Flink之DataSet数据源

谁借莪１个温暖的怀抱￠ 2022-11-06 01:50 214阅读 0赞

### 目录 ###

*   *  （1）Flink Source之Text数据源
     *  （2）Flink Source之JSON数据源
     *  （3）FlinkSource之CSV数据源
     *  （4）Flink Source之MySQL数据源

## （1）Flink Source之Text数据源 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70_pic_center]

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
    // read text file from local files system
    DataSet<String> localLines = env.readTextFile("file:///path/to/my/textfile");
    
    // read text file from an HDFS running at nnHost:nnPort
    DataSet<String> hdfsLines = env.readTextFile("hdfs://nnHost:nnPort/path/to/my/textfile");

本地文件或者HDFS文件地址。

text数据源：

java java java hive
    hive hadoop hive spark
    java python

测试代码：

package com.aikfk.dataset;
    
    import org.apache.flink.api.common.functions.FlatMapFunction;
    import org.apache.flink.api.common.functions.ReduceFunction;
    import org.apache.flink.api.java.DataSet;
    import org.apache.flink.api.java.ExecutionEnvironment;
    import org.apache.flink.api.java.tuple.Tuple2;
    import org.apache.flink.util.Collector;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/3/7 11:10 上午 */
    public class TextSourceJava { 
        public static void main(String[] args) throws Exception { 
            // 准备环境
            ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
            // 读取text数据源
            DataSet<String> dataSet = env.readTextFile("/Users/caizhengjie/IdeaProjects/aikfk_flink/src/main/java/resources/wordcount");
    
            // flatmap
            DataSet<Tuple2<String, Integer>> wordcounts = dataSet.flatMap(new FlatMapFunction<String, Tuple2<String,Integer>>() { 
                @Override
                public void flatMap(String s, Collector<Tuple2<String, Integer>> collector) throws Exception { 
                    for (String word : s.split(" ")){ 
                        collector.collect(new Tuple2<>(word,1));
                    }
                }
            })
            // 指定分区key
            .groupBy(0)
            .reduce(new ReduceFunction<Tuple2<String, Integer>>() { 
                @Override
                public Tuple2<String, Integer> reduce(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) throws Exception { 
                    return new Tuple2<String, Integer>(t1.f0,t1.f1 + t2.f1);
                }
            });
    
            wordcounts.print();
        }
    }

运行结果：

(hadoop,1)
    (java,4)
    (python,1)
    (spark,1)
    (hive,3)

## （2）Flink Source之JSON数据源 ##

DataSet<WordCountPOJO> dataSet = env.readTextFile("file:///path/to/my/jsonfile")

本地文件或者HDFS文件地址。

解析JSON，需要添加依赖包**jackson-databind**

<dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-databind</artifactId>
      <version>2.12.2</version>
    </dependency>

json数据源：

{ "word": "java","count": 1}
    { "word": "hive","count": 11}
    { "word": "spark","count": 21}
    { "word": "hadoop","count": 3}
    { "word": "java","count": 12}
    { "word": "hive","count": 12}

POJO类：

package com.aikfk.dataset;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/3/7 12:55 下午 */
    public class WordCountPOJO { 
        public String word;
        public int count;
    
        public WordCountPOJO() { 
        }
    
        public WordCountPOJO(String word, int count) { 
            this.word = word;
            this.count = count;
        }
    
        @Override
        public String toString() { 
            return "WordCount{" +
                    "word='" + word + '\'' +
                    ", count=" + count +
                    '}';
        }
    }

测试代码：

package com.aikfk.dataset;
    
    import com.fasterxml.jackson.databind.ObjectMapper;
    import org.apache.flink.api.common.functions.MapFunction;
    import org.apache.flink.api.common.functions.ReduceFunction;
    import org.apache.flink.api.java.DataSet;
    import org.apache.flink.api.java.ExecutionEnvironment;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/3/7 11:54 上午 */
    public class JsonSourceJava { 
    
        final static ObjectMapper OBJECT_MAPPER = new ObjectMapper();
    
        public static void main(String[] args) throws Exception { 
    
            // 准备环境
            ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
            // 读取json数据源
            DataSet<WordCountPOJO> dataSet = env.readTextFile("/Users/caizhengjie/IdeaProjects/aikfk_flink/src/main/java/resources/wordcount.json")
                    .map(new MapFunction<String, WordCountPOJO>() { 
                        @Override
                        public WordCountPOJO map(String line) throws Exception { 
                            WordCountPOJO wordCountPOJO = OBJECT_MAPPER.readValue(line,WordCountPOJO.class);
                            return wordCountPOJO;
                        }
                    })
                    .groupBy("word")
                    .reduce(new ReduceFunction<WordCountPOJO>() { 
                        @Override
                        public WordCountPOJO reduce(WordCountPOJO t1, WordCountPOJO t2) throws Exception { 
                            return new WordCountPOJO(t1.word,t1.count + t2.count);
                        }
                    });
    
            dataSet.print();
        }
    }

运行结果：

WordCount{ word='hadoop', count=3}
    WordCount{ word='java', count=13}
    WordCount{ word='spark', count=21}
    WordCount{ word='hive', count=23}

## （3）FlinkSource之CSV数据源 ##

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
    // read a CSV file with three fields
    DataSet<Tuple3<Integer, String, Double>> csvInput = env.readCsvFile("hdfs:///the/CSV/file")
    	                       .types(Integer.class, String.class, Double.class);
    
    // read a CSV file with five fields, taking only two of them
    DataSet<Tuple2<String, Double>> csvInput = env.readCsvFile("hdfs:///the/CSV/file")
                                   .includeFields("10010")  // take the first and the fourth field
    	                       .types(String.class, Double.class);
    
    // read a CSV file with three fields into a POJO (Person.class) with corresponding fields
    DataSet<Person>> csvInput = env.readCsvFile("hdfs:///the/CSV/file")
                             .pojoType(Person.class, "name", "age", "zipcode");

csv数据源：

java,12,1
    java,10,1
    hive,4,1
    spark,2,2
    spark,3,2
    hive,3,1

测试代码：

package com.aikfk.dataset;
    
    import org.apache.flink.api.common.functions.ReduceFunction;
    import org.apache.flink.api.java.DataSet;
    import org.apache.flink.api.java.ExecutionEnvironment;
    import org.apache.flink.api.java.tuple.Tuple2;
    import org.apache.flink.api.java.tuple.Tuple3;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/3/7 2:04 下午 */
    public class CsvSourceJava { 
        public static void main(String[] args) throws Exception { 
    
            // 准备环境
            ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
            // 读取csv数据（方式一：映射POJO类对象）
            DataSet<WordCountPOJO> dataSet1 = env.readCsvFile("/Users/caizhengjie/IdeaProjects/aikfk_flink/src/main/java/resources/wordcount.csv")
                    .pojoType(WordCountPOJO.class,"word","count")
                    .groupBy("word")
                    .reduce(new ReduceFunction<WordCountPOJO>() { 
                        @Override
                        public WordCountPOJO reduce(WordCountPOJO t1, WordCountPOJO t2) throws Exception { 
                            return new WordCountPOJO(t1.word, t1.count + t2.count);
                        }
                    });
    
            dataSet1.print();
    
            /** * WordCount{word='java', count=22} * WordCount{word='spark', count=5} * WordCount{word='hive', count=7} */
    
            // 读取csv数据（方式二：映射成Tuple类，带有三个字段）
            DataSet<Tuple3<String,Integer,Integer>> dataSet2 = env.readCsvFile("/Users/caizhengjie/IdeaProjects/aikfk_flink/src/main/java/resources/wordcount.csv")
                    .types(String.class,Integer.class,Integer.class)
                    .groupBy(0)
                    .reduce(new ReduceFunction<Tuple3<String, Integer, Integer>>() { 
                        @Override
                        public Tuple3<String, Integer, Integer> reduce(Tuple3<String, Integer, Integer> t1, Tuple3<String, Integer, Integer> t2) throws Exception { 
                            return new Tuple3<>(t1.f0,t1.f1 + t2.f1,t2.f2);
                        }
                    });
    
            dataSet2.print();
    
            /** * (java,22,1) * (spark,5,2) * (hive,7,1) */
    
    
            // 读取csv数据（方式二：映射成Tuple类，带有三个字段，只取两个字段）
            DataSet<Tuple2<String,Integer>> dataSet3 = env.readCsvFile("/Users/caizhengjie/IdeaProjects/aikfk_flink/src/main/java/resources/wordcount.csv")
                    // 只取了第一个第二个字段（1表示取，2表示没有取）
                    .includeFields("110")
                    .types(String.class,Integer.class)
                    .groupBy(0)
                    .reduce(new ReduceFunction<Tuple2<String, Integer>>() { 
                        @Override
                        public Tuple2<String, Integer> reduce(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) throws Exception { 
                            return new Tuple2<>(t1.f0, t1.f1 + t2.f1);
                        }
                    });
    
            dataSet3.print();
    
            /** * (java,22) * (spark,5) * (hive,7) */
    
        }
    }

## （4）Flink Source之MySQL数据源 ##

第一步：添加Flink-jdbc依赖包

<dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-jdbc_2.11</artifactId>
      <version>${flink.version}</version>
    </dependency>

第二步：添加mysql-connector-java依赖包

<dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.38</version>
    </dependency>

第三步：创建MySQL表

create table wordcount(
    word varchar(100) not null,
    count int not null);
    
    insert into wordcount(word,count) values("spark",1);
    insert into wordcount(word,count) values("spark",10);
    insert into wordcount(word,count) values("java",2);
    insert into wordcount(word,count) values("java",3);
    insert into wordcount(word,count) values("hive",5);

第四步：编写程序

// Read data from a relational database using the JDBC input format
    DataSet<Tuple2<String, Integer> dbData =
        env.createInput(
          JdbcInputFormat.buildJdbcInputFormat()
                         .setDrivername("org.apache.derby.jdbc.EmbeddedDriver")
                         .setDBUrl("jdbc:derby:memory:persons")
                         .setQuery("select name, age from persons")
                         .setRowTypeInfo(new RowTypeInfo(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.INT_TYPE_INFO))
                         .finish()
        );

测试代码：

package com.aikfk.dataset;
    
    import org.apache.flink.api.common.functions.MapFunction;
    import org.apache.flink.api.common.functions.ReduceFunction;
    import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
    import org.apache.flink.api.java.DataSet;
    import org.apache.flink.api.java.ExecutionEnvironment;
    import org.apache.flink.api.java.typeutils.RowTypeInfo;
    import org.apache.flink.connector.jdbc.JdbcInputFormat;
    import org.apache.flink.types.Row;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/3/7 5:08 下午 */
    public class MySQLSourceJava { 
        public static void main(String[] args) throws Exception { 
            // 准备环境
            ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
            // 读取mysql数据源
            DataSet<Row> dbData = env.createInput(
                            JdbcInputFormat.buildJdbcInputFormat()
                                    .setDrivername("com.mysql.jdbc.Driver")
                                    .setDBUrl("jdbc:mysql://bigdata-pro-m07:3306/flink?serverTimezone=GMT%2B8&useSSL=false")
                                    .setUsername("root")
                                    .setPassword("199911")
                                    .setQuery("select * from wordcount")
                                    .setRowTypeInfo(new RowTypeInfo(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.INT_TYPE_INFO))
                                    .finish()
                    );
    
            /** * DataSet中的数据类型是POJO类 */
            DataSet<WordCountPOJO> dataSet = dbData.map(new MapFunction<Row, WordCountPOJO>() { 
                @Override
                public WordCountPOJO map(Row row) throws Exception { 
                    return new WordCountPOJO(String.valueOf(row.getField(0)), (Integer) row.getField(1));
                }
            })
            .groupBy("word")
            .reduce(new ReduceFunction<WordCountPOJO>() { 
                @Override
                public WordCountPOJO reduce(WordCountPOJO t1, WordCountPOJO t2) throws Exception { 
                    return new WordCountPOJO(t1.word,t1.count + t2.count);
                }
            });
    
            dataSet.print();
    
            /** * WordCount{word='java', count=5} * WordCount{word='spark', count=11} * WordCount{word='hive', count=5} */
    
    // /**
    // * DataSet中的数据类型是Tuple类
    // */
    // DataSet<Tuple2<String,Integer>> dataSet = dbData.map(new MapFunction<Row, Tuple2<String, Integer>>() { 
    // @Override
    // public Tuple2<String, Integer> map(Row row) throws Exception { 
    // return new Tuple2<>(String.valueOf(row.getField(0)), (Integer) row.getField(1));
    // }
    // })
    // .groupBy(0)
    // .reduce(new ReduceFunction<Tuple2<String, Integer>>() { 
    // @Override
    // public Tuple2<String, Integer> reduce(Tuple2<String, Integer> t1, Tuple2<String, Integer> t2) throws Exception { 
    // return new Tuple2<>(t1.f0, t1.f1 + t2.f1);
    // }
    // });
    
        }
    }

运行结果：

WordCount{ word='java', count=5}
    WordCount{ word='spark', count=11}
    WordCount{ word='hive', count=5}

--------------------

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