Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mother/mongoose-timeseries
Mongoose plugin to implement time series data storage for any model you want.
https://github.com/mother/mongoose-timeseries
mongoose series time timeseries
Last synced: about 2 months ago
JSON representation
Mongoose plugin to implement time series data storage for any model you want.
- Host: GitHub
- URL: https://github.com/mother/mongoose-timeseries
- Owner: mother
- License: mit
- Created: 2016-08-05T02:59:44.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-12-20T21:18:45.000Z (about 6 years ago)
- Last Synced: 2024-12-01T08:24:20.890Z (2 months ago)
- Topics: mongoose, series, time, timeseries
- Language: JavaScript
- Size: 11.7 KB
- Stars: 10
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# mongoose-timeseries
a time series data and analytics storage plugin for [Mongoose](http://mongoosejs.com).
[](https://nodei.co/npm/mongoose-timeseries/)
## Installation
```sh
npm install mongoose-timeseries
```
## Usage
#### Create your Schema
```js
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
```js
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
```js
YourDocumentSchema.plugin(timeseries, options(Object))
```
You can apply multiple times with different options:
```js
YourDocumentSchema.plugin(timeseries, options1(Object))
YourDocumentSchema.plugin(timeseries, options2(Object))
YourDocumentSchema.plugin(timeseries, options3(Object))
```
#### Options
```js
target(String)
```
The MongoDB collection name (destination) of the specific time series data.
---
```js
dateField(String)
```
The custom date field of your schema (if applicable).
If not set, defaults to ```document._id.getTimestamp()```
---
```js
resolutions(Array)
```
The time series resolutions you want:
Can include any or all of ['minute', 'hour', 'day', 'month']
---
```js
key(Object)
```
The unique information you'd like your time series to separate and store.
```js
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.
---
```js
data(Object)
```
The data you'd like to keep track of.
```js
data.'attribute'.source(String)
```
The source of the parameter you're tracking.
Can be nested like:
```js
'analytics.metrics.metric1'
```
```js
data.'attribute'.operations(Array of Strings)
```
The operations to perform. Currently supports `'sum'`, `'max'`, and `'min'`
```js
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:
```js
var startDateFromUI = ...
var endDateFromUI = ...
TimeSeriesAnalyticsModel.find({
resolution: 'day',
'date.start': {
$gte: startDateFromUI,
$lte: endDateFromUI
}
} function(err, results) {
console.log(results)
})
```
## Tests (incomplete)
```sh
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
## To-do
- [ ] Tests
- [ ] Auto-indexing
- [ ] Auto-remove (removes source time-series documents automatically after a set interval... capped collection?)
## License
[MIT](LICENSE)