Having trouble loading SparkR into my RStudio. When I try to load a spark context, I get the error:
Could not find function "launchBackend."
I can get sparkR working on my terminal shell, but that's also because I don't have to load a spark context (it seems like it's already there). Any help?
Sys.setenv(SPARK_HOME="~/spark-1.4.1")
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
library(SparkR)
sc <- sparkR.init(master = "local")
sqlContext <- sparkRSQL.init(sc)
One thing which might help is to type the full path in. R doesn't expand the tilde.
Instead of:
Sys.setenv(SPARK_HOME="~/spark-1.4.1")
Something like this:
Sys.setenv(SPARK_HOME="/home/spark-1.4.1")
Related
I've been trying to read from a .csv file on many ways, utilizing SparkContext object. I found it possible through scala.io.Source.fromFile function, but I want to use spark object. Everytime I run function textfile for org.apache.spark.SparkContext I get the same error:
scala> sparkSession.read.csv("file://C:\\Users\\184229\\Desktop\\bigdata.csv")
21/12/29 16:47:32 WARN streaming.FileStreamSink: Error while looking for metadata directory.
java.lang.UnsupportedOperationException: Not implemented by the DistributedFileSystem FileSystem implementation
.....
As it's mentioned in the title I run the code on Windows in IntelliJ
[Edit]
In build.sbt have no redundant or overlapped dependencies. I use hadoop-tools, spark-sql and hadoop-xz.
Have you tried to run your spark-shell using local mode?
spark-shell --master=local
Also pay attention to not use both Hadoop-code and Hadoop-commons as a dependencies since you may have conflicting jars issues.
I've found the solution, precisely one of my colleague did that.
In dependencies build.sbt I changed hadoop-tools to hadoop-commons and it worked out.
I am using RStudio to connect to my HDFS file using SparkR. When I leave Spark analyses running overnight, I get "R session aborted" error the next day. From Spark's documentation on SparkR (https://spark.apache.org/docs/latest/configuration.html), the default value of spark.r.backendConnectionTimeout is set to 6000s. I would like to change this value to something large that my connection doesn't time out after the analyses is done.
I have tried the following:
sparkR.session(master = "local[*]", sparkConfig = list(spark.r.backendConnectionTimeout = 10))
sparkR.session(master = "local[*]", spark.r.backendConnectionTimeout = 10)
I get the same output for both commands:
Spark package found in SPARK_HOME: C:\Spark\spark-2.3.2-bin-hadoop2.7
Launching java with spark-submit command C:\Spark\spark-2.3.2-bin-hadoop2.7/bin/spark-submit2.cmd sparkr-shell C:\Users\XYZ\AppData\Local\Temp\3\RtmpiEaE5q\backend_port696c18316c61
Java ref type org.apache.spark.sql.SparkSession id 1
It seems that the parameter was not passed correctly. Also, I am not sure where to pass that parameter.
Any help would be appreciated.
A similar post is around, but that involves Zeppelin (how to change spark.r.backendConnectionTimeout value?).
Thanks.
I found the solution: it is to modify the spark-defaults.conf file and add the following line:
spark.r.backendConnectionTimeout = 6000000
(or whatever time limit you want)
IMPORTANT note - restart hadoop and yarn services, and try connecting to Spark with SparkR normally:
library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib")))
sparkR.session(master = "local")
You can check if the settings took place or not at http://localhost:4040/environment/
I hope this comes useful for other people.
I am getting below error while trying to save parquet file from local directory using pyspark.
I tried spark 1.6 and 2.2 both give same error
It display's schema properly but gives error at the time of writing file.
base_path = "file:/Users/xyz/Documents/Temp/parquet"
reg_path = "file:/Users/xyz/Documents/Temp/parquet/ds_id=48"
df = sqlContext.read.option( "basePath",base_path).parquet(reg_path)
out_path = "file:/Users/xyz/Documents/Temp/parquet/out"
df2 = df.coalesce(5)
df2.printSchema()
df2.write.mode('append').parquet(out_path)
org.apache.spark.SparkException: Task failed while writing rows
Caused by: java.io.IOException: can not read class org.apache.parquet.format.PageHeader: Required field 'uncompressed_page_size' was not found in serialized data! Struct: PageHeader(type:null, uncompressed_page_size:0, compressed_page_size:0)
In my own case, I was writing a custom Parquet Parser for Apache Tika and I experienced this error. It turned out that if the file is being used by another process, the ParquetReader will not be able to access uncompressed_page_size. Hence, causing the error.
Verify if other processes are not holding on to the file.
Temporary resolved by the spark config:
"spark.sql.hive.convertMetastoreParquet": "false"
Although it would has extra cost, but a walkaround approach by now.
I am trying to read .sas7bdat file from default container. I have tried following till now:
sas_file <- RxSasData("wasbs://container#storageaccount.blob.core.windows.net/abc/xyz.sas7bdat")
sas_df <- rxImport(sas_file)
but I get following error:
The file 'wasbs://container#storageaccount.blob.core.windows.net/abc/xyz.sas7bdat' does not exist.
Could not open data source.
Error in doTryCatch(return(expr), name, parentenv, handler) :
Could not open data source.
File exists at the mentioned location in code. Still it throws error. Can someone please help me this?
According to your code, I think you want to local a SAS data file from HDFS on Azure HDInsight via RxSasData. However, RxSasData seems to be not supported on Hadoop env, as the figure below, please see here.
Please try to copy the file to local filesystem on HDI, then to read.
I R and sparkR I want to change the 'max-ppsize'.
https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
page 93/105 there is a desciption for the command.
How exactly do I type it in when I have open the R or sparkR program on Ubuntu ? I couldnt find a clear description for it else where.
I have looked at the answer on
How to set max ppsize in R?
I can do it in R successfully but in I can't do it in SparkR.
I launch sparkR on ubuntu like this
cd /home/.../R/spark-1.4.0
and then I type
.bin/sparkR --max-ppsize=90000
But I get an error message: "log4j Warn No appenders could be found for logger"