Version: 2.2.3

NQL query examples

Glossary

	Description
field	Key-value pair, a way of storing information, e.g. name=John. In special cases, it is allowed to store an empty value in the field. It is not possible to create a field with an empty/undefined key. The value of the field for a specific data type.
object	A collection of fields that form the basic unit of data storage. It has mandatory/system fields. A blank document has only mandatory/system fields.
collection	A logical container for storing objects. The collection can be empty or it can be distributed. Provides an interface for querying, adding/deleting/modifying documents regardless of their internal organization.
input collection	A collection of objects (input objects) that is passed to the command.
output collection	A collection of objects (output objects) that is the result of executing a command.
stream	Data source. Generates (on demand or continuously) a sequence of objects (collection) of a specific type (origin).
command	Processes a set of data (collections) coming from the input and passes the output result. Commands can be more than one and can form a so-called pipe: source1 \| command1 \| command2 \| .... commandN. Each command has an input and an output of data (collections). The input of a command is a sequence of input objects (collection) which are the result (output) of the previous command or stream. The result of a command is a collection of output objects. A special type of command is a stream, which has only an output.
pipe (nql)	A sequence of commands that processes collections of objects. A pipeline is defined by an nql expression.

Example 1

Scenario

Select the ten oldest people in the IT department and display the full names of these people.

NQL Query

src stream="testdata"  | set fullName = concat(fName, lName, delimiter=",")  | where dep="IT"  | aggr fName=first(fName), lName=first(lName), ctry=first(ctry), age=first(age), docs=first(docs), host=first(host), PD=max(PD), Balance=max(balance) by fullName | sort age desc | limit 10

Description

Below is a step-by-step explanation of the NQL Query.

records

Get the objects from the test database.

Result:

{
    "fName": "Jake",
    "lName": "White",
    "dep": "HR",
    "ctry": "DE",
    "age": 56,
    "host": "www.facebook.com",
    "docs": [
        "IDCard",
        "Passport",
        "DrivingLicense"
    ],
    "PD": 0.43859708144526346,
    "ts": 1673755017972,
    "balance": 9966.23
},
{
    "fName": "Jack",
    "lName": "Magenta",
    "dep": "HR",
    "ctry": "PL",
    "age": 27,
    "host": "pl.wikipedia.org",
    "docs": [
        "Passport"
    ],
    "PD": 0.511210222043333,
    "ts": 1674845669100,
    "balance": 5556.47
},
{
    "fName": "Harry",
    "lName": "Watermelon",
    "dep": "HR",
    "ctry": "US",
    "age": 49,
    "host": "www.google.com",
    "docs": [
        "Passport"
    ],
    "PD": 0.3029903276018222,
    "ts": 1673763725949,
    "balance": 7404.62
},
{"...":"..."}

concat

Add a new field containing first and last names separated by a comma.

set fullName = concat(fName, lName, delimiter=",")

Result:

{
    "fName": "Jake",
    "lName": "White",
    "dep": "HR",
    "ctry": "DE",
    "age": 56,
    "host": "www.facebook.com",
    "docs": [
        "IDCard",
        "Passport",
        "DrivingLicense"
    ],
    "PD": 0.43859708144526346,
    "ts": 1673755017972,
    "balance": 9966.23,
    "fullName":"Jake,White"
},
{
    "fName": "Jack",
    "lName": "Magenta",
    "dep": "HR",
    "ctry": "PL",
    "age": 27,
    "host": "pl.wikipedia.org",
    "docs": [
        "Passport"
    ],
    "PD": 0.511210222043333,
    "ts": 1674845669100,
    "balance": 5556.47, 
    "fullName":"Jake,Magenta"
},
{
    "fName": "Harry",
    "lName": "Watermelon",
    "dep": "HR",
    "ctry": "US",
    "age": 49,
    "host": "www.google.com",
    "docs": [
        "Passport"
    ],
    "PD": 0.3029903276018222,
    "ts": 1673763725949,
    "balance": 7404.62, 
    "fullName":"Harry,Watermelon"
},
{"...":"..."}

where

Select only those people who belong to the IT department.

where dep="IT"

Result:

{
    "lName": "Blue",
    "fName": "Jack",
    "dep":"IT",
    "docs": [
        "Passport"
    ],      
    "PD": 0.8441553273619709,
    "ctry": "PL",
    "host": "www.linkedin.com",
    "balance": 8899.6,
    "age": 60,
    "fullName" : "Jack,Blue"
},
{
    "lName": "Yellow",
    "fName": "Jacob",
    "dep":"IT",
    "docs": [
        "IDCard",
        "Passport"
    ],      
    "PD": 0.8416937579622111,
    "ctry": "IE",
    "host": "www.google.com",
    "balance": 9376.34,
    "age": 60,
    "fullName" : "Jacob,Yellow"
}
,
{"...":"..."}

aggr
The data contains several values of the Balance and PD fields for a person at different ts times. In this example, only one object for each person will be displayed. To do this, you need to aggregate the data by full name (fullName field), taking the maximum values of the Balance and PD fields. The value of the fullName field by which the aggregation will be done will be stored in the aggregation field _id.

aggr fName=first(fName), lName=first(lName), ctry=first(ctry), age=first(age), docs=first(docs), host=first(host), PD=max(PD), Balance=max(balance) by fullName

Result:

{
    "lName": "Blue",
    "fName": "Jack",
    "docs": [
        "Passport"
    ],
    "PD": 0.8441553273619709,
    "ctry": "PL",
    "host": "www.linkedin.com",
    "balance": 8899.6,
    "_id": [
        "Jack,Blue"
    ],
    "age": 60
},
{
    "lName": "Yellow",
    "fName": "Jacob",
    "docs": [
        "IDCard",
        "Passport"
    ],
    "PD": 0.8416937579622111,
    "ctry": "IE",
    "host": "www.google.com",
    "balance": 9376.34,
    "_id": [
        "Jacob,Yellow"
    ],
    "age": 60
},
{"...":"..."}

sort
Sort the results by the age of the people starting with the oldest (desc).

sort age desc

limit
Select the first ten people from a previously sorted list.

limit 10

Result:

[
    {
        "lName": "Blue",
        "fName": "Jack",
        "docs": [
            "Passport"
        ],
        "PD": 0.8441553273619709,
        "ctry": "PL",
        "host": "www.linkedin.com",
        "balance": 8899.6,
        "_id": [
            "Jack,Blue"
        ],
        "age": 60
    },
    {
        "lName": "Yellow",
        "fName": "Jacob",
        "docs": [
            "IDCard",
            "Passport"
        ],
        "PD": 0.8416937579622111,
        "ctry": "IE",
        "host": "www.google.com",
        "balance": 9376.34,
        "_id": [
            "Jacob,Yellow"
        ],
        "age": 60
    },
    {
        "lName": "Copper",
        "fName": "Olive",
        "docs": [
            "IDCard",
            "Passport"
        ],
        "PD": 0.8762584426876398,
        "ctry": "IE",
        "host": "www.linkedin.com",
        "balance": 8586.93,
        "_id": [
            "Olive,Copper"
        ],
        "age": 59
    },
    {
        "lName": "Cinnamon",
        "fName": "Neil",
        "docs": [
            "Passport"
        ],
        "PD": 0.9939145170408105,
        "ctry": "DE",
        "host": "www.google.com",
        "balance": 8096.58,
        "_id": [
            "Neil,Cinnamon"
        ],
        "age": 57
    },
    ,
{"...":"..."}
]

Example 2

Scenario

For each person, display the current balance along with the date from which this balance is valid.

NQL Query method 1

src stream="testdata"  | sort ts desc | aggr latestBalance=first(balance),latestPD=first(PD)  by fName, lName unwind=true

Description

Below is a step-by-step explanation of the NQL Query.

sort
Sort collections by ts field from the largest value (desc).

sort ts desc

aggr
Select the values of the Balance and PD fields from the first object found from the aggregated data by the fName and lName fields. The previous command sorted the values by ts in descending order, so if in this step you select the value of the Balance field and PD field from the first object encountered, these will be the values for the maximum ts, i.e. the last ones.

aggr latestBalance=first(balance),latestPD=first(PD)  by fName, lName unwind=true

Result:

[
    {
        "lName": "Amber",
        "fName": "Matt",
        "latestBalance": 8540.07,
        "latestPD": 0.3231355816784073
    },
    {
        "lName": "Olive",
        "fName": "William",
        "latestBalance": 595.67,
        "latestPD": 0.6249965829144613
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "latestBalance": 3772.04,
        "latestPD": 0.2969195237000811
    },
    {
        "lName": "Ruby",
        "fName": "Paul",
        "latestBalance": 4879.27,
        "latestPD": 0.006748252054172954
    },
    {
        "lName": "Yellow",
        "fName": "Richard",
        "latestBalance": 4011.64,
        "latestPD": 0.5387816649690332
    },  
{"...":"..."}
]

NQL Query method 2

Sequence of three NQLs:

src stream="testdata"  | dst "collTestData"

coll "collTestData" |  fork ( set fullName=concat(fName, " ", lName) | dst "data3"), ( aggr latestTs=max(ts)by fName, lName unwind=true | dst "data4")

coll "data3" |  set latestTs=valColl("data4", "latestTs", {"fName":fName, "lName":lName}) |  where $eq(latestTs,ts) |  sort fullName

Description

Below is a step-by-step explanation of the NQL Query.

dst collTestData

Prepare a collector with test data.

Result:

[
    {
        "lName": "White",
        "fName": "Jake",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.43859708144526344,
        "ctry": "DE",
        "host": "www.facebook.com",
        "balance": 9966.23,
        "dep": "HR",
        "age": 56,
        "ts": 1673755017972
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "docs": [
            "Passport"
        ],
        "PD": 0.511210222043333,
        "ctry": "PL",
        "host": "pl.wikipedia.org",
        "balance": 5556.47,
        "dep": "HR",
        "age": 27,
        "ts": 1674845669100
    },
    ...
]

coll collTestData

Get the test data from the collTestData collector

fork

Execute two NQLs in parallel.

3.1 set, dst

Add a new fullName field which is a composite of the values of the fields: Name , " " (space) and Name. Store the result to a collector with "data3" id .

Result:

[
    {
        "lName": "White",
        "fName": "Jake",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.43859708144526344,
        "ctry": "DE",
        "host": "www.facebook.com",
        "balance": 9966.23,
        "dep": "HR",
        "age": 56,
        "ts": 1673755017972,
        "fullName": "Jake White"
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "docs": [
            "Passport"
        ],
        "PD": 0.511210222043333,
        "ctry": "PL",
        "host": "pl.wikipedia.org",
        "balance": 5556.47,
        "dep": "HR",
        "age": 27,
        "ts": 1674845669100,
        "fullName": "Jack Magenta"
    },
    ...
]

3.2 aggr, dst

Calculate the maximum timestamp (latestTs) for each person and store the result in the collector with "data4" id.

Result:

[
    {
        "lName": "White",
        "fName": "Jake",
        "latestTs": 1675018088389
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "latestTs": 1675201265593
    },
    {
        "lName": "Watermelon",
        "fName": "Harry",
        "latestTs": 1674670980569
    },
    ...
]

coll "data3"

Get the test data from the "data3" collector .

set, valColl

To all objects from the "data3" collector add the latestTs field of which the value is taken for each person from the "data4" collector .

Result:

[
    {
        "lName": "White",
        "fName": "Jake",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.43859708144526344,
        "ctry": "DE",
        "host": "www.facebook.com",
        "balance": 9966.23,
        "dep": "HR",
        "age": 56,
        "ts": 1673755017972,
        "fullName": "Jake White",
        "latestTs": null
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "docs": [
            "Passport"
        ],
        "PD": 0.511210222043333,
        "ctry": "PL",
        "host": "pl.wikipedia.org",
        "balance": 5556.47,
        "dep": "HR",
        "age": 27,
        "ts": 1674845669100,
        "fullName": "Jack Magenta",
        "latestTs": null
    },
    ...
]

where

From the previously prepared set of objects (people), select only those for which latestTs equals ts. These are the objects containing the most recent (latest) PD and balance values.

sort

Sort the result.

Result:

[
    {
        "lName": "Amber",
        "fName": "Connor",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.48863820141709124,
        "ctry": "IE",
        "host": "www.linkedin.com",
        "balance": 1440.24,
        "dep": "ADM",
        "age": 24,
        "ts": 1675165590523,
        "fullName": "Connor Amber",
        "latestTs": 1675165590523
    },
    {
        "lName": "Green",
        "fName": "Connor",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.5409617064298299,
        "ctry": "IE",
        "host": "www.google.com",
        "balance": 5851.46,
        "dep": "ADM",
        "age": 31,
        "ts": 1674903306796,
        "fullName": "Connor Green",
        "latestTs": 1674903306796
    },
    ...
]

Example 3

Scenario

Add a new fullName field to all objects, sort and display them and perform data aggregation in parallel. To do this, perform three NQLs.

NQL Query

src stream="testdata"  |  dst "collTestData"

coll "collTestData" |  fork (set fullName=concat(fName, " ", lName) | limit 100 | dst "collData1"), ( aggr fieldsCount=sum(age) by fName,lName maxBuckets=2 | dst "collData2")

coll "collData1" |  sort fullName

Description

Below is a step-by-step explanation of the NQL Query.

dst
Store all objects from testdata in a new collector with the collTestData identifier.

Result:

[
    {
        "lName": "White",
        "fName": "Jake",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.43859708144526344,
        "balance": 9966.23,
        "ctry": "DE",
        "host": "www.facebook.com",
        "dep": "HR",
        "age": 56,
        "ts": 1673755017972
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "docs": [
            "Passport"
        ],
        "PD": 0.511210222043333,
        "balance": 5556.47,
        "ctry": "PL",
        "host": "pl.wikipedia.org",
        "dep": "HR",
        "age": 27,
        "ts": 1674845669100
    },
    ...
]

coll
The previously created collTestData data collector is the data source for the next step NQL.
fork
On the data from the collTestData collector perform two NQLs in parallel: set... and aggr.... Each of them stores its results in the newly created collData1 and collData1 collectors.

Result for set... stored in collData1 collector:

[
    {
        "lName": "White",
        "fName": "Jake",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.43859708144526344,
        "balance": 9966.23,
        "ctry": "DE",
        "host": "www.facebook.com",
        "dep": "HR",
        "age": 56,
        "ts": 1673755017972,
        "fullName": "Jake White"
    },
    {
        "lName": "Magenta",
        "fName": "Jack",
        "docs": [
            "Passport"
        ],
        "PD": 0.511210222043333,
        "balance": 5556.47,
        "ctry": "PL",
        "host": "pl.wikipedia.org",
        "dep": "HR",
        "age": 27,
        "ts": 1674845669100,
        "fullName": "Jack Magenta"
    },
    ...
]

Result for aggr... stored in collData2 collector:

[
    {
        "fieldsCount": 560,
        "_id": [
            "Jake",
            "White"
        ]
    },
    {
        "fieldsCount": 270,
        "_id": [
            "Jack",
            "Magenta"
        ]
    }
]

coll
The result of the NQL ("set...") from the previous step stored in the collData1 collector is the data source for the next NQL.
sort
Sort the data from the collData1 collector and display the result.

Result:

[
    {
        "fName": "Connor",
        "lName": "Amber",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.7200228784043261,
        "balance": 1118.7,
        "ctry": "IE",
        "host": "www.linkedin.com",
        "dep": "ADM",
        "age": 24,
        "ts": 1673615235896,
        "fullName": "Connor Amber"
    },
    {
        "fName": "Connor",
        "lName": "Green",
        "docs": [
            "IDCard",
            "Passport",
            "DrivingLicense"
        ],
        "PD": 0.8421159777008483,
        "balance": 7882.33,
        "ctry": "IE",
        "host": "www.google.com",
        "dep": "ADM",
        "age": 31,
        "ts": 1673976067457,
        "fullName": "Connor Green"
    },
    ...
]

In this example, the data from the aggregation is ignored in further processing and not displayed at the end.

Example 4

Scenario

Calculate the average value of a person's balance and add it to each person object in the source table.

NQL Query

src stream="testdata" |  aggr avgBalance=avg(balance) by fName, lName unwind=true |  dst "avgBalanceColl"

src stream="testdata" |  set avgBalance=valColl("avgBalanceColl", "avgBalance", {"fName":fName, "lName":lName})

Description

Below is a step-by-step explanation of the NQL Query.

Calculate the average balance value for a person and save it in the avgBalance field.
Save the results to the collector with the avgBalanceColl id.
For each person from the testdata collection, add the avgBalance field whose value is taken from the avgBalanceColl collector from the avgBalance field of the object selected in this collector after the filter avgBalanceColl.fName = testdata.fName and avgBalanceColl.lName = testdata.lName.

[
  {
    "fName": "Jake",
    "lName": "White",
    "dep": "HR",
    "country": "DE",
    "age": 56,
    "host": "www.facebook.com",
    "docs": [
      "IDCard",
      "Passport",
      "DrivingLicense"
    ],
    "PD": 0.43859708144526344,
    "ts": 1673755017972,
    "balance": 9966.23,
    "avgBalance": 6849.086
  },
  {
    "fName": "Jack",
    "lName": "Magenta",
    "dep": "HR",
    "country": "PL",
    "age": 27,
    "host": "pl.wikipedia.org",
    "docs": [
      "Passport"
    ],
    "PD": 0.511210222043333,
    "ts": 1674845669100,
    "balance": 5556.47,
    "avgBalance": 4675.839
  }
  ...
]

Example 5 (real data from the network)

Display five IP clients for which there is the most data.

src stream="netflow"  | isIp(clientIp) | aggr countClientIp=count(clientIp) by clientIp as client unwind=true | sort countClientIp desc | limit 5

Description

Below is a step-by-step explanation of the NQL Query.

Get the data from the netflow stream.

Result (first two objects):

[
    {
        "_stream": "netflow",
        "timestamp": 1677225660000,
        "sessionId": 1727,
        "clientIp": "172.16.70.220",
        "serverIp": "172.16.10.11",
        "protocol": 6,
        "clientPort": 60236,
        "serverPort": 49666,
        "application": 49666,
        "exporterIps": [
            "172.16.100.1"
        ],
        "interfaces": [
            "172.16.100.1[58]",
            "172.16.100.1[5]"
        ],
        "clientFunction": [
            "Workstations"
        ],
        "serverFunction": [
            "Workstations"
        ],
        "clientLocation": [],
        "serverLocation": [],
        "clientRole": [],
        "serverRole": [],
        "clientTcpFlags": 0,
        "serverTcpFlags": 0,
        "tosNumbers": [],
        "mpls": [],
        "asNumbers": [],
        "icmpType": [],
        "clientCountry": null,
        "serverCountry": null,
        "clientAsNumber": null,
        "serverAsNumber": null,
        "activeTime": 92,
        "firstTimestamp": 1677225416260,
        "lastTimestamp": 1677225416352,
        "clientBytes": 3756,
        "serverBytes": 1878,
        "clientPackets": 10,
        "serverPackets": 9,
        "flows": 2,
        "customField1036": null
    },
    {
        "_stream": "netflow",
        "timestamp": 1677225660000,
        "sessionId": 1729,
        "clientIp": "172.16.20.75",
        "serverIp": "8.253.186.44",
        "protocol": 6,
        "clientPort": 51310,
        "serverPort": 80,
        "application": 80,
        "exporterIps": [
            "172.16.100.1"
        ],
        "interfaces": [
            "172.16.100.1[3]",
            "172.16.100.1[5]"
        ],
        "clientFunction": [
            "Workstations"
        ],
        "serverFunction": [],
        "clientLocation": [],
        "serverLocation": [],
        "clientRole": [],
        "serverRole": [],
        "clientTcpFlags": 0,
        "serverTcpFlags": 0,
        "tosNumbers": [],
        "mpls": [],
        "asNumbers": [],
        "icmpType": [],
        "clientCountry": null,
        "serverCountry": null,
        "clientAsNumber": null,
        "serverAsNumber": null,
        "activeTime": 121156,
        "firstTimestamp": 1677225274816,
        "lastTimestamp": 1677225395972,
        "clientBytes": 80,
        "serverBytes": 40,
        "clientPackets": 2,
        "serverPackets": 1,
        "flows": 2,
        "customField1036": null
    }
    ...
]

Select only those objects in which the clientIp field contains the current value ip4 or ip6.

| isIp(clientIp)

Calculate the number of objects for each client ip.

| aggr countClientIp=count(clientIp) by clientIp as client unwind=true

Result (first three values):

[
    {
        "countClientIp": 542,
        "client": "172.16.70.220"
    },
    {
        "countClientIp": 925,
        "client": "172.16.20.75"
    },
    {
        "countClientIp": 2139,
        "client": "172.16.20.141"
    }
    ...
]

Sort the results from the largest value of countClientIp.
Select the first 5 objects from the result.

Result:

[
    {
        "countClientIp": 15744,
        "client": "172.16.70.36"
    },
    {
        "countClientIp": 12255,
        "client": "172.16.60.107"
    },
    {
        "countClientIp": 6888,
        "client": "172.16.10.11"
    },
    {
        "countClientIp": 4520,
        "client": "172.16.70.108"
    },
    {
        "countClientIp": 3777,
        "client": "172.16.20.59"
    }
]

Example 6 (real data from the network)

Scenario

Display ten protocols with the highest network traffic.

NQL Query

src stream="netflowByProtocolAggr" 
| aggr sumClientBytes=sum(clientBytes),sumServerBytes=sum(serverBytes),sumClientPackets=sum(clientPackets),sumServerPackets=sum(serverPackets),sumFlows=sum(flows) by protocol as protocolName unwind=true 
| set _sumBytes1=add(sumClientBytes,sumServerBytes),_sumClientBitsPerSecond4=div(mul(sumClientBytes,8),60),_sumServerBitsPerSecond5=div(mul(sumServerBytes,8),60),_sumPacketsPerSecond6=div(add(sumClientPackets,sumServerPackets),60),_sumFlowsPerSecond7=div(sumFlows, 60) 
| fork (aggr _sumBytes1=sum(_sumBytes1), _sumClientBytes2=sum(sumClientBytes), _sumServerBytes3=sum(sumServerBytes), _sumClientBitsPerSecond4=sum(_sumClientBitsPerSecond4),_sumServerBitsPerSecond5=sum(_sumServerBitsPerSecond5), _sumPacketsPerSecond6=sum(_sumPacketsPerSecond6), _sumFlowsPerSecond7=sum(_sumFlowsPerSecond7), total=count(1) | set protocolName="Total", _isTotalRow=true), (sort _sumBytes1 desc | limit 10) 

Description

Below is a step-by-step explanation of the NQL Query.

Select the data from the netflowByProtocolAggr stream.

src stream="netflowByProtocolAggr"

Result:

[
    {
        "_stream": "netflowByProtocolAggr",
        "timestamp": 1677196980000,
        "protocol": 17,
        "tenantId": 1284495119,
        "activeTime": 14978853,
        "clientBytes": 44116491,
        "serverBytes": 160063,
        "clientPackets": 117479,
        "serverPackets": 1045,
        "flows": 1566,
        "sessions": 676
    },
    {
        "_stream": "netflowByProtocolAggr",
        "timestamp": 1677196980000,
        "protocol": 1,
        "tenantId": 1284495119,
        "activeTime": 1120484,
        "clientBytes": 1228,
        "serverBytes": 399559,
        "clientPackets": 23,
        "serverPackets": 1263,
        "flows": 112,
        "sessions": 23
    },
    {
        "_stream": "netflowByProtocolAggr",
        "timestamp": 1677196980000,
        "protocol": 2,
        "tenantId": 1284495119,
        "activeTime": 79982,
        "clientBytes": 0,
        "serverBytes": 1224,
        "clientPackets": 0,
        "serverPackets": 34,
        "flows": 2,
        "sessions": 1
    }
]

Calculate the sum of the values of the clientBytes, serverBytes, clientPackets and serverPackets fields for each protocol.
Set unwind=true then the value of the protocol field will be in the resulting object in the protocolName field, otherwise the value will be returned in the _id:[<protocol>] field.

| aggr sumClientBytes=sum(clientBytes),
       sumServerBytes=sum(serverBytes), 
       sumClientPackets=sum(clientPackets),
       sumServerPackets=sum(serverPackets), 
       sumFlows=sum(flows) 
  by protocol as protocolName unwind=true 
  

Result:

[
    {
        "protocolName": 17,
        "sumClientBytes": 27629261045,
        "sumServerBytes": 200320556,
        "sumClientPackets": 73431273,
        "sumServerPackets": 736372,
        "sumFlows": 787521
    },
    {
        "protocolName": 1,
        "sumClientBytes": 771406,
        "sumServerBytes": 210319829,
        "sumClientPackets": 13398,
        "sumServerPackets": 706733,
        "sumFlows": 63849
    },
    {
        "protocolName": 2,
        "sumClientBytes": 0,
        "sumServerBytes": 227536,
        "sumClientPackets": 0,
        "sumServerPackets": 6318,
        "sumFlows": 491
    },
    {
        "protocolName": 6,
        "sumClientBytes": 1873217764,
        "sumServerBytes": 11520014000,
        "sumClientPackets": 16168561,
        "sumServerPackets": 9237395,
        "sumFlows": 884914
    },
    {
        "protocolName": 58,
        "sumClientBytes": 0,
        "sumServerBytes": 192648,
        "sumClientPackets": 0,
        "sumServerPackets": 3514,
        "sumFlows": 765
    }
]

Add fields whose values are the result of the following arithmetic expressions:

_sumBytes1 = sumClientBytes + sumServerBytes _sumClientBitsPerSecond4 = (sumClientBytes * 8) / 60 _sumServerBitsPerSecond5 = (sumServerBytes * 8) / 60 _sumPacketsPerSecond6 = (sumClientPackets + sumServerPackets) / 60 _sumFlowsPerSecond7 = sumFlows / 60 _sumFlowsPerSecond7 = sumFlows / 60

| set _sumBytes1=add(sumClientBytes,sumServerBytes), 
      _sumClientBitsPerSecond4=div(mul(sumClientBytes,8),60), 
      _sumServerBitsPerSecond5=div(mul(sumServerBytes,8),60), 
      _sumPacketsPerSecond6=div(add(sumClientPackets,sumServerPackets),60), 
      _sumFlowsPerSecond7=div(sumFlows, 60)      

Result:

[
    {
        "protocolName": 17,
        "sumClientBytes": 27629261045,
        "sumServerBytes": 200320556,
        "sumClientPackets": 73431273,
        "sumServerPackets": 736372,
        "sumFlows": 787521,
        "_sumBytes1": 27829581601,
        "_sumClientBitsPerSecond4": 3683901472.6666665,
        "_sumServerBitsPerSecond5": 26709407.466666665,
        "_sumPacketsPerSecond6": 1236127.4166666667,
        "_sumFlowsPerSecond7": 13125.35
    },
    {
        "protocolName": 1,
        "sumClientBytes": 771406,
        "sumServerBytes": 210319829,
        "sumClientPackets": 13398,
        "sumServerPackets": 706733,
        "sumFlows": 63849,
        "_sumBytes1": 211091235,
        "_sumClientBitsPerSecond4": 102854.13333333333,
        "_sumServerBitsPerSecond5": 28042643.866666667,
        "_sumPacketsPerSecond6": 12002.183333333332,
        "_sumFlowsPerSecond7": 1064.15
    },
    {
        "protocolName": 2,
        "sumClientBytes": 0,
        "sumServerBytes": 227536,
        "sumClientPackets": 0,
        "sumServerPackets": 6318,
        "sumFlows": 491,
        "_sumBytes1": 227536,
        "_sumClientBitsPerSecond4": 0.0,
        "_sumServerBitsPerSecond5": 30338.133333333335,
        "_sumPacketsPerSecond6": 105.3,
        "_sumFlowsPerSecond7": 8.183333333333334
    },
    {
        "protocolName": 6,
        "sumClientBytes": 1873217764,
        "sumServerBytes": 11520014000,
        "sumClientPackets": 16168561,
        "sumServerPackets": 9237395,
        "sumFlows": 884914,
        "_sumBytes1": 13393231764,
        "_sumClientBitsPerSecond4": 249762368.53333333,
        "_sumServerBitsPerSecond5": 1536001866.6666667,
        "_sumPacketsPerSecond6": 423432.6,
        "_sumFlowsPerSecond7": 14748.566666666668
    },
    {
        "protocolName": 58,
        "sumClientBytes": 0,
        "sumServerBytes": 192648,
        "sumClientPackets": 0,
        "sumServerPackets": 3514,
        "sumFlows": 765,
        "_sumBytes1": 192648,
        "_sumClientBitsPerSecond4": 0.0,
        "_sumServerBitsPerSecond5": 25686.4,
        "_sumPacketsPerSecond6": 58.56666666666667,
        "_sumFlowsPerSecond7": 12.75
    }
]

Show results.

Show ten aggregation results by protocol from the highest value of the sum of the sumClientBytes and sumServerBytes fields and one object containing all the summed values (total) of the fields _sumBytes1, sumClientBytes, sumServerBytes, _sumClientBitsPerSecond4, _sumServerBitsPerSecond5 and _sumPacketsPerSecond6, _sumFlowsPerSecond7.

| fork (aggr _sumBytes1=sum(_sumBytes1), 
             _sumClientBytes2=sum(sumClientBytes), 
             _sumServerBytes3=sum(sumServerBytes), 
             _sumClientBitsPerSecond4=sum(_sumClientBitsPerSecond4),
             _sumServerBitsPerSecond5=sum(_sumServerBitsPerSecond5), 
             _sumPacketsPerSecond6=sum(_sumPacketsPerSecond6), 
             _sumFlowsPerSecond7=sum(_sumFlowsPerSecond7), 
             total=count(1) | set protocolName="Total", _isTotalRow=true), (sort _sumBytes1 desc | limit 10) 

Result:

The first object in the following list (protocolName: Total) contains a summary (total) of values.

[
    {
        "_sumBytes1": 41572536009,
        "_sumClientBytes2": 29606497335,
        "_sumServerBytes3": 11966038674,
        "_sumClientBitsPerSecond4": 3947532978,
        "_sumServerBitsPerSecond5": 1595471823.2,
        "_sumPacketsPerSecond6": 1678277.1666666667,
        "_sumFlowsPerSecond7": 29062.8,
        "total": 5,
        "protocolName": "Total",
        "_isTotalRow": true
    },
    {
        "protocolName": 17,
        "sumClientBytes": 27719053914,
        "sumServerBytes": 201911191,
        "sumClientPackets": 73654716,
        "sumServerPackets": 742475,
        "sumFlows": 789663,
        "_sumBytes1": 27920965105,
        "_sumClientBitsPerSecond4": 3695873855.2,
        "_sumServerBitsPerSecond5": 26921492.133333333,
        "_sumPacketsPerSecond6": 1239953.1833333333,
        "_sumFlowsPerSecond7": 13161.05
    },
    {
        "protocolName": 6,
        "sumClientBytes": 1886667955,
        "sumServerBytes": 11552918926,
        "sumClientPackets": 16300918,
        "sumServerPackets": 9267241,
        "sumFlows": 888874,
        "_sumBytes1": 13439586881,
        "_sumClientBitsPerSecond4": 251555727.33333334,
        "_sumServerBitsPerSecond5": 1540389190.1333334,
        "_sumPacketsPerSecond6": 426135.98333333334,
        "_sumFlowsPerSecond7": 14814.566666666668
    },
    {
        "protocolName": 1,
        "sumClientBytes": 775466,
        "sumServerBytes": 210788181,
        "sumClientPackets": 13425,
        "sumServerPackets": 708019,
        "sumFlows": 63973,
        "_sumBytes1": 211563647,
        "_sumClientBitsPerSecond4": 103395.46666666666,
        "_sumServerBitsPerSecond5": 28105090.8,
        "_sumPacketsPerSecond6": 12024.066666666668,
        "_sumFlowsPerSecond7": 1066.2166666666667
    },
    {
        "protocolName": 2,
        "sumClientBytes": 0,
        "sumServerBytes": 227536,
        "sumClientPackets": 0,
        "sumServerPackets": 6318,
        "sumFlows": 491,
        "_sumBytes1": 227536,
        "_sumClientBitsPerSecond4": 0.0,
        "_sumServerBitsPerSecond5": 30338.133333333335,
        "_sumPacketsPerSecond6": 105.3,
        "_sumFlowsPerSecond7": 8.183333333333334
    },
    {
        "protocolName": 58,
        "sumClientBytes": 0,
        "sumServerBytes": 192840,
        "sumClientPackets": 0,
        "sumServerPackets": 3518,
        "sumFlows": 767,
        "_sumBytes1": 192840,
        "_sumClientBitsPerSecond4": 0.0,
        "_sumServerBitsPerSecond5": 25712.0,
        "_sumPacketsPerSecond6": 58.63333333333333,
        "_sumFlowsPerSecond7": 12.783333333333333
    }
]

Example 7 (real data from the network)

Scenario

Show the average amount of network traffic in bytes for each country in 3-hour time frames.

NQL Query

src stream ="netflowByCountryAggr" 
| set sumClientBytesAndServerBytes = add(clientBytes, serverBytes) 
| timeAggr dcCountry0=dc(country),avgSumClientBytesAndServerBytes=avg(sumClientBytesAndServerBytes),countries=join(country),   mintimestamp=min(timestamp),maxtimestamp=max(timestamp) on timestamp interval="3h" 
| set timestampStr=tsToStr(_bucket),mintimestampStr=tsToStr(mintimestamp),maxtimestampStr=tsToStr(maxtimestamp)
| project -dcCountry0, -mintimestamp, -maxtimestamp, -_bucket

Description

Below is a step-by-step explanation of the NQL Query.

Select the data from the netflowByCountryAggr stream and calculate the sum of clientBytes + serverBytes

src stream ="netflowByCountryAggr" | set sumClientBytesAndServerBytes = add(clientBytes, serverBytes)

Result:

[
    {
        "_stream": "netflowByCountryAggr",
        "timestamp": 1677196800000,
        "country": "DE",
        "tenantId": 1284495119,
        "direction": 0,
        "activeTime": 439550000,
        "clientBytes": 382021135,
        "serverBytes": 379897485,
        "clientPackets": 1487888,
        "serverPackets": 1479586,
        "flows": 35164,
        "sessions": 8791,
        "sumClientBytesAndServerBytes": 761918620
    },
    {
        "_stream": "netflowByCountryAggr",
        "timestamp": 1677196800000,
        "country": "SE",
        "tenantId": 1284495119,
        "direction": 0,
        "activeTime": 732700000,
        "clientBytes": 640053594,
        "serverBytes": 638382685,
        "clientPackets": 2492900,
        "serverPackets": 2486417,
        "flows": 58614,
        "sessions": 14654,
        "sumClientBytesAndServerBytes": 1278436279
    },
    {
        "_stream": "netflowByCountryAggr",
        "timestamp": 1677196800000,
        "country": "DK",
        "tenantId": 1284495119,
        "direction": 0,
        "activeTime": 88827750000,
        "clientBytes": 77620570054,
        "serverBytes": 77625811592,
        "clientPackets": 302319389,
        "serverPackets": 302339738,
        "flows": 7106172,
        "sessions": 1776555,
        "sumClientBytesAndServerBytes": 155246381646
    },
   ...
]   

Calculate the average value over a 3-hour interval and save it to the avgSumClientBytesAndServerBytes variable.

In addition, the data from the country field is combined, so as to show a list of countries in a given time interval (countries variable) and the beginning and end of a given time interval (mintimestamp, maxtimestamp variables).

| timeAggr dcCountry0=dc(country),
    avgSumClientBytesAndServerBytes=avg(sumClientBytesAndServerBytes),
    sum1 = sum(sumClientBytesAndServerBytes),
    countries=join(country),    
    mintimestamp=min(timestamp),
    maxtimestamp=max(timestamp) 
    on timestamp interval="3h" 

Result:

[
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643487893.42223,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,...",
        "mintimestamp": 1677196800000,
        "maxtimestamp": 1677207540000,
        "_bucket": 1677196800000
    },
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643537084.8361,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,...",
        "mintimestamp": 1677207600000,
        "maxtimestamp": 1677218340000,
        "_bucket": 1677207600000
    },
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643425062.48611,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,...",
        "mintimestamp": 1677218400000,
        "maxtimestamp": 1677229140000,
        "_bucket": 1677218400000
    },
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643393605.52942,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,...",
        "mintimestamp": 1677229200000,
        "maxtimestamp": 1677231180000,
        "_bucket": 1677229200000
    }
]

Convert variables containing a timestamp value to a readable text value.

| set timestampStr=tsToStr(_bucket),
    mintimestampStr=tsToStr(mintimestamp),
    maxtimestampStr=tsToStr(maxtimestamp)

Result:

[
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643487893.42223,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,...",
        "mintimestamp": 1677196800000,
        "maxtimestamp": 1677207540000,
        "_bucket": 1677196800000,
        "timestampStr": "2023-02-24 00:00:00.000",
        "mintimestampStr": "2023-02-24 00:00:00.000",
        "maxtimestampStr": "2023-02-24 02:59:00.000"
    },
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643537084.8361,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,...",
        "mintimestamp": 1677207600000,
        "maxtimestamp": 1677218340000,
        "_bucket": 1677207600000,
        "timestampStr": "2023-02-24 03:00:00.000",
        "mintimestampStr": "2023-02-24 03:00:00.000",
        "maxtimestampStr": "2023-02-24 05:59:00.000"
    },
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643425062.48611,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,...",
        "mintimestamp": 1677218400000,
        "maxtimestamp": 1677229140000,
        "_bucket": 1677218400000,
        "timestampStr": "2023-02-24 06:00:00.000",
        "mintimestampStr": "2023-02-24 06:00:00.000",
        "maxtimestampStr": "2023-02-24 08:59:00.000"
    },
    {
        "dcCountry0": 4,
        "avgSumClientBytesAndServerBytes": 78643404454.77777,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,...",
        "mintimestamp": 1677229200000,
        "maxtimestamp": 1677231300000,
        "_bucket": 1677229200000,
        "timestampStr": "2023-02-24 09:00:00.000",
        "mintimestampStr": "2023-02-24 09:00:00.000",
        "maxtimestampStr": "2023-02-24 09:35:00.000"
    }
]

Remove the fields you do not want to display from the result .

| project -dcCountry0, -mintimestamp, -maxtimestamp, -_bucket

Result:

[
    {
        "avgSumClientBytesAndServerBytes": 78643487893.42223,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,DE,SE,...",
        "timestampStr": "2023-02-24 00:00:00.000",
        "mintimestampStr": "2023-02-24 00:00:00.000",
        "maxtimestampStr": "2023-02-24 02:59:00.000"
    },
    {
        "avgSumClientBytesAndServerBytes": 78643537084.8361,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,DE,SE,...",
        "timestampStr": "2023-02-24 03:00:00.000",
        "mintimestampStr": "2023-02-24 03:00:00.000",
        "maxtimestampStr": "2023-02-24 05:59:00.000"
    },
    {
        "avgSumClientBytesAndServerBytes": 78643425062.48611,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,DE,SE,...",
        "timestampStr": "2023-02-24 06:00:00.000",
        "mintimestampStr": "2023-02-24 06:00:00.000",
        "maxtimestampStr": "2023-02-24 08:59:00.000"
    },
    {
        "avgSumClientBytesAndServerBytes": 78643408233.22368,
        "countries": "DE,SE,DK,US,DE,SE,DK,US,DE,SE,DK,US,DE,SE,...",
        "timestampStr": "2023-02-24 09:00:00.000",
        "mintimestampStr": "2023-02-24 09:00:00.000",
        "maxtimestampStr": "2023-02-24 09:37:00.000"
    }
]

Example 8 (real data from the network)

Scenario

Threats Trajectory for most suspicious IPs.

NQL Query

src stream="alerts" 
 | valInColl(clientIp, "top10ClientIpLast15Minute_Alerts", "clientIp") 
 | splitAggr countAlertName0=count(alertName) 
             (timeAggr on timestamp interval="1m" dir="desc" bucketAlias="srcEventTimestamp"), 
             (aggr by clientIp as clientIp unwind=true maxBuckets=20) unwind=true 
 | sort countAlertName0 desc 
 | limit 480

Description

Below is a step-by-step explanation of the NQL Query.

Select the data from the alerts stream and take only those objects which have an entry in the top10ClientIpLast15Minute_Alerts collector for client IP value.

src stream="alerts" 
 | valInColl(clientIp, "top10ClientIpLast15Minute_Alerts", "clientIp")

Result:

[
    {
        "id": "61c43b97-0e06e7a1-3e17bce1",
        "timestamp": 1679616180000,
        "alertName": "Brute Force Attack",
        "alertSeverity": "Medium",
        "alertRuleType": "Security",
        "alertTags": [
            "60143f92-3f2b5b74-c234033d",
            "60143f92-3f2b5b74-c2340327"
        ],
        "alertRuleId": "Credential Access_015",
        "alertFlagThresholdLevel": "Critical",
        "alertSeen": false,
        "alertFalsePositive": false,
        "alertComment": null,
        "alertMitreTactic": "Credential Access",
        "alertMitreTechnique": "Brute Force",
        "alertMitreId": "T1110",
        "alertMitreSubtechnique": null,
        "alertDefId": "61a869fa-825ce206-718d6533",
        "alertAckUser": null,
        "alertAckLastUpdate": null,
        "alertFalsePositiveUser": null,
        "alertFalsePositiveLastUpdate": null,
        "alertCommentUser": null,
        "alertCommentLastUpdate": null,
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "172.16.60.31",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.70.243",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [
            "Workstations"
        ],
        "clientIp": "172.16.70.243",
        "serverIp": "172.16.60.31",
        "_clientIp": "172.16.70.243",
        "_countSessions": 949,
        "_firstServerIp": "172.16.60.31",
        "_firstClientAsn": null,
        "_firstServerAsn": null
    },
    {
        "id": "61c43b97-0e06e7a1-3e17bce3",
        "timestamp": 1679616180000,
        "alertName": "Brute Force Attack 2",
        "alertSeverity": "Medium",
        "alertRuleType": "Security",
        "alertTags": [
            "60143f92-3f2b5b74-c234033d",
            "60143f92-3f2b5b74-c2340327"
        ],
        "alertRuleId": "Credential Access_019",
        "alertFlagThresholdLevel": "Critical",
        "alertSeen": false,
        "alertFalsePositive": false,
        "alertComment": null,
        "alertMitreTactic": "Credential Access",
        "alertMitreTechnique": "Brute Force",
        "alertMitreId": "T1110",
        "alertMitreSubtechnique": null,
        "alertDefId": "61a739be-037e4715-be7c2dcf",
        "alertAckUser": null,
        "alertAckLastUpdate": null,
        "alertFalsePositiveUser": null,
        "alertFalsePositiveLastUpdate": null,
        "alertCommentUser": null,
        "alertCommentLastUpdate": null,
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "172.16.60.31",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.70.243",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [
            "Workstations"
        ],
        "clientIp": "172.16.70.243",
        "serverIp": "172.16.60.31",
        "_clientIp": "172.16.70.243",
        "_countSessions": 949,
        "_firstServerIp": "172.16.60.31",
        "_firstClientAsn": null,
        "_firstServerAsn": null
    },
    ...
]

Calculate the number of alerts in groups by time bucket 1m (one month) and clientIp value.

 | splitAggr countAlertName0=count(alertName) 
           (timeAggr on timestamp interval="1m" dir="desc" bucketAlias="srcEventTimestamp"), 
           (aggr by clientIp as clientIp unwind=true maxBuckets=20) unwind=true 

2.1. Calculate the number of alerts in 1m time range buckets.

 | splitAggr countAlertName0=count(alertName) 
           (timeAggr on timestamp interval="1m" dir="desc" bucketAlias="srcEventTimestamp")

Result:

[
    {
        "countAlertName0": 2,
        "srcEventTimestamp": 1679644920000
    },
    {
        "countAlertName0": 2,
        "srcEventTimestamp": 1679644860000
    },
    ...
]

2.2. Calculate the number of alerts in clientIp groups.

 | splitAggr countAlertName0=count(alertName)            
           (aggr by clientIp as clientIp unwind=true maxBuckets=20) unwind=true

Result:

[
    {
        "countAlertName0": 166,
        "clientIp": "172.16.70.243"
    },
    {
        "countAlertName0": 35,
        "clientIp": "172.16.42.30"
    },
    {
        "countAlertName0": 28,
        "clientIp": "110.78.211.84"
    }
]

Result for splitAggr:

[
    {
        "countAlertName0": 2,
        "clientIp": "172.16.70.243",
        "srcEventTimestamp": 1679644620000
    },
    {
        "countAlertName0": 2,
        "clientIp": "172.16.70.243",
        "srcEventTimestamp": 1679644500000
    },
    {
        "countAlertName0": 2,
        "clientIp": "172.16.70.243",
        "srcEventTimestamp": 1679644380000
    },
    ...
]       

Sort the objects by the number of alerts and show the first 480 objects

| sort countAlertName0 desc 
| limit 480

Result:

[
    {
        "countAlertName0": 2,
        "clientIp": "172.16.70.243",
        "srcEventTimestamp": 1679644680000
    },
    {
        "countAlertName0": 2,
        "clientIp": "172.16.70.243",
        "srcEventTimestamp": 1679644620000
    },
    {
        "countAlertName0": 2,
        "clientIp": "172.16.70.243",
        "srcEventTimestamp": 1679644500000
    },
    ...
]

Example 9 (real data from the network)

Scenario

A rule detects a brute force/dictionary attack on specific applications (FTP, HTTPS, HTTP, IMAP, RDP, SSH, IMAP3, LDAP, LDAPS, MYSQL, POP3, POP3S, POSTGRESQL, SMTP, TELNET, TFTP, ASTERISK, VNC, SNMP, MSSQL, SMB, ICQ, NNTP, PCANYWHERE, ORACLELISTENER, SVN, XMPP, SIP, RADMIN2, REXEC, RLOGIN, WS - Management and PowerShell remoting via HTTP, WS - Management and PowerShell remoting via HTTPS, RPCAP, NetBIOS, Kerberos) from a single IP address to the same host.

NQL Query

src stream="netflow" 
| in(serverPort,[21,22,23,25,69,80,88,110,119,139,143,161,220,389,443,445,512,513,636,995,1433,1521,2002,3306,3389,3690,4000,4899,5038,5060,5222,5432,5631,5900,5985,5986,6667]) 
| aggr _countSessions=count(timestamp), 
       _clientIp=first(clientIp), 
       _firstClientCountry=first(clientCountry), 
       _firstClientFunction=first(clientFunction), 
       _serverIp=first(serverIp), 
       _firstServerCountry=first(serverCountry), 
       _firstServerFunction=first(serverFunction), 
       _firstClientIp=first(clientIp),
       _firstServerIp=first(serverIp), 
       _firstServerPort=first(serverPort) by clientIp as clientIp, serverIp as serverIp, serverPort as serverPort unwind=true 
| sort _countSessions desc 
| limit 100 
| set _firstClientAsn=lookup("ip-as","name", {"ip": _clientIp} ), _firstServerAsn=lookup("ip-as","name", {"ip": _serverIp} ) 
| limit 1000

Description

Below is a step-by-step explanation of the NQL Query.

Select objects from the netflow stream for which the value of the serverPort field is on the list: 21,22,23,25,69,80,88,110,119,139,143,161,220,389,443,445,512,513,636,995,1433,1521,2002,3306,3389,3690,4000,4899,5038,5060,5222,5432,5631,5900,5985,5986,6667.

src stream="netflow" 
| in(serverPort,[21,22,23,25,69,80,88,110,119,139,143,161,220,389,443,445,512,513,636,995,1433,1521,2002,3306,3389,3690,4000,4899,5038,5060,5222,5432,5631,5900,5985,5986,6667])

Result:

[
    {
        "_stream": "netflow",
        "timestamp": 1677603840000,
        "sessionId": 1,
        "clientIp": "172.16.60.107",
        "serverIp": "172.105.8.229",
        "protocol": 6,
        "clientPort": 44276,
        "serverPort": 443,
        "application": 443,
        "exporterIps": [
            "172.16.100.1"
        ],
        "interfaces": [
            "172.16.100.1[3]",
            "172.16.100.1[5]"
        ],
        "clientFunction": [
            "Workstations"
        ],
        "serverFunction": [],
        "clientLocation": [],
        "serverLocation": [],
        "clientRole": [],
        "serverRole": [],
        "clientTcpFlags": 0,
        "serverTcpFlags": 0,
        "tosNumbers": [],
        "mpls": [],
        "asNumbers": [],
        "icmpType": [],
        "clientCountry": null,
        "serverCountry": null,
        "clientAsNumber": null,
        "serverAsNumber": null,
        "activeTime": 1368,
        "firstTimestamp": 1677603559044,
        "lastTimestamp": 1677603560436,
        "clientBytes": 1622,
        "serverBytes": 25892,
        "clientPackets": 15,
        "serverPackets": 25,
        "flows": 4
    },
    {
        "_stream": "netflow",
        "timestamp": 1677603840000,
        "sessionId": 3,
        "clientIp": "172.16.20.141",
        "serverIp": "52.97.229.210",
        "protocol": 6,
        "clientPort": 60297,
        "serverPort": 443,
        "application": 443,
        "exporterIps": [
            "172.16.100.1"
        ],
        "interfaces": [
            "172.16.100.1[4]",
            "172.16.100.1[5]"
        ],
        "clientFunction": [
            "Workstations"
        ],
        "serverFunction": [],
        "clientLocation": [],
        "serverLocation": [],
        "clientRole": [],
        "serverRole": [],
        "clientTcpFlags": 0,
        "serverTcpFlags": 0,
        "tosNumbers": [],
        "mpls": [],
        "asNumbers": [],
        "icmpType": [],
        "clientCountry": null,
        "serverCountry": null,
        "clientAsNumber": null,
        "serverAsNumber": null,
        "activeTime": 43948,
        "firstTimestamp": 1677603524348,
        "lastTimestamp": 1677603568296,
        "clientBytes": 40,
        "serverBytes": 1273,
        "clientPackets": 1,
        "serverPackets": 3,
        "flows": 2
    },
    ...
]

Select the first values of the _clientIp, _firstClientCountry, _firstClientFunction, _serverIp, _firstServerCountry, _firstServerFunction, _firstClientIp, _firstServerIp, _firstServerPort fields for the given clientIp, serverIp i serverPort groups and the calculation of the number of objects in these groups (_countSessions).

| aggr _countSessions=count(timestamp), 
       _clientIp=first(clientIp), 
       _firstClientCountry=first(clientCountry), 
       _firstClientFunction=first(clientFunction), 
       _serverIp=first(serverIp), 
       _firstServerCountry=first(serverCountry), 
       _firstServerFunction=first(serverFunction), 
       _firstClientIp=first(clientIp),
       _firstServerIp=first(serverIp), 
       _firstServerPort=first(serverPort) by clientIp as clientIp, serverIp as serverIp, serverPort as serverPort unwind=true 

Result:

[
    {
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "172.105.8.229",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.60.107",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [],
        "clientIp": "172.16.60.107",
        "serverIp": "172.105.8.229",
        "_clientIp": "172.16.60.107",
        "_countSessions": 4,
        "_firstServerIp": "172.105.8.229"
    },
    {
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "52.97.229.210",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.20.141",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [],
        "clientIp": "172.16.20.141",
        "serverIp": "52.97.229.210",
        "_clientIp": "172.16.20.141",
        "_countSessions": 19,
        "_firstServerIp": "52.97.229.210"
    },
    ...
]

Sort the results by the _countSessions field in descending order and select the first 100 objects.

| sort _countSessions desc 
| limit 100

Result:

[
    {
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "209.206.5.62",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.10.76",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [],
        "clientIp": "172.16.10.76",
        "serverIp": "209.206.5.62",
        "_clientIp": "172.16.10.76",
        "_countSessions": 223,
        "_firstServerIp": "209.206.5.62"
    },
    {
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "52.113.194.132",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.43.38",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [],
        "clientIp": "172.16.43.38",
        "serverIp": "52.113.194.132",
        "_clientIp": "172.16.43.38",
        "_countSessions": 146,
        "_firstServerIp": "52.113.194.132"
    },
    ...
]

Add the _firstClientAsn and _firstServerAsn fields whose values are selected from the ip-as and ip-as files (lookups), from the name field for conditions: ip=_clientIp oraz ip=_serverIp. Get the first 1000 objects.

| set _firstClientAsn=lookup("ip-as","name", {"ip": _clientIp} ), _firstServerAsn=lookup("ip-as","name", {"ip": _serverIp} ) 
| limit 1000

Result:

[
    {
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "209.206.5.62",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.10.76",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [],
        "clientIp": "172.16.10.76",
        "serverIp": "209.206.5.62",
        "_clientIp": "172.16.10.76",
        "_countSessions": 223,
        "_firstServerIp": "209.206.5.62",
        "_firstClientAsn": 23456,
        "_firstServerAsn": 65535
    },
    {
        "_firstServerCountry": null,
        "serverPort": 443,
        "_serverIp": "52.113.194.132",
        "_firstServerPort": 443,
        "_firstClientCountry": null,
        "_firstClientIp": "172.16.43.38",
        "_firstClientFunction": [
            "Workstations"
        ],
        "_firstServerFunction": [],
        "clientIp": "172.16.43.38",
        "serverIp": "52.113.194.132",
        "_clientIp": "172.16.43.38",
        "_countSessions": 146,
        "_firstServerIp": "52.113.194.132",
        "_firstClientAsn": 131072,
        "_firstServerAsn": 64512
    },
    ...
]

NQL query examples

Glossary​

Example 1​

Scenario​

NQL Query​

Description​

Example 2​

Scenario​

NQL Query method 1​

Description​

NQL Query method 2​

Description​

Example 3​

Scenario​

NQL Query​

Description​

Example 4​

Scenario​

NQL Query​

Description​

Example 5 (real data from the network)​

Description​

Example 6 (real data from the network)​

Scenario​

NQL Query​

Description​

Example 7 (real data from the network)​

Scenario​

NQL Query​

Description​

Example 8 (real data from the network)​

Scenario​

NQL Query​

Description​

Example 9 (real data from the network)​

Scenario​

NQL Query​

Description​

Glossary

Example 1

Scenario

NQL Query

Description

Example 2

Scenario

NQL Query method 1

Description

NQL Query method 2

Description

Example 3

Scenario

NQL Query

Description

Example 4

Scenario

NQL Query

Description

Example 5 (real data from the network)

Description

Example 6 (real data from the network)

Scenario

NQL Query

Description

Example 7 (real data from the network)

Scenario

NQL Query

Description

Example 8 (real data from the network)

Scenario

NQL Query

Description

Example 9 (real data from the network)

Scenario

NQL Query

Description