前提： 可以参考文章 SpringBoot 接入 Spark
@Resource private SparkSession sparkSession; @Resource private JavaSparkContext javaSparkContext;
测试文件 word.txt
java 代码
public void testSparkText() {
String file = "D:\\TEMP\\word.txt";
JavaRDD<String> fileRDD = javaSparkContext.textFile(file);
JavaRDD<String> wordsRDD = fileRDD.flatMap(line -> Arrays.asList(line.split(" ")).iterator());
JavaPairRDD<String, Integer> wordAndOneRDD = wordsRDD.mapToPair(word -> new Tuple2<>(word, 1));
JavaPairRDD<String, Integer> wordAndCountRDD = wordAndOneRDD.reduceByKey((a, b) -> a + b);
//输出结果
List<Tuple2<String, Integer>> result = wordAndCountRDD.collect();
result.forEach(System.out::println);
}
结果得出，123 有 3 个，456 有 2 个，789 有 1 个
测试文件 testcsv.csv
java 代码
public void testSparkCsv() {
String file = "D:\\TEMP\\testcsv.csv";
JavaRDD<String> fileRDD = javaSparkContext.textFile(file);
JavaRDD<String> wordsRDD = fileRDD.flatMap(line -> Arrays.asList(line.split(",")).iterator());
//输出结果
System.out.println(wordsRDD.collect());
}
输出结果
public void testSparkMysql() throws IOException {
Dataset<Row> jdbcDF = sparkSession.read()
.format("jdbc")
.option("url", "jdbc:mysql://192.168.140.1:3306/user?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai")
.option("dbtable", "(SELECT * FROM xxxtable) tmp")
.option("user", "root")
.option("password", "xxxxxxxxxx*k")
.option("driver", "com.mysql.cj.jdbc.Driver")
.load();
jdbcDF.printSchema();
jdbcDF.show();
//转化为RDD
JavaRDD<Row> rowJavaRDD = jdbcDF.javaRDD();
System.out.println(rowJavaRDD.collect());
}
也可以把表内容输出到文件，添加以下代码
List<Row> list = rowJavaRDD.collect();
BufferedWriter bw;
bw = new BufferedWriter(new FileWriter("d:/test.txt"));
for (int j = 0; j < list.size(); j++) {
bw.write(list.get(j).toString());
bw.newLine();
bw.flush();
}
bw.close();
结果输出
测试文件 testjson.json，内容如下
[{
"name": "name1",
"age": "1"
}, {
"name": "name2",
"age": "2"
}, {
"name": "name3",
"age": "3"
}, {
"name": "name4",
"age": "4"
}]
注意：testjson.json 文件的内容不能带格式，需要进行压缩
java 代码
public void testSparkJson() {
Dataset<Row> df = sparkSession.read().json("D:\\TEMP\\testjson.json");
df.printSchema();
df.createOrReplaceTempView("t");
Dataset<Row> row = sparkSession.sql("select age,name from t where age > 3");
JavaRDD<Row> rowJavaRDD = row.javaRDD();
System.out.println(rowJavaRDD.collect());
}
输出结果
测试文件 testcsv.csv
public void testSparkCsv() {
String file = "D:\\TEMP\\testcsv.csv";
JavaRDD<String> fileRDD = javaSparkContext.textFile(file);
JavaRDD<String> wordsRDD = fileRDD.flatMap(line -> Arrays.asList(line.split(",")).iterator());
//输出结果
System.out.println(wordsRDD.collect());
}
输出结果，发现中文乱码，可恶
原因：textFile 读取文件没有解决乱码问题，但 sparkSession.read() 却不会乱码
解决办法：获取文件方式由 textFile 改成 hadoopFile，由 hadoopFile 指定具体编码
public void testSparkCsv() {
String file = "D:\\TEMP\\testcsv.csv";
String code = "gbk";
JavaRDD<String> gbkRDD = javaSparkContext.hadoopFile(file, TextInputFormat.class, LongWritable.class, Text.class).map(p -> new String(p._2.getBytes(), 0, p._2.getLength(), code));
JavaRDD<String> gbkWordsRDD = gbkRDD.flatMap(line -> Arrays.asList(line.split(",")).iterator());
//输出结果
System.out.println(gbkWordsRDD.collect());
}
输出结果
