BitSwan Analyzers Tutorial

Analyzers and Matrices exhaustive tutorial

General

Analyzer is a special type of processor in bspump, that passes events unchanged, but aggregates the information about them in some object. There are analyzers operating on Matrix, the top-level object of bspump. So far there are 3 types of matrices, SessionMatrix, TimeWindowMatrix and GeoMatrix. The analyzers on these matrices are SessionAnalyzer, TimeWindowAnalyzer and GeoAnalyzer respectively.

Initialization

The Matrix can be initialized outside of the Analyzer and passed as matrix_id into the Analyzer (next chapter), as many analyzers can operate on the same matrix. The other way is to pass all matrix's parameters to the analyzer's constructor (will be discussed later).

SessionMatrix

Initialization

Parameters in the constructor: app, dtype='float_', id=None, config=None. I guess you're familiar with all of them except for dtype. SessionMatrix has columns with different names and types. For instance, you want to keep in the matrix 3 columns: timestamp as integer, tag as string and some_numbers as float submatrix with 2 rows and 2 columns. That brings dtype, which has to be a list in the form dtype=[('timestamp', 'i8'), ('tag', 'U20'), ('some_numbers','(2,2)f8')]. The first tuple member is a name, the second is a type. i8 means 8 bit integer, U20 means unicode string length at most 20 characters and (2,2)f8 means 8 bit float matrix with size (2, 2).

Attributes

Array is the numpy matrix. Consists of rows and columns and cells. N2IMap structure helping to translate row name (unique id) to numeric array index/ I2NMap index-to-row-name translation. ClosedRows is a set, containing temporaly unused row indeces.

Functions

row_index = SessionMatrix.add_row(row_name) adds new row with specific id and returns numerical index of the row in the matrix. row_index = SessionMatrix.get_row_index(row_name) translates row_id to the index in matrix, None if does not exist. row_name = SessionMatrix.get_row_name(row_index) translates row_name to the name or id, None if does not exist. SessionMatrix.close_row(row_index) adds index to ClosedRows. SessionMatrix.flush() deletes closed rows from the matrix.

TimeWindowMatrix

Initialization

Parameters in the constructor (with defaults) app, dtype='float_', resolution=60, columns=15, clock_driven=True, start_time=None, id=None, config=None. TimeWindowMatrix has a time dimension columns, where events with timestamp are aggregated. You may notice, that dtype is not a list unlike SessionMatrix,fist number represent number of columns (first member of tuple) and number of cells inside each column (second member). resolution means number of seconds in each time column of the Matrix. clock_driven=True means, that time window once in a while will be moving forward by adding newest column and deleting the oldest one. The period of advancing is resolution. start_time is a timestamp of starting, if it's None, the start time will be current time.

Attributes

(same as SessionMatrix) additional ones: Start newest timestamp is seconds. End oldest timestamp is seconds. Resolution seconds in each column. Dimensions (number of columns, cells in column) WarmingUpCount array indicating if added row is 'old' enough to be analyzed.

Functions

(same as SessionMatrix) plus: column_index = TimeWindowMatrix.get_column(timestamp) returns column index of the cell the timestamp (in seconds) belongs to. None, if it's outside. TimeWindowMatrix.advance(target_timestamp) possibly move forward the time window

GeoMatrix

Initialization

Parameters in the constructor (with defaults) app, dtype='float_', bbox=None, resolution=5, id=None, config=None. GeoMatrix is fixed size matrix, representing the map. Real GPS coordinates are projected to map coordinates using equirectangular transformation. dtype see SessionMatrix section. bbox is a dictionary with map extreme points max_lat, min_lat, min_lon, max_lon. If None, it takes european coordinates. resolution is the size of the cell in kilometers.

Attributes

Array is the numpy matrix. Consists of rows and columns and cells. BBox is a bounding box (see initialization section). Resolution is the size of the cell in kilometers. MembersToIds SizeWidth width of map in km SizeHeight height of map in km MapWidth number of rows in matrix (alias of Array.shape[0]) MapHeight number of columns in matrix (alias of Array.shape[1])

Functions

is_in_boundaries(lat, lon) tests if coordinates fall into the map, returns boolean. row, column = project_equirectangular(lat, lon) translates real coordinates to map coordinates, using equirectangular transformation. lat, lon = inverse_equirectangular(row, column) translates map coordinates into gps coordinates.

Analyzers initialization

SessionAnalyzer

Parameters of the constructor app, pipeline, matrix_id=None, dtype='float_', analyze_on_clock=False, id=None, config=None. dtype is passed to Matrix if needed. If matrix_id is present, the pre-created matrix will be 'located'. analyze_on_clock enables the call of analyze() function by timer. There should be added 'analyze_period':time_in_seconds to config. Default period is 1 minute.

TimeWindowAnalyzer

Parameters of the constructor app, pipeline, matrix_id=None, dtype='float_', columns=15, analyze_on_clock=False, resolution=60, start_time=None, clock_driven=True, id=None, config=None. If matrix_id is present, the pre-created matrix will be 'located'. analyze_on_clock enables the call of analyze() function by timer. There should be added 'analyze_period':time_in_seconds to config. Default period is 1 minute.

GeoAnalyzer

Parameters in constructor app, pipeline, matrix_id=None, dtype='float_', analyze_on_clock=False, bbox=None, resolution=5, id=None, config=None (see GeoMatrix and SessioAnalyzer).

The lifecycle of an Analyzer

Any Analyzer is a processor, it means, it has process() function. By default it is:

def process(self, context, event):
    if self.predicate(context, event):
        self.evaluate(context, event)
    return event

predicate() should filter incoming events and return boolean. By default always returns True. evaluate() is main function, where the information should be aggregated. Example:

def evaluate(self, context, event):
    row_index = self.TimeWindow.get_row_index(event['some_id'])
    if row_index is None:
        row_index = self.TimeWindow.add_row(event['some_id'])

    column_index = self.TimeWindow.get_column(event['@timestamp']) # not need if SessionMatrix
    if column_index is None:
        # do nothing
        return

    self.TimeWindow.Array[row_index, column_index] = event['some_attribute']
    # self.Sessions.Array['some_column'][row_index] = event['some_attribute']
    ...

Each Analyzer has an analyze() function, which can be called internally or externally, depends on your needs. Typically analyze() produces events based on the object of analyzis (usually Matrix) to some external source. It is specific for each analyzer. If the analyzed object is Matrix, it is not recommended to iterate through the matrix row by row (or cell by cell). Instead use numpy fuctions. Examples:

  1. You have a vector with n rows. You need only those row indeces, where the cell content is more than 10. Use np.where(vector > 10).

  2. You have a matrix with n rows and m columns. You need to find out which rows fully consist of zeros. use np.where(np.all(matrix == 0, axis=1)) to get those row indexes. Instead np.all() you can use np.any() to get all row indexes, where there is at least one zero.

  3. Use np.mean(matrix, axis=1) to get means for all rows.

  4. Usefull numpy functions: np.unique(), np.sum(), np.argmin(), np.argmax().

Other analyzers

There exist other kinds of Analyzer not using the Matrix object. They are LatchAnalyzer which stores last n events. TimeDriftAnalyzer creates a metric of statistic about events' timestamps with current time difference.

About the Author

Margarita Argirova

Margarita is data scientist in LibertyAces

You Might Be Interested in Reading These Articles

BitSwan Features v1910

We've just launched new features: Analyzer profiling, bspump.lookup.MatrixLookup, bspump.lookup.BitMapIndex, MongoDBSink etc.

Continue reading ...

new release features analyzer profiler mongodb

Published on October 31, 2019

BitSwan Features v1907

We've just launched new features: Google Drive Sink, Tableau Sink, KafkaSink, analyzers and lookup refactoring, LatchAnalyzer etc.

Continue reading ...

new release features file google drive matrix kafka latch

Published on July 31, 2019

BitSwan Features v1908

We've just launched new features: PostgreSQLLogicalReplicationSource, ODBC connection, source and sink, asynchronous Generator, etc.

Continue reading ...

new release features odbc postgresql mysql generator matrix kafka latch

Published on September 15, 2019