We continue discussing the ZooKeeper. It is a co-ordination service with elements from group messaging, shared registers and distributed lock services. It provides a interface that guarantees wait-free property and FIFO execution of requests from each client.  Requests across all clients are also linearized.
We were discussing the throughput of ZooKeeper when the system is saturated and with various injected failure.The most dip in throughput occurred with the failures of the leader. On the other hand, failure of the followers is tolerated with a quorum and the leader election algorithm helps mitigate this further.
We now start reading "Modern data Fraud Prevention at Big Data Scale". Feedzai enables companies to move from broad segment based scoring of transactions to individual oriented scoring with machine learning based techniques.Traditional approaches such  as those based on SAS suffered from the limitation that they have become old and difficult to maintain. Second, they are inflexible and unable to keep up with dynamic requirements.Feedzai claims to use a new technology on a new platform. They claim to have highest fraud detection rates with lowest false positives. They have true real time processing. They have true machine learning capabilities. They run on commodity hardware. They are non-intrusive and they are easily deployed.
we will read about them in the coming week. But I have a question to ask will fraud detection go down if we can differentiate that the transaction was indeed made by the genuine user ? do we always need the user to sign in to let us know that he or she is the real user. Will we recognize them by the phone they carry. we allow websites to delegate authentication such as with OAuth. Why don't we delegate it to Androids and  Apple's ?

#Fraud detection service introduction: https://1drv.ms/w/s!Ashlm-Nw-wnWsEv9woJ7ynzJAPpv 

#codingexercise
any contiguous sorted collection with duplicates has linear runtime complexity to find the weighted mean