Dipanshu Kumar
454 lines
12 KiB
Python
454 lines
12 KiB
Python
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import pyodbc
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import pandas as pd
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import clickhouse_connect
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import numpy as np
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from datetime import datetime
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import traceback
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import warnings
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# =========================================================
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# IGNORE WARNINGS
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# =========================================================
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warnings.filterwarnings(
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'ignore',
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'pandas only supports SQLAlchemy connectable'
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)
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print("ETL Started :", datetime.now())
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# =========================================================
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# SQL SERVER CONNECTION
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# =========================================================
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SQL_CONN_STR = (
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'DRIVER={ODBC Driver 17 for SQL Server};'
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'SERVER=10.200.25.65;'
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'DATABASE=CPMIndiaBusinessInsight;'
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'UID=bsgteam_test;'
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'PWD=B$gt3@m#00512;'
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'TrustServerCertificate=yes;'
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)
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# =========================================================
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# CLICKHOUSE CONFIG
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# =========================================================
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CH_CONFIG = {
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'host': '172.188.12.194',
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'port': 8123,
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'username': 'default',
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'password': 'dipanshu_k',
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'database': 'DaburIndia_BI'
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}
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# =========================================================
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# TABLE NAME
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# =========================================================
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TABLE_NAME = ' additional_visibility'
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PROJECT_ID = 41654
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# =========================================================
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# CLEAN DATAFRAME
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# =========================================================
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def clean_dataframe(df):
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try:
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# ---------------------------------------------
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# Replace NaN
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# ---------------------------------------------
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df = df.replace({np.nan: None})
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# ---------------------------------------------
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# Process Column Wise
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# ---------------------------------------------
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for col in df.columns:
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try:
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col_lower = col.lower()
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# =====================================
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# DATE COLUMNS
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# =====================================
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if 'date' in col_lower:
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print(f"Cleaning Date Column : {col}")
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df[col] = pd.to_datetime(
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df[col],
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errors='coerce'
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)
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# Remove invalid dates
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df[col] = df[col].where(
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(df[col].dt.year >= 1970) &
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(df[col].dt.year <= 2100)
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)
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# Convert to Python Date
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df[col] = df[col].apply(
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lambda x:
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x.date()
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if pd.notnull(x)
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else None
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)
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# =====================================
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# INTEGER COLUMNS
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# =====================================
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elif pd.api.types.is_integer_dtype(df[col]):
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df[col] = pd.to_numeric(
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df[col],
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errors='coerce'
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)
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df[col] = df[col].apply(
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lambda x:
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int(x)
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if pd.notnull(x)
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else None
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)
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# =====================================
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# FLOAT COLUMNS
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# =====================================
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elif pd.api.types.is_float_dtype(df[col]):
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non_null = df[col].dropna()
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# ---------------------------------
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# Convert whole float to int
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# Example:
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# 3240.0 -> 3240
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# ---------------------------------
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if len(non_null) > 0 and (
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(non_null % 1 == 0).all()
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):
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df[col] = df[col].apply(
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lambda x:
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int(x)
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if pd.notnull(x)
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else None
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)
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else:
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df[col] = df[col].apply(
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lambda x:
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float(x)
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if pd.notnull(x)
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else None
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)
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# =====================================
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# OBJECT / STRING COLUMNS
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# =====================================
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else:
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cleaned = []
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for val in df[col]:
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# NULL
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if pd.isnull(val):
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cleaned.append(None)
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# INTEGER
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elif isinstance(
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val,
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(
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int,
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np.integer
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)
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):
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cleaned.append(int(val))
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# FLOAT
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elif isinstance(
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val,
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(
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float,
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np.floating
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)
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):
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if np.isnan(val):
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cleaned.append(None)
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else:
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# IMPORTANT FIX
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# Avoid '3240.0' string issue
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if val.is_integer():
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cleaned.append(int(val))
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else:
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cleaned.append(float(val))
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# STRING
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elif isinstance(val, str):
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cleaned.append(val.strip())
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# BOOLEAN
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elif isinstance(val, bool):
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cleaned.append(int(val))
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# DATETIME
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elif isinstance(
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val,
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(
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datetime,
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pd.Timestamp
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)
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):
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cleaned.append(str(val))
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# OTHER
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else:
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cleaned.append(str(val))
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df[col] = cleaned
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except Exception as col_error:
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print("\n================================")
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print(f"COLUMN FAILED : {col}")
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print(str(col_error))
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print("================================")
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return df
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except Exception as clean_error:
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print("\n================================")
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print("DATA CLEAN FAILED")
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print(str(clean_error))
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print("================================")
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return df
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# =========================================================
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# MAIN PROCESS
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# =========================================================
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try:
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# =====================================================
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# CONNECT SQL SERVER
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# =====================================================
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sql_conn = pyodbc.connect(SQL_CONN_STR)
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print("Connected to SQL Server")
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# =====================================================
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# CONNECT CLICKHOUSE
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# =====================================================
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ch_client = clickhouse_connect.get_client(**CH_CONFIG)
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print("Connected to ClickHouse")
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# =====================================================
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# QUERY
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# =====================================================
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query = f"""
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SELECT *
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FROM dbo.[{TABLE_NAME}]
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WHERE Project_Id = {PROJECT_ID}
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"""
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print("\nExecuting Query:")
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print(query)
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# =====================================================
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# CHUNK SIZE
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# =====================================================
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chunk_size = 100000
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total_rows = 0
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# =====================================================
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# READ DATA
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# =====================================================
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for chunk in pd.read_sql(
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query,
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sql_conn,
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chunksize=chunk_size
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):
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try:
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print("\n================================")
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print(f"Processing {len(chunk)} Rows")
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print("================================")
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# =================================================
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# CLEAN DATA
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# =================================================
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chunk = clean_dataframe(chunk)
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# =================================================
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# DEBUG COLUMN TYPES
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# =================================================
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print("\nCOLUMN TYPES")
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for col in chunk.columns:
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sample = chunk[col].dropna()
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if len(sample) > 0:
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print(
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col,
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type(sample.iloc[0]),
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sample.iloc[0]
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)
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# =================================================
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# SAMPLE DATA
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# =================================================
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print("\nSAMPLE DATA")
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print(chunk.head(2))
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# =================================================
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# INSERT INTO CLICKHOUSE
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# =================================================
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print("\nInserting into ClickHouse...")
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ch_client.insert_df(
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table=TABLE_NAME,
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df=chunk,
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database=CH_CONFIG['database']
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)
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total_rows += len(chunk)
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print(
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f"\nInserted Total Rows : {total_rows}"
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)
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except Exception as chunk_error:
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print("\n================================")
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print("CHUNK INSERT FAILED")
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print("================================")
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print(str(chunk_error))
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traceback.print_exc()
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# =============================================
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# SAVE ERROR LOG
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# =============================================
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with open(
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"clickhouse_chunk_error.log",
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"a",
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encoding="utf-8"
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) as log:
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log.write(
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"\n\n================================"
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)
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log.write(
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f"\nTIME : {datetime.now()}"
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)
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log.write(
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f"\nTABLE : {TABLE_NAME}"
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)
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log.write(
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f"\nERROR : {str(chunk_error)}"
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)
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log.write(
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f"\nTRACEBACK :\n"
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f"{traceback.format_exc()}"
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)
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log.write(
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"\n================================"
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)
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continue
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print("\n================================")
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print("ETL COMPLETED SUCCESSFULLY")
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print(f"TOTAL ROWS INSERTED : {total_rows}")
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print("================================")
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# =========================================================
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# MAIN ERROR
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# =========================================================
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except Exception as main_error:
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print("\n================================")
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print("MAIN ERROR")
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print("================================")
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print(str(main_error))
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traceback.print_exc()
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with open(
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"clickhouse_main_error.log",
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"a",
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encoding="utf-8"
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) as log:
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log.write(
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"\n\n================================"
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)
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log.write(
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f"\nTIME : {datetime.now()}"
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)
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log.write(
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f"\nERROR : {str(main_error)}"
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)
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log.write(
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f"\nTRACEBACK :\n"
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f"{traceback.format_exc()}"
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)
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log.write(
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"\n================================"
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)
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# =========================================================
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# CLOSE CONNECTIONS
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# =========================================================
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finally:
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try:
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sql_conn.close()
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print("\nSQL Server Connection Closed")
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except:
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pass
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try:
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ch_client.close()
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print("ClickHouse Connection Closed")
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except:
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pass
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print("\nETL Finished :", datetime.now())
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