PySpark从TimeStampType列向DataFrame添加一列

Question

我有一个看起来像这样的DataFrame.我想在date_time现场操作.

root
 |-- host: string (nullable = true)
 |-- user_id: string (nullable = true)
 |-- date_time: timestamp (nullable = true)

我试图添加一列来提取这一天.到目前为止,我的尝试失败了.

df = df.withColumn("day", df.date_time.getField("day"))

org.apache.spark.sql.AnalysisException: GetField is not valid on fields of type TimestampType;

这也失败了

df = df.withColumn("day", df.select("date_time").map(lambda row: row.date_time.day))

AttributeError: 'PipelinedRDD' object has no attribute 'alias'

知道如何做到这一点？

Answer 1

你可以使用简单map:

df.rdd.map(lambda row:
    Row(row.__fields__ + ["day"])(row + (row.date_time.day, ))
)

另一种选择是注册一个函数并运行SQL查询:

sqlContext.registerFunction("day", lambda x: x.day)
sqlContext.registerDataFrameAsTable(df, "df")
sqlContext.sql("SELECT *, day(date_time) as day FROM df")

最后你可以像这样定义udf:

from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType

day = udf(lambda date_time: date_time.day, IntegerType())
df.withColumn("day", day(df.date_time))

编辑:

实际上,如果您使用原始SQL day函数已经定义(至少在Spark 1.4中),那么您可以省略udf注册.它还提供了许多不同的日期处理功能,包括:

• 干将喜欢year,month,dayofmonth

• 日期算术工具,如date_add,datediff

• 解析器喜欢from_unixtime和格式化date_format

也可以使用简单的日期表达式,如:

current_timestamp() - expr("INTERVAL 1 HOUR")

这意味着您可以构建相对复杂的查询而无需将数据传递给Python.例如:

df =  sc.parallelize([
    (1, "2016-01-06 00:04:21"),
    (2, "2016-05-01 12:20:00"),
    (3, "2016-08-06 00:04:21")
]).toDF(["id", "ts_"])

now = lit("2016-06-01 00:00:00").cast("timestamp") 
five_months_ago = now - expr("INTERVAL 5 MONTHS")

(df
    # Cast string to timestamp
    # For Spark 1.5 use cast("double").cast("timestamp")
    .withColumn("ts", unix_timestamp("ts_").cast("timestamp"))
    # Find all events in the last five months
    .where(col("ts").between(five_months_ago, now))
    # Find first Sunday after the event
    .withColumn("next_sunday", next_day(col("ts"), "Sun"))
    # Compute difference in days
    .withColumn("diff", datediff(col("ts"), col("next_sunday"))))