r - 如何将 csv 读入 sparkR 1.4 版？

Question

作为 spark 的新版本(1.4) 已发布，似乎对 spark 有一个不错的前端接口(interface)。来自 R包名为 sparkR .在 documentation page of R for spark有一个命令可以读取 json文件作为 RDD 对象

people <- read.df(sqlContext, "./examples/src/main/resources/people.json", "json")

我正在尝试从 .csv 读取数据像 this revolutionanalitics' blog 上描述的文件

# Download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv

# Launch SparkR using 
# ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3

# The SparkSQL context should already be created for you as sqlContext
sqlContext
# Java ref type org.apache.spark.sql.SQLContext id 1

# Load the flights CSV file using `read.df`. Note that we use the CSV reader Spark package here.
flights <- read.df(sqlContext, "./nycflights13.csv", "com.databricks.spark.csv", header="true")

注释说我需要一个 spark-csv 包来启用此操作。所以我从这个 github repo下载了这个包使用此命令:

$ bin/spark-shell --packages com.databricks:spark-csv_2.10:1.0.3

但后来我在尝试读取 .csv 时遇到了这样的错误。文件。

> flights <- read.df(sqlContext, "./nycflights13.csv", "com.databricks.spark.csv", header="true")
15/07/03 12:52:41 ERROR RBackendHandler: load on 1 failed
java.lang.reflect.InvocationTargetException
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:127)
    at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:74)
    at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:36)
    at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:163)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:787)
    at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:130)
    at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
    at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:137)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: Failed to load class for data source: com.databricks.spark.csv
    at scala.sys.package$.error(package.scala:27)
    at org.apache.spark.sql.sources.ResolvedDataSource$.lookupDataSource(ddl.scala:216)
    at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:229)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:114)
    at org.apache.spark.sql.SQLContext.load(SQLContext.scala:1230)
    ... 25 more
Error: returnStatus == 0 is not TRUE

关于这个错误意味着什么以及如何解决这个问题的任何想法？

当然我可以尝试阅读 .csv以标准方式，例如:

read.table("data.csv") -> flights

然后我可以转换 R data.frame进入 spark的 DataFrame像这样:

flightsDF <- createDataFrame(sqlContext, flights)

但这不是我喜欢的方式，而且真的很耗时。

Answer 1

每次都必须像这样启动 sparkR 控制台:

sparkR --packages com.databricks:spark-csv_2.10:1.0.3