Evaluates expressions on each data point it receives. 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. See the property EvalNode.As for details on how to reference the results.
stream .eval(lambda: "error_count" / "total_count") .as('error_percent')
The above example will add a new field
error_percent to each
data point with the result of
error_count / total_count where
total_count are existing fields on the data point.
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.
List of names for each expression. The expressions are evaluated in order and the result of a previous expression will be available in later expressions via the name provided.
stream .eval(lambda: "value" * "value", lambda: 1.0 / "value2") .as('value2', 'inv_value2')
The above example calculates two fields from the value and names them
If called the existing fields will be preserved in addition to the new fields being set. If not called then only new fields are preserved.
Optionally intermediate values can be discarded by passing a list of field names. Only fields in the list will be kept. If no list is given then all fields, new and old, are kept.
stream .eval(lambda: "value" * "value", lambda: 1.0 / "value2") .as('value2', 'inv_value2') .keep('value', 'inv_value2')
In the above example the original field
value is preserved.
In addition the new field
value2 is calculated and used in evaluating
inv_value2 but is discarded before the point is sent on to children nodes.
The resulting point has only two fields
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.