Mongodb compass connecting to gcp code#
All source code for this post is written in Python. You will need to adjust the project’s environment variables to fit your own development and Cloud environments. branch master -single-branch -depth 1 -no-tags \ Use the following command to clone the project. In all cases, we will still benefit from the Cloud Function’s horizontal scaling capabilities.Īll source code is all available on GitHub.
We could also split the telemetry data across multiple MongoDB Atlas Collections, based on sensor type, instead of a single collection. For example, we could create individual pipelines of functions and topic/subscriptions for each sensor type. Each type of sensor will transmit data to a different Cloud Function HTTPS endpoint.Īlthough the Cloud Functions will automatically scale horizontally to accommodate additional load created by the volume of telemetry being received, there are also other options to scale the system. All sensors will transmit data to a single Cloud Function HTTPS endpoint.įor Protobuf over HTTP, the architecture will look as follows in the demonstration. Once in Atlas, we will aggregate, transform, analyze, and build machine learning models with the data, using tools such as MongoDB Compass, Jupyter Notebooks, and Google’s AI Platform Notebooks.įor this demonstration, the architecture for JSON over HTTP will look as follows. A second Cloud Function will respond to all published events and push the messages to MongoDB Atlas on GCP. We should observe a reduction in the message size contained in the request payload as we move from JSON to Protobuf, which should reduce system latency and cost.ĭata received by Cloud Functions over HTTP will be published asynchronously to Google Cloud Pub/Sub. We will then switch to using Google’s Protocol Buffers to transmit binary data over HTTP. We will transmit the sensor telemetry data as JSON over HTTP to serverless Google Cloud Function HTTPS endpoints. Prototype IoT Devices used in this Demonstration
The devices contain a variety of common sensors, including humidity and temperature, motion, and light intensity. Each IoT device is installed in a different physical location. In this demonstration, we will collect environmental sensor data from a number of IoT device sensors and stream that telemetry over the Internet to Google Cloud. They may prefer to transmit telemetry over HTTP using TCP, or securely, using HTTPS (HTTP over TLS). Similarly, depending on the performance requirements and the type of application, organizations may not need or want to start out using IoT/IIOT industry standard data and transport protocols, such as MQTT (Message Queue Telemetry Transport) or CoAP (Constrained Application Protocol), over UDP (User Datagram Protocol). Academic institutions, research labs, tech start-ups, and many commercial enterprises want to leverage the Cloud for IoT applications, but may not be ready for a fully-integrated IoT platform or are resistant to Cloud vendor platform lock-in.
In reality, not everyone needs a fully integrated IoT solution. They are capable of scaling to tens-of-thousands of IoT devices or more and massive amounts of streaming telemetry. All of these solutions are marketed as industrial-grade, highly-performant, scalable technology stacks. Amazon has AWS IoT, Microsoft Azure has multiple offering including IoT Central, IBM’s offering including IBM Watson IoT Platform, Alibaba Cloud has multiple IoT/IIoT solutions for different vertical markets, and Google offers Google Cloud IoT platform. Most of the dominant Cloud providers offer IoT (Internet of Things) and IIotT (Industrial IoT) integrated services. Aggregate, analyze, and build machine learning models with the data using tools such as MongoDB Compass, Jupyter Notebooks, and Google’s AI Platform Notebooks. Collect IoT sensor telemetry using Google Protocol Buffers’ serialized binary format over HTTPS, serverless Google Cloud Functions, Google Cloud Pub/Sub, and MongoDB Atlas on GCP, as an alternative to integrated Cloud IoT platforms and standard IoT protocols.
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