Posted On: Jul 26, 2018
AWS Greengrass now allows you to deploy executables written in C, C++ and any other language that supports importing of C libraries. Executable code has the benefits of greater legacy support as customers can more easily re-use code that is already written in C or C++, minimal resource footprint as no language interpreter is required, and an absolute minimum of compute latency for very high-performance use cases such as computer vision or algorithmic trading. Starting today, your executable code acts much like an AWS Lambda function, can be invoked by events or invoke other Lambdas, and can take advantage of other Greengrass functionality such as Local Resource Access. You can mix and match executable code together with Lambda functions written in interpreted languages such as Python or Node.js.
Greengrass now also allows you to change the queue size for locally stored MQTT messages. Greengrass already spools messages that are published to the cloud when the host device is disconnected from the internet, which is important for environments with intermittent connectivity. Now, the queue size is configurable, which lets customers fine tune the balance between data retention and conserving local storage space.
Next, Greengrass allows you to configure the maximum reconnect/retry period for when the core device is disconnected. Greengrass retries connecting to the internet at progressively longer intervals when the host device is disconnected, which is important for environments with limited connectivity. Now the maximum retry period is configurable, which lets customers ensure Greengrass reconnects to the internet quickly once connectivity becomes available.
In addition to these new and enhanced capabilities, Greengrass now has improved messaging throughput performance.
This new update is available to customers at no additional cost and is available in all AWS regions Greengrass is available in. To get started, just download the latest version of Greengrass Core from the AWS Management Console, and have a look at our documentation. To learn more about AWS Greengrass, please visit AWS Greengrass.