Nested Structures
Two kinds of nested structures that behave in special ways are supported: arrays and maps. Fields of either of these types are arrays; when they are part part of query result, which is already an array of objects, they become nested arrays.
Arrays
Arrays behave as a special kind of single value. There is no way to paginate through, filter, filter by, group, or group by the array.
On the other hand, you can choose which fields of the underlying type you want fetched.
For example, given arrays like this:
type SubRequest {
url: String!
status: Int
}
type Request {
date: Date!
datetime: DateTime!
subRequests: [SubRequest!]!
}
You can run a query to get the status by subrequest:
{
requests {
date
subRequests {
# discard the url, only need the status
status
}
}
}
The results would be:
{
"requests": [
{
"date": "2018-01-01",
"subRequests": [{"status": 404}, {"status": 200}, {"status": 404}]
},
{
"date": "2018-01-01",
"subRequests": [{"status": 200}]
}
]
}
Maps
Maps behave like arrays, but can be grouped using the sum function. They are used in aggregated datasets, such as httpRequest1dGroups.
Example maps:
type URLStatsMapElem {
url: String!
requests: Int
bytes: Int
}
type Request {
date: Date!
datetime: DateTime!
urlStatsMap: [URLStatsMapElem!]!
}
Query:
{
requests {
sum {
urlStatsMap {
url
requests
bytes
}
}
dimensions {
date
}
}
}
Response:
{
"requests": [
{
"sum": {
"urlStatsMap": [
{
"url": "hello-world.org/1",
"requests": 123,
"bytes": 1024
},
{
"url": "hello-world.org/10",
"requests": 1230,
"bytes": 10240
}
]
}
"dimensions" {
"date": "2018-10-19"
}
},
...
]
}
Examples
Query array fields in raw datasets:
query NestedFields($zoneTag: string, $dateStart: string, $dateEnd: string, $datetimeStart: string, $datetimeEnd: string) {
viewer {
zones(filter: {zoneTag: $zoneTag}) {
events(limit: 2, filter: {datetime_geq: $datetimeStart,datetime_leq: $datetimeEnd}){
matches {
ruleId
action
source
}
}
}
}
}
Example response:
{
"data": {
"viewer": {
"zones": [
{
"events": [
{
"matches": [
{
"action": "allow",
"ruleId": "rule-id-one",
"source": "asn"
},
{
"action": "block",
"ruleId": "rule-id-two",
"source": "asn"
}
]
}
]
}
]
}
},
"errors": null
}
Query maps fields in aggregated datasets:
query MapCapacity($zoneTag: string, $dateStart: string, $dateEnd: string, $datetimeStart: string, $datetimeEnd: string) {
viewer {
zones(filter: {zoneTag: $zoneTag}) {
httpRequests1mGroups(
limit: 10,
filter: {date_geq: $dateStart, date_leq: $dateEnd, datetime_geq: $datetimeStart, datetime_lt: $datetimeEnd}) {
sum {
countryMap {
clientCountryName
requests
bytes
threats
}
}
dimensions {
datetimeHour
}
}
}
}
}
Example response:
{
"data": {
"viewer": {
"zones": [
{
"httpRequests1mGroups": [
{
"dimensions": {
"datetime": "2019-03-08T17:00:00Z"
},
"sum": {
"countryMap": [
{
"bytes": 51911317,
"clientCountryName": "XK",
"requests": 4492,
"threats": 0
},
{
"bytes": 1816103586,
"clientCountryName": "T1",
"requests": 132423,
"threats": 0
},
...
]
}
}
]
}
]
}
},
"errors": null
}