1. Schema of Connected Car Events
Each event in our connected car dataset contains 23 fields, but we will only concern ourselves with these:
id : String // a unique id for each event car_id : String // a unique id for the car timestamp : long // timestamp (milliseconds since the epoch) longitude : float // GPS longitude latitude : float // GPS latitude consumption : float // fuel consumption (liters per hour) speed : float // speed (kilometers per hour) throttle : float // throttle position (%) engineload : float // engine load (%)
2. Download the connected car data files
Download the data by running the following commands:
wget http://training.ververica.com/trainingData/carInOrder.csv wget http://training.ververica.com/trainingData/carOutOfOrder.csv
These two files contain the same event records, but in one file the data is sorted by timestamp, while in the other the data is out of order (by at most 30 seconds). Except when data is missing (i.e. when the car was turned off), there should be an event every 5 seconds.
3. Generate a Connected Car Event Stream in a Flink program
All exercises should be implemented using event-time characteristics. Event-time decouples the program semantics from serving speed and guarantees consistent results even in case of historic data or data which is delivered out-of-order.
Here’s an example of how you can create a
ConnectedCarEvent records will be served as fast as possible (unlike the
TaxiRideSource, which uses
sleep() to simulate a more realistic data source).
You will need to figure out how to generate appropriate watermarks. You should test each exercise with both the in-order and out-of-order data files, and make sure they produce consistent results.