morpheus_dfp.stages.dfp_file_batcher_stage.DFPFileBatcherStage#

class DFPFileBatcherStage( config, date_conversion_func, period='D', sampling_rate_s=None, start_time=None, end_time=None, sampling=None, )[source]#

Bases: SinglePortStage

This stage groups data in the incoming DataFrame in batches of a time period (per day default), and optionally filtering incoming data to a specific time window. This stage can potentially improve performance by combining multiple small files into a single batch. This stage assumes that the date of the logs can be easily inferred such as encoding the creation time in the file name (for example, AUTH_LOG-2022-08-21T22.05.23Z.json), or using the modification time as reported by the file system. The actual method for extracting the date is encoded in a user-supplied date_conversion_func function.

Note: Setting both sampling_rate_s and sampling will result in an error.

Parameters:

configmorpheus.config.Config: Pipeline configuration instance.
date_conversion_funccallable: A function that takes a file object and returns a datetime object representing the date of the file.
periodstr, optional: Time period to group data by, value must be one of pandas’ offset strings https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
sampling_rate_s: int, optional: Deprecated consider using sampling instead. When defined a subset of the incoming data files will be sampled, taking the first row for each sampling_rate_s seconds.
start_timedatetime, optional: When not None incoming data files will be filtered, excluding any files created prior to start_time
end_timedatetime, optional: When not None incoming data files will be filtered, excluding any files created after end_time
samplingstr, float, int, optional: When non-None a subset of the incoming data files will be sampled. When a string, the value is interpreted as a pandas frequency. The first row for each frequency will be taken. When the value is between [0,1), a percentage of rows will be taken. When the value is greater than 1, the value is interpreted as the random count of rows to take.

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: Returns the name of the stage.
output_ports: Output ports from this stage.
unique_name: Unique name of stage.

Methods

`accepted_types`()	Accepted incoming types for 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`(file_objects)	Batches incoming data according to date, period and sampling, potentially filtering data based on file dates.
`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`()	Returns whether or not 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]#

Accepted incoming types for 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#: Returns the name of the stage.

on_data(file_objects)[source]#

Batches incoming data according to date, period and sampling, potentially filtering data based on file dates.

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]#

Returns whether or not 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.