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
- c
morpheus.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.
- c
- 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 tomorpheus.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
- builder
mrc.Builder
mrc.Builder
object for the pipeline. This should be used to construct/attach the internalmrc.SegmentObject
.- input_nodes
list[mrc.SegmentObject]
List containing the input
mrc.SegmentObject
objects.
- builder
- 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
- builder
mrc.Builder
MRC segment for this stage.
- do_propagatebool, optional
Whether to propagate to build output stages, by default True.
- builder
- 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
andschema.input_types
properties.Derived classes need to override this method, can set the output type(s) on
schema
by callingset_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 aRuntimeError
.
- 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.
- list[
- get_all_inputs()[source]
Get all input senders to this stage.
- Returns
- list[
morpheus.pipeline.pipeline.Sender
] All input senders.
- list[
- get_all_output_stages()[source]
Get all output stages from this stage.
- Returns
- list[
morpheus.pipeline.pipeline.StageBase
] All output stages.
- list[
- get_all_outputs()[source]
Get all output receivers from this stage.
- Returns
- list[
morpheus.pipeline.pipeline.Receiver
] All output receivers.
- list[
- 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 tomorpheus.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.
- list[
- 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.
- list[
- static read_file_to_df(file, cols_exclude)[source]
Read file content to dataframe.
- Parameters
- file
io.TextIOWrapper
Input file object
- cols_excludelist[str]
Dropping columns from a dataframe.
- file
- 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.