I am new to Spark and would like to know if there are other options than those ones below to read data stored in a hdfs from RStudio using SparkR or if I use them correctly. The data could be any kind (pure text, csv, json, xml or any database containing relational tables) and of any size (1kb - several gb).
I know that textFile(sc, path) should no more be used, but are there other possibilities to read such kinds of data besides the read.df function?
The following code uses the read.df and jsonFile but jsonFile produces an error:
Sys.setenv(SPARK_HOME = "C:\\Users\\--\\Downloads\\spark-1.5.0-bin-hadoop2.6")
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
#load the Sparkr library
library(SparkR)
# Create a spark context and a SQL context
sc <- sparkR.init(master="local", sparkPackages="com.databricks:spark-csv_2.11:1.0.3")
sqlContext <- sparkRSQL.init(sc)
#create a sparkR DataFrame
df <- read.df(sqlContext, "hdfs://0.0.0.0:19000/people.json", source = "json")
df <- jsonFile(sqlContext, "hdfs://0.0.0.0:19000/people.json")
read.df works for json, but how do I read text such as log messages which are only separated by a new line? E.g.
> df <- read.df(sqlContext, "hdfs://0.0.0.0:19000/README.txt", "text")
Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
java.lang.ClassNotFoundException: Failed to load class for data source: text.
at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.lookupDataSource(ResolvedDataSource.scala:67)
at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:87)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:114)
at org.apache.spark.sql.api.r.SQLUtils$.loadDF(SQLUtils.scala:156)
at org.apache.spark.sql.api.r.SQLUtils.loadDF(SQLUtils.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:132)
at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:79)
at org.apache.spark.ap
The error with jsonFile is:
> df <- jsonFile(sqlContext, "hdfs://0.0.0.0:19000/people.json")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
java.io.IOException: No input paths specified in job
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:201)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:207)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfu
I don't know why read.df throws an error because I did not restard SparkR or call SparkR.stop()
For the same code besides using the read.df I use the SparkR:::textFile function and the sc instead of the sqlContext(following the outdated Introduction on amplab).
The error message is:
data <- SparkR:::textFile(sc, "hdfs://0.0.0.0:19000/people.json")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
java.lang.IllegalArgumentException: java.net.URISyntaxException: Expected scheme-specific part at index 5: hdfs:
at org.apache.hadoop.fs.Path.initialize(Path.java:206)
at org.apache.hadoop.fs.Path.<init>(Path.java:172)
at org.apache.hadoop.fs.Path.<init>(Path.java:94)
at org.apache.hadoop.fs.Globber.glob(Globber.java:211)
at org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:1644)
at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:257)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:207)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at or
This error looks as the path is not correct, but I don't know why.
What I currently use:
spark-1.5.0-bin-hadoop2.6 hadoop-2.6.0 Windows(8.1) R Version 3.2.2 Rstudio Version 0.99.484
I hope somebody can give me some hints on this issue here.
Try
% hadoop fs -put people.json /
% sparkR
> people <- read.df(sqlContext, "/people.json", "json")
> head(people)
You probably need a library for parsing other files, like DataBricks CSV library:
https://github.com/databricks/spark-csv
Then you would start R with the package loaded, e.g:
$ sparkR --packages com.databricks:spark-csv_2.10:1.0.3
and load your file like:
> df <- read.df(sqlContext, "cars.csv", source = "com.databricks.spark.csv", inferSchema = "true")
This assumes you have the "cars.csv" test file in your hdfs home directory.
hth
来源：https://stackoverflow.com/questions/32585686/options-to-read-large-files-pure-text-xml-json-csv-from-hdfs-in-rstudio-wit