spark-sql:查询临时表时异常(错误No Typetag)?报错

代码用途：简单测试spark-sql的读hdfs文件，然后用sql 简便提取临时数据

报错猜测：个人刚开始怀疑是pom文件依赖的版本有问题，现在也不确定啥问题，具体描述如下： 

1:集群环境spark-shell下执行，很正常，可以很欢快的使用，可以查询people结果。。

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
case class Person(name: String)
var people = sc.textFile("/workspace/xx/tmp/a").map(_.split(" ")).map(p => Person(p(0)))
val peopleSchema = sqlContext.createSchemaRDD(people)
peopleSchema.registerTempTable("people")
var df=sqlContext.sql("select * from people")

2：在本地打包时发现报错，不能编译。

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.SparkContext._
import org.apache.spark.sql
import org.apache.spark.sql._
import org.apache.hadoop.fs.Path
import org.apache.spark.sql.SchemaRDD

object sparkSql {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("sql_data")
    var sc = new SparkContext(conf)
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    case class Person(name: String)
    var people = sc.textFile("/workspace/xx/tmp/a").map(_.split(" ")).map(p => Person(p(0)))
    val peopleSchema = sqlContext.createSchemaRDD(people)
    peopleSchema.registerTempTable("people")
    var df=sqlContext.sql("select * from people")
    df.map(t => t(0)).collect().foreach(println)
  }
}

报错信息：
Information:2015/4/17 20:29 - Compilation completed with 2 errors and 0 warnings in 1 sec
E:\sparkSql.scala
Error:(26, 50) No TypeTag available for Person
    val peopleSchema = sqlContext.createSchemaRDD(people)                                                ^
Error:(26, 50) not enough arguments for method createSchemaRDD: (implicit evidence$1: reflect.runtime.universe.TypeTag[Person])org.apache.spark.sql.SchemaRDD.
Unspecified value parameter evidence$1.

    val peopleSchema = sqlContext.createSchemaRDD(people)

3：pom 

<dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.10</artifactId>
            <version>1.1.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.10</artifactId>
            <version>1.1.0</version>
        </dependency>