Why is clock synchronization so important in big data

Distributed transactions have historically been implemented by the database community in the manner pioneered by the architects of System R* [22] in the 1980s. The primary mechanism by which System R*-style distributed transactions impede throughput and extend latency is the requirement of an agreement protocol between all participating machines at commit time to ensure atomicity and durability. To ensure isolation, all of a transaction’s locks must be held for the full duration of this agreement protocol, which is typically two-phase commit.

The problem with holding locks during the agreement protocol is that two-phase commit requires multiple network round-trips between all participating machines, and therefore the time required to run the protocol can often be considerably greater than the time required to execute all local transaction logic. If a few popularly accessed records are frequently involved in distributed transactions, the resulting extra time that locks are held on these records can have an extremely deleterious effect on overall transactional throughput. [ Calvin: Fast Distributed Transactions for Partitioned Database Systems. Alexander Thomson et al]

This is one reason why we have all the new databases without transaction support – because high transaction rates in a distributed environment (e.g. web or IOT applications in the Cloud) cannot be scaled in face of lock overhead.