![]() ![]() Your approach doesnt seem bad either btw. Oh and yes, records show as fields in the bq gui, but if you have data like that I would definitely nest repeatable fields as record (if they are) if you can. If it ran in data flow you could write these out to an invalid table gcs bucket. For example: JSON 9.8 If the JSON value is not a number, an error is produced. An easy way was to sum the genre fields and use that as the field to group by but the genre fields had to be converted from TRUE to 1 and FALSE to 0. Description Converts a JSON number to a SQL FLOAT64 value. Will still break your pipeline if data types change though. Use Case 2: Converting String Values to Numeric I wanted to see the count of movies by the number of genres each movie was assigned to. (You need a Google BigQuery account to access this data set.) The schema says that most of the columns are numeric. You could also run it over every load in which case updating your schema with additions. Oh there are a few libraries out there to generate schemas from json, you could also try one of those but youd have to run it over a lot of data to be confident. Bigtable might seem ideal but most people prefer more transformations in order to use bigquery. Like you mentioned though, theres going to be some work beforehand to define the data you need. It also should allow you to run data again against the source json table, assuming you store each batch of jsons as a different partition. ![]() Note that if fields change, this can be a pain and data types changing will still break it. This approach should mean you can change the schema afterwards to include more fields as necessary. In that case I'd dump each json into a field into table 1 and another job to json extract scalar or json extract from there into the fields of 2nd table. A schema which best reflects the data you need. I think you can either load the data into bigtable instead or you'll have to create a 'super' schema. I'm sorry I dont know what x is in this case.Īlternatively ingest everything as string as a load then a processing step to convert. A group of bulls wont help it determine the schema. Personally if you know the schema it's better to pass in a schema json file anyway.Īlternatively if you have control over source (which means youd know the schema anyway), you could ensure the first x rows contain rows that reflect you data type, for example letters in a field if it's a string, numbers if it's an integer, etc. Standard SQL supports the CAST function with the FLOAT64 data type, e.g.: SELECT CAST (author.timesec as FLOAT64) FROM LIMIT 1000 Share. ![]() You can specify a schema instead of it auto detecting, also I think you can avoid specifying a schema if the table is already created. The problem with autodetect for schemas in data is that it typically does it on x rows, not the full dataset, so unless your data types can be correctly evaluated in the first say, 100 rows, you're going to have potential problems. Im assuming you're loading data because you dont say. ![]()
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