Enabling Rapid Microservice Development with a Julia SDK
2021-07-30, 19:00–19:30 (UTC), Red

The Optimal Reality platform, by Deloitte Digital Australia, is a modelling and simulation environment built and deployed as Julia microservices. This talk describes our Software Development Kit which enables rapid building and deployment of these microservices. We discuss our use of templates, utility packages, standardised multi-threading and logging functions, and a custom GraphQL interface; and share how they can be used to bolster team creativity and efficiency.

The benefits of microservices architectures are well understood. They have the potential to be more agile, enable each service to pick the best technology for its purpose and be scaled or autoscaled appropriately, and can be easily deployed and managed through common open-source technologies.

Developing a solution with a microservices architecture, however, can have some disadvantages. A developer creating a new microservice must understand the external interfaces to that service, how it is tested and deployed, and how to configure the service to run and scale as required. This increases the skill requirement of a developer and can take time away from what the developer is actually trying to do – create a new bit of functionality. It can also result in inconsistencies in code behaviour and style between services. This problem is compounded when the developers are using a new language and are unfamiliar with what is possible and with best practices, and further compounded when that language is itself rapidly developing.

We faced this challenge when developing a modelling and simulation platform built and deployed as Julia microservices. Julia was a new language for the majority of our team, and we needed to quickly design and deploy many services. Furthermore, we wanted to continue to use recent open-source developments without requiring that all of our developers must stay up to date with package and language advancements. To enable our developers to focus on what they’re best at (i.e., the functionality of the service they’re developing) and mitigate the issues mentioned, we made use of Julia’s excellent ecosystem to create a Software Development Kit (SDK).

In this talk, we describe the components of the SDK, how they enable both efficient development and use of new open-source advances, and how similar approaches can be used by your team as you build microservices. We will discuss how Julia’s package system makes it ideal for SDK use.

Located in a private registry, the SDK includes a microservice template, a utility package template, various utility packages and a custom GraphQL interface. The microservice template is the starting point for a developer writing a new microservice and includes the following functionality:

  • Default communication routes (service execution, liveness etc)
  • Logging behaviour
  • Automatic documentation
  • Asynchronous and multithreading tools
  • CICD scripts (testing, building and cloud deployment)

We will discuss the above, including detailing the various open-source packages used for each function.

We will also describe how providing utility packages in an SDK enables teams to make use of the best open-source developments, which may be developing and changing frequently, through a stable API. For example, when handling large volumes of data, it is often desirable to encode and decode numeric arrays to minimise data transfer. One package in our SDK provides a simple encode and decode interface, where the specifics of what compression packages are being used can be updated as required without the majority of users needing to stay abreast of open-source developments. We will detail this approach and give other examples of where we have found it useful. Finally, we will describe our custom GraphQL interface, which wraps a generic interface in a similar method to the utility packages, enabling developers to quickly interact with and make use of our platform.

To conclude, Julia is a powerful language with an exceptional ecosystem. In this talk, we will demonstrate how it can be used to create an efficient environment for microservice development which lets developers focus on what they’re developing, ensures all services use the best of the open-source community and generally makes things much more straightforward.

Malcolm is a simulation engineer at Deloitte Digital Australia with a background in real-time simulation in the automotive and transport domains. Currently he is developing real-time simulations of large transport networks as part of the Optimal Reality team, and enjoying contributing to Julia.