pyspark collect_set 或 collect_list 与 groupby

Question

如何在 groupby 之后的数据帧上使用 collect_set 或 collect_list。例如：df.groupby('key').collect_set('values')。我得到一个错误：AttributeError: 'GroupedData' object has no attribute 'collect_set'

Answer 1

您需要使用聚合。示例：

from pyspark import SparkContext
from pyspark.sql import HiveContext
from pyspark.sql import functions as F

sc = SparkContext("local")

sqlContext = HiveContext(sc)

df = sqlContext.createDataFrame([
    ("a", None, None),
    ("a", "code1", None),
    ("a", "code2", "name2"),
], ["id", "code", "name"])

df.show()

+---+-----+-----+
| id| code| name|
+---+-----+-----+
|  a| null| null|
|  a|code1| null|
|  a|code2|name2|
+---+-----+-----+

注意上面你必须创建一个 HiveContext。有关处理不同 Spark 版本的信息，请参阅。

(df
  .groupby("id")
  .agg(F.collect_set("code"),
       F.collect_list("name"))
  .show())

+---+-----------------+------------------+
| id|collect_set(code)|collect_list(name)|
+---+-----------------+------------------+
|  a|   [code1, code2]|           [name2]|
+---+-----------------+------------------+

Answer 2

如果你的dataframe很大，你可以尝试使用pandas udf(GROUPED_AGG)来避免内存错误。它也快得多。

Grouped aggregate Pandas UDFs are similar to Spark aggregate functions. Grouped aggregate Pandas UDFs are used with groupBy().agg() and pyspark.sql.Window. It defines an aggregation from one or more pandas.Series to a scalar value, where each pandas.Series represents a column within the group or window. pandas udf

示例：

import pyspark.sql.functions as F

@F.pandas_udf('string', F.PandasUDFType.GROUPED_AGG)
def collect_list(name):
    return ', '.join(name)

grouped_df = df.groupby('id').agg(collect_list(df["name"]).alias('names'))

pyspark collect_set 或 collect_list 与 groupby

pyspark collect_set or collect_list with groupby

list

group-by

set

pyspark

collect