Warning! This page documents an old version of Kapacitor, which is no longer actively developed. Kapacitor v1.4 is the most recent stable version of Kapacitor.

Joins the data from any number of nodes. As each data point is received from a parent node it is paired with the next data points from the other parent nodes with a matching timestamp. Each parent node contributes at most one point to each joined point. A tolerance can be supplied to join points that do not have perfectly aligned timestamps. Any points that fall within the tolerance are joined on the timestamp. If multiple points fall within the same tolerance window than they are joined in the order they arrive.

Aliases are used to prefix all fields from the respective nodes.

The join can be an inner or outer join, see the JoinNode.Fill property.


    var errors = stream
    var requests = stream
    // Join the errors and requests streams
            // Provide prefix names for the fields of the data points.
            .as('errors', 'requests')
            // points that are within 1 second are considered the same time.
            // fill missing values with 0, implies outer join.
            // name the resulting stream
        // Both the "value" fields from each parent have been prefixed
        // with the respective names 'errors' and 'requests'.
        .eval(lambda: "errors.value" / "requests.value"))

In the above example the errors and requests streams are joined and then transformed to calculate a combined field.



Chaining Methods


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.


Prefix names for all fields from the respective nodes. Each field from the parent nodes will be prefixed with the provided name and a '.'. See the example above.

The names cannot have a dot '.' character. ...string)


Fill the data. The fill option implies the type of join: inner or full outer Options are:

  • none - (default) skip rows where a point is missing, inner join.
  • null - fill missing points with null, full outer join.
  • Any numerical value - fill fields with given value, full outer join.
node.fill(value interface{})


Join on specfic dimensions. For example given two measurements:

  1. building_power – tagged by building, value is the total power consumed by the building.
  2. floor_power – tagged by building and floor, values is the total power consumed by the floor.

You want to calculate the percentage of the total building power consumed by each floor.


    var buidling = stream.from().measurement('building_power')
    var floor = stream.from().measurement('floor_power')
                         .groupBy('building', 'floor')
                .as('building', 'floor')
            .eval(lambda: "floor.value" / "building.value")
            ... // Values here are grouped by 'building' and 'floor'
node.on(dims ...string)


The name of this new joined data stream. If empty the name of the left parent is used.

node.streamName(value string)


The maximum duration of time that two incoming points can be apart and still be considered to be equal in time. The joined data point's time will be rounded to the nearest multiple of the tolerance duration.

node.tolerance(value time.Duration)

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.


Create an alert node, which can trigger alerts.


Returns: AlertNode


Select the bottom num points for field and sort by any extra tags or fields.

node.bottom(num int64, field string, fieldsAndTags ...string)

Returns: InfluxQLNode


Count the number of points.

node.count(field string)

Returns: InfluxQLNode


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.


    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

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.
              .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

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

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
node.deadman(threshold float64, interval time.Duration, expr ...tick.Node)

Returns: AlertNode


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

node.derivative(field string)

Returns: DerivativeNode


Produce batch of only the distinct points.

node.distinct(field string)

Returns: InfluxQLNode


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


Select the first point.

node.first(field string)

Returns: InfluxQLNode


Group the data by a set of tags.

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

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

Returns: GroupByNode


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


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


Returns: InfluxDBOutNode


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

node.join(others ...Node)

Returns: JoinNode


Select the last point.

node.last(field string)

Returns: InfluxQLNode


> DEPRECATION WARNING: As of v0.11 you can use the new InfluxQLNode to perform map reduce functions. This way of performing influxql functions will be removed in the 0.12 release.

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


Select the maximum point.

node.max(field string)

Returns: InfluxQLNode


Compute the mean of the data.

node.mean(field string)

Returns: InfluxQLNode


Compute the median of the data. Note, this method is not a selector, if you want the median point use .percentile(field, 50.0).

node.median(field string)

Returns: InfluxQLNode


Select the minimum point.

node.min(field string)

Returns: InfluxQLNode


Select a point at the given percentile. This is a selector function, no interpolation between points is performed.

node.percentile(field string, percentile float64)

Returns: InfluxQLNode


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


Create a new node that shifts the incoming points or batches in time.

node.shift(shift time.Duration)

Returns: ShiftNode


Compute the difference between min and max points.

node.spread(field string)

Returns: InfluxQLNode


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


Compute the standard deviation.

node.stddev(field string)

Returns: InfluxQLNode


Compute the sum of all values.

node.sum(field string)

Returns: InfluxQLNode


Select the top num points for field and sort by any extra tags or fields. int64, field string, fieldsAndTags ...string)

Returns: InfluxQLNode


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

node.union(node ...Node)

Returns: UnionNode


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

node.where(expression tick.Node)

Returns: WhereNode


Create a new node that windows the stream by time.

NOTE: Window can only be applied to stream edges.


Returns: WindowNode