mysql – Project_Bank.csv不是Parquet文件.尾部的预期幻数[80,65,82,49],但发现[110,111,13,10]

2023年8月8日 276次阅读

所以我试图加载csv文件推断自定义架构,但每次我最终得到以下错误：

Project_Bank.csv不是Parquet文件.尾部的预期幻数[80,65,82,49],但发现[110,111,13,10]

这是我的程序的样子和我的csv文件条目,

年龄,职业,婚姻,教育,默认值;平衡;住房;贷款;联系;天;月;持续时间;活动; pdays;以前; poutcome; Y
58;管理;已婚;叔胺;无; 2143;是;无;未知; 5;可; 261; 1; 1; 0;未知;无
44;技师;单;次级;无; 29;是;无;未知; 5;可; 151; 1; 1; 0;未知;无
33,企业家;已婚;次级;无; 2;是;是;未知; 5;可; 76; 1; 1; 0;未知;无

我的代码：

$spark-shell –packages com.databricks：spark-csv_2.10：1.5.0

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import org.apache.spark.sql.types._
import org.apache.spark.sql.SQLContext   
import sqlContext.implicits._    
import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType}

val bankSchema = StructType(Array(
  StructField("age", IntegerType, true),
  StructField("job", StringType, true),
  StructField("marital", StringType, true),
  StructField("education", StringType, true),
  StructField("default", StringType, true),
  StructField("balance", IntegerType, true),
  StructField("housing", StringType, true),
  StructField("loan", StringType, true),
  StructField("contact", StringType, true),
  StructField("day", IntegerType, true),
  StructField("month", StringType, true),
  StructField("duration", IntegerType, true),
  StructField("campaign", IntegerType, true),
  StructField("pdays", IntegerType, true),
  StructField("previous", IntegerType, true),
  StructField("poutcome", StringType, true),
  StructField("y", StringType, true)))


 val df = sqlContext.
  read.
  schema(bankSchema).
  option("header", "true").
  option("delimiter", ";").
  load("/user/amit.kudnaver_gmail/hadoop/project_bank/Project_Bank.csv").toDF()

  df.registerTempTable("people")
  df.printSchema()
  val distinctage = sqlContext.sql("select distinct age from people")

任何建议为什么在推送正确的架构后无法使用csv文件.在此先感谢您的建议.

谢谢
阿米特K.

最佳答案 这里的问题是Data Frame在处理它时需要Parquet文件.为了处理CSV中的数据.在这里你可以做什么.

首先,从数据中删除标题行.

58;management;married;tertiary;no;2143;yes;no;unknown;5;may;261;1;-1;0;unknown;no
44;technician;single;secondary;no;29;yes;no;unknown;5;may;151;1;-1;0;unknown;no
33;entrepreneur;married;secondary;no;2;yes;yes;unknown;5;may;76;1;-1;0;unknown;no

接下来,我们编写以下代码来读取数据.

创建案例类

case class BankSchema(age: Int, job: String, marital:String, education:String, default:String, balance:Int, housing:String, loan:String, contact:String, day:Int, month:String, duration:Int, campaign:Int, pdays:Int, previous:Int, poutcome:String, y:String)

从HDFS读取数据并解析它

val bankData = sc.textFile("/user/myuser/Project_Bank.csv").map(_.split(";")).map(p => BankSchema(p(0).toInt, p(1), p(2),p(3),p(4), p(5).toInt, p(6), p(7), p(8), p(9).toInt, p(10), p(11).toInt, p(12).toInt, p(13).toInt, p(14).toInt, p(15), p(16))).toDF()

然后注册表并执行查询.

bankData.registerTempTable("bankData")
val distinctage = sqlContext.sql("select distinct age from bankData")

这是输出的样子

+---+
|age|
+---+
| 33|
| 44|
| 58|
+---+
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