將列表中的缺失元素作為每個時間窗口組的行插入到DataFrame中

Question

試圖從語法上解決這個問題……似乎是一個困難的問題……基本上，如果未在時間序列時間戳記間隔源數據中捕獲傳感器項，則希望為每個缺少的傳感器項附加一行，每行的值為NULL時間戳窗口

# list of sensor items [have 300 plus; only showing 4 as example]
list = ["temp", "pressure", "vacuum", "burner"]

# sample data
df = spark.createDataFrame([('2019-05-10 7:30:05', 'temp', '99'),\
                            ('2019-05-10 7:30:05', 'burner', 'TRUE'),\
                            ('2019-05-10 7:30:10', 'vacuum', '.15'),\
                            ('2019-05-10 7:30:10', 'burner', 'FALSE'),\
                            ('2019-05-10 7:30:10', 'temp', '75'),\
                            ('2019-05-10 7:30:15', 'temp', '77'),\
                            ('2019-05-10 7:30:20', 'pressure', '.22'),\
                            ('2019-05-10 7:30:20', 'temp', '101'),], ["date", "item", "value"])

# current dilemma => all sensor items are not being captured / only updates to sensors are being captured in current back-end design streaming devices
+------------------+--------+-----+
|              date|    item|value|
+------------------+--------+-----+
|2019-05-10 7:30:05|    temp|   99|
|2019-05-10 7:30:05|  burner| TRUE|

|2019-05-10 7:30:10|  vacuum|  .15|
|2019-05-10 7:30:10|  burner|FALSE|
|2019-05-10 7:30:10|    temp|   75|

|2019-05-10 7:30:15|    temp|   77|

|2019-05-10 7:30:20|pressure|  .22|
|2019-05-10 7:30:20|    temp|  101|
+------------------+--------+-----+

想要捕獲每個時間戳的每個傳感器項，因此可以在旋轉數據幀之前執行正向填充估算[正向填充300 plus cols會導致scala錯誤=>

引發火花：java.lang.StackOverflowError窗口函數？

# desired output
+------------------+--------+-----+
|              date|    item|value|
+------------------+--------+-----+
|2019-05-10 7:30:05|    temp|   99|
|2019-05-10 7:30:05|  burner| TRUE|
|2019-05-10 7:30:05|  vacuum| NULL|
|2019-05-10 7:30:05|pressure| NULL|

|2019-05-10 7:30:10|  vacuum|  .15|
|2019-05-10 7:30:10|  burner|FALSE|
|2019-05-10 7:30:10|    temp|   75|
|2019-05-10 7:30:10|pressure| NULL|

|2019-05-10 7:30:15|    temp|   77|
|2019-05-10 7:30:15|pressure| NULL|
|2019-05-10 7:30:15|  burner| NULL|
|2019-05-10 7:30:15|  vacuum| NULL|

|2019-05-10 7:30:20|pressure|  .22|
|2019-05-10 7:30:20|    temp|  101|
|2019-05-10 7:30:20|  vacuum| NULL|
|2019-05-10 7:30:20|  burner| NULL|
+------------------+--------+-----+

Answer 1

擴展我的評論 ：

您可以直接與不同的日期和的笛卡爾乘積加入您的數據幀sensor_list 。 由於sensor_list很小，因此可以broadcast它。

from pyspark.sql.functions import broadcast

sensor_list = ["temp", "pressure", "vacuum", "burner"]

df.join(
    df.select('date')\
        .distinct()\
        .crossJoin(broadcast(spark.createDataFrame([(x,) for x in sensor_list], ["item"]))),
    on=["date", "item"],
    how="right"
).sort("date", "item").show()
#+------------------+--------+-----+
#|              date|    item|value|
#+------------------+--------+-----+
#|2019-05-10 7:30:05|  burner| TRUE|
#|2019-05-10 7:30:05|pressure| null|
#|2019-05-10 7:30:05|    temp|   99|
#|2019-05-10 7:30:05|  vacuum| null|
#|2019-05-10 7:30:10|  burner|FALSE|
#|2019-05-10 7:30:10|pressure| null|
#|2019-05-10 7:30:10|    temp|   75|
#|2019-05-10 7:30:10|  vacuum|  .15|
#|2019-05-10 7:30:15|  burner| null|
#|2019-05-10 7:30:15|pressure| null|
#|2019-05-10 7:30:15|    temp|   77|
#|2019-05-10 7:30:15|  vacuum| null|
#|2019-05-10 7:30:20|  burner| null|
#|2019-05-10 7:30:20|pressure|  .22|
#|2019-05-10 7:30:20|    temp|  101|
#|2019-05-10 7:30:20|  vacuum| null|
#+------------------+--------+-----+