3. Asynchronous Count Updates with Kafka and Caching (Revised Third Approach)

How it works: This approach builds on the Kafka asynchronous update mechanism but adds a caching layer to provide instant feedback and fast reads. The steps are:

1. Immediate Write of Action to DB: Just like the previous approach, each like/dislike action is recorded as a separate entry in the database right away. This ensures durability and a trace of each user’s action.
2. Update Cache’s Count: The system then updates the cache for the total like/dislike count of that item immediately. For example, if a post had 50 likes in cache, and a new like comes in, the application or a caching layer will increment the cached count to 51. This gives real-time feedback – the next time someone fetches the like count (even the same user immediately after liking), they will see “51” from the cache without waiting for the backend aggregation. This cache is typically a fast in-memory store like Redis or Memcached. The update can be done with an atomic operation (e.g., Redis INCR) to handle concurrent updates safely – ensuring two simultaneous likes both get applied without losing one.
3. Publish Event to Kafka: In parallel, the service still publishes an event to the Kafka topic for likes (so the asynchronous pipeline is informed of the new like). The event will be used by background workers to eventually reconcile the persistent count in the database.
4. Kafka Workers Update Database: Kafka consumer workers operate similarly as in Approach 1: they consume like events in batches. The difference now is that these updates to the database’s aggregate count are somewhat redundant in the short term (because the cache already has the latest count), but they serve to persist the aggregated count for long-term consistency and as a fallback. Workers might batch 100 events and then do an SQL UPDATE posts SET like_count = like_count + 100 WHERE post_id=.... Over time (every few seconds or minutes), the database’s stored count catches up with what the cache shows. If the cache was updated for each like, the DB update should match that total after processing the batch.
5. Cache and DB Convergence: The cache entry for the count can be given a short TTL (Time To Live), say 5 seconds, or some small window. This means every few seconds, if no new updates happen, the cache will expire and the next read will fetch the count from the database (which by then should include all recent updates from the Kafka consumers). This helps correct any discrepancy that might have occurred between cache and database. Alternatively, the system can invalidate the cache or refresh it when the database is updated by the worker (a mini write-through on the aggregated count update).

Read Path: For any client retrieving the like/dislike count, the application will read from the cache. If the cache has a value (not expired), it returns that almost instantly. This ensures that users always see the most up-to-date count (including recent likes) with low latency, as long as the cache is being kept in sync. If the cache entry expired or is missing (cache miss), the service can fall back to the database: fetch the persisted count from DB (which might be slightly behind), return it, and repopulate the cache with that value (cache-aside pattern). However, because of the short TTL and continuous updates, such cache misses for a hot item would be rare. Essentially, the cache acts as the primary source for reads, with the DB as backup and long-term storage.

Pros:

The Kafka + Cache approach provides the best of both worlds – responsive updates and scalable processing:

• Real-Time User Experience: Users see their like/dislike actions reflected immediately in the count, thanks to the cache update. The interface feels instant and interactive, with no waiting for the backend to catch up. Reads hitting the cache are extremely fast (in-memory lookups), which is crucial for high-traffic scenarios where many users are viewing counts.
• Reduced Database Load on Reads: With the cache as the primary source for counts, read traffic to the database for these counts drops dramatically. This frees the database to handle other queries and reduces the chance of it becoming a bottleneck. The cache shields the database from reads, and the batched writes (from Kafka workers) are infrequent and efficient.
• Scalability and Throughput: Like the previous approach, writes are still handled asynchronously via Kafka, allowing the system to handle a massive stream of likes/dislikes. The addition of caching also means that even if the like count is requested very frequently (e.g., a viral post being liked and viewed by thousands of users), those requests don’t overwhelm the DB. The architecture can scale horizontally: more app servers to handle user actions (each updating cache and sending events), more Kafka partitions/workers to handle the firehose of events, and a distributed cache cluster to handle fast reads.

Cons:

While this approach is powerful, it adds more components to manage:

• Higher Complexity: There are now three moving parts (DB, Kafka, Cache) instead of two. Ensuring all three stay in sync and debugging issues can be challenging. Developers must handle cache invalidation properly (a notoriously hard problem) – e.g., if a bug prevents invalidation on a DB update, the cache could serve stale data for longer than intended. Understanding the interplay of TTL, cache updates, and Kafka timing requires careful design and testing.
• Cache Consistency Issues: Despite our strategies, there can be edge cases of inconsistency. For instance, if the Kafka consumers are delayed (say the system is briefly overwhelmed), the cache might carry “unconfirmed” increments for longer. If the cache TTL expires before the DB is updated, users might see the count jump down then up again, which could be confusing. In practice this window is small, but it’s a trade-off: we prefer showing a possibly temporary count increase (optimistic update) for responsiveness, accepting that in rare cases it might momentarily correct downward. Keeping the TTL very short minimizes this inconsistency window but too short a TTL could increase database reads (striking a balance is key).
• Cache Memory Overhead: Storing counts for many items in cache consumes memory. If the app has millions of content items, you wouldn’t cache all of them permanently – likely you cache the hot ones that are being actively liked/viewed. The short TTL helps avoid filling the cache with rarely-accessed items, but it means those items will cause a DB read occasionally when they expire. This is generally fine, but it’s a consideration (this is a balance between cache hit rate and staleness).

Use Cases: The revised third approach combines Kafka for scalable, reliable processing with caching for instant user feedback and fast reads. It handles failures by ensuring no single point (cache or worker) being down will permanently lose data or show wrong counts for long. The use of short TTLs, cache invalidation, and idempotent message processing collectively address the consistency challenges, making this approach suitable for large-scale systems where both performance and accuracy are required. Users get a snappy experience, and the system can handle the load and recover from issues gracefully, at the cost of a more complex architecture that must be carefully managed.