There are some paid third-party translation tools, but there are too many differences at the language level for us to feel comfortable relying on such tools. The situation with Swift is a bit more complicated. Following the standards and commonly accepted good practices was always important for the entire Expo team, so since a few past months we “Kotlinized” 36 packages in our repository.
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Android Studio can do this for you, with mixed results, and React Native itself won’t complain. Migrating from Java to Kotlin isn’t difficult. But iOS and Android developers today tend to use Swift and Kotlin, so why not use those languages instead? Today, most developers in the Expo and React Native native module ecosystem use Objective-C on iOS and Java on Android.
However, native module libraries rarely benefit from being written in C++ instead of a higher level, more ergonomic language.
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This is a great move because much of the code can be shared between Android and iOS and performance improvements in core usually have the most meaningful impact. React Native core has been moving increasingly low-level as more of it is written or rewritten in C++ for TurboModules and Fabric.
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While there is plenty of ongoing work on the current module API, this post is about a longer term project to rethink the way that we write Expo and React Native modules to take full advantage of modern languages, to make it more fun, easy, safe, and consistent across platforms. One topic that we did not discuss at all in that post is the work that we have been doing on module authorship tools for developers that write native modules for Expo and React Native. This change was more than just cosmetic it came with a slew of behind the scenes and user-facing benefits that we only partially covered in " What's new in Expo modules infrastructure". A job requires at least one jobanalysis in order to run.With the release of SDK 43, Expo is migrating from react-native-unimodules to the expo package and "Expo modules". Each jobanalysis object contains the analysis code, input command, hardware selection and the files associated with that particular jobanalysis.
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Jobanalyses refers to the simulation software packages–or analysis code in Rescale terms–that you want to run for that job. Jobvariables are input parameters that a user may specify to run a parameter sweep. A Rescale job is composed of two types of primary objects: jobanalyses and jobvariables. A job can contain one or more jobvariables. In fact, using DRF made our code clearer and more concise because a lot of the features of its serializers and viewsets made our custom validation redundant.īefore going into the details of the API endpoints, let us first take a look at how a Rescale job is defined. DRF worked quite nicely with our multilevel relational model, and migrating our Django forms to DRF serializers was quite easy.
It provides a clear and easily extensible interface for handling serialization, deserialization, authentication, and routing for your API.
The Django REST Framework (DRF) is a powerful toolkit for Django. We ended up using Django REST Framework for this purpose. Fleshing out our internal data models and converting our existing Django views into a public facing RESTful API was quite an undertaking. To enable that, we’ve been working on making a public facing API.Ĭurrently, our web service is a Django app, whose end points our Javascript client calls to create and monitor jobs. One of the items at the top of our customers’ wish list has been the ability to programmatically burst jobs onto the platform. At Rescale, we’ve been quite busy lately with a few exciting features in the works.
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