大纲
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
python
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]:
python
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_type
- udaf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：
python
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 UserDefinedFunction
def calc():
config = Configuration()
# write all the data to one file
config.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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("count", DataTypes.BIGINT())]), func_type="pandas")
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()
bash
+--------------------------------+----------------------+
| 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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("count", DataTypes.BIGINT())]), func_type="pandas")
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()
bash
+--------------------------------+----------------------+
| 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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("avg", DataTypes.FLOAT())]), func_type="pandas")
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()
bash
+--------------------------------+--------------------------------+
| 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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("avg", DataTypes.FLOAT())]), func_type="pandas")
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()
bash
+--------------------------------+--------------------------------+
| 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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("min", DataTypes.FLOAT())]), func_type="pandas")
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()
bash
+--------------------------------+--------------------------------+--------------------------------+
| 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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("max tag", DataTypes.STRING()), DataTypes.FIELD("min", DataTypes.FLOAT()), DataTypes.FIELD("min tag", DataTypes.STRING())]), func_type="pandas")
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()
bash
+--------------------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
| 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
python
@udaf(result_type=DataTypes.ROW([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("max tag", DataTypes.STRING()), DataTypes.FIELD("min", DataTypes.FLOAT()), DataTypes.FIELD("min tag", DataTypes.STRING())]), func_type="pandas")
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()
bash
+--------------------------------+--------------------------------+--------------------------------+--------------------------------+--------------------------------+
| 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
python
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 UserDefinedFunction
def calc():
config = Configuration()
# write all the data to one file
config.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_type=DataTypes.ROW([DataTypes.FIELD("count", DataTypes.BIGINT())]), func_type="pandas")
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_type=DataTypes.ROW([DataTypes.FIELD("avg", DataTypes.FLOAT())]), func_type="pandas")
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_type=DataTypes.ROW([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("min", DataTypes.FLOAT())]), func_type="pandas")
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__':
calc()入参是表中一行（Row）的集合
python
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 UserDefinedFunction
def calc():
config = Configuration()
# write all the data to one file
config.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_type=DataTypes.ROW([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("max tag", DataTypes.STRING()), DataTypes.FIELD("min", DataTypes.FLOAT()), DataTypes.FIELD("min tag", DataTypes.STRING())]), func_type="pandas")
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_type=DataTypes.ROW([DataTypes.FIELD("max", DataTypes.FLOAT()), DataTypes.FIELD("max tag", DataTypes.STRING()), DataTypes.FIELD("min", DataTypes.FLOAT()), DataTypes.FIELD("min tag", DataTypes.STRING())]), func_type="pandas")
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__':
calc()