MongoDB datasource for Grafana

Features

Allows MongoDB to be used as a data source for Grafana by providing a backend plugin API into MongoDB aggregation queries

This package was forked from https://github.com/JamesOsgood/mongodb-grafana which is a purely frontend plugin.

Requirements

• Grafana > 3.x.x
• MongoDB > 3.4.x

Installation

The git repo does not include the dist/ dirctory needed for grafana. Either download a pre-built release from https://github.com/PhracturedBlue/mongodb-grafana-backend/releases or build via make

Install the Grafana plugin components

• Copy the whole mongodb-grafana dir into the Grafana plugins dir ( /usr/local/var/lib/grafana/plugins )
• Restart the Grafana server. If installed via Homebrew, this will be brew services restart grafana

Examples

Create a new data source of type MongoDB as shown below. The MongoDB details are :

• MongoDB URL - mongodb://rpiread:[email protected]:27017,rpi-sensor-data-shard-00-01-ifxxs.mongodb.net:27017,rpi-sensor-data-shard-00-02-ifxxs.mongodb.net:27017/test?ssl=true&replicaSet=rpi-sensor-data-shard-0&authSource=admin
• MongoDB Database - rpi

Then save the data source

Using stage macros

Because MongoDB isn't ideally suited to extracting timeseries data, aggregate pipelines can become quite long with repeated boilerplate for each query. The MongoDB datasource provides the ability to define macros which can be used in panel queries to reduce the amount of duplicated boilerplate. These are very similar to grafana's constant variables except that (a) they support per-query confguration, and (b) they work with alerts

timegroup ==>
{
  "$match": {
    "ts": {
      "$gte": "$from", "$lt": "$to"
    },
    $QUERY
  }
},
{
  "$bucketAuto": {
    "groupBy": "$ts",
    "buckets" : "$maxDataPoints",
    "output": {
      "root": {"$push": "$$ROOT"}
    }
  }
},
{$unwind": "$root"},
{
  "$project: {
    "_id": 1, "data": "$root.data", "ts": "$root.ts"
  }
}

The above example assumes a schema with timeseries stored in ts and data stored under data It will filter data by timerange and parameters specified in the panel qury, and then group into the number of points requested in maxDataPoints. Note that the macro is not legal JSON syntax because $QUERY is unquoted. The QUERY value will be substituted before JSON parsing and the result after substitution will be legal JSON.

To make use of the above filter, a query may look like:

db.timeseries.aggregate(
[
  "$timegroup": {"data.name": "a"},
  {
    "$project": {
      "_id": 0,
      "ts": 1,
      "value": "$data.value"
    }
  }
])

The above would be combined into a single JSON:

db.timeseries.aggregate(
[
  {
    "$match": {
      "ts": {
        "$gte": "$from", "$lt": "$to"
      },
      "data.name": "a"
    }
  },
  {
    "$bucketAuto": {
      "groupBy": "$ts",
      "buckets" : "$maxDataPoints",
      "output": {
        "root": {"$push": "$$ROOT"}
      }
    }
  },
  {$unwind": "$root"},
  {
    "$project: {
      "_id": 1, "data": "$root.data", "ts": "$root.ts"
    }
  }
  {
    "$project": {
      "_id": 0,
      "ts": 1,
      "value": "$data.value"
    }
  }
])

Example 1 - Simple aggregate to rename fields

Import the dashboard in examples\RPI MongoDB - Atlas.json

This should show a graph of light sensor values from a Raspberry PI with an EnviroPHAT board feeding readings every minute into a MongoDB Atlas database.

Clicking on the title of the graph allows you to see the aggregation query being run against the 'RPI Atlas' data source

The query here is

db.sensor_value.aggregate ( [ 
{ "$match" :    {   "sensor_type" : "$sensor",   "host_name" : "$host",   "ts" : { "$gte" : "$from", "$lte" : "$to" } }  },        
 {"$sort" : {"ts" : 1}},            
 {"$project" :   {  "name" : "value",   "value" : "$sensor_value",  "ts" : "$ts",  "_id" : 0} } ])

The API is expecting back documents with the following fields

• name - Name of the series ( will be displayed on the graph)
• value - The float value of the point
• ts - The time of the point as a BSON date

These documents are then converted into the Grafana API

$from and $to are expanded by the plugin as BSON dates based on the range settings on the UI.

Template Variables

$sensor and $host are template variables that are filled in by Grafana based on the drop down. The sample template queries are shown below. They expect documents to be returned with a single _id field.

Example 2 - Using $bucketAuto to push data point aggregation to the server

Grafana tells the backend server the date range along with the size of the buckets that should be used to calculate points. Therefore it's possible to use the MongoDB aggregation operator $bucketAuto to automatically bucket the data points into display points. To support this the backend provides the $maxDataPoints macro so that queries such as the one below can be written

db.sensor_value.aggregate( [ 
{  "$match" :  {  "sensor_type" : "$sensor", "host_name" : "$host" , "ts" : { "$gte" : "$from", "$lt" : "$to" }}},
{  "$bucketAuto" :  { "groupBy" : "$ts",  
                           "buckets" : "$maxDataPoints", 
                            "output" :  {  "maxValue" : { "$max" : "$sensor_value" }  }   }   },  
{  "$project" :  {  "name" : "value",  "value" : "$maxValue",  "ts" : "$_id.min",  "_id" : 0  }  }  ]  )

Note that _id field of the bucketAuto output contains the start and end of the bucket so we can use that as the ts value

The dashboard in examples\RPI MongoDB Bucket - Atlas.json shows this.

Example 3 - Using a Tabel Panel

Table panels are now supported with queries of the form

db.sensor_value.aggregate(
[
    {  "$match" :  {  "ts" : { "$gte" : "$from", "$lt" : "$to"  }}},
    {  "$group":    {  "_id":  { "sensor_name" : "$sensor_name",  "sensor_type" : "$sensor_type"  }, "cnt" : { "$sum" : 1 },  "ts" : { "$max" : "$ts" }  } }, 
    { "$project": {  "name" : { "$concat" : ["$_id.sensor_name",":","$_id.sensor_type" ]},  "value" : "$cnt",  "ts" : 1, "_id" : 0} } 
])

The dashboard in examples\Sensor Values Count - Atlas.json shows this.