java 框架中的大数据处理技术包括:apache hadoop:分布式处理框架包括 hdfs(文件系统)和 mapreduce(编程模型)。apache spark:统一分析引擎,结合 hadoop 处理能力和内存计算。flink:用于处理实时数据流的分布式流处理引擎。
随着大数据的普及,Java 开发人员需要有能力处理大量数据。Java 该框架提供了有效处理大数据的各种技术,本文将介绍一些最受欢迎的技术。
Apache HadoopHadoop 用于处理大数据集的分布式处理框架。它由一套工具组成,包括:
- HDFS (Hadoop 分布式文件系统):分布式文件的存储和管理。
- MapReduce:并行处理大型数据集的编程模型。
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordCount { public static class MyMapper extends Mapper<Object, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); @Override public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String[] tokens = value.toString().split(" "); for (String token : tokens) { word.set(token); context.write(word, one); } } } public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); @Override public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(MyMapper.class); job.setReducerClass(MyReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); } }
Apache Spark
Spark 它结合了统一的分析引擎 Hadoop 处理能力和内存计算。它提供了先进的 API,大数据处理简化。
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import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructType; public class SparkWordCount { public static void main(String[] args) { SparkSession spark = SparkSession.builder().appName("word count").master("local").getOrCreate(); JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext()); JavaRDD<String> lines = jsc.textFile(args[0]); JavaRDD<String> words = lines.flatMap(line -> Arrays.asList(line.split(" ")).iterator()); JavaPairRDD<String, Integer> wordCounts = words.mapToPair(word -> new Tuple2<>(word, 1)).reduceByKey((a, b) -> a + b); StructType schema = DataTypes.createStructType(new StructField[] { DataTypes.createStructField("word", DataTypes.StringType, false), DataTypes.createStructField("count", DataTypes.IntegerType, false) }); Dataset<Row> df = spark.createDataFrame(wordCounts.rdd(), schema); df.show(); } }
Flink
Flink 它是一种实时处理不断增长的数据集的分布式流处理引擎。它可以处理无限的数据流,并提供容错和低延迟。
import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.api.java.utils.ParameterTool; 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; public class FlinkWordCount { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); ParameterTool params = ParameterTool.fromArgs(args); String input = params.has("input") ? params.get("input") : "data.txt"; DataStream<String> text = env.readTextFile(input); DataStream<Tuple2<String, Integer>> counts = text .flatMap(line -> Arrays.asList(line.split(" ")).iterator()) .map(word -> Tuple2.of(word, 1)) .keyBy(0) .timeWindow(Time.seconds(1)) .sum(1); counts.print().setParallelism(1); env.execute(); } }
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