spark记录（16）SparkStreaming On HDFS AND TO MySQL

本测试使用本地文件系统代替HDFS，如需测试HDFS监控请解开注释换成自己的HDFS集群

sparkstreaming只会监控该文件夹下新增的文件，并不会监控到原文件的删除和修改

SparkStreaming On HDFS

代码 1：监控文件夹下新增加的数据并打印到控制台上

/**
 *
 *  Spark standalone or Mesos with cluster deploy mode only:
 *  在提交application的时候  添加 --supervise 选项  如果Driver挂掉 会自动启动一个Driver
 *
 */
public class SparkStreamingOnHDFS {
    public static void main(String[] args) {
        final SparkConf conf = new SparkConf().setMaster("local").setAppName("SparkStreamingOnHDFS");
        
//        final String checkpointDirectory = "hdfs://node1:9000/spark/SparkStreaming/CheckPoint2017";
        final String checkpointDirectory = "./checkpoint";
        
        JavaStreamingContextFactory factory = new JavaStreamingContextFactory() {
            @Override
            public JavaStreamingContext create() {  
                return createContext(checkpointDirectory,conf);
            }
        };
        /**
         * 获取JavaStreamingContext 先去指定的checkpoint目录中去恢复JavaStreamingContext
         * 如果恢复不到，通过factory创建
         */
        JavaStreamingContext jsc = JavaStreamingContext.getOrCreate(checkpointDirectory, factory);
        jsc.start();
        jsc.awaitTermination();
        jsc.close();
    }

//    @SuppressWarnings("deprecation")
    private static JavaStreamingContext createContext(String checkpointDirectory,SparkConf conf) {

        // If you do not see this printed, that means the StreamingContext has
        // been loaded
        // from the new checkpoint
        System.out.println("Creating new context");
        SparkConf sparkConf = conf;
        // Create the context with a 1 second batch size

        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(5));
//        ssc.sparkContext().setLogLevel("WARN");
        /**
         *  checkpoint 保存：
         *        1.配置信息
         *        2.DStream操作逻辑
         *        3.job的执行进度
         *      4.offset
         */
        ssc.checkpoint(checkpointDirectory);
        
        /**
         * 监控的是HDFS上的一个目录，监控文件数量的变化     文件内容如果追加监控不到。
         * 只监控文件夹下新增的文件，减少的文件时监控不到的，文件的内容有改动也监控不到。
         */
//        JavaDStream<String> lines = ssc.textFileStream("hdfs://node1:9000/spark/sparkstreaming");
        JavaDStream<String> lines = ssc.textFileStream("./data");
         
        JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            /**
             * 
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Iterable<String> call(String s) {
                return Arrays.asList(s.split(" "));
            }
        });
        

        JavaPairDStream<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {
            /**
             * 
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2<String, Integer> call(String s) {
                return new Tuple2<String, Integer>(s.trim(), 1);
            }
        });

        JavaPairDStream<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {
            /**
             * 
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Integer call(Integer i1, Integer i2) {
                return i1 + i2;
            }
        });
        
        counts.print();
//        counts.filter(new Function<Tuple2<String,Integer>, Boolean>() {
//
//            /**
//             * 
//             */
//            private static final long serialVersionUID = 1L;
//
//            @Override
//            public Boolean call(Tuple2<String, Integer> v1) throws Exception {
//                System.out.println("*************************");
//                return true;
//            }
//        }).print();
        return ssc;
    }
}

代码 2：该代码源源不断向目标文件夹中写入数据

/**
 * 此复制文件的程序是模拟在data目录下动态生成相同格式的txt文件，用于给sparkstreaming 中 textFileStream提供输入流。
 * @author root
 *
 */
public class CopyFile_data {
    public static void main(String[] args) throws IOException, InterruptedException {
        while(true){
            Thread.sleep(5000);
            String uuid = UUID.randomUUID().toString();
            System.out.println(uuid);
            copyFile(new File("words.txt"),new File(".\data\"+uuid+"----words.txt"));
        }
    }

    public static void copyFile(File fromFile, File toFile) throws IOException {
        FileInputStream ins = new FileInputStream(fromFile);
        FileOutputStream out = new FileOutputStream(toFile);
        byte[] b = new byte[1024*1024];
        @SuppressWarnings("unused")
        int n = 0;
        while ((n = ins.read(b)) != -1) {
            out.write(b, 0, b.length);
        }

        ins.close();
        out.close();
    }
}

SparkStreaming On HDFS AND TO MySQL

代码 1：监控文件夹数据并写入到MySQL中

public class SparkStreamingOnHDFSToMySQL {
    public static void main(String[] args) {
     
        final SparkConf conf = new SparkConf().setMaster("local[1]").setAppName("SparkStreamingOnHDFS");
        // JavaStreamingContext jsc = new JavaStreamingContext(conf,
        // Durations.seconds(5));
        final String checkpointDirectory = "hdfs://node1:9000/spark/SparkStreaming/CheckPoint2017";
        JavaStreamingContextFactory factory = new JavaStreamingContextFactory() {
            @Override
            public JavaStreamingContext create() {
                return createContext(checkpointDirectory,conf);
            }
        };
         
        JavaStreamingContext jsc = JavaStreamingContext.getOrCreate(checkpointDirectory, factory);
        jsc.start();
        jsc.awaitTermination();
        jsc.close();
    }

    private static JavaStreamingContext createContext(String checkpointDirectory,SparkConf conf) {

        // If you do not see this printed, that means the StreamingContext has
        // been loaded
        // from the new checkpoint
        System.out.println("Creating new context");
        SparkConf sparkConf = conf;
        // Create the context with a 1 second batch size
        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(5));
        ssc.checkpoint(checkpointDirectory);
        
        JavaDStream<String> lines = ssc.textFileStream("hdfs://node1:9000/output/");
         
        JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            /**
             * 
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Iterable<String> call(String s) {
                return Arrays.asList(s.split(" "));
            }
        });

        JavaPairDStream<String, Integer> ones = words.mapToPair(new PairFunction<String, String, Integer>() {
            /**
             * 
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2<String, Integer> call(String s) {
                return new Tuple2<String, Integer>(s, 1);
            }
        });

        JavaPairDStream<String, Integer> counts = ones.reduceByKey(new Function2<Integer, Integer, Integer>() {
            /**
             * 
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Integer call(Integer i1, Integer i2) {
                return i1 + i2;
            }
        });
        
        counts.foreachRDD(new VoidFunction<JavaPairRDD<String, Integer>>() {
            
            private static final long serialVersionUID = 1L;
            
            @Override
            public void call(JavaPairRDD<String, Integer> pairRdd) throws Exception {
                
                pairRdd.foreachPartition(new VoidFunction<Iterator<Tuple2<String,Integer>>>() {
                     
                    private static final long serialVersionUID = 1L;

                    @Override
                    public void call(Iterator<Tuple2<String, Integer>> vs) throws Exception {
                        
                        JDBCWrapper jdbcWrapper = JDBCWrapper.getJDBCInstance();
                    
                        List<Object[]> insertParams = new ArrayList<Object[]>();
                        while(vs.hasNext()){
                            Tuple2<String, Integer> next = vs.next();
                            insertParams.add(new Object[]{next._1,next._2});
                        }
                        System.out.println(insertParams);
                        jdbcWrapper.doBatch("INSERT INTO wordcount VALUES(?,?)", insertParams);
                    }
                });
                
            }
        });
        counts.print();
        return ssc;
    }

}

代码 2：向目标文件夹中写入数据

同上。

代码 3：JDBC

class JDBCWrapper implements Serializable { 
    
    /**
     * 
     */
    private static final long serialVersionUID = 1L;

    public static void main(String[] args) {
        JDBCWrapper jdbcWrapper = JDBCWrapper.getJDBCInstance();
        Object[] obj = new Object[]{"1","1","1","1"};
        List<Object[]> list = new ArrayList<Object[]>();
        list.add(obj);
        jdbcWrapper.doBatch("INSERT INTO result VALUES(?,?,?,?)", list);
    }

    private static JDBCWrapper jdbcInstance = null;
    private static LinkedBlockingQueue<Connection> dbConnectionPool = new LinkedBlockingQueue<Connection>();

    static {
        try {
            Class.forName("com.mysql.jdbc.Driver");
        } catch (ClassNotFoundException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }

    public static JDBCWrapper getJDBCInstance() {
        if (jdbcInstance == null) {

            synchronized (JDBCWrapper.class) {
                if (jdbcInstance == null) {
                    jdbcInstance = new JDBCWrapper();
                }
            }
        }

        return jdbcInstance;
    }
 
    private JDBCWrapper() {

        for (int i = 0; i < 10; i++) {

            try {
                Connection conn = DriverManager.getConnection("jdbc:mysql://node2:3306/sparkstreaming", "root",
                        "123456");
                dbConnectionPool.put(conn);
            } catch (Exception e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }

        }

    }
    
    public synchronized Connection getConnection() {
        while (0 == dbConnectionPool.size()) {
            try {
                Thread.sleep(20);
            } catch (InterruptedException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
 
        return dbConnectionPool.poll();
    }

    public int[] doBatch(String sqlText, List<Object[]> paramsList) {
        Connection conn = getConnection();
        PreparedStatement preparedStatement = null;
        int[] result = null;
        try {
            conn.setAutoCommit(false);
            preparedStatement = conn.prepareStatement(sqlText);

            for (Object[] parameters : paramsList) {
                for (int i = 0; i < parameters.length; i++) {
                    preparedStatement.setObject(i + 1, parameters[i]);
                }

                preparedStatement.addBatch();
            }

            result = preparedStatement.executeBatch();

            conn.commit();

        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } finally {
            if (preparedStatement != null) {
                try {
                    preparedStatement.close();
                } catch (SQLException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
            }

            if (conn != null) {
                try {
                    dbConnectionPool.put(conn);
                } catch (InterruptedException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
            }
        }

        return result;
    }

    public void doQuery(String sqlText, Object[] paramsList, ExecuteCallBack callBack) {

        Connection conn = getConnection();
        PreparedStatement preparedStatement = null;
        ResultSet result = null;
        try {

            preparedStatement = conn.prepareStatement(sqlText);

            if(paramsList!=null){
                for (int i = 0; i < paramsList.length; i++) {
                    preparedStatement.setObject(i + 1, paramsList[i]);
                }
            }

            result = preparedStatement.executeQuery();

            callBack.resultCallBack(result);

        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } finally {
            if (preparedStatement != null) {
                try {
                    preparedStatement.close();
                } catch (SQLException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
            }

            if (conn != null) {
                try {
                    dbConnectionPool.put(conn);
                } catch (InterruptedException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
            }
        }

    }
}

interface ExecuteCallBack {
    void resultCallBack(ResultSet result) throws Exception;
}