Detailed Component Design

Deep dive into 2-3 key components. Explain how they work, how they scale, and any relevant algorithms or data structures. Consider drawing detailed diagrams to enhance your explanation...

Consistency

As we can't have two long URLs with the same short URL and we intend to use the same unique id to generate the key, it is essential that we go for consistency over availability in the event of a network partition. Since Cassandra is leaderless, we need to ensure a decent quorum.

Assume Replication factor is 4. We can keep read quorum as 3 and write quorum as 3 ensuring -> read quorum + write quorum > replicas number.

Caching

Use a redis cache cluster. I will have multiple nodes . Additionally, I will maintain a local in memory cache for each app server which will have the hot key. Identification of the hot key can be done by using Redis --hotkeys. Additionally, we can keep the hotkeys in all the nodes and read from any replica. Write should be to all the replicas

We go in this order local cache -> global redis cluster -> DB

In case of cache miss we go in the above order and populate the caches with the value retrieved from DB

Cache invalidation is not a major concern, since we don't support URL update. User needs to create a new tiny url with the updated URL. IN case of deletion or take down of the site, the TTL of cache should help. We can keep a TTL of 1 month to ensure that.

Since it's LRU cache, the older entries will get flushed out to make way for new ones.

Rate limiting

Rate limiting will be done by using a Kong / Apisix Application gateway. Requests will be based on Token Bucket algorithm. In the event of burst traffic, since the number of tokens is predetermined based on our capacity to handle , requests will be rejected if tokens are not available

URL update

URL update or delete is not supported. However, after the expiry of the URL it will get deleted from the DB and from cache ( no need to separately remove from cache as it has a TTL )

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