Analytics Builder 10.16.0 | Using Analytics Builder for Cumulocity IoT | Models and Devices | Connections between models
Connections between models
You can connect multiple models together using output blocks and input blocks. A model that contains an output block such as Measurement Output (for Measurement objects of Cumulocity IoT) will generate a series of events, and this can be consumed by a suitable input block (such as Measurement Input) in another model. For more details, see Keys for identifying a series of events.
When models are connected together using inputs and outputs for the same stream of events, the term chain is used to refer to all of the models that are connected to each other in this way. There may be multiple chains if there are separate groups of models that are connected to each other.
The events from one model can only be consumed by another model when all involved models are deployed in production mode. When the models are deployed in test or simulation mode, virtual devices are used and the events from one model can therefore not be consumed by another model.
When one model has a synchronous output block generating a series of events for a given key and a second model has an input block consuming from that same series of events (that is, with the same key parameters), then this forms a connection from the first model to the second. When the first model triggers the output block, this causes the second model to be evaluated with a new input on its input block.
It is also possible to form connections between models using the output from an asynchronous output block. In this case, when the first model triggers the asynchronous output block, the output is generated and sent to the external system (such as Cumulocity IoT). The data is received back from the external system at some later point in time and causes the evaluation of any other models consuming the data.
Similar to the processing order of wires within a model (see also Processing order of wires), the following applies when an output block in one model generates a series of events that an input block in another model consumes:
*A single model can send the same events to more than one other model. This means, it is possible to have a single model perform some common pre-processing, such as unit conversion or calculating an average (with the Average (Mean) block), and that value to be used by multiple other models.
*Models are executed in order with respect to the connections between models formed using synchronous output so that the source of a connection is always evaluated before the target of a connection. If a model has connections from multiple blocks all triggered from the same initial event, then they will all evaluate first, and the receiving model will evaluate with all of the inputs once.
Connections formed using asynchronous output do not have a specific execution order. A model consuming the output is executed only when the output is received back from the external system.
Similar to the wire restrictions within a model (see also Wire restrictions), there are restrictions on how output blocks and input blocks can be used to connect models together:
*One block across all models is permitted to generate a series of synchronous events for a given key. See also Keys for identifying a series of events. Multiple output blocks generating asynchronous events can be used within a single model or across multiple models.
*No cycles can be created between models using synchronous output. A model that receives events via an input block synchronously generated from another model cannot include an output block that generates synchronous events that the other model would consume. This applies even if one of the models contains two separate parts, such that there is no actual cycle in terms of wires and connections between models. Cycles among models can be created because of asynchronous outputs. Therefore, care must be taken not to introduce indefinite cyclic executions of models.
Any model that does not meet these restrictions when used in combination with the already activated models will cause an error on trying to activate it. This will count for the last element in a cycle of models. For such errors, the problem may be in interactions between models rather than a problem specific to a single model, but existing models that have already been activated will not automatically be deactivated. For example, if multiple models all generate the same series of synchronous events (with the same key), then the first model to be activated can be deployed, but all subsequent models will report an error upon trying to activate them.
For example, there are three models: Model1, Model2, and Model3. A cycle may exist if:
*An output block of Model1 produces a series of synchronous events that is consumed by an input block in Model2, and Model2 contains an output block that generates a second series of synchronous events, and
*Model3 contains an input block that consumes a series of events from Model2, and Model3 also contains an output block that generates a series of synchronous events used by an input block in Model1.
Note that only activating any two of these models can be done without error. If activated in order, only Model3 would have an error. But if Model1 or Model2 were deactivated, then Model3 could be activated. The error will occur even if one of the models does not contain a link from the input block that is part of the chain to the output block that forms part of the chain, such as the example for Model3 below: the events from Model2 do not form a cycle to the To Model1 Measurement output block, but they count as a cycle as they are both in the same model. (In this case, the issue could be resolved by splitting that model into two models, thus removing the cycle).
Example for Model3
Using multiple specific devices in a model with the concurrency level set to more than 1 can lead to connections between models which are deployed across multiple workers. Chains of models using multiple specific devices with high throughput usually scale less well than chains of models all using a single specific device.