java框架在大数据开发中的自动化和优化

// 导入必要的库
import org.apache.spark.sql.SparkSession;

// 创建 Spark Session
SparkSession spark = SparkSession.builder().appName("Sales Report").getOrCreate();

// 从 CSV 文件加载数据
DataFrame salesDF = spark.read().csv("sales.csv");

// 按月分组并计算销售总额
salesDF.groupBy("month").agg(functions.sum("sales")).show();

// 导入必要的库
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.SqlDialect;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

// 创建流执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

// 创建表环境
EnvironmentSettings settings = EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);
tableEnv.getConfig().setSqlDialect(SqlDialect.DEFAULT);

// 定义传感器数据流模式
DataStream<SensorReading> sensorReadings = env.fromElements(new SensorReading("sensor1", 20.0, 1596894199000L));

// 将流转换为表
Table sensors = tableEnv.fromDataStream(sensorReadings);

// 创建窗口并应用 SQL 查询
Table alertTable = tableEnv.sqlQuery(
  "SELECT sensorId, " +
    "AVG(temperature) OVER (PARTITION BY sensorId ORDER BY eventTime RANGE INTERVAL '5 minutes' PRECEDING) AS avgTemp, " +
    "temperature " +
  "FROM sensors " +
  "WHERE temperature > 30.0"
);

// 接收 SQL 查询的结果流
DataStream<Alert> alerts = tableEnv.toAppendStream(alertTable, Alert.class);

// 输出告警
alerts.addSink(System.out::println);

// 执行流
env.execute("Sensor Anomaly Detection");