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hugocasa
43 lines
1.3 KiB
Markdown
43 lines
1.3 KiB
Markdown
# BigQuery
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Arguments use `@name` syntax.
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Name the parameters by adding comments before the statement:
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```sql
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-- @name1 (string)
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-- @name2 (int64) = 0
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SELECT * FROM users WHERE name = @name1 AND age > @name2;
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```
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## Receiving an S3Object as a script parameter
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Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for
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it, downloads the file, and binds it as a `STRING` JSON parameter — Parquet/CSV
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files are decoded server-side into a JSON array of records, JSON/JSONL pass
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through. Consume with `JSON_EXTRACT_ARRAY` / `JSON_VALUE`:
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```sql
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-- @file (s3object)
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SELECT
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CAST(JSON_VALUE(row, '$.id') AS INT64) AS id,
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JSON_VALUE(row, '$.name') AS name
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FROM UNNEST(JSON_EXTRACT_ARRAY(@file)) AS row;
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```
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## Streaming query results to S3
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Add a `-- s3` directive at the top of the script to stream the result set to S3
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instead of returning rows. Windmill writes the file and returns its `S3Object`
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as the script result.
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```sql
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-- s3 prefix=exports/users format=parquet
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SELECT id, name FROM users;
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```
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All keys are optional: `prefix` (object key prefix), `storage` (named storage —
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omit to use the workspace default), `format` (`json` (default), `parquet`, or
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`csv`). Use this for large result sets — rows stream directly to S3 instead of
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being buffered, bypassing the 10000-row return cap.
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