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Hadoop数据库进行数据聚合主要有以下几种方式:
MapReduce是Hadoop的核心计算框架,适用于大规模数据的分布式处理。
Map阶段:
Reduce阶段:
示例代码:
public class WordCount {
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
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(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
Spark提供了更高级的API和更快的执行速度,适合实时数据处理和迭代计算。
RDD操作:
map
、reduceByKey
、groupByKey
等转换操作。aggregateByKey
进行自定义聚合。DataFrame/Dataset API:
示例代码(Spark SQL):
val df = spark.read.text("hdfs://path/to/input")
val words = df.select(explode(split($"value", "\\s+")).as("word"))
val wordCounts = words.groupBy("word").count()
wordCounts.show()
Hive是基于Hadoop的数据仓库工具,提供了类似SQL的查询语言HiveQL。
创建表:
CREATE TABLE word_count (
word STRING,
count INT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' ';
加载数据:
LOAD DATA INPATH 'hdfs://path/to/input' INTO TABLE word_count;
聚合查询:
SELECT word, SUM(count) AS total_count
FROM word_count
GROUP BY word;
Pig是另一个基于Hadoop的高级数据流语言和执行框架。
A = LOAD 'hdfs://path/to/input' USING PigStorage(' ') AS (word:chararray);
B = GROUP A BY word;
C = FOREACH B GENERATE group AS word, SUM(A.count) AS total_count;
DUMP C;
combiner
、partitioner
或自定义分区策略来缓解。通过以上方法,可以在Hadoop生态系统中高效地进行数据聚合操作。选择哪种方法取决于具体的应用场景和性能需求。
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