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Timeseries Aggregations with Date Granularity

Timeseries Aggregations can be performed on the dateTime field. So, you may group the records based upon thedateTimefield and perform aggregations on them. Hypi object has the dateTime fields likehypi_created or hypi_updated. You may use these fields for timeseries aggregations.

In this tutorial, we will work with the following schema to demonstrate how to use the dateGranularity parameter to group numeric fields over time.

type ActorRatings {
rating: Float!
gender: Gender!
}
enum Gender {
Male
Female
NotSpecified
}

First is looking into the types that you want to aggregate.

Aggregation Types#

For each type in our schema, we have two aggregations that we can perform on them. The first one is named "[our_type]" and the second one is named "[our_type]With". Check more about Aggregations here.

Looking at our schema, our aggregations will be

actorRatings(
where: String
): ActorRatingsAggs

and

actorRatingsWith(
where: String
groupBy: [ActorRatingsGroupByOptions!]!
having: String
first: Int
after: String
last: Int
before: String
includeTrashed: Boolean
): [ActorRatingsAggs]

In this tutorial we will focus on usingactorRatingsWithsince it is the one that allows grouping data.

Using GroupBy with dateGranularity#

Now, let's get into some examples.

Let's retrieve the average rating for each day for the actor withhypi.id='actor1'. The aggregation looks like this.

{
aggregate {
actorRatingsWith(
where: "hypi.id='actor1'"
groupBy: [{ field: hypi_created, dateGranularity: DAYS }]) {
rating {
avg
groupValues{
key
value
}
}
}
}
}

Here we are using the actorRatingsWith aggregation with the groupBy parameter pointing at the field hypi_created and passing the dateGranularity: DAYS parameter to indicate that we want to group by the hypi_created field and have the results grouped specifically by the days.

As for the data we want to be returned, we specifyavgand then thegroupValuesso we can know the value of the field we are grouping by -hypi_created.

Required Selections:

Note that the groupValues field is a mandatory selection when using the dateGranularity parameter. This means it MUST be one of the fields you select. Otherwise you will receive an error.

The result will look like this.

{
"data": {
"aggregate": {
"actorRatingsWith": [
{
"rating": {
"avg": 4.5,
"groupValues": [
{
"key": "hypi_created",
"value": "2020-07-09"
}
]
}
},
{
"rating": {
"avg": 4.2,
"groupValues": [
{
"key": "hypi_created",
"value": "2020-07-10"
}
]
}
},
{
"rating": {
"avg": 3.3,
"groupValues": [
{
"key": "hypi_created",
"value": "2020-07-11"
}
]
}
}
]
}
}
}

As you can see, the value field of the groupValues, is displaying dates with different days and the average is calculated for that day.

Similarly we can use all the other aggregations: count , min , max , sum.

Also, just as we did DAYS for the dateGranularity in this tutorial, you can use HOURS , MINUTES and SECONDS as for your needs.

In conclusion, aggregations using dateGranularity boils down to:

  1. Using the right aggregation type - which is named "[your_type]With".
  2. UsinggroupByto specify which field and dateGranularity you would like to group by. In our example hypi_created , DAYS.
  3. Using where if you want to filter before calculating. (It is an ArcQL string. In our example -hypi.id='actor1'.)
  4. Adding the field that you want to aggregate. In our example rating.
  5. Choosing what you want to calculate. In our example avg(others include count , min , max and sum)
  6. Adding groupValues so you can see the value of the field that you grouped by in the result set.