morpheus.stages.input.azure_source_stage.AzureSourceStage

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

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.

name

The name of the stage.

output_ports

Output ports from this stage.

unique_name

Unique name of stage.

Methods

_build(builder, in_ports_streams)[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 StreamPair tuple which contain the input mrc.SegmentObject object and the message data type.

Parameters

buildermrc.Builder

mrc.Builder object for the pipeline. This should be used to construct/attach the internal mrc.SegmentObject.

in_ports_streamsmorpheus.pipeline.pipeline.StreamPair

List of tuples containing the input mrc.SegmentObject object and the message data type.

Returns

typing.List[morpheus.pipeline.pipeline.StreamPair]

List of tuples containing the output mrc.SegmentObject object from this stage and the message data type.

_build_source(seg)[source]

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

Returns

morpheus.pipeline.pipeline.StreamPair:

A tuple containing the output mrc.SegmentObject object from this stage and the message data type.

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

Creates a dataframe for each userid.

Parameters

xtyping.List[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_dfstyping.Dict[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.

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.

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.

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

typing.List[morpheus.pipeline.pipeline.StreamWrapper]

All input stages.

get_all_inputs()[source]

Get all input senders to this stage.

Returns

typing.List[morpheus.pipeline.pipeline.Sender]

All input senders.

get_all_output_stages()[source]

Get all output stages from this stage.

Returns

typing.List[morpheus.pipeline.pipeline.StreamWrapper]

All output stages.

get_all_outputs()[source]

Get all output receivers from this stage.

Returns

typing.List[morpheus.pipeline.pipeline.Receiver]

All output receivers.

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

typing.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.

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

typing.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_arraytyping.List[pd.DataFrame]

List of repeated dataframes.

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.

stop()[source]

Stages can implement this to perform cleanup steps when pipeline is stopped.

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.