LOAD DATA INFILE + progress tracking

MySQL's LOAD DATA INFILE statement does not provide built-in progress tracking capabilities. When using LOAD DATA INFILE, the process reads the data file and inserts its contents into the table, and there is no native mechanism for tracking the progress of the operation.

However, you can implement custom progress tracking to get an estimation of the progress based on the number of lines processed. Here's a general approach to achieve this:

1. Count the Lines in the File: Before executing the LOAD DATA INFILE statement, count the total number of lines in the data file. You can use a simple script to do this:

   pythonCopy code
   with open('your_data_file.csv', 'r') as f:
       num_lines = sum(1 for line in f)
   

2. Execute the LOAD DATA INFILE Statement: Execute the LOAD DATA INFILE statement as usual to import the data into the table.

3. Track Progress During Insertion: While the LOAD DATA INFILE statement is running, you can implement a custom progress tracker to monitor the progress based on the number of lines processed so far. You can achieve this by periodically checking the number of rows inserted into the table and comparing it to the total number of lines in the file.

   For example, in Python, you can use the pymysql library to execute the LOAD DATA INFILE statement, and then use the cursor.rowcount attribute to get the number of rows inserted.

   pythonCopy code
   import pymysql
   
   # Establish a database connection
   connection = pymysql.connect(host='your_host', user='your_user', password='your_password', db='your_db')
   
   # Create a cursor
   cursor = connection.cursor()
   
   # Execute the LOAD DATA INFILE statement
   cursor.execute("LOAD DATA INFILE 'your_data_file.csv' INTO TABLE your_table")
   
   # Get the total number of lines in the file
   with open('your_data_file.csv', 'r') as f:
       num_lines = sum(1 for line in f)
   
   # Track the progress
   while cursor.rowcount < num_lines:
       progress = (cursor.rowcount / num_lines) * 100
       print(f'Progress: {progress:.2f}%')
       # You can adjust the frequency of checking progress using sleep or other mechanisms
       time.sleep(5)
   
   # Close the cursor and connection
   cursor.close()
   connection.close()
   

Keep in mind that tracking progress using the number of rows inserted is an estimation and may not be completely accurate, especially if the data contains any errors or duplicates that result in rejected rows. However, it should give you a rough idea of the progress during the LOAD DATA INFILE operation.

Also, remember that custom progress tracking will introduce additional overhead, and you may want to adjust the frequency of progress checks based on the size of the data and the performance impact on your system.