Documentation

KapacitorLoopbackNode

The kapacitorLoopback node writes data back into the Kapacitor stream. To write data to a remote Kapacitor instance use the InfluxDBOutNode.

Example:

|kapacitorLoopback()
  .database('mydb')
  .retentionPolicy('myrp')
  .measurement('errors')
  .tag('kapacitor', 'true')
  .tag('version', '0.2')
  • Copy
  • Fill window

Beware of infinite loops

It is possible to create infinite loops using the KapacitorLoopback node. Take care to ensure you do not chain tasks together creating a loop.

Avoid name collisions with multiple subscriptions

When using the KapacitorLoopback node, don’t subscribe to identically named databases and retention policies in multiple InfluxDB instances or clusters. If Kapacitor is subscribed to multiple instances of InfluxDB, make each database and retention policy combination unique. For example:

influxdb_1
  └─ db1/rp1

influxdb_2
  └─ db2/rp2
  • Copy
  • Fill window

Available Statistics:

  • points_written: number of points written back to Kapacitor

Constructor

Chaining MethodDescription
kapacitorLoopback ( )Create an kapacitor loopback node that will send data back into Kapacitor as a stream.

Property Methods

SettersDescription
database ( value string)The name of the database.
measurement ( value string)The name of the measurement.
quiet ( )Suppress all error logging events from this node.
retentionPolicy ( value string)The name of the retention policy.
tag ( key stringvalue string)Add a static tag to all data points. Tag can be called more than once.

Chaining Methods

Deadman, Stats


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. Property methods are marked using the . operator.

Database

The name of the database.

kapacitorLoopback.database(value string)
  • Copy
  • Fill window

Measurement

The name of the measurement.

kapacitorLoopback.measurement(value string)
  • Copy
  • Fill window

Quiet

Suppress all error logging events from this node.

kapacitorLoopback.quiet()
  • Copy
  • Fill window

RetentionPolicy

The name of the retention policy.

kapacitorLoopback.retentionPolicy(value string)
  • Copy
  • Fill window

Tag

Add a static tag to all data points. Tag can be called more than once.

kapacitorLoopback.tag(key string, value string)
  • Copy
  • Fill window

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. Chaining methods are marked using the | operator.

Deadman

Helper function for creating an alert on low throughput, a.k.a. 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...
  • Copy
  • Fill window

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)
            .align()
        |derivative('emitted')
            .unit(10s)
            .nonNegative()
        |alert()
            .id('node \'stream0\' in task \'{{ .TaskName }}\'')
            .message('{{ .ID }} is {{ if eq .Level "OK" }}alive{{ else }}dead{{ end }}: {{ index .Fields "emitted" | printf "%0.3f" }} points/10s.')
            .crit(lambda: "emitted" <= 100.0)
    //Do normal processing of data
    data...
  • Copy
  • Fill window

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 usual. 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)
            .slack()
            .channel('#dead_tasks')
    //Do normal processing of data
    data...
  • Copy
  • Fill window

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...
  • Copy
  • Fill window
kapacitorLoopback|deadman(threshold float64, interval time.Duration, expr ...ast.LambdaNode)
  • Copy
  • Fill window

Returns: AlertNode

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.

kapacitorLoopback|stats(interval time.Duration)
  • Copy
  • Fill window

Returns: StatsNode


Was this page helpful?

Thank you for your feedback!


The future of Flux

Flux is going into maintenance mode. You can continue using it as you currently are without any changes to your code.

Read more

InfluxDB 3 Core and Enterprise are now in Beta

InfluxDB 3 Core and Enterprise are now available for beta testing, available under MIT or Apache 2 license.

InfluxDB 3 Core is a high-speed, recent-data engine that collects and processes data in real-time, while persisting it to local disk or object storage. InfluxDB 3 Enterprise is a commercial product that builds on Core’s foundation, adding high availability, read replicas, enhanced security, and data compaction for faster queries. A free tier of InfluxDB 3 Enterprise will also be available for at-home, non-commercial use for hobbyists to get the full historical time series database set of capabilities.

For more information, check out: