ever wondered how we can speed things up when evaluating stuff? Well, the secret sauce involves something called Redis, a super-fast in-memory data store. This guide will walk you through setting up cache keys and Time-To-Live (TTLs) for Redis, specifically for caching evaluation results. This is super important to help us with #6, which is all about making things faster. Let's dive in!

Understanding the Need for Redis Caching

So, why bother with Redis caching in the first place, right? Imagine you're constantly evaluating feature flags or other criteria. This evaluation process can be computationally expensive, especially if it involves complex rules or accessing data from various sources. Each time a user or system needs to check a flag, it could trigger this whole process, slowing things down. That's where Redis swoops in to save the day! Redis acts as a cache, storing the results of these evaluations. When a request comes in, we first check the Redis cache. If the result is there (a cache hit), we can quickly return the cached value. If it's not (a cache miss), we perform the evaluation, store the result in Redis, and then return it. This drastically reduces the load on our systems and speeds up responses. Caching evaluation results also helps us handle bursts of traffic more efficiently. Instead of having to process a ton of requests at once, Redis can serve cached results, ensuring that our systems stay responsive even during peak times. In short, caching with Redis is about improving performance, reducing load, and ensuring a smoother experience for everyone. It's like having a super-efficient assistant that remembers the answers to frequently asked questions, so you don't have to keep figuring them out from scratch.

The Benefits of Using Redis

  • Speed: Redis is incredibly fast because it stores data in memory. This means retrieval times are measured in microseconds. That's lightning fast!
  • Scalability: Redis can handle massive amounts of data and traffic. You can scale it up to meet the growing needs of your application.
  • Simplicity: Redis is easy to set up and use. It offers a simple API and a range of data structures.
  • Reliability: Redis offers persistence options, so you don't lose your data if the system goes down.

Aligning Cache Keys with ADR-11 and Identifiers

Alright, let's get down to the nitty-gritty of cache keys. Cache keys are basically unique identifiers that we use to store and retrieve data in Redis. It's super important to design them carefully so that the cache works efficiently and doesn't become a mess. We're going to align our cache key schema and namespacing with ADR-11 (Architecture Decision Record). This ensures that we're following best practices and staying consistent with our overall system design. The key thing here is making sure that our cache keys are unique, easy to understand, and follow a consistent format. So, what should this format look like? We need to include things like flag identifiers and project identifiers. This helps us to narrow down which specific feature flag evaluation results are stored. It’s like having a clear and organized filing system for our cache data. This systematic approach is also crucial for implementing things like cache invalidation and ensuring that stale data doesn't stick around. When a flag changes or a new rollout is launched, we'll need to invalidate the cache entries associated with it. Having well-defined cache keys makes this process much easier and more reliable. We will keep in mind that the primary goal is to ensure a smooth, efficient, and well-managed caching strategy that perfectly complements our evaluation processes. So, what would this actually look like in practice? A good cache key might look something like this: evaluation:{project_id}:{flag_id}:{user_id}. This structure is clear and easy to understand. We’re using a consistent format, with the components separated by colons. It's straightforward to use this key to retrieve and update the evaluation results for a given flag and a specific user. This systematic approach enhances the maintainability and scalability of our caching strategy, ensuring it adapts smoothly to any future developments.

Namespacing and Organization

  • Namespacing: Start with a prefix like evaluation: to group all evaluation-related keys. This prevents conflicts with other data stored in Redis.
  • Project ID: Include the project ID to scope the cache to a specific project.
  • Flag ID: Add the flag ID to identify the specific feature flag being evaluated.
  • User ID (Optional): Include a user ID if the evaluation is user-specific. This allows for personalized caching.

Serialization Format for Cached Evaluation Payloads

Next, let’s talk about how we're going to serialize the cached evaluation payloads. Serialization is the process of converting complex data structures into a format that can be stored in Redis. We're going to use JSON (JavaScript Object Notation) for this. JSON is a widely used, human-readable format that's easy to parse and understand. This makes it a great choice for our caching needs. The beauty of JSON is that it's super flexible. It allows us to store complex objects, and we can easily extend it to include more data in the future. We're going to structure the cached evaluation payloads using a specific JSON format that includes the evaluation results and any relevant metadata. This format will ensure that the cached data contains all the information needed to make decisions. The JSON structure will include the evaluation result (e.g., true or false), any rollout metadata. This metadata can be super important, like the percentage of users the flag is enabled for or the specific version of the flag configuration. By including this data, we can make informed decisions about feature flags. The goal is to create a structure that's not only efficient for Redis storage but also straightforward to decode and use within our application. So, what might a JSON payload look like? Something like this: json { "result": true, "rollout_metadata": { "percentage": 75, "version": "v1.2" } } .

JSON Structure Details

  • result: A boolean value indicating the evaluation result (true or false).
  • rollout_metadata: An object containing rollout-related information:
    • percentage: The percentage of users the flag is enabled for.
    • version: The version of the flag configuration.

TTLs and Refresh Strategies: Hot vs. Cold Keys

Now, let's get into TTLs (Time-To-Live) and refresh strategies. TTLs are super important for managing our Redis cache. They tell Redis how long a cached entry should live before it's automatically removed. We'll need to think carefully about how long different types of data should be stored. We don’t want data to live for too long, but we also want it to be available when needed. It all comes down to finding the right balance. For this, we'll consider two categories of keys: hot keys and cold keys. Hot keys are those accessed frequently (e.g., feature flags used in core user flows). Cold keys, on the other hand, are accessed less often (e.g., flags used for less critical features or internal tools). The TTLs and refresh strategies will vary significantly depending on whether a key is hot or cold. For hot keys, we might choose a shorter TTL (e.g., a few minutes or even seconds) to ensure that the data is as fresh as possible. We also might want to implement a proactive refresh strategy. This means periodically refreshing the cache before the TTL expires, so that we always have the latest data available. For cold keys, we can use a longer TTL (e.g., hours or even days). This is because we don't need to update the data as frequently. We can also rely on a passive refresh strategy. This means that when a cold key is accessed after its TTL has expired, we refresh it on demand. To manage TTLs and refresh strategies effectively, we need to monitor the cache. We want to see how often each key is accessed, how often it's missed, and how long it takes to refresh. This information helps us optimize our caching strategy. We can use tools like RedisInsight or monitoring dashboards to keep an eye on things. This ongoing monitoring and adjustment ensure our caching strategy is in peak performance.

TTL Recommendations

  • Hot Keys: Short TTLs (e.g., 60 seconds to 5 minutes) and proactive refresh strategies.
  • Cold Keys: Longer TTLs (e.g., 1 hour to 24 hours) and passive refresh strategies.

Refresh Strategies

  • Proactive Refresh: Refresh the cache before the TTL expires (e.g., using a background process).
  • Passive Refresh: Refresh the cache on demand when a key is accessed after its TTL has expired.

Capturing Findings in ADR-11 or Addendum

Finally, we'll capture everything we've learned in doc/adr/0011-invalidate-redis-caches.md or a follow-up ADR addendum. ADRs (Architecture Decision Records) are super important for documenting our design choices. This helps keep everyone on the same page. It’s like creating a historical record of our system. This includes the cache key schema, the serialization format, and the TTLs and refresh strategies we've chosen. If we need to make any adjustments later on, we can update the ADR to reflect those changes. This is important for keeping everything up to date. This ensures that the documentation accurately reflects the current state of our system. It makes sure that anyone who comes along later can understand why certain decisions were made. By documenting all of our decisions, we're not only improving transparency but also streamlining future maintenance and enhancements. So, capturing our findings helps us maintain a well-documented and well-designed caching strategy.

Key Takeaways

  • Document everything: Make sure all design choices are documented.
  • Update when needed: Revise the ADR if changes are made.

Conclusion

Alright, folks! That covers the basics of defining evaluation cache keys and TTLs for Redis. We've gone over the why, the how, and the what. By carefully designing your cache keys, choosing the right serialization format, and setting appropriate TTLs, you can boost your application's performance and make your systems more resilient. Keep in mind that Redis caching is a continuous process. You should constantly monitor your cache performance, adjust your strategies as needed, and always keep your documentation up-to-date. If you want to dive deeper, check out the documentation for Redis and JSON. Happy caching, and thanks for reading!