I am running a hadoop mapreduce job using Cascalog API.I want to take multiple input folders to process the map reduce job.
I have two folders in HDFS rooPath/Folder_1 & rootPath/Folder_2 which contains files that are to be processed in a job.
I am giving the job the input folders throught Pail Tap function :
new PailTap(rootPath + "Folder_1",
JcascalogUtils.getPailTapOptions());
Can I give multiple folders to the same job.
and Can I give a regex fodler Path like rootPath+*/ so that it will process all the folders in the rootPath folder.
Thank you for any help :)
You can use MultiSourceTap like this:
dataSource = new MultiSourceTap(
new PailTap(rootPath + "Folder_1",JcascalogUtils.getPailTapOptions()),
new PailTap(rootPath + "Folder_2",JcascalogUtils.getPailTapOptions())
);
or use GlobHfs
dataSource = new GlobHfs(new PailTap(rootPath,JcascalogUtils.getPailTapOptions()).getScheme() , rootPath + "*");
Related
Is there any low level API in Hadoop allowing multiple reduce tasks running on different machines to assemble a single HDFS as output of their computation?
Something like, a stub HDFS file is created at the beginning of the job then each reducer creates, as output, a variable number of data blocks and assigns them to this file according to a certain order
The answer is no, that would be an unnecessary complication for a rare use case.
What you should do
option 1 - add some code at the end of your hadoop command
int result = job.waitForCompletion(true) ? 0 : 1;
if (result == 0) { // status code OK
// ls job output directory, collect part-r-XXXXX file names
// create HDFS readers for files
// merge them in a single file in whatever way you want
}
All of the required methods are present in hadoop FileSystem api.
option 2 - add job to merge files
You can create a generic hadoop job that would accept directory name as input and pass everything as-is to the single reducer, that would merge results into one output file. Call this job in a pipeline with your main job.
This would work faster for big inputs.
If you want merged output file on local, you can use hadoop command getmerge to combine multiple reduce task files into one single local output file, below is command for same.
hadoop fs -getmerge /output/dir/on/hdfs/ /desired/local/output/file.txt
I would like to overwrite a output directory in mapreduce but it throws an exception as "FileAlreadyExists". Is there way to overwrite a output directory by creating custom output class?
The easiest way, is check if the output directory exists. if it does delete it all it's contents.
To do it, use the [FileSystem][1] class in your driver class.
Path outputPath = new Path("/user/foor/jobOutput");
Job job = new Job();
FileSystem fs = FileSystem.get(outputPath.toUri(),job.getConfiguration());
fs.delete(outputPath, true);
FileOutputFormat.setOutputPath(job, outputPath);
Output file from MapReduce will be in HDFS. HDFS runs on the concept of write once and read many times. So you cannot overwrite a output directory. You have to delete it and write it again through MapReduce
I want to write two different types of output from the same reducer, into two different directories.
I am able to use multipleoutputs feature in hadoop to write to different files, but they both go to the same output folder.
I want to write each file from the same reduce to a different folder.
Is there a way for doing this?
If I try putting for example "hello/testfile", as the second argument, it shows invaid argument. So I m not able to write to different folders.
If the above case is not possible, the is it possible for the mapper to read only specific files from an input folder?
Please help me.
Thanks in advance!
Thanks for the reply. I am able to read a file successfully using then above method. But in distributed mode, I am not able to do so. In the reducer, I have
set:
mos.getCollector("data", reporter).collect(new Text(str_key), new Text(str_val));
(Using multiple outputs, and in Job Conf:
I tried using
FileInputFormat.setInputPaths(conf2, "/home/users/mlakshm/opchk285/data-r-00000*");
as well as
FileInputFormat.setInputPaths(conf2, "/home/users/mlakshm/opchk285/data*");
But, it gives the following error:
cause:org.apache.hadoop.mapred.InvalidInputException: Input Pattern hdfs://mentat.cluster:54310/home/users/mlakshm/opchk295/data-r-00000* matches 0 files
Question 1: Writing output files to different directories - you can do it using the following approaches:
1. Using MultipleOutputs class:
Its great that you are able to create multiple named output files using MultipleOutputs. As you know, we need to add this in your driver code.
MultipleOutputs.addNamedOutput(job, "OutputFileName", OutputFormatClass, keyClass, valueClass);
The API provides two overloaded write methods to achieve this.
multipleOutputs.write("OutputFileName", new Text(Key), new Text(Value));
Now, to write the output file to separate output directories, you need to use an overloaded write method with an extra parameter for the base output path.
multipleOutputs.write("OutputFileName", new Text(key), new Text(value), baseOutputPath);
Please remember to change your baseOutputPath in each of your implementation.
2. Rename/Move the file in driver class:
This is probably the easiest hack to write output to multiple directories. Use multipleOutputs and write all the output files to a single output directory. But the file names need to be different for each category.
Assume that you want to create 3 different sets of output files, the first step is to register named output files in the driver:
MultipleOutputs.addNamedOutput(job, "set1", OutputFormatClass, keyClass, valueClass);
MultipleOutputs.addNamedOutput(job, "set2", OutputFormatClass, keyClass, valueClass);
MultipleOutputs.addNamedOutput(job, "set3", OutputFormatClass, keyClass, valueClass);
Also, create the different output directories or the directory structure you want in the driver code, along with the actual output directory:
Path set1Path = new Path("/hdfsRoot/outputs/set1");
Path set2Path = new Path("/hdfsRoot/outputs/set2");
Path set3Path = new Path("/hdfsRoot/outputs/set3");
The final important step is to rename the output files based on their names. If the job is successful;
FileSystem fileSystem = FileSystem.get(new Configuration);
if (jobStatus == 0) {
// Get the output files from the actual output path
FileStatus outputfs[] = fileSystem.listStatus(outputPath);
// Iterate over all the files in the output path
for (int fileCounter = 0; fileCounter < outputfs.length; fileCounter++) {
// Based on each fileName rename the path.
if (outputfs[fileCounter].getPath().getName().contains("set1")) {
fileSystem.rename(outputfs[fileCounter].getPath(), new Path(set1Path+"/"+anyNewFileName));
} else if (outputfs[fileCounter].getPath().getName().contains("set2")) {
fileSystem.rename(outputfs[fileCounter].getPath(), new Path(set2Path+"/"+anyNewFileName));
} else if (outputfs[fileCounter].getPath().getName().contains("set3")) {
fileSystem.rename(outputfs[fileCounter].getPath(), new Path(set3Path+"/"+anyNewFileName));
}
}
}
Note: This will not add any significant overhead to the job because we are only MOVING files from one directory to another. And choosing any particular approach depends on the nature of your implementation.
In summary, this approach basically writes all the output files using different names to the same output directory and when the job is successfully completed, we rename the base output path and move files to different output directories.
Question 2: Reading specific files from an input folder(s):
You can definitely read specific input files from a directory using MultipleInputs class.
Based on your input path/file names you can pass the input files to the corresponding Mapper implementation.
Case 1: If all the input files ARE IN a single directory:
FileStatus inputfs[] = fileSystem.listStatus(inputPath);
for (int fileCounter = 0; fileCounter < inputfs.length; fileCounter++) {
if (inputfs[fileCounter].getPath().getName().contains("set1")) {
MultipleInputs.addInputPath(job, inputfs[fileCounter].getPath(), TextInputFormat.class, Set1Mapper.class);
} else if (inputfs[fileCounter].getPath().getName().contains("set2")) {
MultipleInputs.addInputPath(job, inputfs[fileCounter].getPath(), TextInputFormat.class, Set2Mapper.class);
} else if (inputfs[fileCounter].getPath().getName().contains("set3")) {
MultipleInputs.addInputPath(job, inputfs[fileCounter].getPath(), TextInputFormat.class, Set3Mapper.class);
}
}
Case 2: If all the input files ARE NOT IN a single directory:
We can basically use the same approach above even if the input files are in different directories. Iterate over the base input path and check the file path name for a matching criteria.
Or, if the files are in complete different locations, the simplest way is to add to multiple inputs individually.
MultipleInputs.addInputPath(job, Set1_Path, TextInputFormat.class, Set1Mapper.class);
MultipleInputs.addInputPath(job, Set2_Path, TextInputFormat.class, Set2Mapper.class);
MultipleInputs.addInputPath(job, Set3_Path, TextInputFormat.class, Set3Mapper.class);
Hope this helps! Thank you.
Copy the MultipleOutputs code into your code base and loosen the restriction on allowable characters. I can't see any valid reason for the restrictions anyway.
Yes you can specify that a input format only processes certain files:
FileInputFormat.setInputPaths(job, "/path/to/folder/testfile*");
If you do amend the code, remember the _SUCCESS file should be written to both folders upon successful job completion - while this isn't a requirement, it is a machanism by which someone can determine if the output in that folder is complete, and not 'truncated' because of an error.
Yes you can do this. All you need to do is generate the file name for a particular key/value pair coming out of the reducer.
If you override a method, you can return the file name depending on what key/value pair you get, and so on. Here is the link that shows you how to do that.
https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CFMQFjAA&url=https%3A%2F%2Fsites.google.com%2Fsite%2Fhadoopandhive%2Fhome%2Fhow-to-write-output-to-multiple-named-files-in-hadoop-using-multipletextoutputformat&ei=y7YBULarN8iIrAf4iPSOBg&usg=AFQjCNHbd8sRwlY1-My2gNYI0yqw4254YQ
I want to overwrite/reuse the existing output directory when I run my Hadoop job daily.
Actually the output directory will store summarized output of each day's job run results.
If I specify the same output directory it gives the error "output directory already exists".
How to bypass this validation?
What about deleting the directory before you run the job?
You can do this via shell:
hadoop fs -rmr /path/to/your/output/
or via the Java API:
// configuration should contain reference to your namenode
FileSystem fs = FileSystem.get(new Configuration());
// true stands for recursively deleting the folder you gave
fs.delete(new Path("/path/to/your/output"), true);
Jungblut's answer is your direct solution. Since I never trust automated processes to delete stuff (me personally), I'll suggest an alternative:
Instead of trying to overwrite, I suggest you make the output name of your job dynamic, including the time in which it ran.
Something like "/path/to/your/output-2011-10-09-23-04/". This way you can keep around your old job output in case you ever need to revisit in. In my system, which runs 10+ daily jobs, we structure the output to be: /output/job1/2011/10/09/job1out/part-r-xxxxx, /output/job1/2011/10/10/job1out/part-r-xxxxx, etc.
Hadoop's TextInputFormat (which I guess you are using) does not allow overwriting an existing directory. Probably to excuse you the pain of finding out you mistakenly deleted something you (and your cluster) worked very hard on.
However, If you are certain you want your output folder to be overwritten by the job, I believe the cleanest way is to change TextOutputFormat a little like this:
public class OverwriteTextOutputFormat<K, V> extends TextOutputFormat<K, V>
{
public RecordWriter<K, V>
getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException
{
Configuration conf = job.getConfiguration();
boolean isCompressed = getCompressOutput(job);
String keyValueSeparator= conf.get("mapred.textoutputformat.separator","\t");
CompressionCodec codec = null;
String extension = "";
if (isCompressed)
{
Class<? extends CompressionCodec> codecClass =
getOutputCompressorClass(job, GzipCodec.class);
codec = (CompressionCodec) ReflectionUtils.newInstance(codecClass, conf);
extension = codec.getDefaultExtension();
}
Path file = getDefaultWorkFile(job, extension);
FileSystem fs = file.getFileSystem(conf);
FSDataOutputStream fileOut = fs.create(file, true);
if (!isCompressed)
{
return new LineRecordWriter<K, V>(fileOut, keyValueSeparator);
}
else
{
return new LineRecordWriter<K, V>(new DataOutputStream(codec.createOutputStream(fileOut)),keyValueSeparator);
}
}
}
Now you are creating the FSDataOutputStream (fs.create(file, true)) with overwrite=true.
Hadoop already supports the effect you seem to be trying to achieve by allowing multiple input paths to a job. Instead of trying to have a single directory of files to which you add more files, have a directory of directories to which you add new directories. To use the aggregate result as input, simply specify the input glob as a wildcard over the subdirectories (e.g., my-aggregate-output/*). To "append" new data to the aggregate as output, simply specify a new unique subdirectory of the aggregate as the output directory, generally using a timestamp or some sequence number derived from your input data (e.g. my-aggregate-output/20140415154424).
If one is loading the input file (with e.g., appended entries) from the local file system to hadoop distributed file system as such:
hdfs dfs -put /mylocalfile /user/cloudera/purchase
Then one could also overwrite/reuse the existing output directory with -f. No need to delete or re-create the folder
hdfs dfs -put -f /updated_mylocalfile /user/cloudera/purchase
Hadoop follows the philosophy Write Once, Read Many times. Thus when you try to write to the directory again, it assumes it has to make a new one (Write once) but it already exists, and so it complains. You can delete it via hadoop fs -rmr /path/to/your/output/. It's better to create a dynamic directory (eg,based on timestamp or hash value) in order to preserve data.
You can create an output subdirectory for each execution by time. For example lets say you are expecting output directory from user and then set it as follows:
FileOutputFormat.setOutputPath(job, new Path(args[1]);
Change this by the following lines:
String timeStamp = new SimpleDateFormat("yyyy.MM.dd.HH.mm.ss", Locale.US).format(new Timestamp(System.currentTimeMillis()));
FileOutputFormat.setOutputPath(job, new Path(args[1] + "/" + timeStamp));
I had a similar use case, I use MultipleOutputs to resolve this.
For example, if I want different MapReduce jobs to write to the same directory /outputDir/. Job 1 writes to /outputDir/job1-part1.txt, job 2 writes to /outputDir/job1-part2.txt (without deleting exiting files).
In the main, set the output directory to a random one (it can be deleted before a new job runs)
FileInputFormat.addInputPath(job, new Path("/randomPath"));
In the reducer/mapper, use MultipleOutputs and set the writer to write to the desired directory:
public void setup(Context context) {
MultipleOutputs mos = new MultipleOutputs(context);
}
and:
mos.write(key, value, "/outputDir/fileOfJobX.txt")
However, my use case was a bit complicated than that. If it's just to write to the same flat directory, you can write to a different directory and runs a script to migrate the files, like: hadoop fs -mv /tmp/* /outputDir
In my use case, each MapReduce job writes to different sub-directories based on the value of the message being writing. The directory structure can be multi-layered like:
/outputDir/
messageTypeA/
messageSubTypeA1/
job1Output/
job1-part1.txt
job1-part2.txt
...
job2Output/
job2-part1.txt
...
messageSubTypeA2/
...
messageTypeB/
...
Each Mapreduce job can write to thousands of sub-directories. And the cost of writing to a tmp dir and moving each files to the correct directory is high.
I encountered this exact problem, it stems from the exception raised in checkOutputSpecs in the class FileOutputFormat. In my case, I wanted to have many jobs adding files to directories that already exist and I guaranteed that the files would have unique names.
I solved it by creating an output format class which overrides only the checkOutputSpecs method and suffocates (ignores) the FileAlreadyExistsException that's thrown where it checks if the directory already exists.
public class OverwriteTextOutputFormat<K, V> extends TextOutputFormat<K, V> {
#Override
public void checkOutputSpecs(JobContext job) throws IOException {
try {
super.checkOutputSpecs(job);
}catch (FileAlreadyExistsException ignored){
// Suffocate the exception
}
}
}
And the in the job configuration, I used LazyOutputFormat and also MultipleOutputs.
LazyOutputFormat.setOutputFormatClass(job, OverwriteTextOutputFormat.class);
you need to add the setting in your main class:
//Configuring the output path from the filesystem into the job
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//auto_delete output dir
OutputPath.getFileSystem(conf).delete(OutputPath);
I want to create a directory inside the working directory of a MapReduce job in Hadoop.
For example by using:
File setupFolder = new File(setupFolderName);
setupFolder.mkdirs();
in my mapper class to write some intermediate files in it. Is it the right way to do it?.
Also after completion of the job how will I access this directory again if I wish so?
Please advice.
If you are using java, you can override the setup method and open the file handler there ( and close it in cleanup ) . This handle will be available to all mappers.
I am assuming that you are not writing all the map output here but some debug/stats. With this handler you can read and write as it is show in this example ( http://wiki.apache.org/hadoop/HadoopDfsReadWriteExample )
if you want to read the whole directory, check out this example https://sites.google.com/site/hadoopandhive/home/how-to-read-all-files-in-a-directory-in-hdfs-using-hadoop-filesystem-api
remember that you will not be able to depend on the the order of data written to the files.
You can override setupReduce() in reducer class, use mkdirs() to create folder and use create() to create file for outputstream.
#Override
protected void setupReduce(Context context) throws IOException {
Configuration conf = context.getConfiguration();
FileSystem fs = FileSystem.get(conf);
fs.mkdirs(new Path("your_path_here"));
}