How to load large text file in Google BigQuery

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05-09-2024

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Loading Massive Text Files into Google BigQuery: A Step-by-Step Guide

Working with large datasets is commonplace in today's data-driven world. Google BigQuery, a serverless data warehouse, offers a powerful platform for analyzing massive datasets. However, loading large text files into BigQuery can present challenges, particularly when exceeding the platform's individual file size limits.

This article delves into the process of loading large text files exceeding 16TB into BigQuery, utilizing insights from Stack Overflow discussions to provide a comprehensive and practical solution.

Understanding the Challenges: File Size Limits and Encryption

As pointed out in a Stack Overflow question, Google BigQuery has a limit of 5TB for unencrypted file loads and 4TB for encrypted file loads. The overall file size limit per load job is 15TB. Additionally, Google Cloud Storage (GCS), the storage service for BigQuery, has a 5TB file size limit.

This poses a significant problem when dealing with files exceeding these limits. How can we handle files larger than 16TB?

The Solution: Splitting, Encrypting, and Loading in Batches

The approach suggested on Stack Overflow involves splitting the large file into smaller manageable chunks, encrypting each chunk, uploading them to GCS, and then loading them into BigQuery in batches.

Here's a detailed breakdown of this process:

1. Splitting the Large File

• Using split Command (Linux/macOS): The split command is a powerful tool for splitting files on Unix-based systems. You can use it to create smaller files of a specific size or number of lines.

  split -b 4G large_file.txt small_file_
  

  This command will split the large_file.txt into smaller files of 4GB each, naming them small_file_aa, small_file_ab, etc.

• Using Programming Languages (Python, Java, etc.): You can write scripts in programming languages like Python to read the large file and write smaller chunks to individual files.

2. Encrypting the Files

• Using openssl (Linux/macOS): The openssl command-line utility allows for encrypting files using various encryption algorithms.

  openssl enc -aes-256-cbc -salt -in small_file_aa -out small_file_aa.enc
  

  This command encrypts the file small_file_aa with AES-256 encryption and saves the encrypted file as small_file_aa.enc.

• Using Cloud-Based Encryption Solutions: Utilize Google Cloud's Key Management Service (KMS) or other cloud-based encryption services to securely encrypt your files.

3. Uploading to GCS

• Using the gsutil command-line tool:

  gsutil cp small_file_aa.enc gs://your-bucket-name/encrypted_files/
  

  This command uploads the encrypted file small_file_aa.enc to your GCS bucket.

• Using the Google Cloud console: You can drag and drop your files into your GCS bucket through the web interface.

4. Loading into BigQuery

• Using the BigQuery console: Navigate to the "Load data" section and specify the source location in GCS, the destination table, and other loading options.

• Using the bq command-line tool:

  bq load --source_format=CSV --autodetect your_dataset.your_table gs://your-bucket-name/encrypted_files/small_file_aa.enc
  

  This command loads the data from the encrypted file into your BigQuery table.

Important Considerations

• Data Schema: Ensure consistency in the data format across all files.
• Encryption Keys: Securely store and manage your encryption keys.
• Load Job Configuration: Adjust load job settings (e.g., partitioning, clustering) for optimal performance.

Additional Tips

• Parallel Processing: Utilize BigQuery's parallel processing capabilities by loading multiple files concurrently.
• Error Handling: Implement robust error handling mechanisms to identify and address issues during the loading process.

Conclusion

Loading massive text files into Google BigQuery requires a strategic approach. By leveraging file splitting, encryption, and batch loading techniques, you can effectively handle files exceeding the individual file size limits. Remember to prioritize data security, manage encryption keys effectively, and optimize load job configurations for optimal performance. With these strategies in place, you can unlock the full potential of Google BigQuery for analyzing large datasets.