Thanks Andrew. Actually my job did not use any data in .lzo format. Here is
the program itself:
import org.apache.spark._
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
object Test {
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("SparkMLTest")
val sc = new SparkContext(sparkConf)
val training = MLUtils.loadLibSVMFile(sc,
"hdfs://url:8020/user/jilei/sparktesttraining_libsvmfmt_10k.txt"
val model = LogisticRegressionWithSGD.train(training, numIterations
= 20)
}
}
I copied this form a github gist and want to have a try. The file is a
libsvm format file and is in HDFS (I removed the actual hdfs url here in the
code.)
And in the spark-env.sh file, I set the evns:
export SPARK_LIBRARY_PATH=/apache/hadoop/lib/native/
export
SPARK_CLASSPATH=/apache/hadoop/share/hadoop/common/hadoop-common-2.2.0.2.0.6.0-61.jar:/apache/hadoop/lib/hadoop-lzo-0.6.0.jar
Here is the content of the /apache/hadoop/lib/native/ folder:
ls /apache/hadoop/lib/native/
libgplcompression.a libgplcompression.so libgplcompression.so.0.0.0
libhadooppipes.a libhadoop.so.1.0.0 libhdfs.a libhdfs.so.0.0.0
libsnappy.so.1
libgplcompression.la libgplcompression.so.0 libhadoop.a
libhadoop.so libhadooputils.a libhdfs.so libsnappy.so
libsnappy.so.1.1.4
Andrew Ash wrote
> Hi Jikai,
>
> It looks like you're trying to run a Spark job on data that's stored in
> HDFS in .lzo format. Spark can handle this (I do it all the time), but
> you
> need to configure your Spark installation to know about the .lzo format.
>
> There are two parts to the hadoop lzo library -- the first is the jar
> (hadoop-lzo.jar) and the second is the native library
> (libgplcompression.{a,so,la} and liblzo2.{a,so,la}). You need the jar on
> the classpath across your cluster, but also the native libraries exposed
> as
> well.
>
> In Spark 1.0.1 I modify entries in spark-env.sh: set SPARK_LIBRARY_PATH to
> include the path to the native library directory
> (e.g. /path/to/hadoop/lib/native/Linux-amd64-64) and SPARK_CLASSPATH to
> include the hadoop-lzo jar.
>
> Hope that helps,
> Andrew
>
>
> On Thu, Aug 7, 2014 at 7:19 PM, Xiangrui Meng <
> mengxr@
> > wrote:
>
>> Is the GPL library only available on the driver node? If that is the
>> case, you need to add them to `--jars` option of spark-submit.
>> -Xiangrui
>>
>> On Thu, Aug 7, 2014 at 6:59 PM, Jikai Lei <
> hangelwen@
> > wrote:
>> > I had the following error when trying to run a very simple spark job
>> (which
>> > uses logistic regression with SGD in mllib):
>> >
>> > ERROR GPLNativeCodeLoader: Could not load native gpl library
>> > java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
>> > at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1738)
>> > at java.lang.Runtime.loadLibrary0(Runtime.java:823)
>> > at java.lang.System.loadLibrary(System.java:1028)
>> > at
>> >
>> com.hadoop.compression.lzo.GPLNativeCodeLoader.
> <clinit>
> (GPLNativeCodeLoader.java:32)
>> > at com.hadoop.compression.lzo.LzoCodec.
> <clinit>
> (LzoCodec.java:71)
>> > at java.lang.Class.forName0(Native Method)
>> > at java.lang.Class.forName(Class.java:247)
>> > at
>> >
>> org.apache.hadoop.conf.Configuration.getClassByNameOrNull(Configuration.java:1659)
>> > at
>> >
>> org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:1624)
>> > at
>> >
>> org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(CompressionCodecFactory.java:128)
>> > at
>> >
>> org.apache.hadoop.io.compress.CompressionCodecFactory.
> <init>
> (CompressionCodecFactory.java:175)
>> > at
>> >
>> org.apache.hadoop.mapred.TextInputFormat.configure(TextInputFormat.java:45)
>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>> > at
>> >
>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
>> > at
>> >
>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
>> > at java.lang.reflect.Method.invoke(Method.java:597)
>> > at
>> >
>> org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:106)
>> > 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:155)
>> > at org.apache.spark.rdd.HadoopRDD$$anon$1.
> <init>
> (HadoopRDD.scala:187)
>> > at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:181)
>> > at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:93)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34)
>> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> > at
>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
>> > at org.apache.spark.scheduler.Task.run(Task.scala:51)
>> > at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
>> > at
>> >
>> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
>> > at
>> >
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)
>> > at java.lang.Thread.run(Thread.java:662)
>> > 14/08/06 20:32:11 ERROR LzoCodec: Cannot load native-lzo without
>> > native-hadoop
>> >
>> >
>> > This is the command I used to submit the job:
>> >
>> > ~/spark/spark-1.0.0-bin-hadoop2/bin/spark-submit \
>> > --class com.jk.sparktest.Test \
>> > --master yarn-cluster \
>> > --num-executors 40 \
>> > ~/sparktest-0.0.1-SNAPSHOT-jar-with-dependencies.jar
>> >
>> >
>> > The actual java command is :
>> >
>> > /usr/java/latest/bin/java -cp
>> >
>> /apache/hadoop/share/hadoop/common/hadoop-common-2.2.0.2.0.6.0-61.jar:/apache/hadoop/lib/hadoop-lzo-0.6.0.jar::/home/jilei/spark/spark-1.0.0-bin-hadoop2/conf:/home/jilei/spark/spark-1.0.0-bin-hadoop2/lib/spark-assembly-1.0.0-hadoop2.2.0.jar:/home/jilei/spark/spark-1.0.0-bin-hadoop2/lib/datanucleus-rdbms-3.2.1.jar:/home/jilei/spark/spark-1.0.0-bin-hadoop2/lib/datanucleus-core-3.2.2.jar:/home/jilei/spark/spark-1.0.0-bin-hadoop2/lib/datanucleus-api-jdo-3.2.1.jar:/apache/hadoop/conf:/apache/hadoop/conf
>> > \
>> > -XX:MaxPermSize=128m \
>> > -Djava.library.path=
>> > -Xms512m -Xmx512m org.apache.spark.deploy.SparkSubmit \
>> > --class com.jk.sparktest.Test \
>> > --master yarn-cluster \
>> > --num-executors 40 \
>> > ~/sparktest-0.0.1-SNAPSHOT-jar-with-dependencies.jar
>> >
>> >
>> > Seems the -Djava.library.path is not set. I also tried the java command
>> > above and supplied the native lib directory to the java.library.path,
>> but
>> > still got the same errors.
>> >
>> > Any idea on what's wrong? Thanks.
>> >
>> >
>> >
>> > --
>> > View this message in context:
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>> Nabble.com.
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