mongoose-timeseries

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, {
   name: 'TimeSeriesDocument',
   dateKey: 'date',
   intervals: ['minute', 'day'],
   keys: [
      {
         key: 'attr1'
      },
      {
         key: 'attr2'
      },
      {
         key: 'info',
         value: function(doc) {
            return doc.sub1 + doc.sub2 + doc.sub3
         }
      }
   ],
   sums: [
      {
         name: 'metric',
         key: 'analytics.metric'
      }
   ]
})

Watch your time series data grow!

Saved time series documents will look like:

{
  interval: 'day',
  timestamp: Mon Aug 01 2016 00:00:00 GMT-0600(MDT),
  created: Mon Aug 01 2016 00:51:09 GMT-0600(MDT),
  updated: Wed Aug 31 2016 22:19:42 GMT-0600(MDT),
  data: {
    count: 5,
    metric: {
      sum: 697,
      count: 5
    }
  },
  key: {
    attr1: 55931aba4f3b26d63810a55d,
    attr2: 5536011b00a57af8243d7e5b,
    info: 'ABC'
  },
  _id: 57 a50178e47cea6f5d7f1c3b
}

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

name(String)

The collection name of the specific time series data.

---

dateKey(String)

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

---

intervals(Array)

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

---

keys(Array)

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

---

keys.key(String)

The name of the key.

---

keys.value(Function)

The function that returns your a value to store on the associated key: Defaults to the name of the key

---

sums(Array)

The sums you'd like to keep track of.

---

sum.name(String)

The name of the sum.

---

sum.key(String)

The key of the sum. Can be nested like:

'analytics.metrics.metric1'

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({
  interval: 'day',
  timestamp: {
    $gte: startDateFromUI,
    $lte: endDateFromUI
  }
} function(err, results) {
  addAverage(results, 'metric')
  var avg = totalAverage(results, 'metric')
})

And once you collect that data you can perform calculations however you please.

For example, calculate the average:

function addAverage(results, metric) {
  for (var i = 0; i < results.length, i++) {
    var result = results[i]
    result.data[metric].average = result.data[metric].sum / result.data[metric].count
  }
}

function totalAverage(results, metric) {
  var totalSum, totalCount = 0
  for (var i = 0; i < results.length; i++) {
    totalSum += results.data[metric].sum
    totalCount += results.data[metric].count
  }
  return totalSum / totalCount
}

Tests (incomplete)

npm install
npm test

License

MIT