标签: bigquery-udf

我正在执行从 Teradata 到 Big query 的迁移。我遇到过在 USING 子句中包含 VALUES 的合并语句。

MERGE INTO department DL
                        USING VALUES
                        (
                        2,'ABC'
                        ) AS V 
                        (Run_Id, Country) 
                          ON DL.department_id = V.Run_Id
                        WHEN MATCHED THEN
                          UPDATE SET 
                            department_description = V.country
                        WHEN NOT MATCHED THEN
                          INSERT
                          (
                          V.Run_Id
                          , V.Country
                          curr
                          ); 

谁能帮我找到它的 BigQuery 等效项。

BigQuery 支持：

1. SQL 和 JavaScript 中的用户定义函数(UDF)。
2. 计算一组行的值并为每行返回一个结果的分析函数。这些函数可以与OVER子句一起使用。有一组预定义的分析函数。

问题#1：“BigQuery 是否支持分析用户定义函数？”

其背后的动机是我想实现Python pandas 代码中常见的拆分-应用-组合模式。这对于组内标准化和使用组统计数据的其他转换很有用。

我在Standart SQL中做了一个小测试：

create or replace function `mydataset.mylen`(arr array<string>) returns int64 as (
  array_length(arr)
);

WITH Produce AS
 (SELECT 'kale' as item, 23 as purchases, 'vegetable' as category
  UNION ALL SELECT 'orange', 2, 'fruit'
  UNION ALL SELECT 'cabbage', 9, 'vegetable'
  UNION ALL SELECT 'apple', 8, 'fruit'
  UNION ALL SELECT 'leek', 2, 'vegetable'
  UNION ALL SELECT 'lettuce', 10, 'vegetable')
SELECT 
  item, 
  purchases, 
  category, 
  `mydataset.mylen`(item) …

我想用动态键提取嵌套的 JSON。我目前可以借助和提取密钥，但解析 JSON 对象的值会产生意外的结果。“[对象对象]”12

{
    "key1":{"ItemID":1,"UseCount":4,"ItemCount":7},
    "key2":{"ItemID":2,"UseCount":5,"ItemCount":8},
    "key3":{"ItemID":3,"UseCount":6,"ItemCount":9}
    ...
}

WITH
sample_logs AS (
    SELECT '{"key1":{"ItemID":1,"UseCount":4,"ItemCount":7},"key2":{"ItemID":2,"UseCount":5,"ItemCount":8},"key3":{"ItemID":3,"UseCount":6,"ItemCount":9}}' as json_string,
    UNION ALL SELECT '{"key4":{"ItemID":1,"UseCount":4,"ItemCount":7},"key5":{"ItemID":2,"UseCount":5,"ItemCount":8}}'
)
SELECT
    json_string,
    key,
    TO_JSON_STRING(value) as value,
FROM sample_logs
CROSS JOIN UNNEST(bqutil.fn.json_extract_keys(json_string)) as key WITH OFFSET
INNER JOIN UNNEST(bqutil.fn.json_extract_values(json_string)) as value WITH OFFSET USING (OFFSET)
;

JSON_STRING1  |  "key1"   |  {"ItemID":1,"UseCount":4,"ItemCount":7}  -- <- not [object Object]
JSON_STRING1  | …