![]() The main sequential path of the transaction process can also ‘fork’ into one or more sub-processes before rejoining the main transaction path. State changes take place at various stages on the path from the point of origination of the transaction to its point of completion. A subsequent state change may be that the proposal has been accepted by the counterparty and the netting set has been amended. One example of an event resulting in a state change might be a transaction has been proposed for removal from a netting set. The full range of state changes which trigger events are pre-defined in the workflow. It is helpful to view an end-to-end transaction consisting of many stages as a sequence of events which result in “state changes”. The first is: How do you ensure that the data being consumed is not corrupted or in an incorrect or unusable form? The second is: How do you build an infrastructure that is scalable yet maintains the highest degree of data integrity? Breaking the transaction down to ‘state changes’ Unreliable data values create fundamental problems. So how do we handle that issue? The business context concerns high value transactions being processed through the post-trade environment, so data integrity is paramount. In many of the conversations we have with clients, whether we’re talking from a technology or a deployment perspective, a key concern is: What happens if there’s a failure or a break? How are transactions recovered? In particular, clients are keen to know whether in that recovery process we can ensure that there are no duplicates or omitted messages around the post-trade operation. Idempotency is important because it allows systems to operate at speed and cater for the high message volumes processed by post-trade systems. In this post we’re looking at idempotency – the ability of a system to handle sets of related messages irrespective of the sequence in which they arrive and to ignore duplicate messages. ![]()
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