引用与 SparkSQL CSV 不明确 [英] Reference is ambiguous with SparkSQL CSV
本文介绍了引用与 SparkSQL CSV 不明确的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试使用自定义架构读取 SparkSQL 2.10 中的一堆 CSV 文件，该架构部分为双精度，部分为字符串，如下所示:
I'm trying to read a bunch of CSV files in SparkSQL 2.10 with a custom schema that is partly Double, partly Strings like this:
// Build the schema
val schemaStringS = "col1 col2"
val schemaStringD = "col3 col4 col5 col6"
val schemaStringS2 = "col7 col8"
val fieldsString = schemaStringS.split(" ")
.map(fieldName => StructField(fieldName, StringType, nullable = true))
val fieldsString2 = schemaStringS2.split(" ")
.map(fieldName => StructField(fieldName, StringType, nullable = true))
val fieldsDouble = schemaStringS.split(" ")
.map(fieldName => StructField(fieldName, DoubleType, nullable = true))
val schema = StructType(fieldsString ++ fieldsDouble ++ fieldsString2)
// Read DataFrame
val input = sqlContext.read.schema(schema)
.option("header", true)
.csv("/files/*.csv")
.toJavaRDD
结果是
Exception in thread "main" org.apache.spark.sql.AnalysisException: Reference 'col6' is ambiguous, could be: col6#0, col6#5.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:264)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:158)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:129)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.types.StructType.foreach(StructType.scala:96)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at org.apache.spark.sql.types.StructType.map(StructType.scala:96)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:129)
at org.apache.spark.sql.execution.datasources.FileSourceStrategy$.apply(FileSourceStrategy.scala:83)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:62)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:79)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87)
at org.apache.spark.sql.Dataset.rdd$lzycompute(Dataset.scala:2547)
at org.apache.spark.sql.Dataset.rdd(Dataset.scala:2544)
at org.apache.spark.sql.Dataset.toJavaRDD(Dataset.scala:2557)
at com.otterinasuit.spark.sensorlog.main.Main$.main(Main.scala:39)
at com.otterinasuit.spark.sensorlog.main.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
我尝试将文件与 cat 合并(仅适用于 PoC)并避免使用 CSV 库(认为这可能是新 Spark 版本中的错误)但无济于事.
I tried merging the files with cat (only feasible for a PoC) and avoiding the CSV library (thinking this might be a bug in the new Spark version) but to no avail.
val input = sc.textFile("/csv/*.csv")
.map(line => line.split(",")).filter(row => !row.contains("col1")).map(x => Row(x))
val input2 = sqlContext.createDataFrame(input, schema)
我在使用常规 DataFrame 和连接时遇到过这个问题，iirc 可以通过指定列名、删除特定列或使用不同的连接来解决这个问题.但是，在这种情况下，我没有那个选项.
I have encountered this problem with regular DataFrames and joins and iirc this can be solved by specifying column names, dropping specific columns or using different joins. However, in this case, I don't have that option.
所有文件中的所有标头都是相同的，正如 head -1 *.csv 所证明的那样.我不明白为什么会发生这种情况.
All headers in all files are identical, as proven by head -1 *.csv. I don't understand why this would occur.
推荐答案
fieldsString 和 fieldsDouble 都指的是 schemaStringS.
val fieldsString = schemaStringS.split(" ")
.map(fieldName => StructField(fieldName, StringType, nullable = true))
//Changing from schemaStringS to schemaStringD
val fieldsDouble = schemaStringD.split(" ")
.map(fieldName => StructField(fieldName, DoubleType, nullable = true))
所以，当你合并时
val schema = StructType(fieldsString ++ fieldsDouble ++ fieldsString2))
它抛出'col6' is ambiguous 错误，
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