我正在使用Spark SQL(我提到它是在Spark中,以防影响SQL语法 - 我还不够熟悉)我有一个表,我试图重新构建。我有一种在本地工作的方法但是当我尝试在AWS EC2实例上运行相同的命令时,我收到错误报告我有一个未解析的运算符'
基本上我的数据看起来像:
userId someString varA
1 "example1" [0,2,5]
2 "example2" [1,20,5]
我使用'爆炸' varA上的sqlContext中的命令。当我在本地运行时,事情会正确返回,但在AWS上它们会失败。
我可以使用以下命令重现这一点:
val data = List(
("1", "example1", Array(0,2,5)), ("2", "example2", Array(1,20,5)))
val distData = sc.parallelize(data)
val distTable = distData.toDF("userId", "someString", "varA")
distTable.registerTempTable("distTable_tmp")
val temp1 = sqlContext.sql("select userId, someString, varA from distTable_tmp")
val temp2 = sqlContext.sql(
"select userId, someString, explode(varA) as varA from distTable_tmp")
在本地,temp1.show()和temp2.show()返回我期望的内容,即:
scala> temp1.show()
+------+----------+----------+
|userId|someString| varA|
+------+----------+----------+
| 1| example1| [0, 2, 5]|
| 2| example2|[1, 20, 5]|
+------+----------+----------+
scala> temp2.show()
+------+----------+----+
|userId|someString|varA|
+------+----------+----+
| 1| example1| 0|
| 1| example1| 2|
| 1| example1| 5|
| 2| example2| 1|
| 2| example2| 20|
| 2| example2| 5|
+------+----------+----+
但在AWS上,temp1 sqlContext命令工作正常,但temp2失败并显示消息:
scala> val temp2 = sqlContext.sql("select userId, someString, explode(varA) as varA from distTable_tmp")
15/11/05 22:46:49 INFO parse.ParseDriver: Parsing command: select userId, someString, explode(varA) as varA from distTable_tmp
15/11/05 22:46:49 INFO parse.ParseDriver: Parse Completed
org.apache.spark.sql.AnalysisException: unresolved operator 'Project [userId#3,someString#4,HiveGenericUdtf#org.apache.hadoop.hive.ql.udf.generic.GenericUDTFExplode(varA#5) AS varA#6];
...
非常感谢。
答案 0 :(得分:1)
问题的根源是您在EC2上使用的Spark版本。 Spark 1.4中引入了explode
函数,因此无法在1.3.1上运行。可以像这样使用RDD
和flatMap
:
import org.apache.spark.sql.Row
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types.{StructType, StructField, IntegerType}
val rows: RDD[Row] = distTable.rdd.flatMap(
row => row.getAs[Seq[Int]](2).map(v => Row.fromSeq(row.toSeq :+ v)))
val newSchema = StructType(
distTable.schema.fields :+ StructField("varA_exploded", IntegerType, true))
sqlContext.createDataFrame(rows, newSchema).show
// userId someString varA varA_exploded
// 1 example1 ArrayBuffer(0, 2, 5) 0
// 1 example1 ArrayBuffer(0, 2, 5) 2
// 1 example1 ArrayBuffer(0, 2, 5) 5
// 2 example2 ArrayBuffer(1, 20... 1
// 2 example2 ArrayBuffer(1, 20... 20
// 2 example2 ArrayBuffer(1, 20... 5
但是它怀疑它值得大惊小怪。