Spark SQL里concat_ws和collect_set的作用

concat_ws: 用指定的字符连接字符串

例如：

连接字符串：

concat_ws("_", field1, field2)，输出结果将会是：“field1_field2”。

数组元素连接：

concat_ws("_", [a,b,c])，输出结果将会是："a_b_c"。

collect_set: 把聚合的数据组合成一个数组，一般搭配group by 使用。

例如有下表T_course;

id	name	course
1	zhang san	Chinese
2	zhang san	Math
3	zhang san	English

spark.sql("select name, collect_set(course) as course_set from T_course group by name");

结果是：

name	course_set
zhang san	[Chinese,Math,English]

贴上套牌车项目代码：

public class TpcCompute2 {
    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder().enableHiveSupport().appName("TpcCompute2").master("local").getOrCreate();
        JavaSparkContext sc = new JavaSparkContext(spark.sparkContext());
        sc.setLogLevel("ERROR");
        //hphm,id,tgsj,lonlat&
        spark.udf().register("getTpc", new ComputeUDF(), DataTypes.StringType);
        spark.sql("use traffic");
        spark.sql("select hphm,concat_ws('&',collect_set(concat_ws('_',id,kk_lon_lat,tgsj))) as concatValue from t_cltgxx t where t.tgsj>'2015-01-01 00:00:00' group by hphm").show(false);
        Dataset<Row> cltgxxDF =
                spark.sql("select hphm,concatValue from (select hphm,getTpc(concat_ws('&',collect_set(concat_ws('_',id,kk_lon_lat,tgsj)))) as concatValue from t_cltgxx t where t.tgsj>'2015-01-01 00:00:00' group by hphm) where concatValue is not null");
        cltgxxDF.show();
 
        //创建集合累加器
        CollectionAccumulator<String> acc = sc.sc().collectionAccumulator();
        cltgxxDF.foreach(new ForeachFunction<Row>() {
            @Override
            public void call(Row row) throws Exception {
                acc.add(row.getAs("concatValue"));
            }
        });
 
        List<String> values = acc.value();
        for (String id : accValues) {
            System.out.println("accValues: " + id);
            Dataset<Row> resultDF = spark.sql("select hphm,clpp,clys,tgsj,kkbh from t_cltgxx where id in (" + id.split("_")[0] + "," + id.split("_")[1] + ")");
            resultDF.show();
            Dataset<Row> resultDF2 = resultDF.withColumn("jsbh", functions.lit(new Date().getTime()))
                    .withColumn("create_time", functions.lit(new Timestamp(new Date().getTime())));
            resultDF2.show();
            resultDF2.write()
                    .format("jdbc")
                    .option("url","jdbc:mysql://lin01.cniao5.com:3306/traffic?characterEncoding=UTF-8")
                    .option("dbtable","t_tpc_result")
                    .option("user","root")
                    .option("password","123456")
                    .mode(SaveMode.Append)
                    .save();
    }
}

spark.sql语句输出样式：