0基础学习PyFlink——使用datagen生成流式数据-CSDN博客

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大纲

在研究Flink的水位线WaterMark技术之前我们可能需要Flink接收到流式数据比如接入Kafka等。这就要求引入其他组件增加了学习的难度。而Flink自身提供了datagen连接器它可以用于生成流式数据让问题内聚在Flink代码内部从而降低学习探索的难度。
本节我们就介绍如何使用datagen生成数据。

可控参数

我们可以使用option方法控制生成的一些规则主要分为“字段级规则”和“表级规则”。

字段级规则

顾名思义字段级规则是指该规则作用于具体哪个字段这就需要指明字段的名称——fields.col_name。

生成方式

字段的生成方式由下面的字符串形式来控制#表示字段的名称下同

fields.#.kind

可选值有

  • random随机方式比如5,2,1,4,6……。
  • sequence顺序方式比如1,2,3,4,5,6……。

数值控制

如果kind是sequence则数值控制使用

  • fields.#.start区间的起始值。
  • fields.#.end区间的结束值。

如果配置了这个两个参数则会生成有限个数的数据。

如果kind是random则数值控制使用

  • fields.#.min随机算法会选取的最小值。
  • fields.#.max随机算法会选取的最大值。

时间戳控制

fields.#.max-past仅仅可以用于TIMESTAMP和TIMESTAMP_LTZ类型的数据。它表示离现在时间戳最大的时间差这个默认值是0。TIMESTAMP和TIMESTAMP_LTZ只支持random模式生成这就需要控制随机值的区间。如果区间太小我们生成的时间可能非常集中。后面我们会做相关测试。

表级规则

生成速度

rows-per-second表示每秒可以生成几条数据。

生成总量

number-of-rows表示一共可以生成多少条数据。如果这个参数不设置则表示可以生成无界流。

结构

生成环境

我们需要流式环境而datagen是Table API的连接器于是使用流式执行环境创建一个流式表环境。

    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)

定义行结构

    schame = Schema.new_builder().column('seed', DataTypes.INT()).build()

这个结构以及支持的生成模式是

TypeSupported Generators
BOOLEANrandom
CHARrandom / sequence
VARCHARrandom / sequence
BINARYrandom / sequence
VARBINARYrandom / sequence
STRINGrandom / sequence
DECIMALrandom / sequence
TINYINTrandom / sequence
SMALLINTrandom / sequence
INTrandom / sequence
BIGINTrandom / sequence
FLOATrandom / sequence
DOUBLErandom / sequence
DATErandom
TIMErandom
TIMESTAMPrandom
TIMESTAMP_LTZrandom
INTERVAL YEAR TO MONTHrandom
INTERVAL DAY TO MONTHrandom
ROWrandom
ARRAYrandom
MAPrandom
MULTISETrandom

定义表信息

下面这个例子就是给seed字段按随机模式生成seed_min和seed_max之间的数值并且每秒生成rows_per_second行。

    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('fields.seed.min', str(seed_min)) \
                        .option('fields.seed.max', str(seed_max)) \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()

案例

随机Int型

每秒生成5行数据每行数据中seed字段值随机在最小值0和最大值100之间。由于没有指定number-of-rows生成的是无界流。

def gen_random_int():
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 0
    seed_max = 100
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.INT()).build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('fields.seed.min', str(seed_min)) \
                        .option('fields.seed.max', str(seed_max)) \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
                            
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()

+----+-------------+
| op |        seed |
+----+-------------+
| +I |          25 |
| +I |          28 |
| +I |          73 |
| +I |          68 |
| +I |          40 |
| +I |          55 |
| +I |           6 |
| +I |          41 |
| +I |          16 |
| +I |          19 |
……

顺序Int型

每秒生成5行数据每行数据中seed字段值从1开始递增一直自增到10。由于设置了最大和最小值生成的是有界流。

def gen_sequence_int():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 1
    seed_max = 10
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.INT()).build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                            .schema(schame) \
                            .option('fields.seed.kind', 'sequence') \
                            .option('fields.seed.start', str(seed_min)) \
                            .option('fields.seed.end', str(seed_max)) \
                            .option('rows-per-second', str(rows_per_second)) \
                            .build()
                            
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()

+----+-------------+
| op |        seed |
+----+-------------+
| +I |           1 |
| +I |           2 |
| +I |           3 |
| +I |           4 |
| +I |           5 |
| +I |           6 |
| +I |           7 |
| +I |           8 |
| +I |           9 |
| +I |          10 |
+----+-------------+
10 rows in set

随机型Int数组

每秒生成5行数据每行数据中seed字段是一个Int型数组数组里面的每个元素也是随机的。

def gen_random_int_array():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.ARRAY(DataTypes.INT())) \
                                .build()
                                
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()

+----+--------------------------------+
| op |                           seed |
+----+--------------------------------+
| +I | [625785630, -933999461, -48... |
| +I | [2087310154, 1602723641, 19... |
| +I | [1299442620, -613376781, -8... |
| +I | [2051511574, 246258035, -16... |
| +I | [2029482070, -1496468635, -... |
| +I | [1230213175, -1506525784, 7... |
| +I | [501476712, 1901967363, -56... |
……

带时间戳的多列数据

每秒生成5行数据每行数据中seed字段值随机在最小值0和最大值100之间timestamp字段随机在当前时间戳和“当前时间戳+max-past”之间。

def gen_random_int_and_timestamp():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 0
    seed_max = 100
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.INT()) \
                                .column('timestamp', DataTypes.TIMESTAMP()) \
                                .build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('fields.seed.min', str(seed_min)) \
                        .option('fields.seed.max', str(seed_max)) \
                        .option('fields.timestamp.kind', 'random') \
                        .option('fields.timestamp.max-past', '0') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
          
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()

由于max-past值为0所以我们看到上例中每秒生成的timestamp 都极接近。

+----+-------------+----------------------------+
| op |        seed |                  timestamp |
+----+-------------+----------------------------+
| +I |          66 | 2023-11-02 13:53:29.082000 |
| +I |           9 | 2023-11-02 13:53:29.146000 |
| +I |          12 | 2023-11-02 13:53:29.146000 |
| +I |          52 | 2023-11-02 13:53:29.146000 |
| +I |          29 | 2023-11-02 13:53:29.146000 |
| +I |          63 | 2023-11-02 13:53:30.066000 |
| +I |          25 | 2023-11-02 13:53:30.066000 |
| +I |          21 | 2023-11-02 13:53:30.066000 |
| +I |          24 | 2023-11-02 13:53:30.066000 |
| +I |           6 | 2023-11-02 13:53:30.066000 |
| +I |          62 | 2023-11-02 13:53:31.067000 |
| +I |          57 | 2023-11-02 13:53:31.067000 |
| +I |          44 | 2023-11-02 13:53:31.067000 |
| +I |           6 | 2023-11-02 13:53:31.067000 |
| +I |          16 | 2023-11-02 13:53:31.067000 |
……

如果我们把max-past放大到比较大的数值timestamp也将大幅度变化。

.option('fields.timestamp.max-past', '10000')

+----+-------------+----------------------------+
| op |        seed |                  timestamp |
+----+-------------+----------------------------+
| +I |          89 | 2023-11-02 13:57:17.342000 |
| +I |          35 | 2023-11-02 13:57:10.915000 |
| +I |          32 | 2023-11-02 13:57:11.045000 |
| +I |          74 | 2023-11-02 13:57:18.407000 |
| +I |          24 | 2023-11-02 13:57:13.603000 |
| +I |          82 | 2023-11-02 13:57:12.139000 |
| +I |          41 | 2023-11-02 13:57:16.129000 |
| +I |          95 | 2023-11-02 13:57:16.592000 |
| +I |          80 | 2023-11-02 13:57:14.364000 |
| +I |          60 | 2023-11-02 13:57:18.994000 |
| +I |          56 | 2023-11-02 13:57:19.330000 |
| +I |          10 | 2023-11-02 13:57:18.876000 |
| +I |          43 | 2023-11-02 13:57:12.449000 |
| +I |          73 | 2023-11-02 13:57:13.183000 |
| +I |          17 | 2023-11-02 13:57:18.736000 |
| +I |          46 | 2023-11-02 13:57:21.368000 |
……

完整代码


from pyflink.datastream import StreamExecutionEnvironment,RuntimeExecutionMode
from pyflink.table import StreamTableEnvironment, TableDescriptor, Schema, DataTypes

def gen_random_int():
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 0
    seed_max = 100
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.INT()).build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('fields.seed.min', str(seed_min)) \
                        .option('fields.seed.max', str(seed_max)) \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
                            
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_sequence_int():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 1
    seed_max = 10
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.INT()).build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                            .schema(schame) \
                            .option('fields.seed.kind', 'sequence') \
                            .option('fields.seed.start', str(seed_min)) \
                            .option('fields.seed.end', str(seed_max)) \
                            .option('rows-per-second', str(rows_per_second)) \
                            .build()
                            
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_sequence_string():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 0
    seed_max = 100
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.STRING()).build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'sequence') \
                        .option('fields.seed.start', str(seed_min)) \
                        .option('fields.seed.end', str(seed_max)) \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
                            
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()

def gen_random_char():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.CHAR(4)).build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
                            
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_random_int_and_timestamp():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    seed_min = 0
    seed_max = 100
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.INT()) \
                                .column('timestamp', DataTypes.TIMESTAMP()) \
                                .build()
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('fields.seed.min', str(seed_min)) \
                        .option('fields.seed.max', str(seed_max)) \
                        .option('fields.timestamp.kind', 'random') \
                        .option('fields.timestamp.max-past', '10000') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
          
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_random_int_array():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.ARRAY(DataTypes.INT())) \
                                .build()
                                
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_random_map():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.MAP(DataTypes.STRING(), DataTypes.INT())) \
                                .build()
                                
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_random_multiset():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.MULTISET(DataTypes.STRING())) \
                                .build()
                                
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
def gen_random_row():
    
    stream_execute_env = StreamExecutionEnvironment.get_execution_environment()
    stream_execute_env.set_runtime_mode(RuntimeExecutionMode.STREAMING)
    stream_table_env = StreamTableEnvironment.create(stream_execution_environment=stream_execute_env)
    
    rows_per_second = 5
    schame = Schema.new_builder().column('seed', DataTypes.ROW([DataTypes.FIELD("id", DataTypes.BIGINT()), DataTypes.FIELD("data", DataTypes.STRING())])) \
                                .build()
                                
    table_descriptor = TableDescriptor.for_connector('datagen') \
                        .schema(schame) \
                        .option('fields.seed.kind', 'random') \
                        .option('rows-per-second', str(rows_per_second)) \
                        .build()
    
    stream_table_env.create_temporary_table('source', table_descriptor)
    
    table = stream_table_env.from_path('source')
    table.execute().print()
    
    
if __name__ == '__main__':
    gen_random_int_and_timestamp()

参考资料

阿里云国内75折 回扣 微信号：monov8
阿里云国际，腾讯云国际，低至75折。AWS 93折 免费开户实名账号 代冲值 优惠多多 微信号：monov8 飞机：@monov6
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