Explanation:
The task is to extract the month from a UNIX timestamp (seconds since epoch) stored as an integer. The correct approach involves converting the integer to a timestamp type before extracting the month.
getMonth, which is not a valid Spark function.substr on the integer, which would incorrectly treat it as a string and split digits, not convert to a date.Date, but Spark interprets numeric casts to Date as days since epoch, not seconds, leading to incorrect dates.Timestamp (seconds since epoch) and uses month() to extract the month.month() directly on the integer, which is invalid as month() requires a date/timestamp.Only Option D correctly handles the conversion and extraction.
Ultimate access to all questions.
No comments yet.
Which of the following code blocks correctly returns a DataFrame with a month column containing the integer month value extracted from the openDate column in storesDF?
Note: The openDate column is of integer type and stores UNIX epoch timestamps (seconds since midnight January 1, 1970).
A sample of storesDF is shown below:
storeId openDate
0 1100746394
1 1474410343
2 1116610009
3 1180035265
4 1408024997
storeId openDate
0 1100746394
1 1474410343
2 1116610009
3 1180035265
4 1408024997
A
storesDF.withColumn("month", getMonth(col("openDate")))
B
storesDF.withColumn("month", substr(col("openDate"), 4, 2))
C
(storesDF.withColumn("openDateFormat", col("openDate").cast("Date")) .withColumn("month", month(col("openDateFormat"))))
D
(storesDF.withColumn("openTimestamp", col("openDate").cast("Timestamp")) .withColumn("month", month(col("openTimestamp"))))
E
storesDF.withColumn("month", month(col("openDate")))