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What we are describing is a modular software architecture, which is a very old idea, see for example Parnas (1972). For example, packing multiple data items into the same network packet or disk block to increase efficiency and reduce utilisation. Using a single entry point has the added advantage that operations can be reordered to better suit the optimal access patterns of the device (current storage devices perform better for linear than random access).Īdditionally, batching provides the opportunity to share out the cost of expensive operations such as I/O or expensive computations. The I/O bandwidth offered by persistent storage devices can improve dramatically when issuing commands from a single thread (and thereby CPU core) instead of contending for bandwidth from all cores. The same reasoning applies to the use of external resources that need synchronization and coordination. This can mean processing a set of data elements in batches, or it can mean performing different processing steps on dedicated hardware threads.
#PASSING NOTION DEFINITION FULL#
This means that giving different tasks to the same CPU core in rapid succession will not benefit from the full performance that could otherwise be achieved: if possible we should structure the program such that its execution alternates less frequently between different tasks. BatchingĬurrent computers are optimized for the repeated execution of the same task: instruction caches and branch prediction increase the number of instructions that can be processed per second while keeping the clock frequency unchanged. The back-pressure may bubble all the way up to the user, at which point responsiveness may degrade, but this mechanism will ensure that the system is resilient under load, and will provide information that may allow the system itself to apply other resources to help distribute the load, see Elasticity. This back-pressure is an important feedback mechanism that allows systems to gracefully respond to load rather than collapse under it. Since it can’t cope and it can’t fail it should communicate the fact that it is under stress to upstream components and so get them to reduce the load.
It is unacceptable for the component under stress to fail catastrophically or to drop messages in an uncontrolled fashion. When one component is struggling to keep-up, the system as a whole needs to respond in a sensible way. This is the antonym of synchronous processing which implies that the client only resumes its own execution once the service has processed the request. The client cannot directly observe, or synchronize with, the execution that occurs within the service. In the context of this manifesto we mean that the processing of a request occurs at an arbitrary point in time, sometime after it has been transmitted from client to service. The Oxford Dictionary defines asynchronous as “not existing or occurring at the same time”.
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