Mongoose-timeseries NPM

mongoose-timeseries

a time series data and analytics storage plugin for Mongoose.

Installation

npm install mongoose-timeseries

Usage

Create your Schema

var YourDocumentSchema = new Schema({
   attr1: { type: Schema.Types.ObjectId, ref: 'attr1' },
   attr2: { type: Schema.Types.ObjectId, ref: 'attr2' },
   date: { type: Date, default: Date.now },
   analytics: {
     metric: { type: Number }
   },
   info: {
      sub1: { type: String },
      sub2: { type: String },
      sub3: { type: String }
   }
})

Apply Plugin

var mongoose = require('mongoose')
var timeseries = require('mongoose-timeseries')

YourDocumentSchema.plugin(timeseries, {
   target: 'TimeSeriesDocument',
   dateField: 'date',
   resolutions: ['minute', 'day'],
   key: {
      attr1: 1,
      attr2: 1,
      info: function(doc) {
         return doc.info.sub1 + doc.info.sub2 + doc.info.sub3
      }
   },
   data: {
      metric: {
         source: 'analytics.metric',
         operations: ['sum', 'max', 'min'],
         calculations: ['average', 'range', 'range_min', 'range_max']
      }
   }
})

Watch your time series data grow!

Your saved time series documents will look like:

{
  date: {
    start: Mon Aug 01 2016 00:00:00 GMT-0600(MDT),
    end: Mon Aug 01 2016 23:58:42 GMT-0600(MDT)
  }
  resolution: 'day',
  count: 5,
  data: {
    metric: {
      count: 5,
      sum: 697,
      min: 100,
      max: 200
    }
  },
  key: {
    attr1: '55931aba4f3b26d63810a55d',
    attr2: '5536011b00a57af8243d7e5b',
    info: 'ABC'
  },
  _id: 57 a50178e47cea6f5d7f1c3b
}

Your queried and found time series documents will look like:

{
  date: {
    start: Mon Aug 01 2016 00:00:00 GMT-0600(MDT),
    end: Mon Aug 01 2016 23:58:42 GMT-0600(MDT)
  }
  resolution: 'day',
  count: 5,
  data: {
    metric: {
      count: 5,
      sum: 697,
      min: 100,
      max: 200,
      average: 139.4,
      range: 100,
      range_min: 39.4,
      range_max: 60.6
    }
  },
  key: {
    attr1: '55931aba4f3b26d63810a55d',
    attr2: '5536011b00a57af8243d7e5b',
    info: 'ABC'
  },
  _id: 57 a50178e47cea6f5d7f1c3b
}

This is because the calculations are performed as middleware during Mongoose Find() executions.

Documentation

Function

YourDocumentSchema.plugin(timeseries, options(Object))

You can apply multiple times with different options:

YourDocumentSchema.plugin(timeseries, options1(Object))
YourDocumentSchema.plugin(timeseries, options2(Object))
YourDocumentSchema.plugin(timeseries, options3(Object))

Options

target(String)

The MongoDB collection name (destination) of the specific time series data.

dateField(String)

The custom date field of your schema (if applicable). If not set, defaults to document._id.getTimestamp()

resolutions(Array)

The time series resolutions you want: Can include any or all of 'minute', 'hour', 'day', 'month'

key(Object)

The unique information you'd like your time series to separate and store.

key.'attribute'(Number | Function)

For each key, use the number '1' to relay the name, or a function that returns your value to store on the key.

data(Object)

The data you'd like to keep track of.

data.'attribute'.source(String)

The source of the parameter you're tracking. Can be nested like:

'analytics.metrics.metric1'

data.'attribute'.operations(Array of Strings)

The operations to perform. Currently supports 'sum', 'max', and 'min'

data.'attribute'.calculations(Array of Strings)

The "post-find" calculations to perform. Currently supports 'average', 'range', 'range_min', and 'range_max'

average = sum / count, range = max - min, range_min = average - min, (must also include average), range_max = max - average (must also include average)

Using the Time Series Data

Now, in your front-end analytics, you can query the time series data like:

var startDateFromUI = ...
var endDateFromUI = ...

TimeSeriesAnalyticsModel.find({
  resolution: 'day',
  'date.start': {
    $gte: startDateFromUI,
    $lte: endDateFromUI
  }
} function(err, results) {
  console.log(results)
})

Tests (incomplete)

npm install
npm test

Assumptions

Original source documents are a continual stream of data being dumped
Documents in the source time-series collection are never themselves found and updated