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237 lines
9.0 KiB
Python
237 lines
9.0 KiB
Python
"""
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This class is a wrapper around read/write classes of cloud,sql,nosql,other packages
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The wrapper allows for application of plugins as pre-post conditions.
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NOTE: Plugins are converted to a pipeline, so we apply a pipeline when reading or writing:
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- upon initialization we will load plugins
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- on read/write we apply a pipeline (if passed as an argument)
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"""
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from transport.plugins import PluginLoader
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import transport
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from transport import providers
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from multiprocessing import Process, RLock
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import time
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import types
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from . import registry
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from datetime import datetime
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import pandas as pd
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import os
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import sys
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import itertools
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import json
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class IO:
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"""
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Base wrapper class for read/write and support for logs
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"""
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def __init__(self,_agent,plugins,_logger=None):
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#
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# We need to initialize the logger here ...
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#
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# registry.init()
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self._logger = _logger if not type(_agent) in [IReader,IWriter] else _agent._logger #transport.get.writer(label='logger') #if registry.has('logger') else None
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# if not _logger and hasattr(_agent,'_logger') :
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# self._logger = getattr(_agent,'_logger')
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self._agent = _agent
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_date = _date = str(datetime.now())
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self._logTable = 'logs' #'_'.join(['logs',_date[:10]+_date[11:19]]).replace(':','').replace('-','_')
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if plugins :
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self._init_plugins(plugins)
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else:
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self._plugins = None
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def setLogger(self,_logger):
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self._logger = _logger
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def log (self,**_args):
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if self._logger :
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_date = str(datetime.now())
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_data = dict({'pid':os.getpid(),'date':_date[:10],'time':_date[11:19]},**_args)
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for key in _data :
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if type(_data[key]) == list :
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_data[key] = [_item.__name__ if type(_item).__name__== 'function' else _item for _item in _data[key]]
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_data[key] = str(_data[key]) if type(_data[key]) not in [list,dict] else json.dumps(_data[key])
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self._logger.write(pd.DataFrame([_data])) #,table=self._logTable)
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def _init_plugins(self,_items):
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"""
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This function will load pipelined functions as a plugin loader
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"""
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registry.plugins.init()
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self._plugins = PluginLoader(registry=registry.plugins)
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[self._plugins.set(_name) for _name in _items]
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self.log(action='init-plugins',caller='read',object=self.getClassName(self),input =[_name for _name in _items])
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# if 'path' in _args and 'names' in _args :
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# self._plugins = PluginLoader(**_args)
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# else:
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# self._plugins = PluginLoader(registry=registry.plugins)
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# [self._plugins.set(_pointer) for _pointer in _args]
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#
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# @TODO: We should have a way to log what plugins are loaded and ready to use
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def meta (self,**_args):
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if hasattr(self._agent,'meta') :
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return self._agent.meta(**_args)
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return []
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def getClassName (self,_object):
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return '.'.join([_object.__class__.__module__,_object.__class__.__name__])
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def close(self):
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if hasattr(self._agent,'close') :
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self._agent.close()
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def apply(self):
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"""
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applying pre/post conditions given a pipeline expression
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"""
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for _pointer in self._plugins :
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_data = _pointer(_data)
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time.sleep(1)
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def apply(self,_query):
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if hasattr(self._agent,'apply') :
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return self._agent.apply(_query)
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return None
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def submit(self,_query):
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return self.delegate('submit',_query)
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def delegate(self,_name,_query):
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if hasattr(self._agent,_name) :
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pointer = getattr(self._agent,_name)
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return pointer(_query)
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return None
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class IReader(IO):
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"""
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This is a wrapper for read functionalities
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"""
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def __init__(self,_agent,_plugins=None,_logger=None):
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super().__init__(_agent,_plugins,_logger)
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self._args = if 'args' in _args else None
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def _stream (self,_data ):
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# self.log(action='streaming',object=self._agent._engine.name, input= type(_data).__name__)
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_shape = []
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for _segment in _data :
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_shape.append(list(_segment.shape))
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if self._plugins :
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yield self._plugins.apply(_segment,self.log)
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else:
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yield _segment
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_objectName = '.'.join([self._agent.__class__.__module__,self._agent.__class__.__name__])
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self.log(action='streaming',object=_objectName, input= {'shape':_shape})
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def read(self,**_args):
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if 'plugins' in _args :
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self._init_plugins(_args['plugins'])
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if self._args :
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_data = self._agent.read(**self._args)
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else:
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_data = self._agent.read(**_args)
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_objectName = '.'.join([self._agent.__class__.__module__,self._agent.__class__.__name__])
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if types.GeneratorType == type(_data):
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return self._stream(_data)
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# if self._plugins :
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# return self._stream(_data)
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# else:
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# _count = 0
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# for _segment in _data :
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# _count += 1
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# yield _segment
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# self.log(action='streaming',object=_objectName, input= {'segments':_count})
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# return _data
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elif type(_data) == pd.DataFrame :
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_shape = _data.shape #[0,0] if not _data.shape[] else list(_data.shape)
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self.log(action='read',object=_objectName, input=_shape)
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if self._plugins :
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_logs = []
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_data = self._plugins.apply(_data,self.log)
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return _data
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class IWriter(IO):
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lock = RLock()
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def __init__(self,_agent,pipeline=None,_logger=None):
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super().__init__(_agent,pipeline,_logger)
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def write(self,_data,**_args):
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if 'plugins' in _args :
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self._init_plugins(_args['plugins'])
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if self._plugins and self._plugins.ratio() > 0 :
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_logs = []
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_data = self._plugins.apply(_data,_logs,self.log)
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# [self.log(**_item) for _item in _logs]
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try:
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# IWriter.lock.acquire()
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self._agent.write(_data,**_args)
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finally:
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# IWriter.lock.release()
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pass
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#
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# The ETL object in its simplest form is an aggregation of read/write objects
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# @TODO: ETL can/should aggregate a writer as a plugin and apply it as a process
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class IETL(IReader) :
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"""
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This class performs an ETL operation by ineriting a read and adding writes as pipeline functions
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"""
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def __init__(self,**_args):
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_source = _args['source']
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_plugins = _source['plugins'] if 'plugins' in _source else None
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# super().__init__(transport.get.reader(**_args['source']))
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super().__init__(transport.get.reader(**_source),_plugins)
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# _logger =
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if 'target' in _args:
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self._targets = _args['target'] if type(_args['target']) == list else [_args['target']]
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else:
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self._targets = []
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self.jobs = []
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#
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# If the parent is already multiprocessing
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if 'token' in _source :
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self._logToken = _source['token']
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self._sourceArgs = _source['args'] if 'args' in _source else None
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self._hasParentProcess = False if 'hasParentProcess' not in _args else _args['hasParentProcess']
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# def run(self) :
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# """
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# We should apply the etl here, if we are in multiprocessing mode
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# """
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# return self.read()
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def run(self,**_args):
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_data = super().read(**_args) if not self._sourceArgs else super().read(**self._sourceArgs)
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self._targets = [transport.get.writer(**_kwargs) for _kwargs in self._targets]
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if types.GeneratorType == type(_data):
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_index = 0
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for _segment in _data :
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_index += 1
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for _writer in self._targets :
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self.post(_segment,writer=_writer,index=_index)
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time.sleep(1)
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else:
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for _writer in self._targets :
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self.post(_data,writer=_writer)
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return _data
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# return _data
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def post (self,_data,**_args) :
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"""
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This function returns an instance of a process that will perform the write operation
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:_args parameters associated with writer object
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"""
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#writer = transport.get.writer(**_args)
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try:
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_action = 'post'
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_shape = dict(zip(['rows','columns'],_data.shape))
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_index = _args['index'] if 'index' in _args else 0
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writer = _args['writer']
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writer.write(_data)
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except Exception as e:
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_action = 'post-error'
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print (e)
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pass
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self.log(action=_action,object=writer._agent.__module__, input= {'shape':_shape,'segment':_index})
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