Experience Handling Large CSV Uploads (5GB) with Complex Validation

Requirements

• Allow users to upload CSV files (up to 5GB).
  
• Validate the data and log all error rows:
  
  • Columns exceeding 200 characters.
    
  • Presence of disallowed special characters.
    
  • A set of columns must be unique (no duplicates within the file and compared to the database).
    
• Return to the user:
  
  • The rows with errors.
    
  • Type of error.
    
  • The problematic value.

Overall Solution

The processing flow is divided into three main phases:

1. User Uploads File to S3

• Frontend uploads the file to S3 using a pre-signed URL or S3 SDK.
  
• Once upload is successful, trigger the validation process via EventBridge or by sending a request to the backend.
  

2. Batch Processing: Data Validation

The validation process is broken into multiple steps to optimize performance and reduce timeout/memory leak risks.

a. Create a Temporary Table in the Database

• Create a temp table mirroring the CSV structure.
• Add extra columns to log errors: column_1_length, column_1_regex, is_error, line_number, etc.

b. Load File from S3 into the Temp Table

• Use LOAD DATA LOCAL INFILE to import the file directly into the database (if S3 is mounted to local).
  

• Optionally preprocess to remove newline characters (\r\n) embedded in values.
  
• Temporarily disable indexes to speed up data loading, then re-enable them.

LOAD DATA LOCAL INFILE ‘/path_to_file.csv’
INTO TABLE temp_table
FIELDS TERMINATED BY ‘,’
ENCLOSED BY ‘”‘
LINES TERMINATED BY ‘\n’
(column_1, column_2)
SET
  column_1 = REPLACE(REPLACE(column_1, ‘\r’, ”), ‘\n’, ”),
  column_2 = REPLACE(REPLACE(column_2, ‘\r’, ”), ‘\n’, ”),
  column_1_length = CHAR_LENGTH(column_1),
  column_2_length = CHAR_LENGTH(column_2),
  column_1_regex = column_1 REGEXP ‘[^a-zA-Z0-9+\-_.:=?&/]+’,
  is_error = IF (column_1_length > 200 OR column_2_length > 200 OR column_1_regex > 0, 1, 0)

c. Retrieve Error Rows and Generate an Error Report

• Select rows with is_error = 1, write them to an error file with row number and error description.

d. Check for Duplicates Within the File

SELECT id_key, GROUP_CONCAT(line_number) AS duplicated_lines
FROM temp_table
GROUP BY id_key
HAVING COUNT(*) > 1;

e. Check for Duplicates Against the Database

Compare id_key in temp_table against the main table in your system.

3. Insert Valid Data into the Main Database

• Only include rows without errors.
• Use INSERT INTO or REPLACE INTO depending on the use case.
• Optionally add logic for default values, audit fields, etc.
  

4. Clean Up Temporary Table

• Drop the temp table after processing to free up resources.
  

Tips & Best Practices

• Disable indexes during large data loads, then re-enable afterward (significantly reduces loading time).
  
• Ensure the CSV is correctly encoded (UTF-8, without BOM).
  
• Avoid line-by-line validation in code — use SQL for efficient bulk validation.
  
• Log all errors in one pass so users can correct them all at once, rather than iteratively.
  

Conclusion

Handling large file uploads isn’t just about raising size limits. You need a smart, efficient processing flow that ensures performance and scalability. With this approach, you can handle even larger files if needed.

Would you like help implementing a similar solution in code or cloud architecture?

Credit: Ton That Bach (Technical Leader in Mynavi TechTus Vietnam)