要帮助BigQuery更有效地并行处理,可以采取以下解决方法:
示例代码:
CREATE TABLE my_table
PARTITION BY DATE(timestamp_column)
CLUSTER BY another_column
AS
SELECT * FROM source_table;
--jobs
参数在命令行或使用configuration.query.priority
字段设置查询的优先级。示例代码:
from google.cloud import bigquery
client = bigquery.Client()
query = """
SELECT * FROM my_table
"""
job_config = bigquery.QueryJobConfig(priority=bigquery.QueryPriority.BATCH)
query_job = client.query(query, job_config=job_config)
results = query_job.result()
for row in results:
print(row)
--range_partitioning
参数或使用clustering_fields
字段进行表分片。示例代码:
CREATE TABLE my_table
PARTITION BY DATE(timestamp_column)
CLUSTER BY another_column
AS
SELECT * FROM source_table;
示例代码:
SELECT column1, column2
FROM my_table
WHERE date_column >= '2022-01-01' AND date_column < '2022-01-31'
通过采取上述方法,可以帮助BigQuery更有效地并行处理数据,提高查询性能和处理速度。