Spark Scala中从字符串到日期的转换

Question

The question is reframed by giving more details.

I have a dataframe "dailyshow" Schema is:

scala> dailyshow.printSchema
root
 |-- year: integer (nullable = true)
 |-- occupation: string (nullable = true)
 |-- showdate: string (nullable = true)
 |-- group: string (nullable = true)
 |-- guest: string (nullable = true)

Sample Data is:

scala> dailyshow.show(5)
+----+------------------+---------+------+----------------+
|year|        occupation| showdate| group|           guest|
+----+------------------+---------+------+----------------+
|1999|             actor|1/11/1999|Acting|  Michael J. Fox|
|1999|          Comedian|1/12/1999|Comedy| Sandra Bernhard|
|1999|television actress|1/13/1999|Acting|   Tracey Ullman|
|1999|      film actress|1/14/1999|Acting|Gillian Anderson|
|1999|             actor|1/18/1999|Acting|David Alan Grier|
+----+------------------+---------+------+----------------+

Below code is used to transform and generate results which return the top 5 occupations between the time period "01/11/1999" and "06/11/1999"

scala> dailyshow.
    withColumn("showdate",to_date(unix_timestamp(col("showdate"),"MM/dd/yyyy").
    cast("timestamp"))).
    where((col("showdate") >= "1999-01-11") and (col("showdate") <= "1999-06-11")).
    groupBy(col("occupation")).agg(count("*").alias("count")).
    orderBy(desc("count")).
    limit(5).show
        +------------------+-----+                                                      
        |        occupation|count|
        +------------------+-----+
        |             actor|   29|
        |           actress|   20|
        |          comedian|    4|
        |television actress|    3|
        | stand-up comedian|    2|
        +------------------+-----+

My question is how to code and get the same result when using RDD?

scala> dailyshow.first
res12: org.apache.spark.sql.Row = [1999,actor,1/11/1999,Acting,Michael J. Fox]

I used SimpleDateFormat to parse the string to date in a DataFrame .

Below is the code:

val format = new java.text.SimpleDateFormat("MM/dd/yyyy")

dailyshow.
  map(x => x.mkString(",")).
  map(x => x.split(",")).
  map(x => format.parse(x(2))).first // returns Mon Jan 11 00:00:00 PST 1999

Answer 1

If I were you I would use spark's internal date functions as defined in org.apache.spark.sql.functions instead of manually doing it myself with simple date and mapping. This is because using dataframe functions is much simpler, much more idiomatic, less error prone and performs much better.

Lets assume you have a dataframe df which has column called dateString which contains a date string in the format MM/dd/yyyy.

Let's also assume you want to convert it to a date in order to extract the year and then display it in the format yyyy.MMMMM.dd

What you can do is:

val dfWithDate = df.withColumn("date", to_date($"dateString")
val dfWithYear = dfWithDate.withColumn("year", year($"date"))
val dfWithOutput = dfWithYear.withColumn("dateOutput", date_format("$date", "yyyy.MMMMM.dd")

Now the year column would contain the year and the dateOutput column would contain the string representation with your format.

Answer 2

Got a lot of deprecation warning while writing this :D

So we have this data in a RDD

val rdd = sc.parallelize(Array(
     Array("1999","actor","1/11/1999","Acting","  Michael J. Fox"),
     Array("1999","Comedian","1/12/1999","Comedy"," Sandra Bernhard"),
     Array("1999","television actress","1/13/1999","Acting","Tracey Ullman"),
     Array("1999","film actress","1/14/1999","Acting","Gillian Anderson"),
     Array("1999","actor","1/18/1999","Acting","David Alan Grier")))

Then as per your question, we do a filter on date:

val filtered = rdd.filter{ x => 
    format.parse(x(2)).after( new java.util.Date("01/10/1999")) && 
    format.parse(x(2)).before(new java.util.Date("01/14/1999")) 
}

Then we get this :

Array[Array[String]] = Array(
Array(1999, actor, 1/11/1999, Acting, "  Michael J. Fox"), 
Array(1999, Comedian, 1/12/1999, Comedy, " Sandra Bernhard"), 
Array(1999, television actress, 1/13/1999, Acting, Tracey Ullman))

Then we group them with the second element as the key and count the number of occurrences :

filtered.keyBy(x => x(1) ).map((_, 1) ).reduceByKey(_+_).map{ case ((a, b) ,c) => (a,c) }

If everything goes right , you should get :

Array[(String, Int)] = Array((television actress,1), (Comedian,1), (actor,1))