How do You Avoid the Bottlenecks and Save Money at the Same Time?

speed-async

If you had a very valuable and talented member of staff, would you send him/her on an errand to collect something from a supplier; a task that might tie them up for hours? Or, if you had a second option whereby you can request the supplier to call you when the product is ready for collection allowing your staff member to make a timely collection, which option might you take? Most people, I would venture, would choose the second option. However, many would favour having both options. Flexibility is always a good thing to have as part of any workflow.

With KantanMT’s announcement that its Software Development Kit (SDK) has been enhanced by the addition of a new Asynchronous Interface the community of KantanMT users has been given this very flexibility. This development in the SDK provides a high speed, high volume asynchronous programme interface in to both Statistical and Neural MT engines. This option complements the already existing synchronous interface option.

For the uninitiated, the synchronous and asynchronous nature of an Application Programming Interface (API) is a function of the time it takes from the initial request for a service to the return of processed data. In the case of synchronous APIs, the expectation is that there will be an immediate return of processed data. The API requests data and then pauses and waits for the processed data to be returned. This has the impact of tying up that hardware whilst it awaits a result to its request. Hence my analogy above of tying up a valuable resource while he/she waits for the supplier to supply the requested product.

In the case of asynchronous APIs, it proceeds to send data on the basis that a resource may not be immediately available, and therefore, the requested service may not be immediately responded to. The API will receive a call back when the required service is available. The asynchronous method avoids the server (and an engineer?) pausing and lying dormant while it awaits a response, thus allowing that server to be employed in other tasks.

Asynchronous processing is used in the case where the user doesn’t want to halt processing while an external request is being dealt with. A synchronous API will block the caller until it returns thus tying up resources until the action has been completed. On the other hand, an asynchronous API will not block the caller and typically will require a call back that will be executed once the work is completed. Asynchronous requests are useful in maintaining functionality in an application rather than tying up resources while waiting on a request.

The buying of hardware is a big capital investment for many companies, particularly start-ups. And staying on top of the biggest and the best is also an endless drain on company resources. Therefore, server time must be optimised to the nth degree. A tied-up server equates to a wasted resource – hence my analogy of the valuable staff member twiddling his/her thumbs while they queue for a collection.

The provision by KantanMT of the asynchronous API function allows users to reduce spending by optimising server time, so making less need for investment in multiple servers. KantanMT is confident that this addition to its SDK will be welcomed by many of its clients; users who while requiring high volume translations, do not need it done in real-time. The Asynchronous interface will provide a solution to any possible bottleneck in server capacity by queuing non-urgent translation requests to be processed when capacity is available. This, KantanMT is confident, will ensure users can avoid spending on costly hardware, thus reducing outlays and so keeping TCO (Total Cost of Ownership) to a minimum for the KantanMT Community. Having a bypass to a bottleneck is always a good thing to have, especially when it equates to less spending.

Aidan Collins is a language industry veteran. He is Marketing Manager at KantanMT.

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