Big Query Export Data Options created numerous files with headers only

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Summary

The issue at hand involves Big Query Export Data Options creating numerous files with headers only when exporting data from Big Query using Python 3.11. This results in a majority of files containing only headers, with a few files containing actual data rows. The google-cloud-bigquery library is being used, specifically version 3.38.0.

Root Cause

The root cause of this issue lies in the export options specified in the query. Key points include:

  • uri: The URI specified for export is gs://test_bucket/test_folder/test_folder_*.csv, which includes a wildcard character *. This can lead to the creation of multiple files.
  • format: The format specified is ‘CSV’, which is correct for comma-separated values.
  • overwrite: The overwrite option is set to true, which means that existing files can be overwritten.
  • header: The header option is set to true, which includes the header row in the exported files.
  • field_delimiter: The field delimiter is set to ‘,’, which is standard for CSV files.

Why This Happens in Real Systems

This issue can occur in real systems due to several reasons, including:

  • Data size: Large datasets can be split into multiple files, leading to the creation of multiple files with headers.
  • Query optimization: The query optimizer may decide to split the data into smaller chunks, resulting in multiple files.
  • Storage limitations: The storage system may have limitations on file size, leading to the creation of multiple files.

Real-World Impact

The real-world impact of this issue includes:

  • Data inconsistencies: The presence of multiple files with headers only can lead to data inconsistencies and errors.
  • Storage waste: The creation of multiple files with minimal data can result in storage waste and increased costs.
  • Processing complexities: The need to process multiple files can add complexity to data processing pipelines.

Example or Code

from google.cloud import bigquery

# Define the client
client = bigquery.Client()

# Define the query
query = """
    EXPORT DATA OPTIONS (
        uri = "gs://test_bucket/test_folder/test_folder_*.csv",
        format = 'CSV',
        overwrite = true,
        header = true,
        field_delimiter = ','
    ) AS
    ( 
        SELECT field1, field2, field3 
        FROM `project_id.dataset_id.test_folder` 
        WHERE DATETIME(field1) BETWEEN "2025-12-12 00:00:00" AND "2026-12-12 23:59:59" 
        ORDER BY field1 
    )
"""

# Execute the query
job = client.query(query)
job.result()

How Senior Engineers Fix It

Senior engineers fix this issue by:

  • Optimizing the query: Optimizing the query to reduce the number of files created.
  • Using a single file: Specifying a single file for export instead of using a wildcard character.
  • Handling headers: Handling headers properly to avoid duplication.
  • Monitoring storage: Monitoring storage usage to avoid waste and inconsistencies.

Why Juniors Miss It

Juniors may miss this issue due to:

  • Lack of experience: Limited experience with Big Query and data export.
  • Insufficient testing: Insufficient testing of the export process.
  • Misunderstanding of options: Misunderstanding of the export options and their impact on the export process.
  • Inadequate monitoring: Inadequate monitoring of storage usage and data consistency.