It's Simple ! No Need of DockerFile, KubernetesPodOperator, LD_LIBRARY_PATH, etc just a basic python operator will do
Points to consider
- GCP Composer Worker's Pod image is ubuntu 1604 (just run a basic python operator with a command os.system('cat /etc/os-release') to check )
- It has unixodbc-dev already installed on the worker's pod image
- Composer creates the bucket and mount it with airflow
- So why not just install the pyodbc from pypi packages and provide the mssql odbc driver as paramter in pyodbc connection method
here 'gs://bucket_created_by_composer' == '/home/airflow/gcs'
gcs bucket created by composer ->
-> data/
-> dags/
Step By Step Approach
Step 1: Install pyodbc, mssql odbc on any ubuntu instances to get the driver files
for consideration lets do it on GCP VM Intance with ubuntu 1804 image
#update the packages
sudo apt update
sudo apt-get update -y
curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add -
curl https://packages.microsoft.com/config/ubuntu/18.04/prod.list | sudo tee /etc/apt/sources.list.d/msprod.list
sudo apt-get update -y
echo Installing mssql-tools and unixODBC developer...
sudo ACCEPT_EULA=Y apt-get install -y mssql-tools unixodbc-dev
sudo apt-get update -y
sudo apt-get install -y mssql-tools #it includes sql_cmd and bcp (we dont need those)
sudo apt install python3-pip #installing pip3
pip3 install pyodbc
Step 2: Get the Driver Files and upload it to the data folder of gcs_bucket created by the composer
cd /opt/microsoft
#now you can see there is one directory 'msodbcsql17', version may change
#we need to upload this directory to the data folder of gcs_bucket
#for this you may choose which ever approach suits you
#copying the directory to /<home/user> for proper zipping/uploading to gcs
cp -r msodbcsql17 /home/<user> #you may need to use sudo
#upload this /home/<user>/msodbcsql17 to any gcs_bucket
gsutil cp -r /home/<user>/msodbcsql17 gs://<your-gcs-bucket>
download this folder from gcs bucket to local and the upload this folder to data folder of gcs bucket created by composer
choose any approach/method, main aim is to get the msodbcsql17 folder in the data folder of gcs bucket created by composer
Final structure:
gcs bucket created by composer ->
-> data/msodbcsql17/
-> dags/<your_dags.py>
Step 3: using this msodbcsql17 drivers for pyodbc connection
EXAMPLE DAG:
import os
import time
import datetime
import argparse
import json
from airflow import DAG
import airflow
from airflow.operators import python_operator
default_dag_args = {
'start_date': airflow.utils.dates.days_ago(0), #
'provide_context': True
}
dag = DAG(
'pyodbc_test',
schedule_interval=None, #change for composer
default_args=default_dag_args
)
def check_connection(**kwargs):
print('hello')
driver='/home/airflow/gcs/data/msodbcsql17/lib64/libmsodbcsql-17.5.so.2.1'
#this is the main driver file, the exact location can be found on gcs_bucket/data folder or check the /etc/odbcinst.in file of ubuntu instance in which you installed the pyodbc earlier
def tconnection(ServerIp,LoginName,Password,mssql_portno):
""" A method which return connection object"""
import pyodbc
pyodbc.pooling = False
try:
sql_conn = pyodbc.connect("DRIVER={4};SERVER={0},{1};UID={2};PWD={3}".format(ServerIp,mssql_portno,LoginName,Password,driver))
except pyodbc.Error as ex:
sqlstate = ex.args[1]
raise
return sql_conn
con=tconnection('<your-server-ip>','<your-login-name>','<your-password>','1433')
#recommendation is to take the password and login from airflow connections
import pandas as pd
q='select * from <your-db-name>.<your-schema-name>.<your-table-name>'
df=pd.read_sql(q,con)
print(df)
Tcheck_connection= python_operator.PythonOperator(
task_id='Tcheck_connection',
python_callable=check_connection,
dag=dag )
#calling the task sequence
Tcheck_connection
PYPI Packages
pyodbc
pandas
Have tested on Composer recently
