Cumulocity allows developers and power users to run real-time IoT business logic inside Cumulocity based on a high-level real-time processing language.
This section introduces the basic concepts of real-time processing and shows how you can develop your own functional business logic at Cumulocity.
On top of Cumulocity you can use the Apama Streaming Analytics engine to define business operations for immediate processing of incoming data from devices or other data sources. These user-defined operations can for example alert applications of new incoming data, create new operations based on the received data (such as sending an alarm when a threshold for a sensor is exceeded), or trigger operations on devices. The operation logic is implemented in Apama’s Event Processing Language (EPL).
Apama’s Event Processing Language covers statements, which are organized into actions and monitors. Monitor files can be edited directly from within Cumulocity using the Streaming Analytics application. Alternatively, you can install Apama on your local machine and develop your applications with Software AG Designer - an Eclipse-based development environment. You can deploy your monitor files as Apama applications to Cumulocity, see Basic functionality for more information.
For further information on using Apama’s Event Processing Language in Cumulocity refer to Using the Apama Event Processing Language below and to Streaming Analytics.
Cumulocity’s real-time processing feature has the following benefits:
The Apama Event Processing Language has a syntax similar to Java. In addition to simple flow control statements such as if, while, for, users can write listeners with the on keyword to react to events.
Apama EPL is documented in the Apama documentation.
As an example, the following statement listens for new temperature sensor readings greater than a particular temperature:
on all Measurement(type="c8y_TemperatureMeasurement") as m {
if m.measurements.getOrDefault("c8y_TemperatureMeasurement").getOrDefault("T").value > 100.0 {
Alarm alarm := new Alarm;
alarm.type := "c8y_TemperatureAlert";
alarm.source := m.source;
alarm.time := currentTime;
alarm.text := "Temperature too high";
alarm.status := "ACTIVE";
alarm.severity := "CRITICAL";
send alarm to Alarm.SEND_CHANNEL;
}
}
Here, Measurement is a pre-defined event containing the measurements. In this example, m is the Measurement event, the listener is filtering for measurements which are c8y_TemperatureMeasurement and the property is c8y_TemperatureMeasurement.T.value which is in degrees Celsius of a temperature sensor (see Fragment library).
Listeners such as the above should be placed in a monitor in the onload statement, and the file must contain using statements for the types used by the listener - for most of the Cumulocity events, these are in the package com.apama.cumulocity. The full list is provided below - for the sake of brevity, we will omit these from further examples:
using com.apama.cumulocity.ManagedObject;
using com.apama.cumulocity.Operation;
using com.apama.cumulocity.Event;
using com.apama.cumulocity.Alarm;
using com.apama.cumulocity.Error;
using com.apama.cumulocity.Measurement;
using com.apama.cumulocity.MeasurementValue;
using com.apama.cumulocity.FindAlarm;
using com.apama.cumulocity.FindAlarmResponse;
using com.apama.cumulocity.FindAlarmResponseAck;
using com.apama.cumulocity.FindManagedObject;
using com.apama.cumulocity.FindManagedObjectResponse;
using com.apama.cumulocity.FindManagedObjectResponseAck;
using com.apama.cumulocity.FindMeasurement;
using com.apama.cumulocity.FindMeasurementResponse;
using com.apama.cumulocity.FindMeasurementResponseAck;
using com.apama.cumulocity.FindOperation;
using com.apama.cumulocity.FindOperationResponse;
using com.apama.cumulocity.FindOperationResponseAck;
using com.apama.cumulocity.FindEvent;
using com.apama.cumulocity.FindEventResponse;
using com.apama.cumulocity.FindEventResponseAck;
using com.apama.cumulocity.SendEmail;
using com.apama.cumulocity.SendSMS;
using com.apama.cumulocity.Util;
using com.apama.util.AnyExtractor;
using com.apama.correlator.timeformat.TimeFormat;
using com.softwareag.connectivity.httpclient.HttpOptions;
using com.softwareag.connectivity.httpclient.Request;
using com.softwareag.connectivity.httpclient.RequestType;
using com.softwareag.connectivity.httpclient.Response;
monitor ListenForHighTemperatures {
action onload() {
on all Measurement(type="c8y_TemperatureMeasurement") as e {
if e.measurements.getOrDefault("c8y_TemperatureMeasurement").getOrDefault("T").value > 100.0 {
// handle the measurement
}
}
}
}
To create a new alarm or operation, create an instance of the relevant event type and use the send statement to send it to the relevant channel (defined with a constant on the event type). Assume that an alarm should be generated immediately if the temperature of a sensor exceeds a defined value. This is done with the following statement:
on all Measurement(type="c8y_TemperatureMeasurement") as m {
if m.measurements.getOrDefault("c8y_TemperatureMeasurement").getOrDefault("T").value > 100.0 {
send Alarm("","c8y_TemperatureAlert",m.source,currentTime,"Temperature too high","ACTIVE","CRITICAL",1,new dictionary<string,any>) to Alarm.SEND_CHANNEL;
}
}
Technically, this statement produces a new alarm event each time a temperature sensor reads more than 100 degrees Celsius and sends it to Cumulocity.
Remote control with EPL is done by sending an operation event. Remote operations are targeted to a specific device. The following example illustrates switching a relay based on temperature readings:
on all Measurement(type="c8y_TemperatureMeasurement") as m {
if m.measurements.getOrDefault("c8y_TemperatureMeasurement").getOrDefault("T").value > 100.0 {
send Operation("",m.source,"PENDING",{"c8y_Relay":<any>{"relayState":"CLOSED"}}) to Operation.SEND_CHANNEL;
}
}
m.source is a placeholder for the ID of the heating that should be triggered.
The params field (the last field) defines the nested content of the operation. In this example we create a c8y_Relay operation and set the relayState to CLOSED. Note that the top-level fields must be dictionary<string, any>, thus the use of the <any> cast operation.
It may be required to query information from the Cumulocity database as part of the ongoing event processing. This is supported by sending events and using listeners to wait for responses. Here is an example that shows how to summarize total sales for vending machines every hour. The sales report data created after a purchase is retrieved from the Cumulocity database.
using com.apama.aggregates.count;
monitor SalesReport {
event SalesReport {
Event e;
ManagedObject customer;
}
event SalesOutput {
integer count;
string customerId;
}
action onload() {
monitor.subscribe(Measurement.SUBSCRIBE_CHANNEL);
on all Event() as e {
monitor.subscribe(FindManagedObjectResponse.SUBSCRIBE_CHANNEL);
integer reqId := integer.getUnique();
on all FindManagedObjectResponse(reqId=reqId) as mor and not FindManagedObjectResponseAck(reqId=reqId) {
route SalesReport(e, mor.managedObject);
}
on FindManagedObjectResponseAck(reqId=reqId) {
monitor.unsubscribe(FindManagedObjectResponse.SUBSCRIBE_CHANNEL);
}
send FindManagedObject(reqId,"",{"childAssetId":e.source}) to FindManagedObject.SEND_CHANNEL;
}
from sr in all SalesReport() within 3600.0 every 3600.0
group by sr.customer.id
select SalesOutput(count(), sr.customer.id) as sales {
send Measurement("", "total_cust_trx", "customer_trx_counterId", currentTime,
{
"total_cust_trx":{
"total":MeasurementValue(sales.count.toFloat(), "COUNT", new dictionary<string,any>)
}
}, {"customer_id":<any> sales.customerId}) to Measurement.SEND_CHANNEL;
}
}
}
In the above example we start by creating definitions for SalesReport and SaleOutput events. These hold the SalesReport (the Event and ManagedObject that identifies a sale) and the information we want to derive from a set of sales: the count and customerId. We listen for Event objects, and send a FindManagedObject request to look up the ManagedObject that the event came from. These SalesReport objects are sent, via the route statement, into a stream query. The stream query fires every hour (3,600 seconds) and selects an aggregate of the sales data per customer, and sends a new measurement representing the sales for that vending machine.
Cumulocity provides several processing modes for API requests:
persistent - Stores data in the Cumulocity database and sends data to the Streaming Analytics engine. Afterwards, Cumulocity returns the result of the request. This is the default mode.
transient - Sends data to the Streaming Analytics engine and immediately returns the results asynchronously but does not store data in Cumulocity’s database. This mode saves storage and processing costs and is useful for example when tracking devices in real time without requiring data to be stored.
quiescent - Behaves similar to the persistent mode with the exception that no real-time notifications will be sent. The quiescent processing mode is applicable only for measurements and events.
cep - Behaves like the transient mode with the exception that no real-time notifications are sent. Currently it is applicable only for measurements and events.
Assume that location updates from cars should be monitored every second while the car is driving, but only be stored once a minute into the database for reporting purposes. This is done using the following statement:
using com.apama.cumulocity.Event;
using com.apama.cumulocity.Measurement;
monitor SendEveryMinute {
dictionary<string, Event> latestUpdates;
action onload() {
monitor.subscribe(Measurement.SUBSCRIBE_CHANNEL);
on all Event() as e {
if e.params.hasKey("c8y_LocationUpdate") {
latestUpdates[e.source] := e;
}
}
on all wait(60.0) {
Event e;
for e in latestUpdates.values() {
send e to Event.SEND_CHANNEL;
}
latestUpdates.clear();
}
}
}