OpenTSDB is very similar to KairosDB but it is less popular and based instead on either Apache Cassandra or HBase. In the following simple example, we are going to start a single-node OpenTSDB based on HBase. We will use a Docker image to avoid a long installation process.

OpenTSDB uses the RESTful API and Telnet to query and ingest data. To install OpenTSDB, download the code from GitHub:

git clone https://github.com/PacktPublishing/Hands-On-Industrial-Internet-of-Things
cd Chapter07/opentsdb

Perform the following steps:

1. Build the image with the following command:

docker build . --tag iiot:opentsdb

2. Start the container:

docker run -v data:/data/hbase -p 4242:4242 --name opentsdb iiot:opentsdb 

3. Check the status with the following command:

docker ps

Now we can push our first time-series using the RESTful API through CURL. You could also use a simple rest API client installed on Chrome, either Postman or an advanced REST client. Execute the following command several times, changing the value and the timestamp:

curl -d '{"metric": "sys.cpu", "timestamp": 1529176746, "value": 80, "tags": {"host": "localhost", "quality" : "GOOD"}' 
      -H "Content-Type: application/json" 
      -X POST http://localhost:4242/api/put

We can then ask about the inserted time-series:

curl -X GET http://localhost:4242/api/search/lookup?m=sys.cpu

Following this, we can retrieve the inserted time-series interpolated every second (avg:duration_seconds), starting from 10 years ago (10y-ago):

http://localhost:4242/api/query?start=10y-ago&m=avg:duration_seconds:sys.cpu{host=localhost,quality=*}

The output should be as follows:

[{"metric":"sys.cpu","tags":{"host":"localhost","quality":"GOOD"},"aggregateTags":[],"dps":{"1529176746":90,"1529176756":100,"1529176766":110,"1529176776":111,"1529176876":211}}]

In OpenTSDB, a time-series data point consists of the following:

• A metric name
• A UNIX timestamp
• A value
• A set of tags (key-value pairs) that describe the time-series that the point belongs to

OpenTSDB supports  down-sampling and interpolation through aggregate functions, such as sum, last, and rate.

This example highlighted two important concepts: tags, and metrics or measures. In the IoT, we refer to tags when we want to provide a name to a measure or when we want to add attributes to a measure. Unfortunately, this naming is not standard and can cause confusion.