What happens if the project level or user level custom quotas are exceeded in BigQuery?
When using BigQuery, it is essential to understand the concept of custom quotas and what happens if these quotas are exceeded. In BigQuery, there are two types of custom quotas: project level and user level.
Project level custom quotas are set at the project level and apply to all users within that project. These quotas define the maximum amount of resources, such as query usage, storage, and streaming inserts, that can be consumed by all users combined. If the project level custom quotas are exceeded, the project may experience limitations or restrictions on certain operations.
For example, if the query usage quota is exceeded, users may be unable to execute new queries until the quota is reset or increased. Similarly, if the storage quota is exceeded, users may be unable to load new data into BigQuery until the quota is increased or existing data is deleted.
User level custom quotas, on the other hand, are set at the individual user level and apply only to that specific user. These quotas define the maximum amount of resources that a user can consume within the project. If a user exceeds their custom quotas, they may experience limitations or restrictions on their operations.
For instance, if a user exceeds their query usage quota, they may be unable to execute new queries until the quota is reset or increased. If the streaming inserts quota is exceeded, the user may be unable to stream new data into BigQuery until the quota is increased or existing data is deleted.
When either project level or user level custom quotas are exceeded, it is important to take appropriate actions to address the issue. This may involve optimizing queries to reduce resource consumption, deleting unnecessary data to free up storage space, or requesting quota increases from Google Cloud Support.
Exceeding project level or user level custom quotas in BigQuery can result in limitations or restrictions on certain operations. It is important to monitor and manage these quotas effectively to ensure smooth and efficient usage of BigQuery resources.
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