No, you cannot directly add partitions to an existing non-partitioned table in BigQuery. BigQuery does not support converting an existing non-partitioned table into a partitioned table or adding partitions to a table that wasn’t originally created with partitioning.
However, there are workarounds to achieve a similar result:
Workaround 1: Create a New Partitioned Table and Migrate Data
1. Create a New Partitioned Table: Define the partitioning scheme you need.
2. Insert Data from the Old Table: Use a SELECT query to copy data from the existing non-partitioned table to the new partitioned table.
Example:
-- Step 1: Create a new partitioned table
CREATE TABLE `project_id.dataset_id.new_table`
PARTITION BY DATE(timestamp_column) -- Replace with your partition column
AS
SELECT * FROM `project_id.dataset_id.old_table`;
3. Update your application or queries to use the new table.
Workaround 2: Use Clustering Instead of Partitioning
If you can’t migrate data, consider clustering the existing table. Clustering optimizes query performance and does not require recreating the table. Clustering can be done on multiple columns, including STRING, DATE, or INTEGER.
Example:
-- Create a clustered table
CREATE TABLE `project_id.dataset_id.new_table`
CLUSTER BY column_name -- Replace with the column(s) to cluster by
AS
SELECT * FROM `project_id.dataset_id.old_table`;
Workaround 3: Add Partitions to an Existing Partitioned Table
If the table is already partitioned (e.g., by date or range), BigQuery will automatically add partitions as new data arrives that falls outside the existing partitions. You don’t need to explicitly define additional partitions.
Example:
# Append new data to an existing partitioned table
from google.cloud import bigquery
import pandas as pd
client = bigquery.Client()
# Sample data
data = {"partition_column": ["2025-01-01"], "data_column": [100]}
df = pd.DataFrame(data)
# Load data into the table
table_id = "project_id.dataset_id.partitioned_table"
job_config = bigquery.LoadJobConfig(
write_disposition="WRITE_APPEND",
time_partitioning=bigquery.TimePartitioning(field="partition_column")
)
job = client.load_table_from_dataframe(df, table_id, job_config=job_config)
job.result()
Workaround 4: Use Sharded Tables Instead of Partitioning
If you can’t migrate or modify your existing table, you can implement sharded tables (e.g., table_2025, table_2026) to emulate partitioning.
Key Considerations
• Migrating data to a partitioned table is better for performance and cost efficiency, especially for large datasets.
• Clustering works well when partitioning isn’t feasible.
• Sharded tables can become difficult to manage over time.
Let me know if you want assistance with any specific approach!