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.
stream .window() .period(10s) .every(10s) // Sum the values for each 10s window of data. .mapReduce(influxql.sum('value')) ...
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')
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 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.
Create a new node that computes the derivative of adjacent points.
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.
Group the data by a set of tags.
Can pass literal * to group by all dimensions. Example:
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".
Create an influxdb output node that will store the incoming data into InfluxDB.
Join this node with other nodes. The data is joined on timestamp.
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.
Create a new node that samples the incoming points or batches.
One point will be emitted every count or duration specified.
Perform the union of this node and all other given nodes.
Create a new node that filters the data stream by a given expression.
Create a new node that windows the stream by time.
NOTE: Window can only be applied to stream edges.