Is there a possibility to have another timestamp as dimension in Druid? - druid

Question

Is it possible to have Druid datasource with 2 (or multiple) timestmaps in it?
I know that Druid is time-based DB and I have no problem with the concept but I'd like to add another dimension with which I can work as with timestamp
e.g. User retention: Metric surely is specified to a certain date, but I also need to create cohorts based on users date of registration and rollup those dates maybe to a weeks, months or filter to only a certain time periods....
If the functionality is not supported, are there any plug-ins? Any dirty solutions?

Answer 1

Although I'd rather wait for official implementation for timestamp dimensions full support in druid to be made, I've found a 'dirty' hack which I've been looking for.
DataSource Schema
First things first, I wanted to know, how much users logged in for each day, with being able to aggregate by date/month/year cohorts
here's the data schema I used:
"dataSchema": {
"dataSource": "ds1",
"parser": {
"parseSpec": {
"format": "json",
"timestampSpec": {
"column": "timestamp",
"format": "iso"
},
"dimensionsSpec": {
"dimensions": [
"user_id",
"platform",
"register_time"
],
"dimensionExclusions": [],
"spatialDimensions": []
}
}
},
"metricsSpec": [
{ "type" : "hyperUnique", "name" : "users", "fieldName" : "user_id" }
],
"granularitySpec": {
"type": "uniform",
"segmentGranularity": "HOUR",
"queryGranularity": "DAY",
"intervals": ["2015-01-01/2017-01-01"]
}
},
so the sample data should look something like (each record is login event):
{"user_id": 4151948, "platform": "portal", "register_time": "2016-05-29T00:45:36.000Z", "timestamp": "2016-06-29T22:18:11.000Z"}
{"user_id": 2871923, "platform": "portal", "register_time": "2014-05-24T10:28:57.000Z", "timestamp": "2016-06-29T22:18:25.000Z"}
as you can see, my "main" timestamp to which I calculate these metrics is timestamp field, where register_time is only the dimension in stringy - ISO 8601 UTC format .
Aggregating
And now, for the fun part: I've been able to aggregate by timestamp (date) and register_time (date again) thanks to Time Format Extraction Function
Query looking like that:
{
"intervals": "2016-01-20/2016-07-01",
"dimensions": [
{
"type": "extraction",
"dimension": "register_time",
"outputName": "reg_date",
"extractionFn": {
"type": "timeFormat",
"format": "YYYY-MM-dd",
"timeZone": "Europe/Bratislava" ,
"locale": "sk-SK"
}
}
],
"granularity": {"timeZone": "Europe/Bratislava", "period": "P1D", "type": "period"},
"aggregations": [{"fieldName": "users", "name": "users", "type": "hyperUnique"}],
"dataSource": "ds1",
"queryType": "groupBy"
}
Filtering
Solution for filtering is based on JavaScript Extraction Function with which I can transform date to UNIX time and use it inside (for example) bound filter:
{
"intervals": "2016-01-20/2016-07-01",
"dimensions": [
"platform",
{
"type": "extraction",
"dimension": "register_time",
"outputName": "reg_date",
"extractionFn": {
"type": "javascript",
"function": "function(x) {return Date.parse(x)/1000}"
}
}
],
"granularity": {"timeZone": "Europe/Bratislava", "period": "P1D", "type": "period"},
"aggregations": [{"fieldName": "users", "name": "users", "type": "hyperUnique"}],
"dataSource": "ds1",
"queryType": "groupBy"
"filter": {
"type": "bound",
"dimension": "register_time",
"outputName": "reg_date",
"alphaNumeric": "true"
"extractionFn": {
"type": "javascript",
"function": "function(x) {return Date.parse(x)/1000}"
}
}
}
I've tried to filter it 'directly' with javascript filter but I haven't been able to convince druid to return the correct records although I've doublecheck it with various JavaScript REPLs, but hey, I'm no JavaScript expert.

Answer 2

Unfortunately Druid has only one time-stamp column that can be used to do rollup plus currently druid treat all the other columns as a strings (except metrics of course) so you can add another string columns with time-stamp values, but the only thing you can do with it is filtering.
I guess you might be able to hack it that way.
Hopefully in the future druid will allow different type of columns and maybe time-stamp will be one of those.

Answer 3

Another solution is add a longMin sort of metric for the timestamp and store the epoch time in that field or you convert the date time to a number and store it (eg 31st March 2021 08:00 to 310320210800)

Answer 4

As for Druid 0.22 it is stated in the documentation that secondary timestamps should be handled/parsed as dimensions of type long. Secondary timestamps can be parsed to longs at ingestion time with a tranformSpec and be transformed back if needed in querying time link.