Presto 中是否有 window 函数来计算每个商家过去 90 天的平均支出？

Question

我想计算过去 90 天每个商家的平均支出。我一直在用 pyspark SQL:

df_spark = df_spark.withColumn("t_unix", F.unix_timestamp(df_spark['date']))

windowSpec = Window.orderBy("t_unix").partitionBy("merchant").rangeBetween(-3 * 30 * 24 * 3600, -1)
average_spending = F.avg(df_spark['amount']).over(windowSpec)

df = df_spark.withColumn("average_spending", average_spending)

df.select('merchant', 'date', "amount", "average_spending").show(5)

+---------+-------------------+-------+----------------+
| merchant|date               |amount |average_spending|
+---------+-------------------+-------+----------------+
| 26      |2017-01-01 01:11:06|  3    |            null|
| 26      |2017-01-01 02:02:15| 54    |             3.0|
| 26      |2017-01-01 02:26:45|  6    |            28.5|
| 26      |2017-01-01 02:40:37|  4    |            21.0|
| 26      |2017-01-01 02:41:51| 85    |           16.75|
+---------+-------------------+-------+----------------+
only showing top 5 rows

现在我想在 AWS Athena (Presto) 中完成。

我试过下面的查询：

但我收到错误信息：

Your query has the following error(s):

SYNTAX_ERROR: line 7:24: Unexpected parameters (varchar(3), integer, varchar) for function date_add. Expected: date_add(varchar(x), bigint, date) , date_add(varchar(x), bigint, time) , date_add(varchar(x), bigint, time with time zone) , date_add(varchar(x), bigint, timestamp) , date_add(varchar(x), bigint, timestamp with time zone)

但是在 date_add('day', -90, "date") 中，我希望 "date" 作为该行的当前时间戳，而不是静态时间戳。

SELECT
   "date",
   "merchant",
   "amount",
   AVG("amount") 
   FROM "table"
   WHERE ("date" BETWEEN date_add('day', -90, "date") and "date")
   GROUP BY "merchant"
   ORDER BY "date"
   LIMIT 5

但我收到错误信息：

Your query has the following error(s):

SYNTAX_ERROR: line 7:24: Unexpected parameters (varchar(3), integer, varchar) for function date_add. Expected: date_add(varchar(x), bigint, date) , date_add(varchar(x), bigint, time) , date_add(varchar(x), bigint, time with time zone) , date_add(varchar(x), bigint, timestamp) , date_add(varchar(x), bigint, timestamp with time zone)

但是在 date_add('day', -90, "date") 我想要 "date" 作为 行的当前时间戳并且不是静态时间戳。

我又做了一次尝试：

SELECT
   unix_date,
   merchant,
   amount,
   AVG(amount)
      OVER 
      (  PARTITION BY merchant
         ORDER BY unix_date
         RANGE BETWEEN INTERVAL '90' DAY PRECEDING AND CURRENT ROW
      ) AVG_S
FROM ...;

但我收到错误信息：

SYNTAX_ERROR: line 5:4: Window frame start value type must be INTEGER or BIGINT(actual interval day to second)

这里有一个类似的未解决问题： Presto SQL window aggregate looking back x hours/minutes/seconds

Answer 1

如果每个商家×日期有一个数据点，这可以使用 Presto's window functions

轻松完成

SELECT
  date, merchant, amount,
  avg(amount) OVER (
    PARTITION BY merchant
    ORDER BY date ASC
    ROWS 89 PRECEDING) -- 89 preceding rows + the current row
  FROM ...
  ORDER BY date ASC -- not necessary, but you likely want the data to be sorted as well

如果每个商家和日期的数据点数量不同，您可以这样做：

SELECT
  c.merchant, c.date, c.amount, avg(preceding.amount)
  FROM your_table c
  JOIN your_table preceding ON c.merchant = preceding.merchant
    AND preceding.date BETWEEN (c.date - INTERVAL '89' DAY) AND c.date
  GROUP BY c.merchant, c.date, c.amount

Answer 2

这对我有用。

CREATE TABLE IF NOT EXISTS full_year_query_parquet
  WITH (format = 'PARQUET',
        parquet_compression = 'SNAPPY',
        external_location='s3://your_s3_bucket/data') AS
SELECT
    a.merchant,
    a.amount,
    a.date,
    avg(preceding.amount)
  FROM "your_table" as a
  JOIN "your_table" as preceding ON a.merchant = preceding.merchant
    AND preceding.date > DATE_ADD('day', -90, a.date)
    AND preceding.date < a.date
  GROUP BY a.merchant, a.amount, a.date

Presto 中是否有 window 函数来计算每个商家过去 90 天的平均支出？

Is there a window function to compute average spending per merchant the last 90 days in Presto?

sql

presto

amazon-athena