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Processor: Range

The Range processor checks that a value is in a range. According to the specified range, it configures a check for the input series. This check returns an anomaly value if a series aggregation value, such as a last value, sum, avg etc., is in the range. This aggregation type is configured by the 'property' attribute, which is set to 'value' if not specified. The output series contains anomaly values, such as 'true' and 'false'. (Previously called 'not_in_range' and 'range_check'.) The range processor generates the output of True when the input matches the specified criteria.

Parameter Description
Input Types Table (number), Table (number, accumulate=True)
Output Types Table (discrete state)
Property A property of input items which is used to check against the range. Enum of either value, sample_count, sum, avg
Anomalous Range (range) Numeric range, either min or max is optional. Float type is acceptable only with property "std_dev", other property values require integers. Min and max can be expressions evaluated into numeric values.
Graph Query (graph_query)

One or more queries on graph specified as strings, or a list of such queries. (String will be deprecated in a future release.) Multiple queries should provide all the named nodes referenced by the expression fields (including additional_properties). Graph query is executed on the "operation" graph. Results of the queries can be accessed using the "query_result" variable with the appropriate index. For example, if querying property set nodes under name "ps", the result will be available as "query_result[0]["ps"]".

In collector processors (*_collector, if_counter) it is used to choose a set of nodes for further processing (for example, all leafs, or all interfaces between leaf and spines)

In other processors it is used for general parameterization and it is only supported as a list of queries.

graph_query: "node("system", role="leaf", name="system").
              node("interface", name="iface").out("link").
              node("link", role="spine_leaf")"
graph_query: ["node("system", role="leaf", name="system")",
              "node("system", role="spine", name="system")"]

Non-collector processors containing the graph_query configuration parameter, can be parameterized to use data from arbitrary nodes in the graph, such as property set nodes. Property sets allow you to parameterize macro level SLAs for individual business units. In the example below, graph_query matches a node of type property_set with label probe_propset. It's accessed using the special query_result variable, where Index 0 means it's the first node in query results. If a query returned N nodes, they could be accessed using indices starting from 0 to N-1. ps is what the actual node is referred to in the query; the rest depends on the structure of the node. The int() casting is required because values of property_set nodes are strings. Here it's assumed that a property set node has the label probe_propset and that the value accumulate_duration was already created.

graph_query: [node("property_set", label="probe_propset", name="ps")]
duration: int(query_result[0]["ps"].values["accumulate_duration"])

Another example is a that probes can validate a compliance requirement; the compliance value may change over time and/or it can be used by more than one probe. Also, a probe can validate NOS versions on devices. In this case, property sets can be used to define the current NOS version requirement. If it changes tomorrow: change the property set value, instead of going under the probe stage.

Anomaly MetricLog Retention Duration Retain anomaly metric data in MetricDb for specified duration in seconds
Anomaly MetricLog Retention Size Maximum allowed size, in bytes of anomaly metric data to store in MetricDB
Anomaly Metric Logging Enable metric logging for anomalies
Enable Streaming (enable_streaming) Makes samples of output stages streamed if enabled. An optional boolean that defaults to False. If set to True, all output stages of this processor are streamed in the generic protobuf schema.
Raise Anomaly (raise_anomaly)

Outputs “true” and “false” values, “true” meaning an appropriate item is anomalous, and "false" meaning the item is not anomalous. When Raise Anomaly is set to True, an actual anomaly is generated in addition to a sample in the output.

Example: Range

Sample Input (NS)

Sample Output (DSS)

If expressions are used for min or max fields of the range property, then they are evaluated for each input item which results into item-specific thresholds. Properties of the respective output item are extended by range_min or range_max properties with calculated values.

Sample Input (NS)

Sample Output (DSS)