第 10 节 DataStream之source讲解（java）_datastreamsource

上篇：第 9 节 Flink scala shell代码调试

---

Flink API的抽象级别

---

1、DataStream API之Data Sources

1. source是程序的数据源输入，你可以通过StreamExecutionEnvironment.addSource(sourceFunction)来为你的程序添加一个source。
2. flink提供了大量的已经实现好的source方法，你也可以自定义source
   通过实现sourceFunction接口来自定义无并行度的source，
   或者你也可以通过实现ParallelSourceFunction 接口 or 继承RichParallelSourceFunction 来自定义有并行度的source。

---

2、DataStream API之Data Sources

1. 基于文件
   readTextFile(path)
   读取文本文件，文件遵循TextInputFormat 读取规则，逐行读取并返回。
2. 基于socket
   socketTextStream从socker中读取数据，元素可以通过一个分隔符切开。
3. 基于集合
   fromCollection(Collection)
   通过java 的collection集合创建一个数据流，集合中的所有元素必须是相同类型的。
4. 自定义输入
   addSource 可以实现读取第三方数据源的数据
   系统内置提供了一批connectors，连接器会提供对应的source支持【kafka】

---

3、内置Connectors

1. Apache Kafka (source/sink)
2. Apache Cassandra (sink)
3. Elasticsearch (sink)
4. Hadoop FileSystem (sink)
5. RabbitMQ (source/sink)
6. Apache ActiveMQ (source/sink)
7. Redis (sink)

---

4、Source 容错性保证

---

5、代码操作

（1）在pom文件依赖：

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>org.example.flink01</groupId>
    <artifactId>flink01</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.6.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.6.1</version>
           <!-- //   <scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.6.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.6.1</version>
        </dependency>

    </dependencies>

    <build>
        <plugins>
            <!-- 编译插件 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <encoding>UTF-8</encoding>
                </configuration>
            </plugin>
            <!-- scala编译插件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.1.6</version>
                <configuration>
                    <scalaCompatVersion>2.11</scalaCompatVersion>
                    <scalaVersion>2.11.12</scalaVersion>
                    <encoding>UTF-8</encoding>
                </configuration>
                <executions>
                    <execution>
                        <id>compile-scala</id>
                        <phase>compile</phase>
                        <goals>
                            <goal>add-source</goal>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                    <execution>
                        <id>test-compile-scala</id>
                        <phase>test-compile</phase>
                        <goals>
                            <goal>add-source</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <!-- 打jar包插件(会包含所有依赖) -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>2.6</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                    <archive>
                        <manifest>
                            <!-- 可以设置jar包的入口类(可选) -->
                            <mainClass>xuwei.streaming.SocketWindowWordCountJava</mainClass>
                        </manifest>
                    </archive>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105

（2）代码编写：

package xuwei.streaming;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import java.util.ArrayList;

/**
 * Collection作为数据源
 */
public class StreamingFromCollection {
    public static void main(String[] args)throws Exception {
        //获取flink的运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        ArrayList<Integer> data = new ArrayList<>();
        data.add(10);
        data.add(15);
        data.add(20);

        //指定数据源
        DataStreamSource<Integer> collectionData = env.fromCollection(data);

        //通过map数据进行处理
        DataStream<Integer> num = collectionData.map(new MapFunction<Integer, Integer>() {
            @Override
            public Integer map(Integer value) throws Exception {
                return value + 1;
            }
        });
        //直接打印
        num.print();

        env.execute("StreamingFromCollection");

    }
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39

（3）控制台打印信息：