• Parameters and filters
• Timeseries graph
• Activity log
• Top N cards - source
• Top N cards - destination
• TCP Flags
• Executive summary

Use this workflow to build and test queries:

• Install and configure theGraphiQL ↗ app to authenticate to the Cloudflare Analytics GraphQL API. Cloudflare recommends token authentication. Refer toConfigure an Analytics API token, for more information.
• Construct the queries in the GraphiQL. You can use the introspective documentation in the GraphQL client to explore the nodes available. For further information about queries, refer toQuerying basics.
• Test your queries by running them from GraphiQL or by passing them as the payload in a cURL request to the GraphQL API endpoint.
• Use the queries in your application to provide data for your dashboard widgets.

These examples use the account ID for the Cloudflare account that you are querying. You can define this as a variable (accountTag) and reference it in your queries.

The queries also use a filter to specify the time interval that you want to query. The filter uses a start time and end time to define the time interval. You use different attributes to specify the start and end times, depending on the time period that you want to query. Refer toFiltering for further information about filters.

The following example queries for data with dates greater than or equal todate_geq and less than or equal todate_leq:

{
"accountTag":"{account-id}",
"filter":{
"AND":[{"date_geq":"2020-01-19"},{"date_leq":"2020-01-20"}]
}
}

This table lists Network Analytics datasets (nodes) and thedatetimeDimension that you should use when querying data for a given time selection.

When you want an aggregated view of data, use theGroups query nodes. For example, theipFlows1mAttacksGroups dataset represents minute-wise rollup reports of attack activity. For more detail, refer toDatasets.

The table below lists the start and end time attributes that are valid for query nodes representing different time ranges.

Use the following query to build the timeseries graph in network analytics:

queryipFlowTimeseries(
$accountTag:string
$filter:AccountIpFlows1mGroupsFilter_InputObject
){
viewer{
accounts(filter: {accountTag: $accountTag }){
ipFlows1mGroups(
limit:1000
filter: $filter
orderBy: datetimeMinute_ASC
){
dimensions{
timestamp: datetimeMinute
attackMitigationType
attackId
}
sum{
bits
packets
}
}
}
}
}

This query returns an activity log summarizing minute-wise rollups of attack traffic in IP flows. The query groups the data by the fields listed in thedimensions object.

queryipFlowEventLog(
$accountTag:string
$filter:AccountIpFlows1mAttacksGroupsFilter_InputObject
){
viewer{
accounts(filter: {accountTag: $accountTag }){
ipFlows1mAttacksGroups(
limit:10
filter: $filter
orderBy: [min_datetimeMinute_ASC]
){
dimensions{
attackId
attackDestinationIP
attackDestinationPort
attackMitigationType
attackSourcePort
attackType
}
avg{
bitsPerSecond
packetsPerSecond
}
min{
datetimeMinute
bitsPerSecond
packetsPerSecond
}
max{
datetimeMinute
bitsPerSecond
packetsPerSecond
}
sum{
bits
packets
}
}
}
}
}

This query returns data about the top source IPs.Thelimit parameter controls the amount of records returned for each node. In the following code, the highlighted lines indicate where you configurelimit.

queryGetTopNBySource(
$accountTag:string
$filter:AccountIpFlows1mGroupsFilter_InputObject
$portFilter:AccountIpFlows1mGroupsFilter_InputObject
){
viewer{
accounts(filter: {accountTag: $accountTag }){
topNPorts: ipFlows1mGroups(
limit:5
filter: $portFilter
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: sourcePort
ipProtocol
}
}
topNASN: ipFlows1mGroups(
limit:5
filter: $filter
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: sourceIPAsn
description: sourceIPASNDescription
}
}
topNIPs: ipFlows1mGroups(
limit:5
filter: $filter
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: sourceIP
}
}
topNColos: ipFlows1mGroups(
limit:10
filter: $filter
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: coloCity
coloCode
}
}
topNCountries: ipFlows1mGroups(
limit:10
filter: $filter
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: coloCountry
}
}
topNIPVersions: ipFlows1mGroups(
limit:2
filter: $filter
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: ipVersion
}
}
}
}
}

This query returns data about the top destination IPs. Thelimit parameter controls the amount of records returned. In the following code, the highlighted lines indicate that the query returns the five highest results.

queryGetTopNByDestination(
$accountTag:string
$filter:AccountIpFlows1mGroupsFilter_InputObject
$portFilter:AccountIpFlows1mGroupsFilter_InputObject
){
viewer{
accounts(filter: {accountTag: $accountTag }){
topNIPs: ipFlows1mGroups(
filter: $filter
limit:5
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: destinationIP
}
}
topNPorts: ipFlows1mGroups(
filter: $portFilter
limit:5
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
metric: destinationPort
ipProtocol
}
}
}
}
}

This query extracts the number of TCP packets from the minute-wise rollups of IP flows, and groups the results by TCP flag value. It useslimit: 8 to display the top eight results, and presents them in descending order.

Add the following line to the filter to indicate that you want to view TCP data:

{"ipProtocol":"TCP"}

queryGetTCPFlags(
$accountTag:string
$filter:AccountIpFlows1mGroupsFilter_InputObject
){
viewer{
accounts(filter: {accountTag: $accountTag }){
tcpFlags: ipFlows1mGroups(
filter: $filter
limit:8
orderBy: [sum_(bits/packets)_DESC]
){
sum{
count: (bits/packets)
}
dimensions{
tcpFlags
}
}
}
}
}

The executive summary query summarizes overall activity, therefore it only filters by the selected time interval, and ignores all filters applied to the analytics.Use different queries, depending on the time interval you want to examine and what kind of traffic the account is seeing.

If the time interval is absolute, for example March 25th 09:00 to March 25th 17:00, then execute a query for attacks within those times.Use the appropriate query node, for exampleipFlows1dGroups, for the time interval.

queryGetPreviousAttacks($accountTag:string,$filter:filter){
viewer{
accounts(filter: {accountTag: $accountTag}){
${queryNode}(limit:1000,filter: $filter){
dimensions{
attackId
}
sum{
packets
bits
}
}
}
}
}

If the time interval is relative to the current time, for example the last 24 hours or the last 30 minutes, then make a query to theipFlows1mGroup node to check whether there were attacks in the past five minutes. Attacks within the past five minutes are classed as ongoing: the Activity Log displaysPresent.The query response lists theattackID values of ongoing attacks.

queryGetOngoingAttackIds($accountTag:string,$filter:filter){
viewer{
accounts(filter: {accountTag: $accountTag }){
ipFlows1mGroups(limit:1000,filter: $filter){
dimensions{
attackId
}
}
}
}
}

If there are ongoing attacks, query theipFlows1mAttacksGroups node, filtering with theattackID values from the previous query. The query below returns the maximum bit and packet rates.

queryGetOngoingAttacks($accountTag:string,$filter:filter){
viewer{
accounts(filter: {accountTag: $accountTag }){
ipFlows1mAttacksGroups(limit:1000,filter: $filter){
dimensions{
attackId
}
max{
bitsPerSecond
packetsPerSecond
}
}
}
}
}

If there are no ongoing attacks, use theGetPreviousAttacks query to display data for attacks within an absolute time interval.