问题描述
我对 Apache Spark 非常陌生，我正在尝试将 SchemaRDD 与我的管道分隔文本文件一起使用.我在 Mac 上使用 Scala 10 独立安装了 Spark 1.5.2.我有一个包含以下代表性数据的 CSV 文件，我试图根据记录的第一个值(列)将以下内容拆分为 4 个不同的文件.我非常感谢我能得到的任何帮助.
I am very new to Apache Spark and am trying to use SchemaRDD with my pipe delimited text file. I have a standalone installation of Spark 1.5.2 on my Mac using Scala 10. I have a CSV file with the following representative data and I am trying to split the following into 4 different files based on the first value (column) of the record. I would very much appreciate any help I can get with this.
1|1.8|20140801T081137|115810740
2|20140714T060000|335|22159892|3657|0.00|||181
2|20140714T061500|335|22159892|3657|0.00|||157
2|20140714T063000|335|22159892|3657|0.00|||156
2|20140714T064500|335|22159892|3657|0.00|||66
2|20140714T070000|335|22159892|3657|0.01|||633
2|20140714T071500|335|22159892|3657|0.01|||1087
3|34|Starz
3|35|VH1
3|36|CSPAN: Cable Satellite Public Affairs Network
3|37|Encore
3|278|CMT: Country Music Television
3|281|Telehit
4|625363|1852400|Matlock|9212|The Divorce
4|625719|1852400|Matlock|16|The Rat Pack
4|625849|1846952|Smallville|43|Calling
推荐答案
注意:您的 csv 文件每行的字段数不同 - 这无法按原样解析为 DataFrame.(SchemaRDD 已重命名为 DataFrame.)如果您的 csv 文件格式正确，您可以执行以下操作:
Note: Your csv file does not have the same number of fields in each row - this cannot be parsed as is into a DataFrame. (SchemaRDD has been renamed to DataFrame.) Here is something you can do if your csv file were well-formed:
使用 --packages com.databricks:spark-csv_2.10:1.3.0 启动 spark-shell 或 spark-submit 以便轻松解析 csv 文件(见这里).在 Scala 中，您的代码将是，假设您的 csv 文件有一个标题 - 如果是，则更容易引用列:
launch spark-shell or spark-submit with --packages com.databricks:spark-csv_2.10:1.3.0 in order to parse csv files easily (see here). In Scala, your code would be, assuming your csv file has a header - if yes, it is easier to refer to columns:
val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("inferSchema", "true").option("delimiter", '|').load("/path/to/file.csv")
// assume 1st column has name col1
val df1 = df.filter( df("col1") === 1) // 1st DataFrame
val df2 = df.filter( df("col1") === 2) // 2nd DataFrame etc...
由于您的文件格式不正确，您必须以不同方式解析每一行，例如，执行以下操作:
Since your file is not well formed, you would have to parse each of the different lines differently, so for example, do the following:
val lines = sc.textFile("/path/to/file.csv")
case class RowRecord1( col1:Int, col2:Double, col3:String, col4:Int)
def parseRowRecord1( arr:Array[String]) = RowRecord1( arr(0).toInt, arr(1).toDouble, arr(2), arr(3).toInt)
case class RowRecord2( col1:Int, col2:String, col3:Int, col4:Int, col5:Int, col6:Double, col7:Int)
def parseRowRecord2( arr:Array[String]) = RowRecord2( arr(0).toInt, arr(1), arr(2).toInt, arr(3).toInt, arr(4).toInt, arr(5).toDouble, arr(8).toInt)
val df1 = lines.filter(_.startsWith("1")).map( _.split('|')).map( arr => parseRowRecord1( arr )).toDF
val df2 = lines.filter(_.startsWith("2")).map( _.split('|')).map( arr => parseRowRecord2( arr )).toDF
这篇关于Apache Spark 使用管道分隔的 CSV 文件的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！