google-bigquery

问题 I have a table of orders that looks something like this: WITH my_table_of_orders AS ( SELECT 1 AS order_id, DATE(2019, 5, 12) AS date, 5 AS customer_id, TRUE AS is_from_particular_store UNION ALL SELECT 2 AS order_id, DATE(2019, 5, 11) AS date, 5 AS customer_id, TRUE AS is_from_particular_store UNION ALL SELECT 3 AS order_id, DATE(2019, 5, 11) AS date, 4 AS customer_id, FALSE AS is_from_particular_store ) My actual table contains ~59 million rows. What I would like to do is essentially return

问题 I have a table that looks like this: I am looking for a table that gives a frequency count of the elements in the fields l_0, l_1, l_2, l_3 . For example the output should look like this: | author_id | year | l_o.name | l_0.count| l1.name | l1.count | l2.name | l2.count| l3.name | l3.count| | 2164089123 | 1987 | biology | 3 | botany | 3 | | | | | | 2595831531 | 1987 | computer science | 2 | simulation | 2 | computer simulation | 2 | mathematical model | 2 | Edit: In some cases the array field

问题 I'm thinking of using BigQuery's JavaScript UDF as a critical component in a new data architecture. It would be used to logically process each row loaded into the main table, and also to process each row during periodical and ad-hoc aggregation queries. Using an SQL UDF for the same purpose seems to be unfeasible because each row represents a complex object, and implementing the business logic in SQL, including things such as parsing complex text fields, gets ugly very fast. I just read the

问题 I trained a TensorFlow classifier and created it as a model in BigQuery ML using CREATE MODEL . Now I would like to use ML.PREDICT to batch predict using this model. I get the error "Invalid table-valued function ml.predict Column inputs is not found in the input data to the PREDICT function." Here's my query: select * from ml.predict ( model test.digital_native_classifier_kf, (select * from dataset_id.features_table_id) ) In the BigQuery documentation, they give an example for a TensorFlow

问题 I am using ST_MAKEPOLYGON function in BigQuery as follows: with data AS ( SELECT 61680 AS id, 139.74862575531006 AS lon, 35.674973127377314 AS lat union all SELECT 61680, 139.75087881088257, 35.673909836018375 union all SELECT 61680, 139.747037887573, 35.6765767531247 union all SELECT 61680, 139.75308895111, 35.6813525780394 union all SELECT 61680, 139.747509956359, 35.6798884869144 union all SELECT 61680, 139.754590988159, 35.6799930657428 union all SELECT 61680, 139.754977226257, 35

问题 I am using ST_MAKEPOLYGON function in BigQuery as follows: with data AS ( SELECT 61680 AS id, 139.74862575531006 AS lon, 35.674973127377314 AS lat union all SELECT 61680, 139.75087881088257, 35.673909836018375 union all SELECT 61680, 139.747037887573, 35.6765767531247 union all SELECT 61680, 139.75308895111, 35.6813525780394 union all SELECT 61680, 139.747509956359, 35.6798884869144 union all SELECT 61680, 139.754590988159, 35.6799930657428 union all SELECT 61680, 139.754977226257, 35

问题 Can't write Biq Query by following Error. Python 3.5.6 pandas-gbq 0.13.1 google-cloud-bigquery 1.24.0 ImportError: pandas-gbq requires google-cloud-bigquery: cannot import name 'TimeoutGuard' code: sample_dataframe = pd.DataFrame(data_rows,columns['shop_name','category','nearest_station','telephone_number','address','DL_time']) print(sample_dataframe) sample_dataframe.to_gbq('NTT.aikidou2025', 'robotic-column-270803',if_exists = 'replace') 回答1: I tried !pip install google-cloud-bigquery==1.10

问题 Can't write Biq Query by following Error. Python 3.5.6 pandas-gbq 0.13.1 google-cloud-bigquery 1.24.0 ImportError: pandas-gbq requires google-cloud-bigquery: cannot import name 'TimeoutGuard' code: sample_dataframe = pd.DataFrame(data_rows,columns['shop_name','category','nearest_station','telephone_number','address','DL_time']) print(sample_dataframe) sample_dataframe.to_gbq('NTT.aikidou2025', 'robotic-column-270803',if_exists = 'replace') 回答1: I tried !pip install google-cloud-bigquery==1.10

问题 Can anyone help explain what is the transaction isolation level in google cloud BigQuery? It does not appear to be any documents on this. We know that in other databases, e.g. sql server database, there are transaction isolation levels : read uncommitted, read committed, repeatable read, snapshot, serialisable. Thanks. 回答1: There is not much information about it, but in this migration guide we can find some comparisons between BigQuery and Teradata which can give us a clue. As its said in the

问题 We don't have decimal data type in BigQuery now. So I have to use float But In Bigquery float division 0.029*50/100=0.014500000000000002 Although 0.021*50/100=0.0105 To round the value up I have to use round(floatvalue*10000)/10000. Is this the right way to deal with decimal data type now in BigQuery? 回答1: Note that this question deserves a different answer now. The premise of the question is "We don't have decimal data type in BigQuery now." But now we do: You can use NUMERIC : SELECT CAST(