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Version: 1.0.0 (Latest)

Aggregation & Correlation

Common Syntax​

Both aggregation and correlation statements have the following generic structure:

<function-command> [<function-specific-params>] <timespan-param> <group-by-clause> <where-clause>

Some example correlation statements:

count timespan=5m group_by field1 where field3 > 100
distinct_count(myfield) timespan=30s
temporal(ordered=true) [ padasRule="internal_error" || padasRule="new_network_connection" ] timespan=1m group_by internal_ip, remote_ip

Common Parameters for Functions​

All correlation statements evaluate streaming events for a given time window (defined via timespan parameter) and optionally groups them according to selected fields (defined via group_by clause). For counting aggregation/correlation statements it's also possible to limit the results by providing a query expression (defined via where clause).

Argument Order​

Correlation statements must start with the one of the available functions, followed by function specific parameters (if any). Common argument order and descriptions are provided in the following table.

OrderKeywordRequiredDescriptionExample
1timespanYesSpecifies time window to perform aggregated function.
The value should be an integer followed by one of the following identifiers:
s for second(s)
m for minute(s)
h for hour(s)
d for day(s)		timespan=5m
timespan=1h
2group_byNoGroup correlation results according to specified field(s).group_by field1, field2
3whereNoFilter events according to specified query expression.where field1 > 100

Aggregation Functions​

PDL provides several built-in aggregation functions that can be used to analyze event data over a specified time window. These functions can be broadly categorized into counting and statistical aggregations:

Counting Aggregations​

  • count: Counts the total number of events
  • count(field): Counts events where the specified field exists
  • distinct_count(field) or dc(field): Counts unique values of the specified field

Statistical Aggregations​

  • avg(field): Calculates the average value of the specified field
  • median(field): Calculates the median value of the specified field
  • min(field): Finds the minimum value of the specified field
  • max(field): Finds the maximum value of the specified field
  • variance(field): Calculates the variance of the specified field
  • stddev(field): Calculates the standard deviation of the specified field

All aggregation functions support the common parameters (timespan, group_by, and where clauses) as described above.

Aggregation Function Details​

For details please visit Aggregation Functions

Correlation Functions​

Temporal​

Description​

Temporal correlation statement checks for all the events matching the expression array within the time frame defined. If the boolean value ordered is set to true, then all the events are expected to occur in the given order. The result may also contain count of events for each group specified by group_by separately.

Syntax and Functions​

... | temporal(<ordered-param>) [ <expression> || <expression> || ... ] <timespan-param> <group-by-clause> <where-clause>

Ordered parameter: order is assigned either true or false as value (e.g. ordered=true) to specify whether the events are expected to match expression array order. Expression array: The array consists of one or more expressions separated by double-pipe || character (e.g. [ field1="valu*" || field3 < 100 AND field4=false>])

Temporal Examples​

The following table provides examples of available functionality based on the following JSON value:

JSON InputExpressionExpected Output
  {"field1":"value1", "field2":99}
  {"field1":"value1", "field2":124}
  {"field1":"value2", "field2":123}
  {"field1":"value2", "field2":125}
  temporal [ field1?="value" || field2 < 100 ] timespan=2m
  {
    "padasRule": "myrule8",
    "padasTemporal": {
      "result": {
        "field1?=\"value\"": [
          {
            "field1": "value1",
            "field2": 99
          },
          {
            "field1": "value1",
            "field2": 124
          },
          {
            "field1": "value2",
            "field2": 123
          },
          {
            "field1": "value2",
            "field2": 125
          },
        ],
        "field2<100": [
          {
            "field1": "value1",
            "field2": 99
          }
        ]
      }
    }
  }
  {"field1":"sometext", "field2":90}
  {"field1":"value1", "field2":124}
  {"field1":"value2", "field2":95}
  {"field1":"value2", "field2":123}
  temporal(ordered=true) [ field1?="value" || field2 < 100 ] timespan=2m group_by field2
    {
      "padasRule": "myrule9",
      "padasTemporal": {
        "groupBy": {
          "field2": 95
        },
        "result": {
          "field2<100": [
            {
              "field1": "value2",
              "field2": 95
            }
          ],
          "field1?=\"value\"": [
            {
              "field1": "value2",
              "field2": 95
            }
          ]
        }
      }
    }