No, BigQuery does not support partitioning on string columns directly. Partitioning in BigQuery is limited to:
1. Time Partitioning:
• Based on DATE, TIMESTAMP, or DATETIME columns.
2. Integer Range Partitioning:
• Based on integer columns with a defined range.
Workaround for Partitioning on String Values
If you need to partition data based on string values, you can use one of these approaches:
1. Use Clustered Tables
BigQuery allows clustering on string columns. Clustering organizes data based on specified columns, improving query performance for those columns. While it’s not partitioning, it serves a similar purpose for filtering.
Example:
from google.cloud import bigquery
# Define table configuration with clustering
table = bigquery.Table("your-project-id.your-dataset-id.your-table-id")
table.clustering_fields = ["string_column"] # Specify string column for clustering
# Create the table
client = bigquery.Client()
client.create_table(table)
print(f"Table {table.table_id} created with clustering.")
2. Map Strings to Integers
You can map your string values to integers and use integer range partitioning.
Example:
If you have strings like ["A", "B", "C"], you can map them to integers [1, 2, 3]. Then use integer range partitioning on the mapped column.
# Mapping string to integer before loading into BigQuery
data = {
"partition_column": [1, 2, 3], # Mapped integers
"original_column": ["A", "B", "C"]
}
df = pd.DataFrame(data)
3. Use a Pseudo-Partition
Instead of native partitioning, add a STRING column to represent categories and filter the data in queries. This approach does not provide the storage and query optimization benefits of native partitioning.
Example:
SELECT *
FROM `your-project-id.your-dataset-id.your-table-id`
WHERE string_column = "desired_value"
4. Use a DATE-Based Proxy
If string values correspond to dates (e.g., year-month), you can convert them into DATE format and use time partitioning.
Example:
df['partition_column'] = pd.to_datetime(df['string_column'], format="%Y-%m")
Key Considerations:
• Performance: Native partitioning is more efficient than pseudo-partitions.
• Cost: Filtering by string without clustering may increase query costs.
• Schema Design: Choose an approach that aligns with your query patterns.
Let me know if you’d like help implementing one of these approaches!