A StreamNode represents the source of data being
streamed to Kapacitor via any of its inputs.
The stream node allows you to select which portion of the stream
you want to process.
stream variable in stream tasks is an instance of
stream .from() .database('mydb') .retentionPolicy('myrp') .measurement('mymeasurement') .where(lambda: "host" =~ /logger\d+/) .window() ...
The above example selects only data points from the database
and retention policy
myrp and measurement
host matches the regex
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 database name. If empty any database will be used.
The measurement name If empty any measurement will be used.
The retention policy name If empty any retention policy will be used.
Optional duration for truncating timestamps. Helpful to ensure data points land on specfic boundaries Example:
stream .from().measurement('mydata') .truncate(1s)
All incoming data will be truncated to 1 second resolution.
Filter the current stream using the given expression.
This expression is a Kapacitor expression. Kapacitor
expressions are a superset of InfluxQL WHERE expressions.
Expression docs for more information.
If empty then all data points are considered to match.
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.
Creates a new stream node that can be further filtered using the Database, RetentionPolicy, Measurement and Where properties. From can be called multiple times to create multiple independent forks of the data stream.
// Select the 'cpu' measurement from just the database 'mydb' // and retention policy 'myrp'. var cpu = stream.from() .database('mydb') .retentionPolicy('myrp') .measurement('cpu') // Select the 'load' measurement from any database and retention policy. var load = stream.from() .measurement('load') // Join cpu and load streams and do further processing. cpu.join(load) .as('cpu', 'load') ...
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 windows the stream by time.
NOTE: Window can only be applied to stream edges.