Skip to main content

Loading data into MongoDB

QuickStart Module

This quickstart module shows you how to update data into MongoDB.

Methods of loading data - all licenses

info

This section presumes you have already completed MongoDB Data Modelling and that you have created the sensor type

OpenDataDSL makes loading data into MongoDB very easy, here are some ways you can do it using any OpenDataDSL license.

Directly creating data from ODSL code

We can create objects of type sensor directly in ODSL code as shown below:

s9754 = object as sensor
name = "Bromley"
temperature = TimeSeries("2022-10-03T09:30:27", "SPARSE", 15)
humidity = TimeSeries("2022-10-03T09:30:27", "SPARSE", 32)
end
save ${object:s9754}

Use a transformer to create data

An OpenDataDSL transformer maps data from an input source to output data objects.

Let us assume we get sensor data in JSON format similar to below:

{
"time":"2022-10-03T11:30:00",
"sensors":[
{
"id":"s9754",
"temperature":15.5,
"humidity":33
},
{
"id":"s9879",
"temperature":14.5,
"humidity":19
}
]
}

We can create our objects using the following transformer:

sensor_tx = transform input into sensor as var
create with sensors
unique id = var.id
temperature = TimeSeries(input.time, "SPARSE", var.temperature)
humidity = TimeSeries(input.time, "SPARSE", var.humidity)
end
save ${transformer:sensor_tx}

This assumes that the sensors have already been created, otherwise they will fail the name null check.

info

Two very important aspects of loading data into MongoDB via OpenDataDSL:

  • If the document already exists, the data is merged with the existing document and then the quality checks are performed
  • Timeseries are created if they don't exist and appended to if they do exist

We can now load our data from a file using the following script (assuming the data is in a file /data/sensor_data.json)

input = ${file:"/data/sensor_data.json"}
result = ${transformer:"sensor_tx"}.run(input)
save ${object:result}

Saving to your own collection

As well as saving to the default private collection, you can also save to any named collection.

We just have to make a small modification to the save command to specify the name of the collection to save to, in this case sensors

s9797 = object as sensor
name = "Bromley"
temperature = TimeSeries("2022-10-03T09:30:27", "SPARSE", 15)
humidity = TimeSeries("2022-10-03T09:30:27", "SPARSE", 32)
end
save ${object:"sensors"/s9797}
note

If the named collection doesn't exist, it will be created on the fly

Retrieving data from your own collection

To search for data in your own collection, just specify the name of the collection to search in, e.g.:

find ${object:"sensors"}

To retrieve a single document using it's _id property

sensor = ${object:"sensors"/"s9797"}
print sensor

Methods of loading data - commercial licenses

Here are some ways you can load data into MongoDB using a commercial OpenDataDSL license.

Using the REST API

You can update the sensor data using a REST API call as follows:

POST https://api.opendatadsl.com/object/v1/private
Authorization: Bearer {{token}}

[
{
"_id": "s9754",
"_type": "sensor",
"humidity": {
"_type":"VarTimeSeries",
"calendar": "SPARSE",
"data":[
{"time":"2022-10-03T17:30:00", "value":12}
]
},
"temperature": {
"_type":"VarTimeSeries",
"calendar": "SPARSE",
"data":[
{"time":"2022-10-03T17:30:00", "value":9}
]
}
},
{
"_id": "s9879",
"_type": "sensor",
"humidity": {
"_type":"VarTimeSeries",
"calendar": "SPARSE",
"data":[
{"time":"2022-10-03T17:30:00", "value":10}
]
},
"temperature": {
"_type":"VarTimeSeries",
"calendar": "SPARSE",
"data":[
{"time":"2022-10-03T17:30:00", "value":9.5}
]
}
}
]

Using a scheduled process

If you have a Premium or Enterprise License, you can schedule a process to run at regular intervals to collect the data from the source, transform it and load it into MongoDB.

This can be built either as a Workflow using re-usable Actions or as a Script.

This method is explained in detail here