Splitting Large gz Compressed Files into Smaller Compressed Files using Python and Airflow
When working with large gz compressed files, you may encounter errors when trying to load them into tools like Apache Airflow. This is especially true when the size of a single compressed file exceeds 5 GB. To overcome this limitation, you can split large gz compressed files into smaller ones, making it easier to load and process them.
Key Concepts
- gz compression format
- Python programming
- Apache Airflow
- File splitting
Splitting Large gz Compressed Files
To split large gz compressed files, you can use Python's built-in libraries. The following steps demonstrate how to split a gz file into smaller chunks.
Step 1: Read the gz file
To read a gz file in Python, you can use the gzip module. This module provides an interface to compress and decompress files in the gzip format.
import gzip
with gzip.open('large_file.gz', 'rb') as f\_in:
content = f\_in.read()
Step 2: Split the content
Once you have read the content of the gz file, you can split it into smaller chunks using the split() function. This function splits a string into a list where each word is a list item.
chunk\_size = 1024 * 1024 * 1024 # 1 GB
chunks = [content[i:i + chunk\_size] for i in range(0, len(content), chunk\_size)]
Step 3: Write the chunks to new gz files
Finally, you can write each chunk to a new gz file using the gzip.open() function. Make sure to specify the 'wb' mode to write binary data.
for i, chunk in enumerate(chunks):
with gzip.open(f'split_file_{i}.gz', 'wb') as f\_out:
f\_out.write(chunk)
Integrating with Apache Airflow
Now that you have split the large gz file into smaller ones, you can load them into Apache Airflow without encountering errors due to file size limitations.
Step 1: Create a new Airflow DAG
To create a new DAG in Airflow, you can use the following template.
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python\_operator import PythonOperator
default\_args = {
'owner': 'airflow',
'depends\_on\_past': False,
'start\_date': datetime(2023, 3, 10),
'email\_on\_failure': False,
'email\_on\_retry': False,
retries': 1,
'retry\_delay': timedelta(minutes=5),
}
dag = DAG(
'split\_gz\_file',
default\_args=default\_args,
schedule\_interval=timedelta(days=1),
)
Step 2: Define a Python function to split the gz file
Define a Python function that implements the file splitting logic shown earlier.
def split\_gz\_file():
# Splitting logic here
Step 3: Add a PythonOperator to the DAG
Finally, add a PythonOperator to the DAG that calls the split\_gz\_file() function.
split\_gz\_task = PythonOperator(
task\_id='split\_gz\_file',
python\_callable=split\_gz\_file,
dag=dag,
)
- Large gz compressed files can cause errors when loading them into tools like Apache Airflow.
- To overcome this limitation, you can split large gz files into smaller ones using Python's built-in libraries.
- By splitting the files, you can load and process them in Apache Airflow without encountering size limitations.