Get Started#

  • Time to Complete: 30 minutes

  • Programming Language: Python 3

Prerequisites#

Docker Configuration#

  1. Run Docker as Non-Root: Follow the steps in Manage Docker as a non-root user.

  2. Configure Proxy (if required):

    • Set up proxy settings for Docker client and containers as described in Docker Proxy Configuration.

    • Example ~/.docker/config.json:

      {
  "proxies": {
    "default": {
      "httpProxy": "http://<proxy_server>:<proxy_port>",
      "httpsProxy": "http://<proxy_server>:<proxy_port>",
      "noProxy": "127.0.0.1,localhost"
    }
  }
}

    • Configure the Docker daemon proxy as per Systemd Unit File.

  3. Enable Log Rotation:

    • Add the following configuration to /etc/docker/daemon.json:

      {
  "log-driver": "json-file",
  "log-opts": {
    "max-size": "10m",
    "max-file": "5"
  }
}

    • Reload and restart Docker:

      sudo systemctl daemon-reload
sudo systemctl restart docker

Clone source code#

git clone https://github.com/open-edge-platform/edge-ai-libraries.git
cd edge-ai-libraries/microservices/time-series-analytics/docker

Build Docker Image#

Navigate to the application directory and build the Docker image:

docker compose build

Note:
To include copyleft licensed sources when building the Docker image, use the below command:

docker compose build --build-arg COPYLEFT_SOURCES=true

Push Docker Images (Optional)#

To push images to a Docker registry:

  1. Update the following fields in edge-ai-libraries/microservices/time-series-analytics/docker/.env:

    • DOCKER_REGISTRY

    • DOCKER_USERNAME

    • DOCKER_PASSWORD

  2. Push the images:

    docker login $DOCKER_REGISTRY
docker compose push

Configuration Details#

Note: For the default deployment, no need to change anything in the configuration.

Time Series Analytics Microservice uses the User Defined Function(UDF) deployment package(TICK Scripts, UDFs, Models) which is already built-in to the container image. By default, we have a simple UDF python script at edge-ai-libraries/microservices/time-series-analytics/udfs/temperature_classifier.py which does not use any model file for inferencing, it just does a simple check to filter the temperature points which are less than 20 OR greater than 25. The corresponding tick script is available at edge-ai-libraries/microservices/time-series-analytics/temperature_classifier.tick.

Directory (edge-ai-libraries/microservices/time-series-analytics/) details is as below:

config.json:#

Key

Description

Example Value

udfs

Configuration for the User-Defined Functions (UDFs).

See below for details.

UDFs Configuration:

The udfs section specifies the details of the UDFs used in the task.

Key

Description

Example Value

name

The name of the UDF script.

"temperature_classifier"

Note: The maximum allowed size for config.json is 5 KB.

Alerts Configuration:

The alerts section defines the settings for alerting mechanisms, such as MQTT protocol. Please note the MQTT broker needs to be available.

MQTT Configuration:

The mqtt section specifies the MQTT broker details for sending alerts.

Key

Description

Example Value

mqtt_broker_host

The hostname or IP address of the MQTT broker.

"ia-mqtt-broker"

mqtt_broker_port

The port number of the MQTT broker.

1883

name

The name of the MQTT broker configuration.

"my_mqtt_broker"

config/:#

  • kapacitor_devmode.conf would be updated as per the above config.json at runtime for usage.

udfs/:#

  • Contains the python script to process the incoming data.

tick_scripts/:#

  • The TICKScript temperature_classifier.tick determines processing of the input data coming in. Mainly, has the details on execution of the UDF file and publishing of alerts.

Deploy with Docker Compose#

Navigate to the application directory and run the Docker container:

docker compose up -d

Ingesting Temperature Data into the Time Series Analytics Microservice#

Run the following script to ingest temperature data into the Time Series Analytics Microservice:

pip3 install -r simulator/requirements.txt
python3 simulator/temperature_input.py --port 5000

Verify the Temperature Classifier Results#

Run below commands to see the filtered temperature results:

docker logs -f ia-time-series-analytics-microservice

Accessing the Swagger UI#

The Time Series Analytics Microservice provides an interactive Swagger UI at http://<host_ip>:5000/docs. Please refer API documentation.

Bring down the microservice#

docker compose down -v

Troubleshooting#

  • Check container logs to catch any failures:

    docker logs -f ia-time-series-analytics-microservice
docker logs -f ia-time-series-analytics-microservice | grep -i error

# Debugging UDF errors if container is not restarting and providing expected results
docker exec -it ia-time-series-analytics-microservice bash
$ cat /tmp/log/kapacitor/kapacitor.log | grep -i error

Other Deployment options#

Supporting Resources#