Semantic Data Aggregator

Docker-based prototype that deploys the Semantic Data Aggregator (SDA).

The aggregator is composed of three main elements: the context broker, the context registry, and the data fabric. The first two elements are represented by the Scorpio Broker while the latter is a combination of agents that ingest/deliver data to/from the so-called data substrate represented by Kafka. These agents are dynamically configured by the Weaver which subscribes to the context broker for news definitions of data sources and data consumers.

The Weaver leverages Apache NiFi to distribute data among data sources and data consumers that are attached to the Semantic Data Aggregator. NiFi enables the definition of graph flows that implement data ingestion mechanisms such as retrieving Prometheus metrics by polling Prometheus REST API, or data delivery mechanisms such as fetching data from Kafka and sending it out to HTTP-based data consumers.

Kafka plays the role of the data substrate in the SDA, hence NiFi relies on Kafka as the distributed reliable storage system to read/write data in the defined graph flows. For the sake of simplicity, the weaver configures NiFi processors to connect to the same Kafka instance that Scorpio runs for its internal communication bus.

Table of Contents

1. Prototype
   1. Microservices
      1. Weaver
      2. Application Manager
      3. Context Catalog
      4. Source Manager
      5. Complex Publisher
      6. Experimenter
2. Scenarios
   1. All-in-one scenario
   2. Data Source specific scenarios
      1. Prometheus
      2. gNMI
      3. Kafka
3. NGSI-LD Information Models
4. SDA Orchestration
5. Stream processing aplications management
6. Postman collections

Prototype

Requirements

• Docker (Tested with version 19.03.13)
• Docker-compose (Tested with version 1.27.4)

Microservices

The SDA is composed of several microservices that are deployed as Docker containers. These microservices are briefly introduced in this section.

Weaver

The weaver container implements the orchestration of Apache NiFi flows and Apache Flink jobs by consuming context information defined in the Context Broker.

Application-Manager

The app-manager container offers a catalog of applications that can be used in the SDA. Within the scope of the SDA, these applications are NiFi templates and Flink applications, i.e., JARs. The app-manager implements a REST API that facilitates the on-boarding of applications in the SDA. This REST API takes as input the type of application, a name, a description, and the file of the application. The app-manager gathers all this information and makes sure that the application is properly uploaded in the target runner - NiFi or Flink - and that the context associated to the application is created in the Context Broker.

The full OpenAPI specification is available here.

Context-Catalog

The context-catalog container provides a static web server where JSON-LD vocabularies can be uploaded. Interactions with the NGSI-LD API can link to this webserver rather than appending the JSON-LD vocabulary in the request's body.

Source-Manager

The source-manager utility automates the discovery of Kafka topics available in a 5G-EVE monitoring platform. The source-manager provides a REST API through which users can specify the address of 5G-EVE's Kafka Broker and a string filter - usecase - to discover Kafka topics with maching names. Using this information the source-manager sends a request to the DCM service in 5G-EVE. Lastly, those discovered topics are created as new context information within the Context Broker.

The full OpenAPI specification is available here.

Complex-Publisher

The complex-publishers utility allows for generating synthetic metrics compliant with 5G-EVE's data model or 5Gr-LogParser's data model.

Experimenter

The experimenter container emulates an application that receives updates from Tasks running in the SDA. This container subscribes to the Context Broker in order to receive NGSI-LD notifications related with the Tasks. These notifications are sent to stdout. The experimenter container serves as a first step towards a GUI to visualize the status of Tasks running in the SDA.

Scenarios

All-in-one Scenario

To deploy a scenario that comprises the SDA along with all the supported data sources, execute the following command:

docker-compose up

In case you are interested in running the prototype in background (kafka or scorpio logs may be annoying), use the following command:

docker-compose up -d

Tear the scenario down as follows:

docker-compose down

Data Source Specific Scenarios

In addition to the all-in-one setup, this repository includes different docker-compose files that deploy scenarios in which the SDA is integrated with a specific data source. These scenarios come handy for debugging purposes and showcasing demos.

Prometheus-based Data Sources

This scenario deploys a Prometheus instance that is integrated as a data source for the SDA. Additionally, two node-exporter microservices are deployed to emulate two separate machines that send metrics to Prometheus - although both node exporters collect data from the same machine (host machine).

1. Deploy the scenario with the following command:

docker-compose up  -f docker-compose-prometheus.yml -d

2. The MetricSource_Demo Postman collection provides a demo pipeline that periodically fetches a metric from Prometheus.

3. Lastly, tear the scenario down with:

docker-compose -f docker-compose-prometheus.yml down

gNMI-based Data Sources (Arista cEOS)

If you are interested in running the gNMI-based data collection prototype, follow the next steps:

The purpose of this prototype is collect data of gNMI telemetry-based sources from the Semantic Data Aggregator. For this proof of concept with gNMI data sources, the prototype has two main resources: docker instances of Arista cEOS routers as network devices and YANG-based data sources that support the gNMI management protocol and a CLI client that provides a full support of gNMI RPCs called gNMIc to request the configuration and operational status from these telemetry-based network devices.

To get a fine-grained view on how to extract telemetry information of Arista cEOS routers using the gNMIc client from our semantic data aggregator, follow the gNMI Telemetry Proof of Concept Recipe.

1. Before starting docker-compose it is necessary to import the Arista cEOS router docker image. Specifically, the scenario uses one of the latest available Arista cEOS versions cEOS-lab-4.25.4M. Download it first from the Arista software section (it is the 64-bit version).

2. The command to import the image is:

docker import cEOS64-lab-4.25.4M.tar.xz ceos-image:4.25.4M

3. Then you can start the docker-compose:

docker-compose -f docker-compose-arista.yml up

4. Run the utility script ceos-startup.sh to configure a simple network topology with two Arista cEOS devices interconnected through a single link::

cd docker/ceos-arista
./ceos-startup.sh

Following picture depicts the network topology and addressing:

5. The TelemetrySource_Demo Postman collection provides a demo pipeline that subscribes to a YANG XPath of a network device through the gNMI protocol.

6. Tear the scenario down with:

docker-compose -f docker-compose-arista.yml down

Kafka-based Data Sources

Kafka is another type of data source supported by the SDA. More precisely, the ICT-17 5G-EVE project represents a use case that provides monitoring data through Kafka. In this use case, the SDA enables interoperability by integrating a data source from an ICT-17 environment, and then aggregating and delivering the data to other domains such as 5Growth.

1. For the 5G-EVE use case, we simply re-use the same Kafka instance to work as both the data source and the SDA's data substrate. Let's deploy the all-in-one scenario:

docker-compose up  -d

2. The EVESource_Demo Postman collection provides a demo pipeline that subscribes to a Kafka topic.

3. To generate synthetic data compliant with 5G-EVE data model, the complex-publisher microservice offers a utility that generates random data into a Kafka topic. For instance, execute the following command to generate 10 new metrics:

docker exec -it complex-publisher python3 /5GEVE-publisher/publisher.py kafka:9092 spain-5tonic.topic-1 10

4. Once you are done, tear the scenario down with:

docker-compose down

NGSI-LD Information Models

This repository contains NGSI-LD information models that characterize those data sources currently supported by the semantic data aggregator (SDA). More information can be found here

SDA Orchestration

In order to orchestrate the life cycle management of the Semantic Data Aggregator by an external application or system, the prototype uses he NGSI-LD API like an interface that allows translating orders from an external orchestrator component to requests to the SDA and extending the NGSI-LD data model for that. This is an approach to orchestrate the state of those NGSI-LD entities that represent the different stages in the data pipelines and model the activity of the Data Aggregator agents.

To get a full view of the SDA orchestration process and to be able to build a full data pipeline for data aggregation, see Semantic Data Aggregator Orchestration.

Stream Processing Aplications Management

The SDA makes use of the Apache Flink engine, as part of its aggregation agent, to allow the execution of stream processing applications. The SDA allows to dynamically orchestrate the upload and submission of stream processing applications to the Flink engine in order to easily manage their execution.

For more information on how SDA internally manages the uploading and execution of stream processing applications, see Stream Processing Applications Management.

Postman Collections

This repository contains Postman collections that you can use to play with the REST APIs of some of the components present in the prototype. We recommend downloading Postman Desktop for an better user experience.

• NGSI-LD API Orchestrator Postman collection has a set of requests that can be used to interact with the NGSI-LD Scorpio Broker in order to model a full NGSI-LD datapipeline for data aggregation and orchestrate the life cycle of the entities involved in it, based on the NGSI-LD API. This collection includes most of the Entity, Subscription, and Context Source operations that are commonly used in NGSI-LD. The requests contained in this collection can be utilized with other NGSI-LD compliant broker such as Orion-LD or Stellio.

• Flink REST API provides example requests for the supported operations in Apache Flink REST API.

• YANG Catalog REST API provides a collection of sample requests for interacting with the REST API of YANG Catalog. For more details, visit Contribute to YANG Catalog webpage.