![]() ![]() Please note: The relationship between data size and slot usage is not always linear. This can help to improve the performance of queries that are run frequently. BigQuery can cache the results of queries so that they can be reused later. This will make it easier for BigQuery to read and process the data. If you have a large dataset, you can partition the data into smaller chunks. ![]() For example, if you are querying a table with a large number of rows, you can use a hash join algorithm instead of a nested loop join algorithm. Some algorithms are more efficient than others for certain types of data. ![]() Here are some tips for optimizing queries: If you are concerned about the performance of the second query, you can try to optimize the query by using a more efficient algorithm or by partitioning the data. This is because BigQuery needs to read each row in the dataset before it can process it. When the data is doubled, the query needs to scan twice as many rows, which will take twice as long and require twice as many slots. Based on the information you have provided, it seems likely that the longer runtime and fewer slots used in use case 2 are due to the fact that the data was doubled in size. ![]()
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