Hi Sourav,
The error seems to be caused by the fact that your URL starts with
"file://" instead of "file:///".
Also, I believe the current version of the package for Spark 1.4 with Scala
2.11 should be "com.databricks:spark-csv_2.11:1.1.0".
-Jey
On Mon, Jun 29, 2015 at 12:23 PM, Sourav Mazumder <
sourav.mazumde...@gmail.com> wrote:
> Hi Jey,
>
> Thanks for your inputs.
>
> Probably I'm getting error as I'm trying to read a csv file from local
> file using com.databricks.spark.csv package. Probably this package has hard
> coded dependency on Hadoop as it is trying to read input format from
> HadoopRDD.
>
> Can you please confirm ?
>
> Here is what I did -
>
> Ran the spark-shell as
>
> bin/spark-shell --packages com.databricks:spark-csv_2.10:1.0.3.
>
> Then in the shell I ran :
> val df =
> sqlContext.read.format("com.databricks.spark.csv").load("file://home/biadmin/DataScience/PlutoMN.csv")
>
>
>
> Regards,
> Sourav
>
> 15/06/29 15:14:59 INFO spark.SparkContext: Created broadcast 0 from
> textFile at CsvRelation.scala:114
> java.lang.RuntimeException: Error in configuring object
> at
> org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:109)
> at
> org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:75)
> at
> org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:133)
> at org.apache.spark.rdd.HadoopRDD.getInputFormat(HadoopRDD.scala:190)
> at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:203)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)
> at
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:32)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)
> at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1251)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
> at org.apache.spark.rdd.RDD.take(RDD.scala:1246)
> at org.apache.spark.rdd.RDD$$anonfun$first$1.apply(RDD.scala:1286)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
> at org.apache.spark.rdd.RDD.first(RDD.scala:1285)
> at
> com.databricks.spark.csv.CsvRelation.firstLine$lzycompute(CsvRelation.scala:114)
> at
> com.databricks.spark.csv.CsvRelation.firstLine(CsvRelation.scala:112)
> at
> com.databricks.spark.csv.CsvRelation.inferSchema(CsvRelation.scala:95)
> at com.databricks.spark.csv.CsvRelation.<init>(CsvRelation.scala:53)
> at
> com.databricks.spark.csv.DefaultSource.createRelation(DefaultSource.scala:89)
> at
> com.databricks.spark.csv.DefaultSource.createRelation(DefaultSource.scala:39)
> at
> com.databricks.spark.csv.DefaultSource.createRelation(DefaultSource.scala:27)
> at
> org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:265)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:114)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:104)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:19)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:24)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)
> at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:28)
> at $iwC$$iwC$$iwC$$iwC.<init>(<console>:30)
> at $iwC$$iwC$$iwC.<init>(<console>:32)
> at $iwC$$iwC.<init>(<console>:34)
> at $iwC.<init>(<console>:36)
> at <init>(<console>:38)
> at .<init>(<console>:42)
> at .<clinit>(<console>)
> at java.lang.J9VMInternals.initializeImpl(Native Method)
> at java.lang.J9VMInternals.initialize(J9VMInternals.java:200)
> at .<init>(<console>:7)
> at .<clinit>(<console>)
> at java.lang.J9VMInternals.initializeImpl(Native Method)
> at java.lang.J9VMInternals.initialize(J9VMInternals.java:200)
> at $print(<console>)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:60)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:37)
> at java.lang.reflect.Method.invoke(Method.java:611)
> at
> org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
> at
> org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
> at
> org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
> at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
> at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
> at
> org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
> at
> org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
> at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
> at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
> at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
> at org.apache.spark.repl.SparkILoop.org
> $apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
> at
> org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
> at
> org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
> at
> org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
> at
> scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
> at org.apache.spark.repl.SparkILoop.org
> $apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
> at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
> at org.apache.spark.repl.Main$.main(Main.scala:31)
> at org.apache.spark.repl.Main.main(Main.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:60)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:37)
> at java.lang.reflect.Method.invoke(Method.java:611)
> at
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:664)
> at
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:169)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:192)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:111)
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> Caused by: java.lang.reflect.InvocationTargetException
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:60)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:37)
> at java.lang.reflect.Method.invoke(Method.java:611)
> at
> org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:106)
> ... 83 more
>
>
>
> On Mon, Jun 29, 2015 at 10:02 AM, Jey Kottalam <j...@cs.berkeley.edu>
> wrote:
>
>> Actually, Hadoop InputFormats can still be used to read and write from
>> "file://", "s3n://", and similar schemes. You just won't be able to
>> read/write to HDFS without installing Hadoop and setting up an HDFS cluster.
>>
>> To summarize: Sourav, you can use any of the prebuilt packages (i.e.
>> anything other than "source code").
>>
>> Hope that helps,
>> -Jey
>>
>> On Mon, Jun 29, 2015 at 7:33 AM, ayan guha <guha.a...@gmail.com> wrote:
>>
>>> Hi
>>>
>>> You really donot need hadoop installation. You can dowsload a pre-built
>>> version with any hadoop and unzip it and you are good to go. Yes it may
>>> complain while launching master and workers, safely ignore them. The only
>>> problem is while writing to a directory. Of course you will not be able to
>>> use any hadoop inputformat etc. out of the box.
>>>
>>> ** I am assuming its a learning question :) For production, I would
>>> suggest build it from source.
>>>
>>> If you are using python and need some help, please drop me a note off
>>> line.
>>>
>>> Best
>>> Ayan
>>>
>>> On Tue, Jun 30, 2015 at 12:24 AM, Sourav Mazumder <
>>> sourav.mazumde...@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> I'm trying to run Spark without Hadoop where the data would be read and
>>>> written to local disk.
>>>>
>>>> For this I have few Questions -
>>>>
>>>> 1. Which download I need to use ? In the download option I don't see
>>>> any binary download which does not need Hadoop. Is the only way to do this
>>>> to download the source code version and compile the same ?
>>>>
>>>> 2. Which installation/quick start guideline I should use for the same.
>>>> So far I didn't see any documentation which specifically addresses the
>>>> Spark without Hadoop installation/setup unless I'm missing out one.
>>>>
>>>> Regards,
>>>> Sourav
>>>>
>>>
>>>
>>>
>>> --
>>> Best Regards,
>>> Ayan Guha
>>>
>>
>>
>
