NVIDIA Docs Hub NVIDIA Morpheus NVIDIA Morpheus (25.02.01) morpheus.stages.input.appshield_source_stage.AppShieldSourceStage

morpheus.stages.input.appshield_source_stage.AppShieldSourceStage

class AppShieldSourceStage(c, input_glob, plugins_include, cols_include, cols_exclude=None, watch_directory=False, max_files=- 1, sort_glob=False, recursive=True, queue_max_size=128, batch_timeout=5.0, encoding='latin1')[source]

Bases: morpheus.pipeline.preallocator_mixin.PreallocatorMixin, morpheus.pipeline.execution_mode_mixins.GpuAndCpuMixin, morpheus.pipeline.single_output_source.SingleOutputSource

Source stage is used to load Appshield messages from one or more plugins into a dataframe. It normalizes nested json messages and arranges them into a dataframe by snapshot and source.

Parameters

cmorpheus.config.Config: Pipeline configuration instance.
input_globstr: Input glob pattern to match files to read. For example, /input_dir/<source>/snapshot-*/*.json would read all files with the ‘json’ extension in the directory input_dir.
plugins_includeList[str], default = None: Plugins for appshield to be extracted.
cols_includeList[str], default = None: Raw features to extract from appshield plugins data.
cols_excludeList[str], default = None: Columns that aren’t essential should be excluded. If None, [“SHA256”] will be used.
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_filesint, default = -1: Max number of files to read. Useful for debugging to limit startup time. Default value of -1 is unlimited.
sort_globbool, default = False: If true the list of files matching input_glob will be processed in sorted order.
recursivebool, default = True: If true, events will be emitted for the files in subdirectories matching input_glob.
queue_max_sizeint, default = 128: Maximum queue size to hold the file paths to be processed that match input_glob.
batch_timeoutfloat, default = 5.0: Timeout to retrieve batch messages from the queue.
encodingstr, default = latin1: Encoding to read a file.

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 intput 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_source_split`(x, source)	Combines plugin dataframes from multiple snapshot and split dataframe per source.
`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.
`files_to_dfs`(x, cols_include, cols_exclude, ...)	Load plugin files into a dataframe, then segment the dataframe by source.
`fill_interested_cols`(plugin_df, cols_include)	Fill missing interested plugin columns.
`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_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.
`load_df`(filepath, cols_exclude, encoding)	Reads a file into a dataframe.
`load_meta_cols`(filepath_split, plugin, plugin_df)	Loads meta columns to dataframe.
`read_file_to_df`(file, cols_exclude)	Read file content to dataframe.
`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_source_split(x, source)[source]

Combines plugin dataframes from multiple snapshot and split dataframe per source.

Parameters

xlist[pd.DataFrame]: Dataframes from multiple sources.
sourcestr: source column name to group it.

Returns

dict[str, pandas.DataFrame]: Grouped dataframes by source.

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:

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.

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

static files_to_dfs(x, cols_include, cols_exclude, plugins_include, encoding)[source]

Load plugin files into a dataframe, then segment the dataframe by source.

Parameters

xlist[str]: List of file paths.
cols_includelist[str]: Columns that needs to include.
cols_excludelist[str]: Columns that needs to exclude.
plugins_include: list[str]: For each path in x, a list of plugins to load additional meta cols from.
encodingstr: Encoding to read a file.

Returns

dict[str, pandas.DataFrame]: Grouped dataframes by source.

static fill_interested_cols(plugin_df, cols_include)[source]

Fill missing interested plugin columns.

Parameters

plugin_dfpandas.DataFrame: Snapshot plugin dataframe
cols_includelist[str]: Columns that needs to be included.

Returns

pandas.DataFrame: The columns added dataframe.

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_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 intput 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.

static load_df(filepath, cols_exclude, encoding)[source]

Reads a file into a dataframe.

Parameters

filepathstr: Path to a file.
cols_excludelist[str]: Columns that needs to exclude.
encodingstr: Encoding to read a file.

Returns

pandas.DataFrame: The parsed dataframe.

Raises

JSONDecodeError: If not able to decode the json file.

static load_meta_cols(filepath_split, plugin, plugin_df)[source]

Loads meta columns to dataframe.

Parameters

filepath_splitlist[str]: Splits of file path.
pluginstr: Plugin name to which the data belongs to.
plugin_df: pd.DataFrame: DataFrame to which the meta columns will be added to.

Returns

pandas.DataFrame: The parsed dataframe.

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 read_file_to_df(file, cols_exclude)[source]

Read file content to dataframe.

Parameters

fileio.TextIOWrapper: Input file object
cols_excludelist[str]: Dropping columns from a dataframe.

Returns

pandas.DataFrame: The columns added dataframe

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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