EvalNode

Warning! This page documents an old version of Kapacitor, which is no longer actively developed. Kapacitor v1.2 is the most recent stable version of Kapacitor.

Evaluates expressions on each data point it receives. A list of expressions may be provided and will be evaluated in the order they are given and results of previous expressions are made available to later expressions. See the property EvalNode.As for details on how to reference the results.

Example:

    stream
        .eval(lambda: "error_count" / "total_count")
          .as('error_percent')

The above example will add a new field error_percent to each data point with the result of error_count / total_count where error_count and total_count are existing fields on the data point.

Properties

Property methods modify state on the calling node. They do not add another node to the pipeline, and always return a reference to the calling node.

As

List of names for each expression. The expressions are evaluated in order and the result of a previous expression will be available in later expressions via the name provided.

Example:

    stream
        .eval(lambda: "value" * "value", lambda: 1.0 / "value2")
            .as('value2', 'inv_value2')

The above example calculates two fields from the value and names them value2 and inv_value2 respectively.

node.as(names ...string)

Keep

If called the existing fields will be preserved in addition to the new fields being set. If not called then only new fields are preserved.

Optionally intermediate values can be discarded by passing a list of field names. Only fields in the list will be kept. If no list is given then all fields, new and old, are kept.

Example:

    stream
        .eval(lambda: "value" * "value", lambda: 1.0 / "value2")
            .as('value2', 'inv_value2')
        .keep('value', 'inv_value2')

In the above example the original field value is preserved. In addition the new field value2 is calculated and used in evaluating inv_value2 but is discarded before the point is sent on to children nodes. The resulting point has only two fields value and inv_value2.

node.keep(fields ...string)

Chaining Methods

Chaining methods create a new node in the pipeline as a child of the calling node. They do not modify the calling node.

Alert

Create an alert node, which can trigger alerts.

node.alert()

Returns: AlertNode

Deadman

Helper function for creating an alert on low throughput, aka deadman's switch.

  • Threshold – trigger alert if throughput drops below threshold in points/interval.
  • Interval – how often to check the throughput.
  • Expressions – optional list of expressions to also evaluate. Useful for time of day alerting.

Example:

    var data = stream.from()...
    // Trigger critical alert if the throughput drops below 100 points per 10s and checked every 10s.
    data.deadman(100.0, 10s)
    //Do normal processing of data
    data....

The above is equivalent to this Example:

    var data = stream.from()...
    // Trigger critical alert if the throughput drops below 100 points per 10s and checked every 10s.
    data.stats(10s)
          .derivative('collected')
              .unit(10s)
              .nonNegative()
          .alert()
              .id('node \'stream0\' in task \'{{ .TaskName }}\'')
              .message('{{ .ID }} is {{ if eq .Level "OK" }}alive{{ else }}dead{{ end }}: {{ index .Fields "collected" | printf "%0.3f" }} points/10s.')
              .crit(lamdba: "collected" <= 100.0)
    //Do normal processing of data
    data....

The id and message alert properties can be configured globally via the 'deadman' configuration section.

Since the AlertNode is the last piece it can be further modified as normal. Example:

    var data = stream.from()...
    // Trigger critical alert if the throughput drops below 100 points per 1s and checked every 10s.
    data.deadman(100.0, 10s).slack().channel('#dead_tasks')
    //Do normal processing of data
    data....

You can specify additional lambda expressions to further constrain when the deadman's switch is triggered. Example:

    var data = stream.from()...
    // Trigger critical alert if the throughput drops below 100 points per 10s and checked every 10s.
    // Only trigger the alert if the time of day is between 8am-5pm.
    data.deadman(100.0, 10s, lambda: hour("time") >= 8 AND hour("time") <= 17)
    //Do normal processing of data
    data....
node.deadman(threshold float64, interval time.Duration, expr ...tick.Node)

Returns: AlertNode

Derivative

Create a new node that computes the derivative of adjacent points.

node.derivative(field string)

Returns: DerivativeNode

Eval

Create an eval node that will evaluate the given transformation function to each data point. A list of expressions may be provided and will be evaluated in the order they are given and results of previous expressions are made available to later expressions.

node.eval(expressions ...tick.Node)

Returns: EvalNode

GroupBy

Group the data by a set of tags.

Can pass literal * to group by all dimensions. Example:

    .groupBy(*)
node.groupBy(tag ...interface{})

Returns: GroupByNode

HttpOut

Create an http output node that caches the most recent data it has received. The cached data is available at the given endpoint. The endpoint is the relative path from the API endpoint of the running task. For example if the task endpoint is at "/api/v1/task/<task_name>" and endpoint is "top10", then the data can be requested from "/api/v1/task/<task_name>/top10".

node.httpOut(endpoint string)

Returns: HTTPOutNode

InfluxDBOut

Create an influxdb output node that will store the incoming data into InfluxDB.

node.influxDBOut()

Returns: InfluxDBOutNode

Join

Join this node with other nodes. The data is joined on timestamp.

node.join(others ...Node)

Returns: JoinNode

MapReduce

Perform a map-reduce operation on the data. The built-in functions under influxql provide the selection,aggregation, and transformation functions from the InfluxQL language.

MapReduce may be applied to either a batch or a stream edge. In the case of a batch each batch is passed to the mapper independently. In the case of a stream all incoming data points that have the exact same time are combined into a batch and sent to the mapper.

node.mapReduce(mr MapReduceInfo)

Returns: ReduceNode

Sample

Create a new node that samples the incoming points or batches.

One point will be emitted every count or duration specified.

node.sample(rate interface{})

Returns: SampleNode

Stats

Create a new stream of data that contains the internal statistics of the node. The interval represents how often to emit the statistics based on real time. This means the interval time is independent of the times of the data points the source node is receiving.

node.stats(interval time.Duration)

Returns: StatsNode

Union

Perform the union of this node and all other given nodes.

node.union(node ...Node)

Returns: UnionNode

Where

Create a new node that filters the data stream by a given expression.

node.where(expression tick.Node)

Returns: WhereNode

Window

Create a new node that windows the stream by time.

NOTE: Window can only be applied to stream edges.

node.window()

Returns: WindowNode