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.
Example:
var errors = stream |from() .measurement('errors') var requests = stream |from() .measurement('requests') // Join the errors and requests streams errors |join(requests) // Provide prefix names for the fields of the data points. .as('errors', 'requests') // points that are within 1 second are considered the same time. .tolerance(1s) // fill missing values with 0, implies outer join. .fill(0.0) // name the resulting stream .streamName('error_rate') // Both the "value" fields from each parent have been prefixed // with the respective names 'errors' and 'requests'. |eval(lambda: "errors.value" / "requests.value") .as('rate') ...
In the above example the errors
and requests
streams are joined and then transformed to calculate a combined field.
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.
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.
node.as(names ...string)
The delimiter for the field name prefixes. Can be the empty string.
node.delimiter(value string)
Fill the data. The fill option implies the type of join: inner or full outer Options are:
When using a numerical or null fill, the fields names are determined by copying the field names from another point. This doesn't work well when different sources have different field names. Use the DefaultNode and DeleteNode to finalize the fill operation if necessary.
Example:
var maintlock = stream |from() .measurement('maintlock') .groupBy('service') var requests = stream |from() .measurement('requests') .groupBy('service') // Join the maintlock and requests streams // The intent it to drop any points in maintenance mode. maintlock |join(requests) // Provide prefix names for the fields of the data points. .as('maintlock', 'requests') // points that are within 1 second are considered the same time. .tolerance(1s) // fill missing fields with null, implies outer join. // a better default per field will be set later. .fill('null') // name the resulting stream. .streamName('requests') |default() // default maintenance mode to false, overwriting the null value if present. .field('maintlock.mode', false) // default the requests to 0, again overwriting the null value if present. .field('requests.value', 0.0) // drop any points that are in maintenance mode. |where(lambda: "maintlock.mode") |...
node.fill(value interface{})
Join on a subset of the group by dimensions. This is a special case where you want a single point from one parent to join with multiple points from a different parent.
For example given two measurements:
You want to calculate the percentage of the total building power consumed by each floor. Since you only have one point per building you need it to join multiple times with the points from each floor. By defining the on
dimensions as building
we are saying that we want points that only have the building tag to be joined with more specifc points that more tags, in this case the floor
tag. In other words while we have points with tags building and floor we only want to join on the building tag.
Example:
var building = stream |from() .measurement('building_power') .groupBy('building') var floor = stream |from() .measurement('floor_power') .groupBy('building', 'floor') building |join(floor) .as('building', 'floor') .on('building') |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 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.
Create an alert node, which can trigger alerts.
node|alert()
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
Combine this node with itself. The data are combined on timestamp.
node|combine(expressions ...ast.LambdaNode)
Returns: CombineNode
Count the number of points.
node|count(field string)
Returns: InfluxQLNode
Compute a cumulative sum of each point that is received. A point is emitted for every point collected.
node|cumulativeSum(field string)
Returns: InfluxQLNode
Helper function for creating an alert on low throughput, a.k.a. deadman's switch.
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) .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...
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...
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 ...ast.LambdaNode)
Returns: AlertNode
Create a node that can set defaults for missing tags or fields.
node|default()
Returns: DefaultNode
Create a node that can delete tags or fields.
node|delete()
Returns: DeleteNode
Create a new node that computes the derivative of adjacent points.
node|derivative(field string)
Returns: DerivativeNode
Compute the difference between points independent of elapsed time.
node|difference(field string)
Returns: InfluxQLNode
Produce batch of only the distinct points.
node|distinct(field string)
Returns: InfluxQLNode
Compute the elapsed time between points
node|elapsed(field string, unit time.Duration)
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. The results are available to later expressions.
node|eval(expressions ...ast.LambdaNode)
Returns: EvalNode
Select the first point.
node|first(field string)
Returns: InfluxQLNode
Flatten points with similar times into a single point.
node|flatten()
Returns: FlattenNode
Group the data by a set of tags.
Can pass literal * to group by all dimensions. Example:
|groupBy(*)
node|groupBy(tag ...interface{})
Returns: GroupByNode
Compute the holt-winters (https://docs.influxdata.com/influxdb/latest/query_language/functions/#holt-winters) forecast of a data set.
node|holtWinters(field string, h int64, m int64, interval time.Duration)
Returns: InfluxQLNode
Compute the holt-winters (https://docs.influxdata.com/influxdb/latest/query_language/functions/#holt-winters) forecast of a data set. This method also outputs all the points used to fit the data in addition to the forecasted data.
node|holtWintersWithFit(field string, h int64, m int64, interval time.Duration)
Returns: InfluxQLNode
Create an HTTP output node that caches the most recent data it has received. The cached data are 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 /kapacitor/v1/tasks/<task_id>
and endpoint is top10
, then the data can be requested from /kapacitor/v1/tasks/<task_id>/top10
.
node|httpOut(endpoint string)
Returns: HTTPOutNode
Creates an HTTP Post node that POSTS received data to the provided HTTP endpoint. HttpPost expects 0 or 1 arguments. If 0 arguments are provided, you must specify an endpoint property method.
node|httpPost(url ...string)
Returns: HTTPPostNode
Create an influxdb output node that will store the incoming data into InfluxDB.
node|influxDBOut()
Returns: InfluxDBOutNode
Join this node with other nodes. The data are joined on timestamp.
node|join(others ...Node)
Returns: JoinNode
Create a node that can trigger autoscale events for a kubernetes cluster.
node|k8sAutoscale()
Returns: K8sAutoscaleNode
Create an kapacitor loopback node that will send data back into Kapacitor as a stream.
node|kapacitorLoopback()
Returns: KapacitorLoopbackNode
Select the last point.
node|last(field string)
Returns: InfluxQLNode
Create a node that logs all data it receives.
node|log()
Returns: LogNode
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
Compute the mode of the data.
node|mode(field string)
Returns: InfluxQLNode
Compute a moving average of the last window points. No points are emitted until the window is full.
node|movingAverage(field string, window int64)
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 node that tracks number of consecutive points in a given state.
node|stateCount(expression ast.LambdaNode)
Returns: StateCountNode
Create a node that tracks duration in a given state.
node|stateDuration(expression ast.LambdaNode)
Returns: StateDurationNode
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.
node|top(num 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 ast.LambdaNode)
Returns: WhereNode
Create a new node that windows the stream by time.
NOTE: Window can only be applied to stream edges.
node|window()
Returns: WindowNode
© 2015 InfluxData, Inc.
Licensed under the MIT license.
https://docs.influxdata.com/kapacitor/v1.3/nodes/join_node/