java框架在大数据分析中的作用和潜力

public class LogAnalyzerMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    private static final IntWritable ONE = new IntWritable(1);

    @Override
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 解析日志记录并提取 IP 地址
        String ipAddress = ...;

        // 发射 IP 地址和计数 1
        context.write(new Text(ipAddress), ONE);
    }
}

public class LogAnalyzerReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

    @Override
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        // 累加每个 IP 地址的计数
        int count = 0;
        for (IntWritable value : values) {
            count += value.get();
        }

        // 发射 IP 地址和总计数
        context.write(key, new IntWritable(count));
    }
}

// 创建 SparkSession
SparkSession spark = SparkSession.builder().appName("SparkSQLExample").getOrCreate();

// 加载 CSV 文件
DataFrame df = spark.read().csv("file.csv");

// 注册临时视图
df.createOrReplaceTempView("table");

// 执行 SQL 查询
DataFrame result = spark.sql("SELECT * FROM table WHERE column_name = 'value'");

// 输出结果
result.show();