admin管理员组

文章数量:1410674

In a BigQuery notebook, if I want to run a query and store the results into a dataframe, I can use the API client:

client = bigquery.Client()
df = client.query(query).to_dataframe()

or I can use BigFrames:

df = bpd.read_gbq(query)

Which of these libraries is more performant?

In a BigQuery notebook, if I want to run a query and store the results into a dataframe, I can use the API client:

client = bigquery.Client()
df = client.query(query).to_dataframe()

or I can use BigFrames:

df = bpd.read_gbq(query)

Which of these libraries is more performant?

  • dataframe
  • google-cloud-platform
  • google-bigquery
Share Improve this question edited Mar 31 at 1:55 Sourav Dutta 4843 silver badges9 bronze badges asked Mar 6 at 18:32 humptydumptyhumptydumpty 1432 silver badges7 bronze badges
Add a comment  | 

1 Answer 1

Reset to default 1

BigFrames is better to use due to its performance, scalability, and optimized execution for larger datasets, its use of Arrow and BigQuery Storage Read API. As your data warehouse will require more dataset and to be performant, it is highly recommended to use BigFrames.

On the other hand, BigQuery API client.query().to\_dataframe is simpler for small queries, it provides you a structured way to query data, load data, and manage (ETL) but it will be limited to the complexity of the query, network latency, data size.

本文标签： dataframePerformance of BigQuery API Client vs BigQuery BigFramesStack Overflow

版权声明：本文标题：dataframe - Performance of BigQuery API Client vs BigQuery BigFrames? - Stack Overflow 内容由网友自发贡献，该文观点仅代表作者本人， 转载请联系作者并注明出处：http://www.betaflare.com/web/1744956655a2634395.html， 本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容，一经查实，本站将立刻删除。