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我有一个数据框作为多个连接的结果。我想调查是否有重复的。但每次我调查时, Dataframe 看起来都不一样。特别是,下面的命令会导致不同的IDs
,但结果的数量保持不变。
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
import pyspark.sql.functions as f
from pyspark.sql.functions import lit
# Create a Spark session
spark = SparkSession.builder.appName("CreateDataFrame").getOrCreate()
# User input for number of rows
n_a = 10
n_a_c = 5
n_a_c_d = 3
n_a_c_e = 4
# Define the schema for the DataFrame
schema_a = StructType([StructField("id1", StringType(), True)])
schema_a_b = StructType(
[
StructField("id1", StringType(), True),
StructField("id2", StringType(), True),
StructField("extra", StringType(), True),
]
)
schema_a_c = StructType(
[
StructField("id1", StringType(), True),
StructField("id3", StringType(), True),
]
)
schema_a_c_d = StructType(
[
StructField("id3", StringType(), True),
StructField("id4", StringType(), True),
]
)
schema_a_c_e = StructType(
[
StructField("id3", StringType(), True),
StructField("id5", StringType(), True),
]
)
# Create a list of rows with increasing integer values for "id1" and a constant value of "1" for "id2"
rows_a = [(str(i),) for i in range(1, n_a + 1)]
rows_a_integers = [str(i) for i in range(1, n_a + 1)]
rows_a_b = [(str(i), str(1), "A") for i in range(1, n_a + 1)]
def get_2d_list(ids_part_1: list, n_new_ids: int):
rows = [
[
(str(i), str(i) + "_" + str(j))
for i in ids_part_1
for j in range(1, n_new_ids + 1)
]
]
return [item for sublist in rows for item in sublist]
rows_a_c = get_2d_list(ids_part_1=rows_a_integers, n_new_ids=n_a_c)
rows_a_c_d = get_2d_list(ids_part_1=[i[1] for i in rows_a_c], n_new_ids=n_a_c_d)
rows_a_c_e = get_2d_list(ids_part_1=[i[1] for i in rows_a_c], n_new_ids=n_a_c_e)
# Create the DataFrame
df_a = spark.createDataFrame(rows_a, schema_a)
df_a_b = spark.createDataFrame(rows_a_b, schema_a_b)
df_a_c = spark.createDataFrame(rows_a_c, schema_a_c)
df_a_c_d = spark.createDataFrame(rows_a_c_d, schema_a_c_d)
df_a_c_e = spark.createDataFrame(rows_a_c_e, schema_a_c_e)
# Join everything
df_join = (
df_a.join(df_a_b, on="id1")
.join(df_a_c, on="id1")
.join(df_a_c_d, on="id3")
.join(df_a_c_e, on="id3")
)
# Nested structure
# show
df_nested = df_join.withColumn("id3", f.struct(f.col("id3")))
for i, index in enumerate([(5, 3), (4, 3), (3, None)]):
remaining_columns = list(set(df_nested.columns).difference(set([f"id{index[0]}"])))
df_nested = (
df_nested.groupby(*remaining_columns)
.agg(f.collect_list(f.col(f"id{index[0]}")).alias(f"id{index[0]}_tmp"))
.drop(f"id{index[0]}")
.withColumnRenamed(
f"id{index[0]}_tmp",
f"id{index[0]}",
)
)
if index[1]:
df_nested = df_nested.withColumn(
f"id{index[1]}",
f.struct(
f.col(f"id{index[1]}.*"),
f.col(f"id{index[0]}"),
).alias(f"id{index[1]}"),
).drop(f"id{index[0]}")
# Investigate for duplicates in id3 (should be unique)
df_test = df_nested.select("id2", "extra", f.explode(f.col("id3")["id3"]).alias("id3"))
for i in range(5):
df_test.groupby("id3").count().filter(f.col("count") > 1).show()
最后一个命令打印两个my case不同结果之一。有时:
+---+-----+
|id3|count|
+---+-----+
|6_4| 2|
+---+-----+
有时候
+---+-----+
|id3|count|
+---+-----+
|9_3| 2|
+---+-----+
如果有帮助,我使用Databricks Runtime版本11。3 LTS(包括Apache Spark 3。3.0,Scala 2.12)
此外,不能有重复的理解,我的基础上的设计的代码。找到的副本似乎是一个bug!?
也许可以作为连接不会导致任何重复的潜在证据:
df_join.groupby("id3", "id4", "id5").count().filter(f.col("count") > 1).show()
是空的
1条答案
按热度按时间bpzcxfmw1#
列
"id3"
的构造方式是随机的,所以每次执行都会得到不同的结果,你需要定义一个orderBy()来得到相同的结果,所以在该列上添加一个orderBy()后,如下所示:现在,对于多次执行,您将始终获得相同的结果。
记住,spark求值是惰性的,因此Dag将为每个动作重新构造,在本例中是show()。
因此,如果你的代码不是确定性的,它每次都会给予不同的输出。