morpheus_dfp.stages.dfp_rolling_window_stage.DFPRollingWindowStage#

class DFPRollingWindowStage( c, min_history, min_increment, max_history, cache_dir='./.cache/dfp', )[source]#

Bases: SinglePortStage

This stage groups incomming messages into a rolling time window, emitting them only when the history requirements are met specified by the min_history, min_increment and max_history parameters.

Incoming data is cached to disk (cache_dir) to reduce memory ussage. This computes a row hash for the first and last rows of the incoming DataFrame as such all data contained must be hashable, any non-hashable values such as lists should be dropped or converted into hashable types in the DFPFileToDataFrameStage.

Parameters:

cmorpheus.config.Config: Pipeline configuration instance.
min_historyint: Exclude users with less than min_history records, setting this to 1 effectively disables this feature.
min_incrementint: Exclude incoming batches for users where less than min_increment new records have been added since the last batch, setting this to 0 effectively disables this feature.
max_historyint or str: When not None, include up to max_history records. When max_history is an int, then the last max_history records will be included. When max_history is a str it is assumed to represent a duration parsable by [pandas.Timedelta](https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.html) and only those records within the window of [latest timestamp - max_history, latest timestamp] will be included.
cache_dirstr: Path to cache directory, cached items will be stored in a subdirectory under this directory named rolling-user-data. This directory, along with cache_dir will be created if it does not already exist.

Attributes:

has_multi_input_ports: Indicates if this stage has multiple input ports.
has_multi_output_ports: Indicates if this stage has multiple output ports.
input_ports: Input ports to this stage.
is_built: Indicates if this stage has been built.
is_pre_built: Indicates if this stage has been built.
name: Stage name.
output_ports: Output ports from this stage.
unique_name: Unique name of stage.

Methods

`accepted_types`()	Input types accepted by this stage.
`build`(builder[, do_propagate])	Build this stage.
`can_build`([check_ports])	Determines if all inputs have been built allowing this node to be built.
`can_pre_build`([check_ports])	Determines if all inputs have been built allowing this node to be built.
`compute_schema`(schema)	Compute the schema for this stage based on the incoming schema from upstream stages.
`get_all_input_stages`()	Get all input stages to this stage.
`get_all_inputs`()	Get all input senders to this stage.
`get_all_output_stages`()	Get all output stages from this stage.
`get_all_outputs`()	Get all output receivers from this stage.
`get_needed_columns`()	Stages which need to have columns inserted into the dataframe, should populate the `self._needed_columns` dictionary with mapping of column names to `morpheus.common.TypeId`.
`join`()	Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped.
`on_data`(message)	Emits a new message containing the rolling window for the user if and only if the history requirments are met, returns `None` otherwise.
`start_async`()	This function is called along with on_start during stage initialization.
`stop`()	Stages can implement this to perform cleanup steps when pipeline is stopped.
`supported_execution_modes`()	Returns a tuple of supported execution modes of this stage.
`supports_cpp_node`()	Whether this stage supports a C++ node.

_build(builder, input_nodes)[source]#

This function is responsible for constructing this stage’s internal mrc.SegmentObject object. The input of this function contains the returned value from the upstream stage.

The input values are the mrc.Builder for this stage and a list of parent nodes.

Parameters:

buildermrc.Builder: mrc.Builder object for the pipeline. This should be used to construct/attach the internal mrc.SegmentObject.
input_nodeslist[mrc.SegmentObject]: List containing the input mrc.SegmentObject objects.

Returns:

list[mrc.SegmentObject]: List of tuples containing the output mrc.SegmentObject object from this stage.

accepted_types()[source]#

Input types accepted by this stage.

build(builder, do_propagate=True)[source]#

Build this stage.

Parameters:

buildermrc.Builder: MRC segment for this stage.
do_propagatebool, optional: Whether to propagate to build output stages, by default True.

can_build(check_ports=False)[source]#

Determines if all inputs have been built allowing this node to be built.

Parameters:

check_portsbool, optional: Check if we can build based on the input ports, by default False.

Returns:

bool: True if we can build, False otherwise.

can_pre_build(check_ports=False)[source]#

Determines if all inputs have been built allowing this node to be built.

Parameters:

check_portsbool, optional: Check if we can build based on the input ports, by default False.

Returns:

bool: True if we can build, False otherwise.

compute_schema(schema)[source]#

Compute the schema for this stage based on the incoming schema from upstream stages.

Incoming schema and type information from upstream stages is available via the schema.input_schemas and schema.input_types properties.

Derived classes need to override this method, can set the output type(s) on schema by calling set_type for all output ports. For example a simple pass-thru stage might perform the following:

>>> for (port_idx, port_schema) in enumerate(schema.input_schemas):
...     schema.output_schemas[port_idx].set_type(port_schema.get_type())
>>>

If the port types in upstream_schema are incompatible the stage should raise a RuntimeError.

get_all_input_stages()[source]#

Get all input stages to this stage.

Returns:

list[morpheus.pipeline.pipeline.StageBase]: All input stages.

get_all_inputs()[source]#

Get all input senders to this stage.

Returns:

list[morpheus.pipeline.pipeline.Sender]: All input senders.

get_all_output_stages()[source]#

Get all output stages from this stage.

Returns:

list[morpheus.pipeline.pipeline.StageBase]: All output stages.

get_all_outputs()[source]#

Get all output receivers from this stage.

Returns:

list[morpheus.pipeline.pipeline.Receiver]: All output receivers.

get_needed_columns()[source]#: Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.common.TypeId. This will ensure that the columns are allocated and populated with null values.

property has_multi_input_ports: bool#

Indicates if this stage has multiple input ports.

Returns:

bool: True if stage has multiple input ports, False otherwise.

property has_multi_output_ports: bool#

Indicates if this stage has multiple output ports.

Returns:

bool: True if stage has multiple output ports, False otherwise.

property input_ports: list[Receiver]#

Input ports to this stage.

Returns:

list[morpheus.pipeline.pipeline.Receiver]: Input ports to this stage.

property is_built: bool#

Indicates if this stage has been built.

Returns:

bool: True if stage is built, False otherwise.

property is_pre_built: bool#

Indicates if this stage has been built.

Returns:

bool: True if stage is built, False otherwise.

async join()[source]#: Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped. Typically this is called after stop during a graceful shutdown, but may not be called if the pipeline is terminated.

property name: str#: Stage name.

on_data(message)[source]#

Emits a new message containing the rolling window for the user if and only if the history requirments are met, returns None otherwise.

property output_ports: list[Sender]#

Output ports from this stage.

Returns:

list[morpheus.pipeline.pipeline.Sender]: Output ports from this stage.

async start_async()[source]#: This function is called along with on_start during stage initialization. Allows stages to utilize the asyncio loop if needed.

stop()[source]#: Stages can implement this to perform cleanup steps when pipeline is stopped.

supported_execution_modes()[source]#

Returns a tuple of supported execution modes of this stage. By default this returns (ExecutionMode.GPU,). Subclasses can override this method to specify different execution modes.

For most stages the values will be static, and this can be accomplished by making use of either the CpuOnlyMixin or GpuAndCpuMixin mixins.

However, complex stages may choose to make this decision at runtime, in which case this method should be overridden. directly within the stage class.

supports_cpp_node()[source]#: Whether this stage supports a C++ node.

property unique_name: str#

Unique name of stage. Generated by appending stage id to stage name.

Returns:

str: Unique name of stage.