python - 是否可以编写一个 BigQuery 来检索 PyPI 下载随时间的分箱计数？

Question

以下代码是针对 google 的 BigQuery 的 SQL 查询，它计算我的 PyPI 包在过去 30 天内被下载的次数。

#standardSQL
SELECT COUNT(*) AS num_downloads
FROM `the-psf.pypi.downloads*`
WHERE file.project = 'pycotools'
  -- Only query the last 30 days of history
  AND _TABLE_SUFFIX
    BETWEEN FORMAT_DATE(
      '%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
    AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())

是否可以修改此查询，以便我在包上传后每 30 天获取一次下载次数？输出将是一个 .csv，看起来像这样:

date          count
01-01-2016    10
01-02-2016    20
    ..        ..
01-05-2018    100

Answer 1

我建议使用 EXTRACT 或 MONTH() 并只计算 file.project 字段，因为它会让查询运行得更快。您可以使用的查询是:

#standardSQL
SELECT
  EXTRACT(MONTH FROM _PARTITIONDATE) AS month_, 
  EXTRACT(YEAR FROM _PARTITIONDATE) AS year_,
  count(file.project) as count
FROM
  `the-psf.pypi.downloads*`
WHERE
  file.project= 'pycotools'
    GROUP BY 1, 2
    ORDER by 1 ASC

我用公共(public)数据集试了一下:

#standardSQL
SELECT
  EXTRACT(MONTH FROM pickup_datetime) AS month_, 
  EXTRACT(YEAR FROM pickup_datetime) AS year_,
  count(rate_code) as count
FROM
  `nyc-tlc.green.trips_2015`
WHERE
  rate_code=5
GROUP BY 1, 2
ORDER by 1 ASC

或使用旧版

SELECT
  MONTH(pickup_datetime) AS month_, 
  YEAR(pickup_datetime) AS year_,
  count(rate_code) as count
FROM
  [nyc-tlc:green.trips_2015]
  WHERE
  rate_code=5
  GROUP BY 1, 2
  ORDER by 1 ASC

结果是:

month_  year_   count    
1       2015    34228    
2       2015    36366    
3       2015    42221    
4       2015    41159    
5       2015    41934    
6       2015    39506        

我看到您正在使用 _TABLE_SUFFIX，所以如果您正在查询分区表，您可以使用 _PARTITIONDATE列而不是格式化日期并使用 date_sub 函数。这也将使用更少的计算时间。

查询自one partition :

SELECT
  [COLUMN]
FROM
  [DATASET].[TABLE]
WHERE
  _PARTITIONDATE BETWEEN '2016-01-01'
  AND '2016-01-02'