def loadData(fpath, dbFunc):
if not(os.path.exists(fpath)):
raise NameError('Path not exist')
allDone = getAllDone()
if allDone.count(fpath):
print 'Already Done!'
return
print 'Loading File "%s"...'%os.path.basename(fpath)
subjCont = csvAnalyzer.analyzeFile(fpath)
print 'Inserting To DB...'
succ = dbFunc(subjCont)
if succ:
logFile(fpath)
print 'Done!'
else:
print 'Error, please see logs...'
def __init__(self):
# 1. Target
self.db_name = 'postgres'
self.user_name = 'postgres'
self.password = '******'
self.schema_name = 'DB_Schema_Name'
self.tableName = 'DB_Table_Name' # Target Table name, will create it from data source files columns if not exists
# 2. Source
self.dataDir = '~/Downloads/Kaggle/Data' # folder with files to upload to db - search recursive in this folder for
# files that return true for self.filesFormatFunc condition. it can load multipal files to same table. when it fails
# next time it starts, its skips the already success files uploaded
self.filesFormatFunc = lambda f : re.match('.*Test_.*\.txt') <> None # files filter to upload from dataDir table
# 3. Other Tunings and configurations of data parsing and manipulations:
self.upload_bulk_size = 1000 # Bulk size for uploading to DB, we can use the same number also for reading file
# Put None to cancel bulking and load all the file as 1 bulk
self.dataAnalyzerFunLazy = lambda f, h: csvAnalyzer.analyzeFile(f, header = h, delimiter = ',', #delimeter fot csv
manipulationFunc = None, #dictionary for functions to manipulate fields in the csv. example 1
additionalConstFields = None, #additionalFields to add fields for table. see example 2
topN = self.upload_bulk_size) # file reading bulk size
