This is archived documentation for InfluxData product versions that are no longer maintained. For newer documentation, see the latest InfluxData documentation.
A UDFNode is a node that can run a User Defined Function (UDF) in a separate process.
A UDF is a custom script or binary that can communicate via Kapacitor's UDF RPC protocol. The path and arguments to the UDF program are specified in Kapacitor's configuration. Using TICKscripts you can invoke and configure your UDF for each task.
See the README.md for details on how to write your own UDF.
UDFs are configured via Kapacitor's main configuration file.
Example:
[udf]
[udf.functions]
# Example moving average UDF.
[udf.functions.movingAverage]
prog = "/path/to/executable/moving_avg"
args = []
timeout = "10s"
UDFs are first class objects in TICKscripts and are referenced via their configuration name.
Example:
// Given you have a UDF that computes a moving average
// The UDF can define what its options are and then can be
// invoked via a TICKscript like so:
stream
.from()...
.movingAverage()
.field('value')
.size(100)
.as('mavg')
.httpOut('movingaverage')
NOTE: The UDF process runs as the same user as the Kapacitor daemon. As a result make the user is properly secured as well as the configuration file.
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