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.

Performs a reduce operation on the data stream. In the map-reduce framework it is assumed that several different partitions of the data can be 'mapped' in parallel while only one 'reduce' operation will process all of the data stream.


        // Sum the values for each 10s window of data.


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.


The name of the field, defaults to the name of MR function used (i.e. influxql.mean -> 'mean') string)


Use the time of the selected point instead of the time of the batch.

Only applies to selector MR functions like first, last, top, bottom, etc. Aggregation functions always use the batch time.


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


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

node.derivative(field string)

Returns: DerivativeNode


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


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


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 idependently. 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


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


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