相关疑难解决方法(0)

有人可以帮我解决Spark DataFrame中的这个问题吗？

当我这样做时,myFloatRDD.toDF()我收到一个错误:

TypeError:无法推断类型的模式:类型'float'

我不明白为什么......

例:

myFloatRdd = sc.parallelize([1.0,2.0,3.0])
df = myFloatRdd.toDF()

谢谢

我正在尝试加载SVM文件并将其转换为一个,DataFrame因此我可以使用PipelineSpark 的ML模块(ML).我刚刚在Ubuntu 14.04上安装了一个新的Spark 1.5.0(没有spark-env.sh配置).

我my_script.py是:

from pyspark.mllib.util import MLUtils
from pyspark import SparkContext

sc = SparkContext("local", "Teste Original")
data = MLUtils.loadLibSVMFile(sc, "/home/svm_capture").toDF()

我正在使用: ./spark-submit my_script.py

我收到错误:

Traceback (most recent call last):
File "/home/fred-spark/spark-1.5.0-bin-hadoop2.6/pipeline_teste_original.py", line 34, in <module>
data = MLUtils.loadLibSVMFile(sc, "/home/fred-spark/svm_capture").toDF()
AttributeError: 'PipelinedRDD' object has no attribute 'toDF'

我无法理解的是,如果我跑:

data = MLUtils.loadLibSVMFile(sc, "/home/svm_capture").toDF()

直接在PySpark shell中,它的工作原理.

我想从PySpark读取存储在S3上的Parquet数据。

我从这里下载了spark：

http://www.apache.org/dist/spark/spark-2.1.0/spark-2.1.0-bin-hadoop2.7.tgz

并天真地将其安装到Python

cd python
python setup.py install

这似乎工作正常，我可以导入pyspark，创建SparkContext等。但是，当我阅读一些可公开访问的镶木地板数据时，会得到以下信息：

import pyspark
sc = pyspark.SparkContext('local[4]')
sql = pyspark.SQLContext(sc)
df = sql.read.parquet('s3://bucket-name/mydata.parquet')

我收到以下异常

Py4JJavaError: An error occurred while calling o55.parquet.
: java.io.IOException: No FileSystem for scheme: s3
    at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:372)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:344)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:370)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:152)
    at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:441)
    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:498)
    at …