diff --git a/README.md b/README.md index 5aacca2..e69de29 100644 --- a/README.md +++ b/README.md @@ -1 +0,0 @@ -# generator-ms \ No newline at end of file diff --git a/spec_generator/key_files/additional_validation.csv b/spec_generator/key_files/additional_validation.csv new file mode 100644 index 0000000..a3b705b --- /dev/null +++ b/spec_generator/key_files/additional_validation.csv @@ -0,0 +1,7 @@ +column,validations +school_id,"1000000000,9999999999" +cluster_id,"100000000,999999999" +block_id,"10000,99999" +district_id,"100,999" +state_id,"1,99" +month,"1,12" \ No newline at end of file diff --git a/spec_generator/key_files/dataset_keys.csv b/spec_generator/key_files/dataset_keys.csv new file mode 100644 index 0000000..56a2f48 --- /dev/null +++ b/spec_generator/key_files/dataset_keys.csv @@ -0,0 +1,92 @@ 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+CubeToCubeIncrement,School_Statistics,school_statistics_average_pupil_teacher_ratio_by_district,district_level,ingestion.school_details,"block_id,district_id",block_id,"academic_year,district_id,total_teachers,students_enrolled","string,string,number,number","academic_year,district_id",ingestion.school_statistics_average_pupil_teacher_ratio_by_block,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_average_pupil_teacher_ratio_by_district,"sum_total_teachers,sum_students_enrolled",,, +CubeToCubeIncrement,School_Statistics,school_statistics_average_pupil_teacher_ratio_by_state,state_level,ingestion.school_details,"district_id,state_id",district_id,"academic_year,state_id,total_teachers,students_enrolled","string,string,number,number","academic_year,state_id",ingestion.school_statistics_average_pupil_teacher_ratio_by_district,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_average_pupil_teacher_ratio_by_state,"sum_total_teachers,sum_students_enrolled",,, +EventToCubeIncrement,School_Statistics,school_statistics_category_wise_average_pupil_teacher_ratio_by_grade,grade_level,ingestion.school_details,school_id,school_id,"academic_year,school_id,grade,total_teachers,students_enrolled","string,string,number,number,number","academic_year,school_id,grade",event,"total_teachers,students_enrolled",null,null,sum,ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_grade,"sum_total_teachers,sum_students_enrolled",,, +CubeToCubeIncrement,School_Statistics,school_statistics_category_wise_average_pupil_teacher_ratio_by_school,school_level,ingestion.school_details,school_id,school_id,"academic_year,school_id,total_teachers,students_enrolled","string,string,number,number","academic_year,school_id",ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_grade,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_school,"sum_total_teachers,sum_students_enrolled",,, +CubeToCubeIncrement,School_Statistics,school_statistics_category_wise_average_pupil_teacher_ratio_by_cluster,cluster_level,ingestion.school_details,"school_id,cluster_id",school_id,"academic_year,cluster_id,total_teachers,students_enrolled","string,string,number,number","academic_year,cluster_id",ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_school,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_cluster,"sum_total_teachers,sum_students_enrolled",,, +CubeToCubeIncrement,School_Statistics,school_statistics_category_wise_average_pupil_teacher_ratio_by_block,block_level,ingestion.school_details,"cluster_id,block_id",cluster_id,"academic_year,block_id,total_teachers,students_enrolled","string,string,number,number","academic_year,block_id",ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_cluster,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_block,"sum_total_teachers,sum_students_enrolled",,, +CubeToCubeIncrement,School_Statistics,school_statistics_category_wise_average_pupil_teacher_ratio_by_district,district_level,ingestion.school_details,"block_id,district_id",block_id,"academic_year,district_id,total_teachers,students_enrolled","string,string,number,number","academic_year,district_id",ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_block,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_district,"sum_total_teachers,sum_students_enrolled",,, +CubeToCubeIncrement,School_Statistics,school_statistics_category_wise_average_pupil_teacher_ratio_by_state,state_level,ingestion.school_details,"district_id,state_id",district_id,"academic_year,state_id,total_teachers,students_enrolled","string,string,number,number","academic_year,state_id",ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_district,"sum_total_teachers,sum_students_enrolled",null,null,sum,ingestion.school_statistics_category_wise_average_pupil_teacher_ratio_by_state,"sum_total_teachers,sum_students_enrolled",,, diff --git a/spec_generator/key_files/dimension_keys.csv b/spec_generator/key_files/dimension_keys.csv new file mode 100644 index 0000000..f78283a --- /dev/null +++ b/spec_generator/key_files/dimension_keys.csv @@ -0,0 +1,3 @@ +program,dimension_name,dimension_col,dimension_datatype,target_table +SAC,school_details,"grade,school_id,school_name,school_type,school_category,cluster_id,cluster_name,block_id,block_name,district_id,district_name,state_id,state_name","number,number,string,string,string,number,string,number,string,number,string,number,string", ingestion.school_details +School_Statistics,school_details,"grade,school_id,school_name,school_type,school_category,cluster_id,cluster_name,block_id,block_name,district_id,district_name,state_id,state_name","number,number,string,string,string,number,string,number,string,number,string,number,string", ingestion.school_details diff --git a/spec_generator/key_files/event_keys.csv b/spec_generator/key_files/event_keys.csv new file mode 100644 index 0000000..9296078 --- /dev/null +++ b/spec_generator/key_files/event_keys.csv @@ -0,0 +1,5 @@ +program,event_name,event_col,event_datatype +SAC,students_attendance,"date,school_id,grade,gender,total_students,students_attendance_marked,students_marked_present","string,number,number,string,number,number,number" +SAC,teachers_attendance,"date,school_id,grade,total_teachers,teachers_attendance_marked,teachers_marked_present","string,number,number,number,number,number" +School_Statistics,school_statistics_students,"academic_year,school_id,grade,gender,student_category,students_enrolled,cwsn_enrolled","number,number,number,string,string,number,number" +School_Statistics,school_statistics_teachers,"academic_year,school_id,grade,total_teachers,students_enrolled","number,number,number,number,number,number" diff --git a/spec_generator/spec_generator.py b/spec_generator/spec_generator.py new file mode 100644 index 0000000..1beba8b --- /dev/null +++ b/spec_generator/spec_generator.py @@ -0,0 +1,27 @@ +import json +from flask import Flask,request,Response +from spec_key_mapping import EventSpec,DimensionSpec,DatasetSpec +app = Flask(__name__) + + +@app.route('/generator/spec',methods=['POST']) +def SpecGenerator(): + spec_type=request.json['spec_type'] + try: + if(spec_type=='EventSpec'): + return EventSpec(request,Response) + + elif(spec_type=='DimensionSpec'): + return DimensionSpec(request,Response) + + elif (spec_type == 'DatasetSpec'): + return DatasetSpec(request,Response) + else: + return Response(json.dumps({"Message": "Spec Type is not correct"})) + except Exception as error: + print(error) + return Response(json.dumps({"Message": "Given Input Is Not Correct"})) + + +if (__name__ == "__main__"): + app.run(debug=True,port=3002) diff --git a/spec_generator/spec_key_mapping.py b/spec_generator/spec_key_mapping.py new file mode 100644 index 0000000..d20dbe0 --- /dev/null +++ b/spec_generator/spec_key_mapping.py @@ -0,0 +1,245 @@ +import json +import os +import re +from datetime import date + +import pandas as pd +todays_date = date.today() +CeatedSpecList=[] +def KeysMaping(Program, InputKeys, template, SpecFile, Response): + template = template + '.json' + SpecFile = SpecFile + '.json' + Program = Program + 'Specs' + if not os.path.exists(Program): + os.makedirs(Program) + if os.path.exists(os.path.dirname(os.path.abspath(__file__)) + '/' + Program + '/' + SpecFile): + os.remove(os.path.dirname(os.path.abspath(__file__)) + '/' + Program + '/' + SpecFile) + with open(os.path.dirname(os.path.abspath(__file__)) + '/template/' + template, 'r') as fs: + valueOfTemplate = fs.readlines() + if (len(InputKeys) != 0): + for valueOfTemplate in valueOfTemplate: + ToreplaceString = valueOfTemplate + templateKeys = re.findall("(?<=<)(.*?)(?=>)", ToreplaceString) + for key in templateKeys: + replaceStr = '<' + key + '>' + ToreplaceString = ToreplaceString.replace(replaceStr, str(InputKeys[key])) + with open(os.path.dirname(os.path.abspath(__file__)) + '/' + Program + '/' + SpecFile, 'a') as fs: + fs.write(ToreplaceString) + CeatedSpecList.append({"filename": SpecFile}) + return Response(json.dumps({"Message": "Spec created successfully", "SpecFiles":CeatedSpecList, "code": 200})) + else: + print('ERROR : InputKey is empty') + return Response(json.dumps({"Message": "InputKey is empty"})) + + +InputKeys = {} + + +def EventSpec(request, Response): + Template = "Event" + Program = request.json['program'] + EventKeys = request.json['key_file'] + ValidationKeys = request.json['validation_keys'] + Path = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + ValidationKeys + ########## Reading additional validation csv file ########### + df_validation = pd.read_csv(Path) + if len(df_validation) == 0: + return Response(json.dumps({"Message": ValidationKeys + " is empty"})) + Validation_items = df_validation.values.tolist() + Validationcol_list = [] + validation_list = [] + try: + for item in Validation_items: + Validationcol_list.append(item[0]) + validation_list.append(item[1]) + validation_dict = (dict(zip(Validationcol_list, validation_list))) + + ########## Reading Eventtkeys csv file ################# + EventPath = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + EventKeys + df_event = pd.read_csv(EventPath) + if len(df_event) == 0: + return Response(json.dumps({"Message": EventKeys + " is empty"})) + df_event = df_event.loc[df_event['program'] == Program] + E_keys = df_event.keys().tolist() + event_items = df_event.values.tolist() + for value in event_items: + event = (dict(zip(E_keys, value))) + EventName = event['event_name'] + EventColumn = [x.strip() for x in event['event_col'].split(',')] + DataTypes = [x.strip() for x in event['event_datatype'].split(',')] + EventDict = dict(zip(EventColumn, DataTypes)) + ColumnsDataType = [] + for event_col in EventColumn: + if event_col.casefold() == 'date': + ColumnsDataType.append({"type": "string", "shouldnotnull": True, "format": "date"}) + elif (event_col.casefold() == 'grade') | (event_col.casefold() == 'class'): + ColumnsDataType.append({"type": "number", "shouldnotnull": True, "minimum": 1, "maximum": 12}) + elif event_col.casefold() in ['year','academic_year']: + ColumnsDataType.append({"type": "number", "shouldnotnull": True, "minimum":((todays_date.year)-5), "maximum":int(todays_date.year)}) + elif event_col in Validationcol_list: + min = int(str(validation_dict[event_col]).split(',')[0]) + max = int(str(validation_dict[event_col]).split(',')[1]) + ColumnsDataType.append({"type": "number", "shouldnotnull": True, "minimum": min, "maximum": max}) + else: + ColumnsDataType.append({"type": EventDict[event_col].strip(), "shouldnotnull": True}) + InputKeys.update({"EventName": json.dumps(EventName), + "EventObject": json.dumps(dict(zip(EventColumn, ColumnsDataType))), + "EventList": json.dumps(EventColumn)}) + KeysMaping(Program, InputKeys, Template, 'event_' + EventName, Response) + except Exception as error: + print(error) + return KeysMaping(Program, InputKeys, Template, 'event_' + EventName, Response) + + + +def DimensionSpec(request, Response): + Template = "Dimension" + Program = request.json['program'] + DimensionKeys = request.json['key_file'] + ValidationKeys = request.json['validation_keys'] + Path = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + ValidationKeys + + ########## Reading additional validation csv file ########### + df_validation = pd.read_csv(Path) + if len(df_validation) == 0: + return Response(json.dumps({"Message": ValidationKeys + " is empty"})) + Validation_items = df_validation.values.tolist() + Validationcol_list = [] + validation_list = [] + for item in Validation_items: + Validationcol_list.append(item[0]) + validation_list.append(item[1]) + validation_dict = (dict(zip(Validationcol_list, validation_list))) + + ########## Reading DimensionKey csv file ################# + DimensionPath = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + DimensionKeys + df_dimension = pd.read_csv(DimensionPath) + if len(df_dimension) == 0: + return Response(json.dumps({"Message": DimensionKeys + " is empty"})) + df_dimension = df_dimension.loc[df_dimension['program'] == Program] + Dim_keys = df_dimension.keys().tolist() + Dim_items = df_dimension.values.tolist() + for value in Dim_items: + event = (dict(zip(Dim_keys, value))) + DimensionName = event['dimension_name'] + DimensionColumn = [x.strip() for x in event['dimension_col'].split(',')] + DataTypes = [x.strip() for x in event['dimension_datatype'].split(',')] + TargetTable=[x.strip() for x in event['target_table'].split(',')] + print(TargetTable) + DimensionDict = dict(zip(DimensionColumn, DataTypes)) + ColumnsDataType = [] + for dimension_col in DimensionColumn: + if (dimension_col.casefold() == 'grade') | (dimension_col.casefold() == 'class'): + ColumnsDataType.append({"type": "number", "shouldnotnull": True, "minimum": 1, "maximum": 12}) + elif dimension_col in Validationcol_list: + min = int(str(validation_dict[dimension_col]).split(',')[0]) + max = int(str(validation_dict[dimension_col]).split(',')[1]) + ColumnsDataType.append({"type": "number", "shouldnotnull": True, "minimum": min, "maximum": max}) + else: + ColumnsDataType.append({"type": DimensionDict[dimension_col].strip(), "shouldnotnull": True}) + InputKeys.update({"DimensionName": json.dumps(DimensionName), + "DimensionObject": json.dumps(dict(zip(DimensionColumn, ColumnsDataType))), + "DimensionList": json.dumps(DimensionColumn), + "TargetTable":json.dumps(dict(zip(TargetTable, [{"type": "string","shouldnotnull": True}])))}) + return KeysMaping(Program, InputKeys, Template, 'dimension_' + DimensionName, Response) + + +def DatasetSpec(request, Response): + DatasetKeys = request.json['key_file'] + Program = request.json['program'] + ValidationKeys = request.json['validation_keys'] + Path = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + ValidationKeys + ########## Reading additional validation csv file ########### + df_validation = pd.read_csv(Path) + if len(df_validation) == 0: + return Response(json.dumps({"Message": ValidationKeys + " is empty"})) + Validation_items = df_validation.values.tolist() + Validationcol_list = [] + validation_list = [] + for item in Validation_items: + Validationcol_list.append(item[0]) + validation_list.append(item[1]) + validation_dict = (dict(zip(Validationcol_list, validation_list))) + + ########## Reading Datasetkeys csv file ################# + DatasetPath = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + DatasetKeys + df_dataset = pd.read_csv(DatasetPath) + if len(df_dataset) == 0: + return Response(json.dumps({"Message": DatasetKeys + " is empty"})) + df_dataset = df_dataset.loc[df_dataset['program'] == Program] + D_keys = df_dataset.keys().tolist() + Dataset_items = df_dataset.values.tolist() + try: + for value in Dataset_items: + dataset = (dict(zip(D_keys, value))) + DatasetName = dataset['dataset_name'] + Template = dataset['template'] + if dataset['template'] in ['CubeToCube', 'CubeToCubeIncrement', 'CubeToCubePer', 'CubeToCubePerIncrement', + 'E&CToCubePer', 'E&CToCubePerIncrement']: + Template = 'CubeToCube' + elif dataset['template'] in ['EventToCube', 'EventToCubeIncrement', 'EventToCubePer', + 'EventToCubePerIncrement']: + Template = 'EventToCube' + elif dataset['template'] in ['CubeToCubePerFilter', 'CubeToCubePerFilterIncrement', 'CubeToCubeFilter', + 'CubeToCubeFilterIncrement']: + Template = 'CubeToCubeFilter' + else: + return Response(json.dumps({"Message": "Template name is not correct", "Template": Template, "Dataset": DatasetName})) + DimensionCol = [x.strip() for x in dataset['dimension_col'].split(',')] + DimensionTable = [x.strip() for x in dataset['dimension_table'].split(',')] + MergeOnCol = [x.strip() for x in dataset['merge_on_col'].split(',')] + DatasetColumn = [x.strip() for x in dataset['dataset_col'].split(',')] + DataTypes = [x.strip() for x in dataset['dataset_datatype'].split(',')] + DatasetDict = dict(zip(DatasetColumn, DataTypes)) + GroupByCol = [x.strip() for x in dataset['group_by_col'].split(',')] + AggFunction = [x.strip() for x in dataset['agg_function'].split(',')] + TargetTable = [x.strip() for x in dataset['target_table'].split(',')] + UpdateCol = [x.strip() for x in dataset['update_col'].split(',')] + AggCol = [x.strip() for x in dataset['agg_col'].split(',')] + AggColTable = [x.strip() for x in dataset['agg_col_table'].split(',')] + FilterCol = [x.strip() for x in str(dataset['filter_col']).split(',')] + FilterType = [x.strip() for x in str(dataset['filter_type']).strip('{}').split(',')] + Filter = [x.strip() for x in str(dataset['filter']).split(',')] + Numerator=[x.strip() for x in str(dataset['numerator']).split(',')] + Denominator=[x.strip() for x in str(dataset['denominator']).split(',')] + ColumnsDataType = [] + for datasetcol in DatasetColumn: + if datasetcol.casefold() == 'date': + ColumnsDataType.append({"type": "string", "shouldnotnull": True, "format": "date"}) + elif (datasetcol.casefold() == 'grade') | (datasetcol.casefold() == 'class'): + ColumnsDataType.append({"type": "number", "shouldnotnull": True, "minimum": 1, "maximum": 12}) + elif datasetcol in Validationcol_list: + min = int(str(validation_dict[datasetcol]).split(',')[0]) + max =int(str(validation_dict[datasetcol]).split(',')[1]) + ColumnsDataType.append({"type": "number", "shouldnotnull": True,"minimum":min,"maximum":max}) + else: + ColumnsDataType.append({"type": DatasetDict[datasetcol].strip(), "shouldnotnull": True}) + InputKeys.update( + {"DatasetName": DatasetName, "DimensionTable": json.dumps(dict(zip(DimensionTable, [{"type": "string"}]))), + "DimensionCol": json.dumps(dict(zip(DimensionCol, ([{"type": "string"}]) * len(DimensionCol)))), + "MergeOnCol": json.dumps(dict(zip(MergeOnCol, [{"type": "string"}]))), + "DatasetObject": json.dumps(dict(zip(DatasetColumn, ColumnsDataType))), + "DatasetList": json.dumps(DatasetColumn), "GroupByList": json.dumps(GroupByCol), + "GroupByObject": json.dumps(dict(zip(GroupByCol, ([{"type": "string"}]) * len(GroupByCol)))), + "AggFunction": json.dumps(dict(zip(AggFunction, ([{"type": "string"}]) * len(AggFunction)))), + "AggCol": json.dumps(dict(zip(AggCol, ([{"type": "string"}]) * len(AggCol)))), + "TargetTable": json.dumps(dict(zip(TargetTable, [{"type": "string"}]))), + "UpdateCol": json.dumps(dict(zip(UpdateCol, ([{"type": "number"}]) * len(UpdateCol)))), + "NumeratorCol":json.dumps(dict(zip(Numerator,([{"type":"number"}])))), + "DenominatorCol":json.dumps(dict(zip(Denominator,([{"type":"number"}]))))}) + if Template == "EventToCube": + InputKeys.update(InputKeys) + elif Template == "CubeToCube": + InputKeys.update({"AggColTable": json.dumps(dict(zip(AggColTable, [{"type": "string"}])))}) + elif Template == "CubeToCubeFilter": + InputKeys.update({"AggColTable": json.dumps(dict(zip(AggColTable, [{"type": "string"}]))), + "FilterCol": json.dumps(dict(zip(FilterCol, [{"type": "string"}]))), + "FilterType": json.dumps(dict(zip(FilterType, [{"type": "string"}]))), + "Filter": json.dumps(dict(zip(Filter, [{"type": "string"}])))}) + else: + print("ERROR: Template name is not correct") + return Response(json.dumps({"Message": "Template name is not correct", "Template": Template})) + KeysMaping(Program, InputKeys, Template, DatasetName, Response) + except Exception as error: + print(error) + return KeysMaping(Program, InputKeys, Template, DatasetName, Response) \ No newline at end of file diff --git a/spec_generator/template/CubeToCube.json b/spec_generator/template/CubeToCube.json new file mode 100644 index 0000000..600b425 --- /dev/null +++ b/spec_generator/template/CubeToCube.json @@ -0,0 +1,116 @@ +{ + "ingestion_type": "dataset", + "dataset_name": "", + "input": { + "type": "object", + "properties": { + "dataset_name": { + "type": "string" + }, + "dimensions": { + "type": "object", + "properties": { + "table": { + "type": "object", + "properties": + }, + "column": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "merge_on_col": { + "type": "object", + "properties": + } + } + }, + "dataset": { + "type": "object", + "properties": { + "items": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + }, + "group_by": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + }, + "aggregate": { + "type": "object", + "properties": { + "function": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "target_table": { + "type": "object", + "properties": + }, + "numerator_col":{ + "type": "object", + "properties": + }, + "denominator_col":{ + "type": "object", + "properties": + }, + "update_cols": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "columns": { + "type": "array", + "items": { + "type": "object", + "properties": { + "table": { + "type": "object", + "properties": + }, + "column": { + "type": "array", + "items": { + "type": "object", + "properties": + } + } + } + } + } + }, + "required": [ + "function", + "target_table", + "update_col", + "columns" + ] + } + }, + "required": [ + "items" + ] + } + }, + "required": [ + "dataset_name", + "dataset" + ] + } +} diff --git a/spec_generator/template/CubeToCubeFilter.json b/spec_generator/template/CubeToCubeFilter.json new file mode 100644 index 0000000..f1acd6f --- /dev/null +++ b/spec_generator/template/CubeToCubeFilter.json @@ -0,0 +1,137 @@ +{ + "ingestion_type": "dataset", + "dataset_name": "", + "input": { + "type": "object", + "properties": { + "dataset_name": { + "type": "string" + }, + "dimensions": { + "type": "object", + "properties": { + "table": { + "type": "object", + "properties": + }, + "column": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "merge_on_col": { + "type": "object", + "properties": + } + } + }, + "dataset": { + "type": "object", + "properties": { + "items": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + }, + "group_by": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + }, + "aggregate": { + "type": "object", + "properties": { + "function": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "target_table": { + "type": "object", + "properties": + },"numerator_col":{ + "type": "object", + "properties": + }, + "denominator_col":{ + "type": "object", + "properties": + }, + "update_cols": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "columns": { + "type": "array", + "items": { + "type": "object", + "properties": { + "table": { + "type": "object", + "properties": + }, + "column": { + "type": "array", + "items": { + "type": "object", + "properties": + } + } + } + } + }, + "filters": { + "type": "array", + "items": { + "type": "object", + "properties": { + "column": { + "type": "object", + "properties": + }, + "filter_type": { + "type": "object", + "properties": + }, + "filter": { + "type": "object", + "properties": + } + } + } + } + }, + "required": [ + "function", + "target_table", + "update_col", + "columns", + "filters" + ] + } + }, + "required": [ + "items" + ] + } + }, + "required": [ + "dataset_name", + "dataset" + ] + } +} + diff --git a/spec_generator/template/Dataset.json b/spec_generator/template/Dataset.json new file mode 100644 index 0000000..598e1e0 --- /dev/null +++ b/spec_generator/template/Dataset.json @@ -0,0 +1,60 @@ +{ + "ingestion_type": "dataset", + "dataset_name": "school_details", + "input": { + "type": "object", + "properties": { + "dataset_name": { + "type": "string" + }, + "dataset": { + "type": "object", + "properties": { + "items": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + }, + "aggregate": { + "type": "object", + "properties": { + "target_table": { + "type": "object", + "properties": + } + }, + "numerator_col":{ + "type": "object", + "properties": + }, + "denominator_col":{ + "type": "object", + "properties": + }, + "update_cols": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "required": [ + "target_table", + "update_col" + ] + } + }, + "required": [ + "items" + ] + } + }, + "required": [ + "dataset_name", + "dataset" + ] + } +} diff --git a/spec_generator/template/Dimension.json b/spec_generator/template/Dimension.json new file mode 100644 index 0000000..c0bb36e --- /dev/null +++ b/spec_generator/template/Dimension.json @@ -0,0 +1,29 @@ +{ + "ingestion_type": "dimension", + "dimension_name": , + "input": { + "type": "object", + "properties": { + "dimension_name": { + "type": "string" + }, + "dimension": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + }, + "target_table": + { + "type": "object", + "properties": + } + }, + "required": [ + "dimension_name", + "dimension" + ] + } +} diff --git a/spec_generator/template/Event.json b/spec_generator/template/Event.json new file mode 100644 index 0000000..bfde64d --- /dev/null +++ b/spec_generator/template/Event.json @@ -0,0 +1,24 @@ +{ + "ingestion_type": "event", + "event_name": , + "input": { + "type": "object", + "properties": { + "event_name": { + "type": "string" + }, + "event": { + "type": "array", + "items": { + "type": "object", + "properties": , + "required": + } + } + }, + "required": [ + "event_name", + "event" + ] + } +} \ No newline at end of file diff --git a/spec_generator/template/EventToCube.json b/spec_generator/template/EventToCube.json new file mode 100644 index 0000000..5c5b828 --- /dev/null +++ b/spec_generator/template/EventToCube.json @@ -0,0 +1,111 @@ +{ + "ingestion_type": "dataset", + "dataset_name": "", + "input": { + "type": "object", + "properties": { + "dataset_name": { + "type": "string" + }, + "dimensions": { + "type": "object", + "properties": { + "table": { + "type": "object", + "properties": + }, + "column": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "merge_on_col": { + "type": "object", + "properties": + } + } + }, + "dataset": { + "type": "object", + "properties": { + "items": { + "type": "array", + "items": { + "type": "object", + "properties":, + "required": + } + }, + "group_by": { + "type": "array", + "items": { + "type": "object", + "properties":, + "required": + } + }, + "aggregate": { + "type": "object", + "properties": { + "function": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "target_table": { + "type": "object", + "properties": + },"numerator_col":{ + "type": "object", + "properties": + }, + "denominator_col":{ + "type": "object", + "properties": + }, + "update_cols": { + "type": "array", + "items": { + "type": "object", + "properties": + } + }, + "columns": { + "type": "array", + "items": { + "type": "object", + "properties": { + "column": { + "type": "array", + "items": { + "type": "object", + "properties": + } + } + } + } + } + }, + "required": [ + "function", + "targetTable", + "update_cols", + "columns" + ] + } + }, + "required": [ + "items" + ] + } + }, + "required": [ + "dataset_name", + "dataset" + ] + } +} \ No newline at end of file diff --git a/transformer_generator/key_files/transformer_dataset_mapping.csv b/transformer_generator/key_files/transformer_dataset_mapping.csv new file mode 100644 index 0000000..2f31464 --- /dev/null +++ b/transformer_generator/key_files/transformer_dataset_mapping.csv @@ -0,0 +1,92 @@ +template,program,event_name,dataset_name +EventToCubePerIncrement,SAC,students_attendance,students_attendance_compliance_by_school +CubeToCubePerFilterIncrement,SAC,students_attendance,students_attendance_compliance_by_cluster +CubeToCubePerIncrement,SAC,students_attendance,students_attendance_compliance_by_block +CubeToCubePerIncrement,SAC,students_attendance,students_attendance_compliance_by_district +CubeToCubePerIncrement,SAC,students_attendance,students_attendance_compliance_by_state +EventToCubePerIncrement,SAC,students_attendance,students_average_attendance_by_grade +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_by_school +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_by_cluster +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_by_block +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_by_district +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_by_state +EventToCubePerIncrement,SAC,students_attendance,students_average_attendance_gender_wise_by_grade +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_gender_wise_by_school +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_gender_wise_by_cluster +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_gender_wise_by_block +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_gender_wise_by_district +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_gender_wise_by_state +EventToCubePerIncrement,SAC,students_attendance,students_average_attendance_category_wise_by_school +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_category_wise_by_cluster +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_category_wise_by_block +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_category_wise_by_district +CubeToCubePerIncrement,SAC,students_attendance,students_average_attendance_category_wise_by_state +EventToCubePerIncrement,SAC,teachers_attendance,teachers_attendance_compliance_by_school +CubeToCubePerFilterIncrement,SAC,teachers_attendance,teachers_attendance_compliance_by_cluster +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_attendance_compliance_by_block +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_attendance_compliance_by_district +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_attendance_compliance_by_state +EventToCubePerIncrement,SAC,teachers_attendance,teachers_average_attendance_by_school +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_average_attendance_by_cluster +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_average_attendance_by_block +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_average_attendance_by_district +CubeToCubePerIncrement,SAC,teachers_attendance,teachers_average_attendance_by_state +EventToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_schools_by_cluster +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_schools_by_block +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_schools_by_district +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_schools_by_state +E&CToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_category_wise_schools_by_cluster +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_category_wise_schools_by_block +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_category_wise_schools_by_district +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_category_wise_schools_by_state +EventToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_enrollment_by_grade +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_enrollment_by_school +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_enrollment_by_cluster +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_enrollment_by_block +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_enrollment_by_district +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_enrollment_by_state +EventToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_gender_wise_enrollment_by_grade +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_gender_wise_enrollment_by_school +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_gender_wise_enrollment_by_cluster +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_gender_wise_enrollment_by_block +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_gender_wise_enrollment_by_district +CubeToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_gender_wise_enrollment_by_state +E&CToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_cwsn_enrollment_by_grade +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_cwsn_enrollment_by_school +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_cwsn_enrollment_by_cluster +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_cwsn_enrollment_by_block +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_cwsn_enrollment_by_district +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_cwsn_enrollment_by_state +E&CToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_grade_wise_enrollment_by_school +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_grade_wise_enrollment_by_cluster +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_grade_wise_enrollment_by_block +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_grade_wise_enrollment_by_district +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_grade_wise_enrollment_by_state +E&CToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_students_category_wise_enrollment_by_grade +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_students_category_wise_enrollment_by_school +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_students_category_wise_enrollment_by_cluster +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_students_category_wise_enrollment_by_block +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_students_category_wise_enrollment_by_district +CubeToCubePerIncrement,School_Statistics,school_statistics_students,school_statistics_students_category_wise_enrollment_by_state +EventToCubeIncrement,School_Statistics,school_statistics_students,school_statistics_total_teachers_by_school +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_total_teachers_by_cluster +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_total_teachers_by_block +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_total_teachers_by_district +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_total_teachers_by_state +E&CToCubePerIncrement,School_Statistics,school_statistics_teachers,school_statistics_grade_wise_teachers_by_school +CubeToCubePerIncrement,School_Statistics,school_statistics_teachers,school_statistics_grade_wise_teachers_by_cluster +CubeToCubePerIncrement,School_Statistics,school_statistics_teachers,school_statistics_grade_wise_teachers_by_block +CubeToCubePerIncrement,School_Statistics,school_statistics_teachers,school_statistics_grade_wise_teachers_by_district +CubeToCubePerIncrement,School_Statistics,school_statistics_teachers,school_statistics_grade_wise_teachers_by_state +EventToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_average_pupil_teacher_ratio_by_grade +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_average_pupil_teacher_ratio_by_school +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_average_pupil_teacher_ratio_by_cluster +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_average_pupil_teacher_ratio_by_block +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_average_pupil_teacher_ratio_by_district +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_average_pupil_teacher_ratio_by_state +EventToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_category_wise_average_pupil_teacher_ratio_by_grade +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_category_wise_average_pupil_teacher_ratio_by_school +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_category_wise_average_pupil_teacher_ratio_by_cluster +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_category_wise_average_pupil_teacher_ratio_by_block +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_category_wise_average_pupil_teacher_ratio_by_district +CubeToCubeIncrement,School_Statistics,school_statistics_teachers,school_statistics_category_wise_average_pupil_teacher_ratio_by_state diff --git a/transformer_generator/key_files/transformer_dimension_mapping.csv b/transformer_generator/key_files/transformer_dimension_mapping.csv new file mode 100644 index 0000000..f2643a1 --- /dev/null +++ b/transformer_generator/key_files/transformer_dimension_mapping.csv @@ -0,0 +1,2 @@ +template,dimension_name +Dataset,school_details \ No newline at end of file diff --git a/transformer_generator/templates/CubeToCube.py b/transformer_generator/templates/CubeToCube.py new file mode 100644 index 0000000..de3b145 --- /dev/null +++ b/transformer_generator/templates/CubeToCube.py @@ -0,0 +1,44 @@ +import configparser +import os +from sqlalchemy import create_engine +import pandas as pd +from urllib.parse import quote + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + df_dataset = pd.read_sql('select * from {Table};',con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_dataset.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + try: + for index,row in df_snap.iterrows(): + print(row) + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {ReplaceFormat};'''\ + .format(','.join(map(str,values))) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + diff --git a/transformer_generator/templates/CubeToCubeIncrement.py b/transformer_generator/templates/CubeToCubeIncrement.py new file mode 100644 index 0000000..99781ff --- /dev/null +++ b/transformer_generator/templates/CubeToCubeIncrement.py @@ -0,0 +1,45 @@ +import configparser +import os +from sqlalchemy import create_engine +import pandas as pd +from urllib.parse import quote + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + df_dataset = pd.read_sql('select * from {Table};',con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_dataset.merge(df_dimension, on={MergeOnCol}, how='inner') + + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable} As main_table({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {IncrementFormat};'''\ + .format(','.join(map(str,values)),{UpdateCol}) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + diff --git a/transformer_generator/templates/CubeToCubePer.py b/transformer_generator/templates/CubeToCubePer.py new file mode 100644 index 0000000..831ada8 --- /dev/null +++ b/transformer_generator/templates/CubeToCubePer.py @@ -0,0 +1,46 @@ +import configparser +import os +from sqlalchemy import create_engine +import pandas as pd +from urllib.parse import quote + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + df_dataset = pd.read_sql('select * from {Table};',con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_dataset.merge(df_dimension, on={MergeOnCol}, how='inner') + + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + df_agg['percentage'] = ((df_agg['{NumeratorCol}'] / df_agg['{DenominatorCol}']) * 100) ### Calculating Percentage + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {ReplaceFormat};'''\ + .format(','.join(map(str,values))) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + diff --git a/transformer_generator/templates/CubeToCubePerFilter.py b/transformer_generator/templates/CubeToCubePerFilter.py new file mode 100644 index 0000000..71a60be --- /dev/null +++ b/transformer_generator/templates/CubeToCubePerFilter.py @@ -0,0 +1,55 @@ +import os +import pandas as pd +import configparser +from urllib.parse import quote +from sqlalchemy import create_engine + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def filterTransformer(valueCols={ValueCols}): + df_dataset = pd.read_sql('select * from {Table}', con=con) ### reading dataset from database + {DatasetCasting} + df_dimension=pd.read_sql('select {DimensionCols} from {DimensionTable}',con=con) ### reading DimensionDataset from Database + df_dimension_merge = df_dataset.merge(df_dimension, on={MergeOnCol}, how='inner') ### mapping dataset with dimension + + df_total = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) ### aggregation before filter + + df_filter = df_dimension_merge.loc[df_dataset{FilterCol}{FilterType}{Filter}] ### applying filter + df_filter= df_filter.groupby({GroupBy}, as_index=False).agg({AggCols}) ### aggregation after filter + df_merge = df_filter.merge(df_total, on={GroupBy}, how='inner') ### merging aggregated DataFrames + df_merge_col_list = df_merge.columns.to_list() + numerator=df_merge_col_list[-2] + denominator = df_merge_col_list[-1] + df_merge['percentage'] = ((df_merge[numerator] / df_merge[denominator]) * 100) ### Calculating Percentage + + col_list = df_merge.columns.to_list() + df_snap = df_merge[col_list] + df_snap.columns = valueCols + try: + for index, row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {ReplaceFormat};'''.format(','.join(map(str, values))) + cur.execute(query) + except Exception as error: + print(error) + +filterTransformer() + + + + diff --git a/transformer_generator/templates/CubeToCubePerFilterIncrement.py b/transformer_generator/templates/CubeToCubePerFilterIncrement.py new file mode 100644 index 0000000..86e0fe7 --- /dev/null +++ b/transformer_generator/templates/CubeToCubePerFilterIncrement.py @@ -0,0 +1,56 @@ +import os +import pandas as pd +import configparser +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def filterTransformer(valueCols={ValueCols}): + df_dataset = pd.read_sql('select * from {Table}', con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}',con=con) ### reading DimensionDataset from Database + df_dimension_merge = df_dataset.merge(df_dimension, on={MergeOnCol},how='inner') ### mapping dataset with dimension + + df_total = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) ### aggregation before filter + + df_filter = df_dimension_merge.loc[df_dataset{FilterCol}{FilterType}{Filter}] ### applying filter + df_filter = df_filter.groupby({GroupBy}, as_index=False).agg({AggCols}) ### aggregation after filter + df_merge = df_filter.merge(df_total, on={GroupBy}, how='inner') ### merging aggregated DataFrames + df_merge_col_list=df_merge.columns.to_list() + numerator = df_merge_col_list[-2] + denominator = df_merge_col_list[-1] + df_merge['percentage'] = ((df_merge[numerator] / df_merge[denominator]) * 100) ### Calculating Percentage + col_list = df_merge.columns.to_list() + df_snap = df_merge[col_list] + df_snap.columns = valueCols + df_snap.columns = valueCols + try: + for index, row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable} As main_table({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {IncrementFormat},percentage=(({QueryNumerator})/({QueryDenominator}))*100;'''.format(','.join(map(str, values)),{UpdateCols}) + cur.execute(query) + except Exception as error: + print(error) + +filterTransformer() + + + + diff --git a/transformer_generator/templates/CubeToCubePerIncrement.py b/transformer_generator/templates/CubeToCubePerIncrement.py new file mode 100644 index 0000000..6697e13 --- /dev/null +++ b/transformer_generator/templates/CubeToCubePerIncrement.py @@ -0,0 +1,46 @@ +import configparser +import os +from sqlalchemy import create_engine +import pandas as pd +from urllib.parse import quote + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + df_dataset = pd.read_sql('select * from {Table};',con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_dataset.merge(df_dimension, on={MergeOnCol}, how='inner') + + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + df_agg['percentage'] = ((df_agg['{NumeratorCol}'] / df_agg['{DenominatorCol}']) * 100) ### Calculating Percentage + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable} As main_table({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {IncrementFormat},percentage=(({QueryNumerator})/({QueryDenominator}))*100;'''\ + .format(','.join(map(str,values)),{UpdateCols}) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + diff --git a/transformer_generator/templates/Dataset.py b/transformer_generator/templates/Dataset.py new file mode 100644 index 0000000..ad9b86a --- /dev/null +++ b/transformer_generator/templates/Dataset.py @@ -0,0 +1,57 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def Datainsert(valueCols={ValueCols}): + df_dataset=pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {DatasetCasting} + col_list = df_dataset.columns + df_snap = df_dataset[col_list] + try: + for index,row in df_snap.iterrows(): + values = [] + for i in col_list: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values});'''\ + .format(','.join(map(str,values))) + cur.execute(query) + except Exception as error: + print(error) + +Datainsert() + + + + + + + + + + + + + + + + + diff --git a/transformer_generator/templates/E&CToCubePer.py b/transformer_generator/templates/E&CToCubePer.py new file mode 100644 index 0000000..7796c4c --- /dev/null +++ b/transformer_generator/templates/E&CToCubePer.py @@ -0,0 +1,60 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] +# aws_key = config['CREDs']['AWS_ACCESS_KEY'] +# aws_secret=config['CREDs']['AWS_SECRET_ACCESS_KEY'] +# bucket_name=config['CREDs']['AWS_BUCKET_NAME'] +# object_key=config['CREDs']['AWS_OBJ_KEY'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + # path = 's3://{AWSKey}:{AWSSecretKey}@{BucketName}/{ObjKey}'.format(aws_key, aws_secret, bucket_name, object_key) + # df_events = pd.read_csv(smart_open(path)) + df_events = pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {EventCasting} + df_dataset=pd.read_sql('select * from {Table};',con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_events.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + merge_on_col_list=[] + for i,j in (df_agg.columns.to_list(),df_dataset.columns.to_list()): + if(i==j): + merge_on_col_list.append(j) + df_merge=df_agg.merge(df_dataset,on=merge_on_col_list,how='inner') + df_merge_col_list=df_merge.columns.to_list() + numerator=df_merge_col_list[-2] + denominator=df_merge_col_list[-1] + df_merge['percentage'] = ((df_merge[numerator] / df_merge[denominator]) * 100) ### Calculating Percentage + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {ReplaceFormat};'''\ + .format(','.join(map(str,values))) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() diff --git a/transformer_generator/templates/E&CToCubePerIncrement.py b/transformer_generator/templates/E&CToCubePerIncrement.py new file mode 100644 index 0000000..dc02495 --- /dev/null +++ b/transformer_generator/templates/E&CToCubePerIncrement.py @@ -0,0 +1,60 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] +# aws_key = config['CREDs']['AWS_ACCESS_KEY'] +# aws_secret=config['CREDs']['AWS_SECRET_ACCESS_KEY'] +# bucket_name=config['CREDs']['AWS_BUCKET_NAME'] +# object_key=config['CREDs']['AWS_OBJ_KEY'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + # path = 's3://{AWSKey}:{AWSSecretKey}@{BucketName}/{ObjKey}'.format(aws_key, aws_secret, bucket_name, object_key) + # df_events = pd.read_csv(smart_open(path)) + df_events = pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {EventCasting} + df_dataset=pd.read_sql('select * from {Table};',con=con) + {DatasetCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_events.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + merge_on_col_list=[] + for i,j in (df_agg.columns.to_list(),df_dataset.columns.to_list()): + if(i==j): + merge_on_col_list.append(j) + df_merge=df_agg.merge(df_dataset,on=merge_on_col_list,how='inner') + df_merge_col_list=df_merge.columns.to_list() + df_merge_col_list = df_merge.columns.to_list() + numerator = df_merge_col_list[-2] + denominator = df_merge_col_list[-1] + df_merge['percentage'] = ((df_merge[numerator] / df_merge[denominator]) * 100) ### Calculating Percentage + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index, row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable} As main_table({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {IncrementFormat},percentage=(({QueryNumerator})/({QueryDenominator}))*100;'''.format(','.join(map(str, values)),{UpdateCols}) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() diff --git a/transformer_generator/templates/EventToCube.py b/transformer_generator/templates/EventToCube.py new file mode 100644 index 0000000..80fa5bb --- /dev/null +++ b/transformer_generator/templates/EventToCube.py @@ -0,0 +1,66 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] +# aws_key = config['CREDs']['AWS_ACCESS_KEY'] +# aws_secret=config['CREDs']['AWS_SECRET_ACCESS_KEY'] +# bucket_name=config['CREDs']['AWS_BUCKET_NAME'] +# object_key=config['CREDs']['AWS_OBJ_KEY'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + # path = 's3://{AWSKey}:{AWSSecretKey}@{BucketName}/{ObjKey}'.format(aws_key, aws_secret, bucket_name, object_key) + # df_events = pd.read_csv(smart_open(path)) + df_events = pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {EventCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_events.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {ReplaceFormat};'''\ + .format(','.join(map(str,values))) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + + + + + + + + + + + + + + + + diff --git a/transformer_generator/templates/EventToCubeIncrement.py b/transformer_generator/templates/EventToCubeIncrement.py new file mode 100644 index 0000000..98013e6 --- /dev/null +++ b/transformer_generator/templates/EventToCubeIncrement.py @@ -0,0 +1,66 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] +# aws_key = config['CREDs']['AWS_ACCESS_KEY'] +# aws_secret=config['CREDs']['AWS_SECRET_ACCESS_KEY'] +# bucket_name=config['CREDs']['AWS_BUCKET_NAME'] +# object_key=config['CREDs']['AWS_OBJ_KEY'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + # path = 's3://{AWSKey}:{AWSSecretKey}@{BucketName}/{ObjKey}'.format(aws_key, aws_secret, bucket_name, object_key) + # df_events = pd.read_csv(smart_open(path)) + df_events = pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {EventCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_events.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable} As main_table({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {IncrementFormat};'''\ + .format(','.join(map(str,values)),{UpdateCol}) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + + + + + + + + + + + + + + + + diff --git a/transformer_generator/templates/EventToCubePer.py b/transformer_generator/templates/EventToCubePer.py new file mode 100644 index 0000000..04edaf6 --- /dev/null +++ b/transformer_generator/templates/EventToCubePer.py @@ -0,0 +1,67 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] +# aws_key = config['CREDs']['AWS_ACCESS_KEY'] +# aws_secret=config['CREDs']['AWS_SECRET_ACCESS_KEY'] +# bucket_name=config['CREDs']['AWS_BUCKET_NAME'] +# object_key=config['CREDs']['AWS_OBJ_KEY'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + # path = 's3://{AWSKey}:{AWSSecretKey}@{BucketName}/{ObjKey}'.format(aws_key, aws_secret, bucket_name, object_key) + # df_events = pd.read_csv(smart_open(path)) + df_events = pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {EventCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_events.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + df_agg['percentage'] = ((df_agg['{NumeratorCol}'] / df_agg['{DenominatorCol}']) * 100) ### Calculating Percentage + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable}({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {ReplaceFormat};'''\ + .format(','.join(map(str,values))) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() + + + + + + + + + + + + + + + + + diff --git a/transformer_generator/templates/EventToCubePerIncrement.py b/transformer_generator/templates/EventToCubePerIncrement.py new file mode 100644 index 0000000..308d07d --- /dev/null +++ b/transformer_generator/templates/EventToCubePerIncrement.py @@ -0,0 +1,50 @@ +import os +import configparser +import pandas as pd +from urllib.parse import quote +from sqlalchemy import create_engine + + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] +# aws_key = config['CREDs']['AWS_ACCESS_KEY'] +# aws_secret=config['CREDs']['AWS_SECRET_ACCESS_KEY'] +# bucket_name=config['CREDs']['AWS_BUCKET_NAME'] +# object_key=config['CREDs']['AWS_OBJ_KEY'] + +engine='postgresql://'+user+':%s@'+host+':'+port+'/'+database +con=create_engine(engine %quote(password)) +cur = con.connect() + +def aggTransformer(valueCols={ValueCols}): + # path = 's3://{AWSKey}:{AWSSecretKey}@{BucketName}/{ObjKey}'.format(aws_key, aws_secret, bucket_name, object_key) + # df_events = pd.read_csv(smart_open(path)) + df_events = pd.read_csv(os.path.dirname(os.path.abspath(__file__)) + "/events/" + {KeyFile}) + {EventCasting} + df_dimension = pd.read_sql('select {DimensionCols} from {DimensionTable}', con=con) + df_dimension_merge = df_events.merge(df_dimension, on={MergeOnCol}, how='inner') + df_agg = df_dimension_merge.groupby({GroupBy}, as_index=False).agg({AggCols}) + df_agg['percentage'] = ((df_agg['{NumeratorCol}'] / df_agg['{DenominatorCol}']) * 100) ### Calculating Percentage + col_list = df_agg.columns.to_list() + df_snap = df_agg[col_list] + df_snap.columns = valueCols + try: + for index,row in df_snap.iterrows(): + values = [] + for i in valueCols: + values.append(row[i]) + query = ''' INSERT INTO {TargetTable} As main_table({InputCols}) VALUES ({Values}) ON CONFLICT ({ConflictCols}) DO UPDATE SET {IncrementFormat},percentage=(({QueryNumerator})/({QueryDenominator}))*100;'''\ + .format(','.join(map(str,values)),{UpdateCols}) + cur.execute(query) + except Exception as error: + print(error) + +aggTransformer() diff --git a/transformer_generator/transformer_generator.py b/transformer_generator/transformer_generator.py new file mode 100644 index 0000000..6b7b8f3 --- /dev/null +++ b/transformer_generator/transformer_generator.py @@ -0,0 +1,21 @@ +import json +from flask import Flask, request, Response +from transformer_keys_mapping import collect_keys, dimension_data_insert + +app = Flask(__name__) + + +@app.route('/generator', methods=['POST']) +def TransformerGenerator(): + try: + if request.json['operation'] == 'dataset': + return collect_keys(request, Response) + elif request.json['operation'] == 'dimension': + return dimension_data_insert(request, Response) + except Exception as error: + print(error) + return Response(json.dumps({"Message": "Transformer not created"})) + + +if __name__ == "__main__": + app.run(debug=True, host='0.0.0.0', port=3003) diff --git a/transformer_generator/transformer_keys_mapping.py b/transformer_generator/transformer_keys_mapping.py new file mode 100644 index 0000000..a2af8ad --- /dev/null +++ b/transformer_generator/transformer_keys_mapping.py @@ -0,0 +1,251 @@ +import configparser +import json +import os +import re +import pandas as pd +import psycopg2 as pg + +configuartion_path = os.path.dirname(os.path.abspath(__file__)) + "/transformers/config.ini" +print(configuartion_path) +config = configparser.ConfigParser() +config.read(configuartion_path); + +port = config['CREDs']['port'] +host = config['CREDs']['host'] +user = config['CREDs']['user'] +password = config['CREDs']['password'] +database = config['CREDs']['database'] + +CeatedTransformersList = [] + + +def KeysMapping(InputKeys, Template, Transformer, Response): + if os.path.exists(os.path.dirname(os.path.abspath(__file__)) + '/transformers/' + Transformer): + os.remove(os.path.dirname(os.path.abspath(__file__)) + '/transformers/' + Transformer) + with open(os.path.dirname(os.path.abspath(__file__)) + '/templates/' + Template, 'r') as fs: + valueOfTemplate = fs.readlines() + if len(InputKeys) != 0: + for valueOfTemplate in valueOfTemplate: + ToreplaceString = valueOfTemplate + templateKeys = re.findall("(?<={)(.*?)(?=})", ToreplaceString) + for key in templateKeys: + replaceStr = '{' + key + '}' + ToreplaceString = ToreplaceString.replace(replaceStr, str(InputKeys[key])) + with open(os.path.dirname(os.path.abspath(__file__)) + '/transformers/' + Transformer, 'a') as fs: + fs.write(ToreplaceString) + CeatedTransformersList.append({"filename": Transformer}) + return Response(json.dumps({"Message": "Transformer created successfully", "TransformerFiles": CeatedTransformersList, "code": 200})) + else: + print('ERROR : InputKey is Empty') + return Response(json.dumps({"Message": "InputKey is empty"})) + +InputKeys = {} +def dimension_data_insert(request, Response): + Dimension = request.json['ingestion_name'] + KeyFile = request.json['key_file'] + Path = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + KeyFile + df = pd.read_csv(Path) + if len(df) == 0: + return Response(json.dumps({"Message": KeyFile + " is empty"})) + df = df.loc[df['dimension_name'] == Dimension] + Dimensionkeys = df.keys().tolist() + DimensionValues = df.values.tolist() + for value in DimensionValues: + TemplateDatasetMapping = (dict(zip(Dimensionkeys, value))) + DimensionName = TemplateDatasetMapping['dimension_name'] + Transformer = DimensionName + '.py' + TranformerType = TemplateDatasetMapping['template'] + Template = TranformerType + '.py' + con = pg.connect(database=database, user=user, password=password, host=host, port=port) + cur = con.cursor() + DatasetQueryString = '''SELECT dimension_data FROM spec.dimension WHERE dimension_name='{}';'''.format( + DimensionName) + cur.execute(DatasetQueryString) + con.commit() + if cur.rowcount == 1: + for records in cur.fetchall(): + for record in list(records): + DimensionObject = list(record['input']['properties']['dimension']['items']['properties'].keys()) + DimensionArray=list(record['input']['properties']['dimension']['items']['required']) + TargetTable = list(record['input']['properties']['target_table']['properties'].keys()) + string_col_list = [] + DatasetCasting = [] + df = pd.json_normalize(record['input']['properties']['dimension']['items']['properties']) + for cols in DimensionObject: + col = cols + '.type' + if (df[col] == "string").item(): + string_col_list.append(cols) + if len(string_col_list) != 0: + DatasetCasting.append('df_dataset.update(df_dataset[' + json.dumps(string_col_list) + '].applymap("\'{}\'".format))') + + if TranformerType == 'Dataset': + InputKeys.update({'ValueCols':DimensionArray, "KeyFile": json.dumps(Dimension + '.csv'), + 'DatasetCasting': ','.join(DatasetCasting), + 'TargetTable':','.join(TargetTable), + 'InputCols': ','.join(DimensionArray), + 'Values': '{}'}) + else: + return Response(json.dumps({"Message": "Transformer type is not correct", "TransformerType": TranformerType, + "Dataset": DimensionName})) + print(Transformer,':transformer:::::::::::') + else: + return Response(json.dumps({"Message": "No dimension found " + Dimension})) + return KeysMapping(InputKeys, Template,Transformer, Response) + + + +def collect_keys(request, Response): + KeyFile = request.json['key_file'] + Program = request.json['program'] + EventName = request.json['ingestion_name'] + Path = os.path.dirname(os.path.abspath(__file__)) + "/key_files/" + KeyFile + ####### Reading Transformer Mapping Key Files ################ + try: + df = pd.read_csv(Path) + if len(df) == 0: + return Response(json.dumps({"Message": KeyFile + " is empty"})) + df = df.loc[df['program'] == Program] + df = df.loc[df['event_name'] == EventName] + Datasetkeys = df.keys().tolist() + DatasetValues = df.values.tolist() + for value in DatasetValues: + TemplateDatasetMapping = (dict(zip(Datasetkeys, value))) + DatasetName = TemplateDatasetMapping['dataset_name'] + Transformer = DatasetName + '.py' + TranformerType = TemplateDatasetMapping['template'] + Template = TranformerType + '.py' + con = pg.connect(database=database, user=user, password=password, host=host, port=port) + cur = con.cursor() + EventQueryString = ''' SELECT event_data FROM spec.event WHERE event_name='{}';'''.format(EventName) + cur.execute(EventQueryString) + con.commit() + if cur.rowcount == 1: + DatasetQueryString = '''SELECT dataset_data FROM spec.dataset WHERE dataset_name='{}';'''.format( + DatasetName) + cur.execute(DatasetQueryString) + con.commit() + if cur.rowcount == 1: + for records in cur.fetchall(): + for record in list(records): + Dataset = record['input']['properties']['dataset']['properties'] + DatasetObject = list(Dataset['items']['items']['properties'].keys()) + DatasetArray=list(Dataset['items']['items']['required']) + Dimensions = record['input']['properties']['dimensions']['properties'] + ReplaceFormat = [] + IncrementFormat = [] + PercentageIncrement = [] + UpdateCols = [] + DatasetCasting = [] + EventCasting = [] + string_col_list = [] + NumeratorCol = \ + list(Dataset['aggregate']['properties']['numerator_col']['properties'].keys())[0] + DenominatorCol = \ + list(Dataset['aggregate']['properties']['denominator_col']['properties'].keys())[0] + fun = list(Dataset['aggregate']['properties']['function']['items']['properties'].keys()) + UpdateColList = [] + df = pd.json_normalize(Dataset['items']['items']['properties']) + for cols in DatasetObject: + col = cols + '.type' + if (df[col] == "string").item(): + string_col_list.append(cols) + if len(string_col_list) != 0: + DatasetCasting.append('df_dataset.update(df_dataset[' + json.dumps( + string_col_list) + '].applymap("\'{}\'".format))') + EventCasting.append('df_events.update(df_events[' + json.dumps( + string_col_list) + '].applymap("\'{}\'".format))') + if not (NumeratorCol.lower()).startswith(('sum_', 'count_')): + UpdateColList.append(fun[0] + '_' + NumeratorCol) + else: + UpdateColList.append(NumeratorCol) + if not (DenominatorCol.lower()).startswith(('sum_', 'count_')): + UpdateColList.append(fun[0] + '_' + DenominatorCol) + UpdateColList.append("percentage") + else: + UpdateColList.append(DenominatorCol) + UpdateColList.append("percentage") + for i in UpdateColList: + if i == 'percentage': + ReplaceFormat.append(i + '=EXCLUDED.' + i) + else: + ReplaceFormat.append(i + '=EXCLUDED.' + i) + UpdateCols.append('row["' + i + '"]') + IncrementFormat.append(i + '=main_table.' + i + '::numeric+{}::numeric') + PercentageIncrement.append('main_table.' + i + '::numeric+{}::numeric') + # col = list(Dataset['aggregate']['properties']['columns']['items']['properties']['column']['items']['properties'].keys()) + agg_col=[NumeratorCol,DenominatorCol] + AggCols = (dict(zip(agg_col, (fun * len(agg_col))))) + InputKeys.update({'AWSKey': '{}', 'AWSSecretKey': '{}', 'BucketName': '{}', 'ObjKey': '{}', + 'Values': '{}', 'EventCasting': ','.join(EventCasting), + 'DatasetCasting': ','.join(DatasetCasting), 'ValueCols': DatasetArray, + 'GroupBy': list(Dataset['group_by']['items']['properties'].keys()), + 'AggCols': AggCols, + 'DimensionTable': list(Dimensions['table']['properties'].keys())[0], + 'DimensionCols': ','.join( + list(Dimensions['column']['items']['properties'].keys())), + 'MergeOnCol': list(Dimensions['merge_on_col']['properties'].keys()), + 'TargetTable': list(Dataset['aggregate']['properties']['target_table'][ + 'properties'].keys())[0], + 'InputCols': ','.join(DatasetArray), + 'ConflictCols': ','.join( + list(Dataset['group_by']['items']['properties'].keys())), + 'IncrementFormat': ','.join(IncrementFormat), + 'ReplaceFormat': ','.join(ReplaceFormat), + 'UpdateCols': ','.join(UpdateCols * 2), + 'UpdateCol': ','.join(UpdateCols), + "KeyFile": json.dumps(EventName + '.csv')}) + print(Template, '::::::::::::Template::::::::::::') + if TranformerType in ['EventToCube', 'EventToCubeIncrement']: + InputKeys.update(InputKeys) + elif TranformerType in ['EventToCubePer', 'EventToCubePerIncrement']: + InputKeys.update({'NumeratorCol': NumeratorCol, 'DenominatorCol': DenominatorCol, + 'QueryDenominator': PercentageIncrement[1], + 'QueryNumerator': PercentageIncrement[0]}) + elif TranformerType in ['CubeToCube', 'CubeToCubeIncrement']: + table = list( + Dataset['aggregate']['properties']['columns']['items']['properties']['table'][ + 'properties'].keys()) + InputKeys.update({'Table': table[0]}) + elif TranformerType in ['CubeToCubePer', 'CubeToCubePerIncrement', 'E&CToCubePerIncrement', + 'E&CToCubePer']: + table = list( + Dataset['aggregate']['properties']['columns']['items']['properties']['table'][ + 'properties'].keys()) + InputKeys.update( + {'Table': table[0], 'NumeratorCol': NumeratorCol, 'DenominatorCol': DenominatorCol, + 'QueryDenominator': PercentageIncrement[1], + 'QueryNumerator': PercentageIncrement[0]}) + elif TranformerType in ['CubeToCubePerFilter', 'CubeToCubePerFilterIncrement']: + table = list( + Dataset['aggregate']['properties']['columns']['items']['properties']['table'][ + 'properties'].keys()) + filter = Dataset['aggregate']['properties']['filters']['items']['properties'] + InputKeys.update( + {'Table': table[0], 'FilterCol': list(filter['column']['properties'].keys()), + 'FilterType': list(filter['filter_type']['properties'].keys())[0], + 'Filter': list(filter['filter']['properties'].keys())[0], + 'DimensionTable': list(Dimensions['table']['properties'].keys())[0], + 'NumeratorCol': NumeratorCol, 'DenominatorCol': DenominatorCol, + 'QueryDenominator': PercentageIncrement[1], + 'QueryNumerator': PercentageIncrement[0]}) + else: + return Response(json.dumps( + {"Message": "Transformer type is not correct", "TransformerType": TranformerType, + "Dataset": DatasetName})) + print(Transformer, ':::::::::::Transformer:::::::::::::::') + KeysMapping(InputKeys, Template, Program + '_' + Transformer, Response) + else: + print('ERROR : No dataset found') + return Response(json.dumps({"Message": "No dataset found " + DatasetName})) + else: + print('ERROR : No Event Found') + return Response(json.dumps({"Message": "No event found " + EventName})) + if cur is not None: + cur.close() + if con is not None: + con.close() + except Exception as error: + print(error) + return Response( + json.dumps({"Message": "Transformer not created ", "transformerFiles": Transformer, "code": 400})) + return KeysMapping(InputKeys, Template, Program + '_' + Transformer, Response) diff --git a/transformer_generator/transformers/config.ini b/transformer_generator/transformers/config.ini new file mode 100644 index 0000000..c223745 --- /dev/null +++ b/transformer_generator/transformers/config.ini @@ -0,0 +1,9 @@ +[CREDs] + +# Postgres credentials #################### + +host = postgres_app +port = 5432 +database = cqube +user = cqube_user +password = Tibil@123 diff --git a/transformer_generator/transformers/events/school_details.csv b/transformer_generator/transformers/events/school_details.csv new file mode 100644 index 0000000..329c6f7 --- /dev/null +++ b/transformer_generator/transformers/events/school_details.csv @@ -0,0 +1,10002 @@ +grade,school_id,school_name,school_type,school_category,cluster_id,cluster_name,block_id,block_name,district_id,district_name,state_id,state_name +8,1000000007,school_1000000007,urban,upper_primary,100000004,cluster_100000004,10007,block_10007,102,district_102,9,state_9 +12,1000000001,school_1000000001,rural,senior_secondary,100000006,cluster_100000006,10008,block_10008,105,district_105,7,state_7 +12,1000000007,school_1000000007,rural,senior_secondary,100000001,cluster_100000001,10005,block_10005,110,district_110,1,state_1 +10,1000000004,school_1000000004,rural,upper_primary,100000006,cluster_100000006,10000,block_10000,108,district_108,7,state_7 +9,1000000002,school_1000000002,rural,upper_primary,100000003,cluster_100000003,10002,block_10002,110,district_110,8,state_8 +4,1000000003,school_1000000003,rural,primary,100000006,cluster_100000006,10004,block_10004,101,district_101,2,state_2 +9,1000000010,school_1000000010,urban,upper_primary,100000000,cluster_100000000,10007,block_10007,104,district_104,3,state_3 +4,1000000008,school_1000000008,urban,primary,100000005,cluster_100000005,10001,block_10001,101,district_101,3,state_3 +11,1000000009,school_1000000009,rural,senior_secondary,100000003,cluster_100000003,10001,block_10001,107,district_107,4,state_4 +12,1000000005,school_1000000005,rural,senior_secondary,100000009,cluster_100000009,10000,block_10000,102,district_102,8,state_8 +2,1000000003,school_1000000003,rural,primary,100000005,cluster_100000005,10004,block_10004,102,district_102,5,state_5 +7,1000000005,school_1000000005,rural,secondary,100000006,cluster_100000006,10000,block_10000,109,district_109,2,state_2 +3,1000000002,school_1000000002,urban,primary,100000004,cluster_100000004,10005,block_10005,101,district_101,2,state_2 +6,1000000006,school_1000000006,rural,secondary,100000009,cluster_100000009,10004,block_10004,106,district_106,3,state_3 +1,1000000007,school_1000000007,rural,primary,100000008,cluster_100000008,10002,block_10002,106,district_106,1,state_1 +12,1000000001,school_1000000001,urban,senior_secondary,100000008,cluster_100000008,10009,block_10009,101,district_101,8,state_8 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