We will need to introduce the following features to implement the functionalities we discussed above:

Feature #1: Find all the people on Facebook that are in a user’s friend circle.

Feature #2: We want all the user’s friends on Facebook to be suggested on Instagram as well. Since Instagram is a different platform, all of its connections need to be copied to a separate database.

Feature #3: Sync the Facebook stories list with Instagram.

Feature #4: Limit the request rate from users. The same request cannot be sent from the other platform until a specified amount of time has elapsed since the request from the first platform.

Feature #5: Identify the morphed versions of abused and profane words so posts containing them can be flagged inappropriate.

Feature #6: Group the similar gibberish posts together so a decoding pattern can be observed.

Feature #7: Mining for patterns in posts by a user needs to be done on a high-performance cluster. You need to suggest an optimal assignment of posts to cluster nodes so that their processing power is optimally utilized.

Feature #8: Find the smallest sequence of topics mentioned by a user, which overlaps with the topics mentioned by their friend.

Feature #9: To recommend ads to Instagram users, we want to use Facebook’s recommendation tree. Recreate the decision tree, given that the original tree is serialized in the form of preorder and inorder traversals.