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

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 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

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

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