免费做调查的网站,wordpress 文章设置,网站栏目收录,安徽建设工程网大纲 UDAF入参并非表中一行#xff08;Row#xff09;的集合计算每个人考了几门课计算每门课有几个人考试计算每个人的平均分计算每课的平均分计算每个人的最高分和最低分 入参是表中一行#xff08;Row#xff09;的集合计算每个人的最高分、最低分以及所属的课程计算每课… 大纲 UDAF入参并非表中一行Row的集合计算每个人考了几门课计算每门课有几个人考试计算每个人的平均分计算每课的平均分计算每个人的最高分和最低分 入参是表中一行Row的集合计算每个人的最高分、最低分以及所属的课程计算每课的最高分数、最低分数以及所属人 完整代码入参并非表中一行Row的集合入参是表中一行Row的集合 在前面几篇文章中我们学习了非聚合类的用户自定义函数。这节我们将介绍最简单的聚合函数UDAF。
UDAF
我们对比下UDAF和UDF的定义
def udaf(f: Union[Callable, AggregateFunction, Type] None,input_types: Union[List[DataType], DataType, str, List[str]] None,result_type: Union[DataType, str] None, accumulator_type: Union[DataType, str] None,deterministic: bool None, name: str None,func_type: str general) - Union[UserDefinedAggregateFunctionWrapper, Callable]:def udf(f: Union[Callable, ScalarFunction, Type] None,input_types: Union[List[DataType], DataType, str, List[str]] None,result_type: Union[DataType, str] None,deterministic: bool None, name: str None, func_type: str general,udf_type: str None) - Union[UserDefinedScalarFunctionWrapper, Callable]:可以发现
udaf比udf多了一个参数accumulator_typeudaf比udf少了一个参数udf_type
accumulator中文是“累加器”。我们可以将其看成聚合过后比如GroupBy的成批数据每批都要走一次函数。 举一个例子我们对图中左侧的成绩单使用人名name进行聚类然后计算出最高分数。即算出每个人考出的最高分数是多少。 如图所示聚合后的数据每个都会经过accumulator计算。计算出来的值的类型就是accumulator_type。这个类型的数据是中间态它并不是最终UDAF返回的数据类型——result_type。具体这块的知识我们会在后面讲解。 为了方便讲解我们就以上面例子来讲解其使用。先贴出准备的代码
from pyflink.common import Configuration
from pyflink.table import (EnvironmentSettings, TableEnvironment, Schema)
from pyflink.table.types import DataTypes
from pyflink.table.table_descriptor import TableDescriptor
from pyflink.table.expressions import lit, col
from pyflink.common import Row
from pyflink.table.udf import udf,udtf,udaf,udtaf
import pandas as pd
from pyflink.table.udf import UserDefinedFunctiondef word_count():config Configuration()# write all the data to one fileconfig.set_string(parallelism.default, 1)env_settings EnvironmentSettings \.new_instance() \.in_batch_mode() \.with_configuration(config) \.build()t_env TableEnvironment.create(env_settings)row_type_tab_source DataTypes.ROW([DataTypes.FIELD(name, DataTypes.STRING()), DataTypes.FIELD(score, DataTypes.FLOAT()), DataTypes.FIELD(class, DataTypes.STRING())])students_score [(张三, 80.0, English),(李四, 75.0, English),(王五, 90.0, English),(赵六, 85.0, English),(张三, 60.0, Math),(李四, 95.0, Math),(王五, 90.0, Math),(赵六, 70.0, Math),(孙七, 60.0, Math),]tab_source t_env.from_elements(students_score, row_type_tab_source )我们在tab_source表中录入了学生的成绩信息其中包括姓名name、成绩score和科目class。
入参并非表中一行Row的集合
计算每个人考了几门课
按姓名name聚类UDTF统计聚类后集合的个数并返回别名UDTF返回的列名select出数据
udaf(result_typeDataTypes.ROW([DataTypes.FIELD(count, DataTypes.BIGINT())]), func_typepandas)def exam_count(pandas_df: pd.DataFrame):return Row(pandas_df.count())tab_student_exam_count tab_source.group_by(col(name)) \.aggregate(exam_count(col(name)).alias(count)) \.select(col(name), col(count)) tab_student_exam_count.execute().print()------------------------------------------------------
| name | count |
------------------------------------------------------
| 孙七 | 1 |
| 张三 | 2 |
| 李四 | 2 |
| 王五 | 2 |
| 赵六 | 2 |
------------------------------------------------------
5 rows in set计算每门课有几个人考试
按姓名class聚类UDTF统计聚类后集合的个数并返回别名UDTF返回的列名select出数据 udaf(result_typeDataTypes.ROW([DataTypes.FIELD(count, DataTypes.BIGINT())]), func_typepandas)def exam_count(pandas_df: pd.DataFrame):return Row(pandas_df.count())tab_class_exam_count tab_source.group_by(col(class)) \.aggregate(exam_count(col(class)).alias(count)) \.select(col(class), col(count)) tab_class_exam_count.execute().print()------------------------------------------------------
| class | count |
------------------------------------------------------
| English | 4 |
| Math | 5 |
------------------------------------------------------
2 rows in set计算每个人的平均分
按姓名name聚类UDTF统计聚类后集合的均值并返回别名UDTF返回的列名select出数据 udaf(result_typeDataTypes.ROW([DataTypes.FIELD(avg, DataTypes.FLOAT())]), func_typepandas)def avg_score(pandas_df: pd.DataFrame):return Row(pandas_df.mean())tab_student_avg_score tab_source.group_by(col(name)) \.aggregate(avg_score(col(score)).alias(avg)) \.select(col(name), col(avg)) tab_student_avg_score.execute().print()----------------------------------------------------------------
| name | avg |
----------------------------------------------------------------
| 孙七 | 60.0 |
| 张三 | 70.0 |
| 李四 | 85.0 |
| 王五 | 90.0 |
| 赵六 | 77.5 |
----------------------------------------------------------------
5 rows in set计算每课的平均分
按姓名class聚类UDTF统计聚类后集合的均值并返回别名UDTF返回的列名select出数据 udaf(result_typeDataTypes.ROW([DataTypes.FIELD(avg, DataTypes.FLOAT())]), func_typepandas)def avg_score(pandas_df: pd.DataFrame):return Row(pandas_df.mean())tab_class_avg_score tab_source.group_by(col(class)) \.aggregate(avg_score(col(score)).alias(avg)) \.select(col(class), col(avg)) tab_class_avg_score.execute().print()----------------------------------------------------------------
| class | avg |
----------------------------------------------------------------
| English | 82.5 |
| Math | 75.0 |
----------------------------------------------------------------
2 rows in set计算每个人的最高分和最低分
按姓名name聚类UDTF统计聚类后集合的最大值和最小值并返回别名UDTF返回的列名select出数据 udaf(result_typeDataTypes.ROW([DataTypes.FIELD(max, DataTypes.FLOAT()), DataTypes.FIELD(min, DataTypes.FLOAT())]), func_typepandas)def max_min_score(pandas_df: pd.DataFrame):return Row(pandas_df.max(), pandas_df.min())tab_student_max_min_score tab_source.group_by(col(name)) \.aggregate(max_min_score(col(score)).alias(max, min)) \.select(col(name), col(max), col(min)) tab_student_max_min_score.execute().print()------------------------------------------------------------------------------------------------
| name | max | min |
------------------------------------------------------------------------------------------------
| 孙七 | 60.0 | 60.0 |
| 张三 | 80.0 | 60.0 |
| 李四 | 95.0 | 75.0 |
| 王五 | 90.0 | 90.0 |
| 赵六 | 85.0 | 70.0 |
------------------------------------------------------------------------------------------------
5 rows in set入参是表中一行Row的集合
计算每个人的最高分、最低分以及所属的课程
按姓名name聚类UDTF统计聚类后集合中分数最大值、最小值分数最大值所在行的课程名和分数最小值所在行的课程名并返回别名UDTF返回的列名select出数据 udaf(result_typeDataTypes.ROW([DataTypes.FIELD(max, DataTypes.FLOAT()), DataTypes.FIELD(max tag, DataTypes.STRING()), DataTypes.FIELD(min, DataTypes.FLOAT()), DataTypes.FIELD(min tag, DataTypes.STRING())]), func_typepandas)def max_min_score_with_class(pandas_df: pd.DataFrame):return Row(pandas_df[score].max(), pandas_df.loc[pandas_df[score].idxmax(), class], pandas_df[score].min(), pandas_df.loc[pandas_df[score].idxmin(), class])tab_student_max_min_score tab_source.group_by(col(name)) \.aggregate(max_min_score_with_class.alias(max, class(max), min, class(min))) \.select(col(name), col(max), col(class(max)), col(min), col(class(min))) tab_student_max_min_score.execute().print()----------------------------------------------------------------------------------------------------------------------------------------------------------------
| name | max | class(max) | min | class(min) |
----------------------------------------------------------------------------------------------------------------------------------------------------------------
| 孙七 | 60.0 | Math | 60.0 | Math |
| 张三 | 80.0 | English | 60.0 | Math |
| 李四 | 95.0 | Math | 75.0 | English |
| 王五 | 90.0 | English | 90.0 | English |
| 赵六 | 85.0 | English | 70.0 | Math |
----------------------------------------------------------------------------------------------------------------------------------------------------------------
5 rows in set计算每课的最高分数、最低分数以及所属人
按姓名class聚类UDTF统计聚类后集合中分数最大值、最小值分数最大值所在行的人名和分数最小值所在行的人名并返回别名UDTF返回的列名select出数据 udaf(result_typeDataTypes.ROW([DataTypes.FIELD(max, DataTypes.FLOAT()), DataTypes.FIELD(max tag, DataTypes.STRING()), DataTypes.FIELD(min, DataTypes.FLOAT()), DataTypes.FIELD(min tag, DataTypes.STRING())]), func_typepandas)def max_min_score_with_name(pandas_df: pd.DataFrame):return Row(pandas_df[score].max(), pandas_df.loc[pandas_df[score].idxmax(), name], pandas_df[score].min(), pandas_df.loc[pandas_df[score].idxmin(), name])tab_class_max_min_score tab_source.group_by(col(class)) \.aggregate(max_min_score_with_name.alias(max, name(max), min, name(min))) \.select(col(class), col(max), col(name(max)), col(min), col(name(min))) tab_class_max_min_score.execute().print()----------------------------------------------------------------------------------------------------------------------------------------------------------------
| class | max | name(max) | min | name(min) |
----------------------------------------------------------------------------------------------------------------------------------------------------------------
| English | 90.0 | 王五 | 75.0 | 李四 |
| Math | 95.0 | 李四 | 60.0 | 张三 |
----------------------------------------------------------------------------------------------------------------------------------------------------------------
2 rows in set完整代码
入参并非表中一行Row的集合
from pyflink.common import Configuration
from pyflink.table import (EnvironmentSettings, TableEnvironment, Schema)
from pyflink.table.types import DataTypes
from pyflink.table.table_descriptor import TableDescriptor
from pyflink.table.expressions import lit, col
from pyflink.common import Row
from pyflink.table.udf import udf,udtf,udaf,udtaf
import pandas as pd
from pyflink.table.udf import UserDefinedFunctiondef word_count():config Configuration()# write all the data to one fileconfig.set_string(parallelism.default, 1)env_settings EnvironmentSettings \.new_instance() \.in_batch_mode() \.with_configuration(config) \.build()t_env TableEnvironment.create(env_settings)row_type_tab_source DataTypes.ROW([DataTypes.FIELD(name, DataTypes.STRING()), DataTypes.FIELD(score, DataTypes.FLOAT()), DataTypes.FIELD(class, DataTypes.STRING())])students_score [(张三, 80.0, English),(李四, 75.0, English),(王五, 90.0, English),(赵六, 85.0, English),(张三, 60.0, Math),(李四, 95.0, Math),(王五, 90.0, Math),(赵六, 70.0, Math),(孙七, 60.0, Math),]tab_source t_env.from_elements(students_score, row_type_tab_source )udaf(result_typeDataTypes.ROW([DataTypes.FIELD(count, DataTypes.BIGINT())]), func_typepandas)def exam_count(pandas_df: pd.DataFrame):return Row(pandas_df.count())tab_student_exam_count tab_source.group_by(col(name)) \.aggregate(exam_count(col(name)).alias(count)) \.select(col(name), col(count)) tab_student_exam_count.execute().print()tab_class_exam_count tab_source.group_by(col(class)) \.aggregate(exam_count(col(class)).alias(count)) \.select(col(class), col(count)) tab_class_exam_count.execute().print()udaf(result_typeDataTypes.ROW([DataTypes.FIELD(avg, DataTypes.FLOAT())]), func_typepandas)def avg_score(pandas_df: pd.DataFrame):return Row(pandas_df.mean())tab_student_avg_score tab_source.group_by(col(name)) \.aggregate(avg_score(col(score)).alias(avg)) \.select(col(name), col(avg)) tab_student_avg_score.execute().print()tab_class_avg_score tab_source.group_by(col(class)) \.aggregate(avg_score(col(score)).alias(avg)) \.select(col(class), col(avg)) tab_class_avg_score.execute().print()udaf(result_typeDataTypes.ROW([DataTypes.FIELD(max, DataTypes.FLOAT()), DataTypes.FIELD(min, DataTypes.FLOAT())]), func_typepandas)def max_min_score(pandas_df: pd.DataFrame):return Row(pandas_df.max(), pandas_df.min())tab_student_max_min_score tab_source.group_by(col(name)) \.aggregate(max_min_score(col(score)).alias(max, min)) \.select(col(name), col(max), col(min)) tab_student_max_min_score.execute().print()if __name__ __main__:word_count()入参是表中一行Row的集合
from pyflink.common import Configuration
from pyflink.table import (EnvironmentSettings, TableEnvironment, Schema)
from pyflink.table.types import DataTypes
from pyflink.table.table_descriptor import TableDescriptor
from pyflink.table.expressions import lit, col
from pyflink.common import Row
from pyflink.table.udf import udf,udtf,udaf,udtaf
import pandas as pd
from pyflink.table.udf import UserDefinedFunctiondef word_count():config Configuration()# write all the data to one fileconfig.set_string(parallelism.default, 1)env_settings EnvironmentSettings \.new_instance() \.in_batch_mode() \.with_configuration(config) \.build()t_env TableEnvironment.create(env_settings)row_type_tab_source DataTypes.ROW([DataTypes.FIELD(name, DataTypes.STRING()), DataTypes.FIELD(score, DataTypes.FLOAT()), DataTypes.FIELD(class, DataTypes.STRING())])students_score [(张三, 80.0, English),(李四, 75.0, English),(王五, 90.0, English),(赵六, 85.0, English),(张三, 60.0, Math),(李四, 95.0, Math),(王五, 90.0, Math),(赵六, 70.0, Math),(孙七, 60.0, Math),]tab_source t_env.from_elements(students_score, row_type_tab_source )udaf(result_typeDataTypes.ROW([DataTypes.FIELD(max, DataTypes.FLOAT()), DataTypes.FIELD(max tag, DataTypes.STRING()), DataTypes.FIELD(min, DataTypes.FLOAT()), DataTypes.FIELD(min tag, DataTypes.STRING())]), func_typepandas)def max_min_score_with_class(pandas_df: pd.DataFrame):return Row(pandas_df[score].max(), pandas_df.loc[pandas_df[score].idxmax(), class], pandas_df[score].min(), pandas_df.loc[pandas_df[score].idxmin(), class])tab_student_max_min_score tab_source.group_by(col(name)) \.aggregate(max_min_score_with_class.alias(max, class(max), min, class(min))) \.select(col(name), col(max), col(class(max)), col(min), col(class(min))) tab_student_max_min_score.execute().print()udaf(result_typeDataTypes.ROW([DataTypes.FIELD(max, DataTypes.FLOAT()), DataTypes.FIELD(max tag, DataTypes.STRING()), DataTypes.FIELD(min, DataTypes.FLOAT()), DataTypes.FIELD(min tag, DataTypes.STRING())]), func_typepandas)def max_min_score_with_name(pandas_df: pd.DataFrame):return Row(pandas_df[score].max(), pandas_df.loc[pandas_df[score].idxmax(), name], pandas_df[score].min(), pandas_df.loc[pandas_df[score].idxmin(), name])tab_class_max_min_score tab_source.group_by(col(class)) \.aggregate(max_min_score_with_name.alias(max, name(max), min, name(min))) \.select(col(class), col(max), col(name(max)), col(min), col(name(min))) tab_class_max_min_score.execute().print()if __name__ __main__:word_count()
