This example demonstrates DataSQRL's capabilities by replicating shipment delivery in Manhattan. It serves as an ideal use-case for streaming, showcasing real-time shipment tracking. Monitor and manage shipments as they move through the city and answer queries like the number of shipments per customer and their current locations. Amazingly, all of this is achieved in just 33 lines of code, imports included. How cool is that?
Run the example with:
docker run -it -p 8081:8081 -p 8888:8888 --rm -v $PWD:/build datasqrl/cmd:latest run -c logistics_package_test.jsonNormally, there is no need to regenerate the test data if you are using pre-configured sets. However, if you wish to modify or experiment with the data, here's how you can generate your own test data.
- Remove the
datagen/generatedfolder. - Then navigate to the logistics root directory and run the following commands:
python datagen/src/generate_test_data.pyto generate fresh test data. This command generates three folders under thegeneratedfolder:assets: In this folder, you can find the plotted route for each event when a vehicle status was emitted. Alongside them, there is alogistics.gifwhich is an animation created from the plots. We only commit the lastpngfile to show the full route; any other file is irrelevant, as they are needed for the gif creation. We didn't add automation to delete them because this way you can use them for your experiments.db: In this folder, you can find the entities which could live in an RDBMS.stream: In this folder, you can find the events which would be emitted by a sensor on the vehicle or by a scanner in the distribution center.
See Python Utilities for details.