如何利用Java实现仓库管理系统的大数据分析和商

// 采集数据并存储到HDFS

Configuration conf = new Configuration();

Job job = Job.getInstance(conf, "Data Collection");

job.setJarByClass(DataCollection.class);

job.setMapperClass(DataCollectionMapper.class);

job.setOutputKeyClass(NullWritable.class);

job.setOutputValueClass(Text.class);

FileInputFormat.addInputPath(job, new Path("input/data.txt"));

FileOutputFormat.setOutputPath(job, new Path("output/raw-data"));

job.waitForCompletion(true);

// 将数据存储到HBase

Configuration hbaseConf = HBaseConfiguration.create();

Connection connection = ConnectionFactory.createConnection(hbaseConf);

Admin admin = connection.getAdmin();

TableName tableName = TableName.valueOf("warehouse");

HTableDescriptor tableDescriptor = new HTableDescriptor(tableName);

HColumnDescriptor columnDescriptor = new HColumnDescriptor("data");

tableDescriptor.addFamily(columnDescriptor);

admin.createTable(tableDescriptor);

Table table = connection.getTable(tableName);

Put put = new Put(Bytes.toBytes("row-1"));

put.addColumn(Bytes.toBytes("data"), Bytes.toBytes("column-1"), Bytes.toBytes("value-1"));

table.put(put);

// 加载数据到Spark DataFrame

SparkSession spark = SparkSession.builder()

.appName("Data Cleaning")

.master("local")

.getOrCreate();

Dataset<Row> dataFrame = spark.read()

.format("csv")

.option("header", "true")

.load("output/raw-data/part-00000");

// 数据清洗与预处理

Dataset<Row> cleanedDataFrame = dataFrame.na().drop();

// 加载数据到Flink DataSet

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

DataSet<Tuple2<String, Double>> dataSet = env.readCsvFile("output/cleaned-data/part-00000")

.ignoreFirstLine()

.types(String.class, Double.class);

// 数据分析与挖掘

DataSet<Tuple2<String, Double>> averageByCategory = dataSet.groupBy(0)

.reduceGroup(new GroupReduceFunction<Tuple2<String, Double>, Tuple2<String, Double>>() {

@Override

public void reduce(Iterable<Tuple2<String, Double>> values,

Collector<Tuple2<String, Double>> out) throws Exception {

String category = null;

double sum = 0;

int count = 0;

for (Tuple2<String, Double> value : values) {

category = value.f0;

sum += value.f1;

count++;

}

out.collect(new Tuple2<>(category, sum / count));

}

});

// 加载报表模板

InputStream input = new FileInputStream(new File("resources/template.jrxml"));

JasperReport jasperReport = JasperCompileManager.compileReport(input);

// 生成报表

JasperPrint jasperPrint = JasperFillManager.fillReport(jasperReport, null, new JREmptyDataSource());

JasperExportManager.exportReportToPdfFile(jasperPrint, "output/report.pdf");

// 发送邮件

Properties props = new Properties();

props.put("mail.smtp.auth", "true");

props.put("mail.smtp.starttls.enable", "true");

props.put("mail.smtp.host", "smtp.gmail.com");

props.put("mail.smtp.port", "587");

Session session = Session.getInstance(props,

new javax.mail.Authenticator() {

protected PasswordAuthentication getPasswordAuthentication() {

return new PasswordAuthentication("your_email", "your_password");

}

});

Message message = new MimeMessage(session);

message.setFrom(new InternetAddress("from@example.com"));

message.setRecipients(Message.RecipientType.TO,

InternetAddress.parse("to@example.com"));

message.setSubject("Report");

message.setText("Please find the attached report.");

MimeBodyPart messageBodyPart = new MimeBodyPart();

Multipart multipart = new MimeMultipart();

messageBodyPart = new MimeBodyPart();

String file = "output/report.pdf";

String fileName = "report.pdf";

DataSource source = new FileDataSource(file);

messageBodyPart.setDataHandler(new DataHandler(source));

messageBodyPart.setFileName(fileName);

multipart.addBodyPart(messageBodyPart);

message.setContent(multipart);

Transport.send(message);