列数据值一致性检查 PySpark SQL

Question

我有两个 table 具有相同的列名、相同的数据、相同的行数，但行的顺序可能不同。现在我 select 来自 table_1 的 A 列和来自 table_2 的 A 列并比较这些值。我如何使用 PySpark 实现此目的 SQL 我可以执行 sha2/md5 校验和比较吗？

from pyspark import SparkContext, SparkConf
from pyspark.sql import HiveContext
from pyspark.sql.types import *
from pyspark.sql import Row
import pyspark.sql.functions as f

app_name="test"
table1="DB1.department"
table2="DB2.department"
conf = SparkConf().setAppName(app_name)
sc = SparkContext(conf=conf)
sqlContext = HiveContext(sc)

query1="select * from %s" %(table1)
df1 = sqlContext.sql(query1)
query2="select * from %s" %(table2)
df2 = sqlContext.sql(query2)
df3=sqlContext.sql(SELECT  DB1.departmentid FROM  DB1.department a FULL JOIN 
DB2.department b ON a.departmentid = b.departmentid WHERE   a.departmentid 
IS NULL OR b.departmentid IS NULL)
df5=sqlContext.sql("select md5(departmentid) from department1")

Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/spark/python/pyspark/sql/context.py", line 580, in sql
return DataFrame(self._ssql_ctx.sql(sqlQuery), self)
File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 
813, in __call__
File "/usr/lib/spark/python/pyspark/sql/utils.py", line 51, in deco
raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: u"cannot resolve 'md5(departmentid)' 
due to data type mismatch: argument 1 requires binary type, however,

'departmentid'是bigint类型；第 1 行位置 11"

当尝试使用 md5 校验和时，它说它需要二进制类型，但部门 ID 是 bigint

表 1:

departmentid  departmentname   departmentaddress
 1            A           Newyork
 2            B           Newjersey
 3            C           SanJose
 4            D           WashingtonDC
 5            E           Mexico
 6            F           Delhi
 7            G           Pune
 8            H           chennai

表 2:

departmentid   departmentname   departmentaddress
 7            G         Pune
 8            H         chennai
 1            A         Newyork
 2            B         Newjersey
 3            C         SanJose
 4            D         WashingtonDC
 5            E         Mexico
 6            F         Delhi

在 table 中，两个行的顺序刚刚改变，但数据仍然如此，现在从技术上讲，这两个 table 是相同的。除非添加新行或修改值，否则这两个 table 是相同的（以表为例和解释，实际上我们处理大数据）

Answer 1

最简单的解决方案是：

def is_identical(x, y):
    return (x.count() == y.count()) and (x.subtract(y).count() == 0)

示例数据：

df1 = spark.createDataFrame(
    [(1, "A", "Newyork"), (2, "B", "Newjersey"),
    (3, "C", "SanJose"), (4, "D", "WashingtonDC"), (5, "E", "Mexico"), (6, "F", "Delhi"),
    (7, "G", "Pune"), (8, "H", "chennai")],
    ("departmentid", "departmentname", "departmentadd"))

df2 = spark.createDataFrame(
    [(7, "G", "Pune"), (8, "H", "chennai"), (1, "A", "Newyork"), (2, "B", "Newjersey"),
    (3, "C", "SanJose"), (4, "D", "WashingtonDC"), (5, "E", "Mexico"), (6, "F", "Delhi")],
    ("departmentid", "departmentname", "departmentadd"))

df3 = spark.createDataFrame(
    [(1, "A", "New York"), (2, "B", "New Jersey"),
    (3, "C", "SanJose"), (4, "D", "WashingtonDC"), (5, "E", "Mexico"), (6, "F", "Delhi"),
    (7, "G", "Pune"), (8, "H", "chennai")],
    ("departmentid", "departmentname", "departmentadd"))

df4 = spark.createDataFrame(
    [(3, "C", "SanJose"), (4, "D", "WashingtonDC"), (5, "E", "Mexico"), (6, "F", "Delhi")],
    ("departmentid", "departmentname", "departmentadd"))

支票：

is_identical(df1, df2)
# True
is_identical(df1, df3)
# False
is_identical(df1, df4)
# False
is_identical(df4, df4)
# True

有外连接

from pyspark.sql.functions import col, coalesce, lit

from functools import reduce
from operator import and_

def is_identical_(x, y, keys=("departmentid", )):
    def both_null(c):
        return (col("x.{}".format(c)).isNull() & 
                col("y.{}".format(c)).isNull())
    def both_equal(c):
        return coalesce((col("x.{}".format(c)) == 
                col("y.{}".format(c))), lit(False))



    p = reduce(and_, [both_null(c) | both_equal(c) for c in x.columns if c not in keys])

    return (x.alias("x").join(y.alias("y"), list(keys), "full_outer")
            .where(~p).count() == 0)

你会得到相同的结果：

is_identical_(df1, df2)
# True
is_identical_(df1, df3)
# False
is_identical_(df1, df4)
# False
is_identical_(df4, df4)
# True

md5 不适合你，因为它不是聚合函数。它计算特定值的校验和。

列数据值一致性检查 PySpark SQL

Column data value consistency check PySpark SQL

apache-spark

apache-spark-sql

pyspark

spark-dataframe

pyspark-sql