NVIDIA Docs Hub NVIDIA Morpheus NVIDIA Morpheus (25.02.01) morpheus.stages.input.azure_source_stage.AzureSourceStage

morpheus.stages.input.azure_source_stage.AzureSourceStage

class AzureSourceStage(c, input_glob, watch_directory=False, max_files=-1, file_type=<FileTypes.Auto: 0>, repeat=1, sort_glob=False, recursive=True, queue_max_size=128, batch_timeout=5.0)[source]

Bases: morpheus.stages.input.autoencoder_source_stage.AutoencoderSourceStage

Source stage is used to load Azure Active Directory messages.

Adds the following derived features:

appincrement: Increments every time the logs contain a distinct app.
locincrement: Increments every time a log contains a distinct city within a day.
logcount: Tracks the number of logs generated by a user within a day.

Parameters

cmorpheus.config.Config: Pipeline configuration instance.
input_globstr: Input glob pattern to match files to read. For example, /input_dir/*.json would read all files with the ‘json’ extension in the directory input_dir.
watch_directorybool, default = False: The watch directory option instructs this stage to not close down once all files have been read. Instead it will read all files that match the ‘input_glob’ pattern, and then continue to watch the directory for additional files. Any new files that are added that match the glob will then be processed.
max_files: int, default = -1: Max number of files to read. Useful for debugging to limit startup time. Default value of -1 is unlimited.
file_typemorpheus.common.FileTypes, default = ‘FileTypes.Auto’.: Indicates what type of file to read. Specifying ‘auto’ will determine the file type from the extension. Supported extensions: ‘json’, ‘csv’
repeat: int, default = 1: How many times to repeat the dataset. Useful for extending small datasets in debugging.
sort_globbool, default = False: If true the list of files matching input_glob will be processed in sorted order.
recursive: bool, default = True: If true, events will be emitted for the files in subdirectories that match input_glob.
queue_max_size: int, default = 128: Maximum queue size to hold the file paths to be processed that match input_glob.
batch_timeout: float, default = 5.0: Timeout to retrieve batch messages from the queue.

Attributes

df_type_str: Returns the DataFrame module that should be used for the given execution mode.
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_count: Return None for no max input count
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: The name of the stage.
output_ports: Output ports from this stage.
unique_name: Unique name of stage.

Methods

`batch_user_split`(x, userid_column_name, ...)	Creates a dataframe for each userid.
`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.
`change_columns`(df)	Removes characters (_,.,{,},:) from the names of the dataframe columns.
`compute_schema`(schema)	Compute the schema for this stage based on the incoming schema from upstream stages.
`derive_features`(df, feature_columns)	Derives feature columns from the AzureAD (logs) source columns.
`files_to_dfs_per_user`(x, userid_column_name, ...)	After loading the input batch of AzureAD logs into a dataframe, this method builds a dataframe for each set of userid rows in accordance with the specified filter condition.
`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_df_class`()	Returns the DataFrame class that should be used for the given execution mode.
`get_df_pkg`()	Returns the DataFrame package that should be used for the given execution mode.
`get_match_pattern`(glob_split)	Return a file match pattern
`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`.
`is_stop_requested`()	Returns `True` if a stop has been requested.
`join`()	Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped.
`repeat_df`(df, repeat_count)	This function iterates over the same dataframe to extending small datasets in debugging with incremental updates to the `event_dt` and `eventTime` columns.
`request_stop`()	Request the source to stop processing data.
`set_needed_columns`(needed_columns)	Sets the columns needed to perform preallocation.
`start_async`()	This function is called along with on_start during stage initialization.
`stop`()	This method is invoked by the pipeline whenever there is an unexpected shutdown.
`supported_execution_modes`()	Returns a tuple of supported execution modes of this stage.
`supports_cpp_node`()	Specifies whether this Stage is capable of creating C++ nodes.

_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.

_build_source(builder)[source]

Abstract method all derived Source classes should implement. Returns the same value as build.

Returns

mrc.SegmentObject:: The MRC node for this stage.

_build_sources(builder)[source]

Abstract method all derived Source classes should implement. Returns the same value as build.

Returns

mrc.SegmentObject:: The MRC nodes for this stage.

static batch_user_split(x, userid_column_name, userid_filter, datetime_column_name='event_dt')[source]

Creates a dataframe for each userid.

Parameters

xlist[pd.DataFrame]: List of dataframes.
userid_column_namestr: Name of a dataframe column used for categorization.
userid_filterstr: Only rows with the supplied userid are filtered.
datetime_column_namestr: Name of the dataframe column used to sort the rows.

Returns

user_dfsdict[str, pd.DataFrame]: Dataframes, each of which is associated with a single userid.

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.

static change_columns(df)[source]

Removes characters (_,.,{,},:) from the names of the dataframe columns.

Parameters

dfpd.DataFrame: Dataframe that requires column renaming.

Returns

dfpd.DataFrame: Dataframe with renamed columns.

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:

Copy
Copied!

            
            >>> 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.

static derive_features(df, feature_columns)[source]

Derives feature columns from the AzureAD (logs) source columns.

Parameters

dfpd.DataFrame: Dataframe for deriving columns.
feature_columnstyping.List[str]: Names of columns that are need to be derived.

Returns

dftyping.List[pd.DataFrame]: Dataframe with actual and derived columns.

property df_type_str: Literal['cudf', 'pandas']: Returns the DataFrame module that should be used for the given execution mode.

static files_to_dfs_per_user(x, userid_column_name, feature_columns, userid_filter=None, repeat_count=1)[source]

After loading the input batch of AzureAD logs into a dataframe, this method builds a dataframe for each set of userid rows in accordance with the specified filter condition.

Parameters

xtyping.List[str]: List of messages.
userid_column_namestr: Name of the column used for categorization.
feature_columnstyping.List[str]: Feature column names.
userid_filterstr: Only rows with the supplied userid are filtered.
repeat_countstr: Number of times the given rows should be repeated.

Returns

df_per_usertyping.Dict[str, pd.DataFrame]: Dataframe per userid.

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_df_class()[source]

Returns the DataFrame class that should be used for the given execution mode.

get_df_pkg()[source]

Returns the DataFrame package that should be used for the given execution mode.

get_match_pattern(glob_split)[source]: Return a file match pattern

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_count: int: Return None for no max input count

property input_ports: list[morpheus.pipeline.receiver.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.

is_stop_requested()[source]

Returns True if a stop has been requested.

Returns

bool:: True if a stop has been requested, 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 on its own.

property name: str

The name of the stage. Used in logging. Each derived class should override this property with a unique name.

Returns

str: Name of a stage.

property output_ports: list[morpheus.pipeline.sender.Sender]

Output ports from this stage.

Returns

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

static repeat_df(df, repeat_count)[source]

This function iterates over the same dataframe to extending small datasets in debugging with incremental updates to the event_dt and eventTime columns.

Parameters

dfpd.DataFrame: To be repeated dataframe.
repeat_countint: Number of times the given dataframe should be repeated.

Returns

df_arraylist[pd.DataFrame]: List of repeated dataframes.

request_stop()[source]: Request the source to stop processing data.

set_needed_columns(needed_columns)[source]

Sets the columns needed to perform preallocation. This should only be called by the Pipeline at build time. The needed_columns shoudl contain the entire set of columns needed by any other stage in this segment.

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]: This method is invoked by the pipeline whenever there is an unexpected shutdown. Subclasses should override this method to perform any necessary cleanup operations.

supported_execution_modes()[source]

Returns a tuple of supported execution modes of this stage.

supports_cpp_node()[source]: Specifies whether this Stage is capable of creating C++ nodes. During the build phase, this value will be combined with CppConfig.get_should_use_cpp() to determine whether or not a C++ node is created. This is an instance method to allow runtime decisions and derived classes to override base implementations.

property unique_name: str

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

Returns

str: Unique name of stage.

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