I have been testing a map reduce job on a single node and it seems to work but now that I am trying to run it on a remote cluster I am getting a ClassNotFoundExcepton. My code is structured as follows:
public class Pivot {
public static class Mapper extends TableMapper<ImmutableBytesWritable, ImmutableBytesWritable> {
#Override
public void map(ImmutableBytesWritable rowkey, Result values, Context context) throws IOException {
(map code)
}
}
public static class Reducer extends TableReducer<ImmutableBytesWritable, ImmutableBytesWritable, ImmutableBytesWritable> {
public void reduce(ImmutableBytesWritable key, Iterable<ImmutableBytesWritable> values, Context context) throws IOException, InterruptedException {
(reduce code)
}
}
public static void main(String[] args) {
Configuration conf = HBaseConfiguration.create();
conf.set("fs.default.name", "hdfs://hadoop-master:9000");
conf.set("mapred.job.tracker", "hdfs://hadoop-master:9001");
conf.set("hbase.master", "hadoop-master:60000");
conf.set("hbase.zookeeper.quorum", "hadoop-master");
conf.set("hbase.zookeeper.property.clientPort", "2222");
Job job = new Job(conf);
job.setJobName("Pivot");
job.setJarByClass(Pivot.class);
Scan scan = new Scan();
TableMapReduceUtil.initTableMapperJob("InputTable", scan, Mapper.class, ImmutableBytesWritable.class, ImmutableBytesWritable.class, job);
TableMapReduceUtil.initTableReducerJob("OutputTable", Reducer.class, job);
job.waitForCompletion(true);
}
}
The error I am receiving when I try to run this job is the following:
java.lang.RuntimeException: java.lang.ClassNotFoundException: Pivot$Mapper
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:857)
...
Is there something I'm missing? Why is the job having difficulty finding the mapper?
When running a job from Eclipse it's important to note that Hadoop requires you to launch your job from a jar. Hadoop requires this so it can send your code up to HDFS / JobTracker.
In your case i imagine you haven't bundled up your job classes into a jar, and then run the program 'from the jar' - resulting in a CNFE.
Try building a jar and running from the command line using hadoop jar myjar.jar ..., once this works then you can test running from within Eclipse
Related
I am running a hadoop job which is working fine when I am running it without yarn in pseudo-distributed mode, but it is giving me class not found exception when running with yarn
16/03/24 01:43:40 INFO mapreduce.Job: Task Id : attempt_1458775953882_0002_m_000003_1, Status : FAILED
Error: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.hadoop.keyword.count.ItemMapper not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2195)
at org.apache.hadoop.mapreduce.task.JobContextImpl.getMapperClass(JobContextImpl.java:186)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:745)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
Caused by: java.lang.ClassNotFoundException: Class com.hadoop.keyword.count.ItemMapper not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2101)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2193)
... 8 more
Here is the source-code for the job
Configuration conf = new Configuration();
conf.set("keywords", args[2]);
Job job = Job.getInstance(conf, "item count");
job.setJarByClass(ItemImpl.class);
job.setMapperClass(ItemMapper.class);
job.setReducerClass(ItemReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
Here is the command I am running
hadoop jar ~/itemcount.jar /user/rohit/tweets /home/rohit/outputs/23mar-yarn13 vodka,wine,whisky
Edit Code, after suggestion
package com.hadoop.keyword.count;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Mapper.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import org.json.simple.parser.ParseException;
public class ItemImpl {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.set("keywords", args[2]);
Job job = Job.getInstance(conf, "item count");
job.setJarByClass(ItemImpl.class);
job.setMapperClass(ItemMapper.class);
job.setReducerClass(ItemReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
public static class ItemMapper extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
JSONParser parser = new JSONParser();
#Override
public void map(Object key, Text value, Context output) throws IOException,
InterruptedException {
JSONObject tweetObject = null;
String[] keywords = this.getKeyWords(output);
try {
tweetObject = (JSONObject) parser.parse(value.toString());
} catch (ParseException e) {
e.printStackTrace();
}
if (tweetObject != null) {
String tweetText = (String) tweetObject.get("text");
if(tweetText == null){
return;
}
tweetText = tweetText.toLowerCase();
/* StringTokenizer st = new StringTokenizer(tweetText);
ArrayList<String> tokens = new ArrayList<String>();
while (st.hasMoreTokens()) {
tokens.add(st.nextToken());
}*/
for (String keyword : keywords) {
keyword = keyword.toLowerCase();
if (tweetText.contains(keyword)) {
output.write(new Text(keyword), one);
}
}
output.write(new Text("count"), one);
}
}
String[] getKeyWords(Mapper<Object, Text, Text, IntWritable>.Context context) {
Configuration conf = (Configuration) context.getConfiguration();
String param = conf.get("keywords");
return param.split(",");
}
}
public static class ItemReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
#Override
protected void reduce(Text key, Iterable<IntWritable> values, Context output)
throws IOException, InterruptedException {
int wordCount = 0;
for (IntWritable value : values) {
wordCount += value.get();
}
output.write(key, new IntWritable(wordCount));
}
}
}
Running in full distributed mode your TaskTracker/NodeManager (the thing running your mapper) is running in a separate JVM and it sounds like your class is not making it onto that JVM's classpath.
Try using the -libjars <csv,list,of,jars> command line arg on job invocation. This will have Hadoop distribute the jar to the TaskTracker JVM and load your classes from that jar. (Note, this copies the jar out to each node in your cluster and makes it available only for that specific job. If you have common libraries that would need to be invoked for a lot of jobs, you'd want to look into using the Hadoop distributed cache.)
You may also want to try yarn -jar ... when launching your job versus hadoop -jar ... since that's the new/preferred way to launch yarn jobs.
Can you check the content of your itemcount.jar ?( jar -tvf itemcount.jar). I faced this issue once only to find that the .class was missing from the jar.
I had the same error a few days ago.
Changing map and reduce classes to static fixed my problem.
Make your map and reduce classes inner classes.
Control constructors of map and reduce classes (i/o values and override statement)
Check your jar command
old one
hadoop jar ~/itemcount.jar /user/rohit/tweets /home/rohit/outputs/23mar-yarn13 vodka,wine,whisky
new
hadoop jar ~/itemcount.jar com.hadoop.keyword.count.ItemImpl /user/rohit/tweets /home/rohit/outputs/23mar-yarn13 vodka,wine,whisky
add packageName.mainclass after you specified .jar file
Try-catch
try {
tweetObject = (JSONObject) parser.parse(value.toString());
} catch (Exception e) { **// Change ParseException to Exception if you don't only expect Parse error**
e.printStackTrace();
return; **// return from function in case of any error**
}
}
extends Configured and implement Tool
public class ItemImpl extends Configured implements Tool{
public static void main (String[] args) throws Exception{
int res =ToolRunner.run(new ItemImpl(), args);
System.exit(res);
}
#Override
public int run(String[] args) throws Exception {
Job job=Job.getInstance(getConf(),"ItemImpl ");
job.setJarByClass(this.getClass());
job.setJarByClass(ItemImpl.class);
job.setMapperClass(ItemMapper.class);
job.setReducerClass(ItemReducer.class);
job.setMapOutputKeyClass(Text.class);//probably not essential but make it certain and clear
job.setMapOutputValueClass(IntWritable.class); //probably not essential but make it certain and clear
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
add public static map
add public static reduce
I'm not an expert about this topic but This implementation is from one of my working projects. Try this if doesn't work for you I would suggest you check the libraries you added to your project.
Probably first step will solve it but
If these steps doesn't work , share the code with us.
When I submit hadoop job It always said
WARN [JobClient] Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same
How can I fix this?
I am using CDH 4.6.0.
You should use something like below driver code to start your MapReduce job to get rid of warning (although it doesn't do any harm):
public class MyClass extends Configured implements Tool {
public int run(String [] args) throws IOException {
JobConf conf = new JobConf(getConf(), MyClass.class);
// run the job here.
return 0;
}
public static void main(String [] args) throws Exception {
int status = ToolRunner.run(new MyClass(), args); // calls your run() method.
System.exit(status);
}
}
Hadoop's DistributedCache documentation doesn't seem to sufficently describe how to use the distributed cache. Here is the example given:
// Setting up the cache for the application
1. Copy the requisite files to the FileSystem:
$ bin/hadoop fs -copyFromLocal lookup.dat /myapp/lookup.dat
$ bin/hadoop fs -copyFromLocal map.zip /myapp/map.zip
$ bin/hadoop fs -copyFromLocal mylib.jar /myapp/mylib.jar
$ bin/hadoop fs -copyFromLocal mytar.tar /myapp/mytar.tar
$ bin/hadoop fs -copyFromLocal mytgz.tgz /myapp/mytgz.tgz
$ bin/hadoop fs -copyFromLocal mytargz.tar.gz /myapp/mytargz.tar.gz
2. Setup the application's JobConf:
JobConf job = new JobConf();
DistributedCache.addCacheFile(new URI("/myapp/lookup.dat#lookup.dat"),
job);
DistributedCache.addCacheArchive(new URI("/myapp/map.zip", job);
DistributedCache.addFileToClassPath(new Path("/myapp/mylib.jar"), job);
DistributedCache.addCacheArchive(new URI("/myapp/mytar.tar", job);
DistributedCache.addCacheArchive(new URI("/myapp/mytgz.tgz", job);
DistributedCache.addCacheArchive(new URI("/myapp/mytargz.tar.gz", job);
3. Use the cached files in the Mapper
or Reducer:
public static class MapClass extends MapReduceBase
implements Mapper<K, V, K, V> {
private Path[] localArchives;
private Path[] localFiles;
public void configure(JobConf job) {
// Get the cached archives/files
File f = new File("./map.zip/some/file/in/zip.txt");
}
public void map(K key, V value,
OutputCollector<K, V> output, Reporter reporter)
throws IOException {
// Use data from the cached archives/files here
// ...
// ...
output.collect(k, v);
}
}
I've been searching around for over an hour trying to figure out how to use this. After piecing together a few other SO questions, here's what I came up with:
public static void main(String[] args) throws Exception {
Job job = new Job(new JobConf(), "Job Name");
JobConf conf = job.getConfiguration();
DistributedCache.createSymlink(conf);
DistributedCache.addCacheArchive(new URI("/ProjectDir/LookupTable.zip", job);
// *Rest of configuration code*
}
public static class MyMapper extends Mapper<Object, Text, Text, IntWritable>
{
private Path[] localArchives;
public void configure(JobConf job)
{
// Get the cached archive
File file1 = new File("./LookupTable.zip/file1.dat");
BufferedReader br1index = new BufferedReader(new FileInputStream(file1));
}
public void map(Object key, Text value, Context context) throws IOException, InterruptedException
{ // *Map code* }
}
Where am I supposed to call the void configure(JobConf job) function?
Where do I use the private Path[] localArchives object?
Is my code in the configure() function the correct way to access files within an archive and to link a file with a BufferedReader?
I will answer your questions w.r.t new API and common practices in use for distributed cache
Where am I supposed to call the void configure(JobConf job) function?
Framework will call protected void setup(Context context) method once at beginning of every map task, the logic associated with using cache files is usually handled here. For example, reading file and storing data in variable to be used in map() function which is called after setup()
Where do I use the private Path[] localArchives object?
It will be typically used in setup() method to retrieve path of cache files . Something like this.
Path[] localArchive =DistributedCache.getLocalCacheFiles(context.getConfiguration());
Is my code in the configure() function the correct way to access
files within an archive and to link a file with a BufferedReader?
Its missing a call to method to retrive path where cache files are stored (shown above). Once the path is retrieved the file(s) can be read as below.
FSDataInputStream in = fs.open(localArchive);
BufferedReader br = new BufferedReader(new InputStreamReader(in));
As we know, new need to pack all needed class into the job-jar and upload it to server. it's so slow, i will to know whether there is a way which to specify the thirdpart jar include executing map-red job, so that i could only pack my classes with out dependencies.
PS(i found there is a "-libjar" command, but i doesn't figure out how to use it. Here is the link http://blog.cloudera.com/blog/2011/01/how-to-include-third-party-libraries-in-your-map-reduce-job/)
Those are called generic options.
So, to support those, your job should implement Tool.
Run your job like --
hadoop jar yourfile.jar [mainClass] args -libjars <comma seperated list of jars>
Edit:
To implement Tool and extend Configured, you do something like this in your MapReduce application --
public class YourClass extends Configured implements Tool {
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new YourClass(), args);
System.exit(res);
}
public int run(String[] args) throws Exception
{
//parse you normal arguments here.
Configuration conf = getConf();
Job job = new Job(conf, "Name of job");
//set the class names etc
//set the output data type classes etc
//to accept the hdfs input and outpur dir at run time
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
return job.waitForCompletion(true) ? 0 : 1;
}
}
For me I had to specify -libjar option before the arguments. Otherwise it was considered as an argument.
How do i copy a file that is required for a hadoop program, to all compute nodes? I am aware that -file option for hadoop streaming does that. How do i do this for java+hadoop?
Exactly the same way.
Assuming you use the ToolRunner / Configured / Tool pattern, the files you specify after the -files option will be in the local dir when your mapper / reducer / combiner tasks run:
public class Driver extends Configured implements Tool {
public static void main(String args[]) {
ToolRunner.run(new Driver(), args);
}
public int run(String args[]) {
Job job = new Job(getConf());
// ...
job.waitForCompletion(true);
}
}
public class MyMapper extends Mapper<K1, V1, K2, V2> {
public void setup(Context context) {
File myFile = new File("file.csv");
// do something with file
}
// ...
}
You can then execute with:
#> hadoop jar myJar.jar Driver -files file.csv ......
See the Javadoc for GenericOptionsParser for more info