I am new in hadoop and currently working on hadoop. I have a small query.
I have around 10 files in input folder which I need to pass to my map reduce program. I want the file Name in my mapper as my fileName contains the time at which this file got created. I saw people using FileSplit to get the file Name in mapper. If let say my input files contains million of lines then every time mapper code will be called, it will get the file Name and then extract the time from the file, which is obvious a repeated time consuming thing for the same file. Once I get the time in the mapper I do not have to again and again assign the time from the file.
How can I achieve this?
You could use Mapper's setup method to get the filename, as setup method is gaurenteed to run only once before map() method gets initialized like this:
public class MapperRSJ extends Mapper<LongWritable, Text, CompositeKeyWritableRSJ, Text> {
String filename;
#Override
protected void setup(Context context) throws IOException, InterruptedException {
FileSplit fsFileSplit = (FileSplit) context.getInputSplit();
filename = context.getConfiguration().get(fsFileSplit.getPath().getParent().getName()));
}
#Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// process each key value pair
}
}
Related
I need to know the row index of the partitions of the input file that I'm using. I could force this in the original file by concatenating the row index to the data but I'd rather have a way of doing this in Hadoop. I have this in my mapper...
String id = context.getConfiguration().get("mapreduce.task.partition");
But "id" is 0 in every case. In the "Hadoop: The Definitive Guide" it mentions accessing properties like the partition id "can be accessed from the context object passed to all methods of the Mapper or Reducer". It does not, from what I can tell, actually go into how to access this information.
I went through the documentation for the Context object and it seems like the above is the way to do it and the script does compile. But since I'm getting 0 for every value, I'm not sure if I'm actually using the right thing and I'm unable to find any detail online that could help in figuring this out.
Code used to test...
public class Test {
public static class TestMapper extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String id = context.getConfiguration().get("mapreduce.task.partition");
context.write(new Text("Test"), new Text(id + "_" + value.toString()));
}
}
public static class TestReducer extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
for(Text value : values) {
context.write(key, value);
}
}
}
public static void main(String[] args) throws Exception {
if(args.length != 2) {
System.err.println("Usage: Test <input path> <output path>");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(Test.class);
job.setJobName("Test");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(TestMapper.class);
job.setReducerClass(TestReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
Two options are:
Use the offset instead of the row number
Track the line number in the mapper
For the first one, the key which is LongWritable tells you the offset of the line being processed. Unless your lines are exactly the same length, you won't be able to calculate the line number from an offset, but it does allow you to determine ordering if thats useful.
The second option is to just track it in the mapper. You could change your code to something like:
public static class TestMapper extends Mapper<LongWritable, Text, Text, Text> {
private long currentLineNum = 0;
private Text test = new Text("Test");
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
context.write(test, new Text(currentLineNum + "_" + value));
currentLineNum++;
}
}
You could also represent your matrix as lines of tuples and include the row and col on every tuple so when you're reading in the file, you have that information. If you use a file that is just space or comma seperated values that make up a 2D array, it'll be extremely hard to figure out what line (row) you are currently working on in the mapper
In the run method of the Driver class, I want to fetch a String value (from the mapper function) and want to write it to a file. I used the following code, but null was returned. Please help
Mapper
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
context.getConfiguration().set("feedName", feedName);
}
Driver Class
#Override
public int run(String[] args) throws Exception {
String lineVal = conf.get("feedName")
}
Configuration is one way.
If you want to pass non-counter types of values back to the driver, you can utilize HDFS for that.
Either write to your main output context (key and values) that you emit from your job.
Or alternatively use MultipleOutputs, if you do not want to mess with your standard job output.
For example, you can write any kind of properties as Text keys and Text values from your mappers or reducers.
Once control is back to your driver, simply read from HDFS. For example you can store your name/values to the Configuration object to be used by the next job in your sequence:
public void load(Configuration targetConf, Path src, FileSystem fs) throws IOException {
InputStream is = fs.open(src);
try {
Properties props = new Properties();
props.load(new InputStreamReader(is, "UTF8"));
for (Map.Entry prop : props.entrySet()) {
String name = (String)prop.getKey();
String value = (String)prop.getValue();
targetConf.set(name, value);
}
} finally {
is.close();
}
}
Note that if you have multiple mappers or reducers where you write to MultipleOutputs, you will end up with multiple {name}-m-##### or {name}-r-##### files.
In that case, you will need to either read from every output file or run a single reducer job to combine your outputs into one and then just read from one file as shown above.
Using configuration you can only do the viceversa.
You can set values in Driver class
public int run(String[] args) throws Exception {
conf.set("feedName",value);
}
and set get those in Mapper class
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
String lineVal = conf.get("feedName");
}
UPDATE
One option to your question is write data to a file and store it in HDFS, and then access them in Driver class. These files can be treated as "Intermediate Files".
Just try it and see.
I need to process and manipulate many images in a Hadoop job, the input will be over the network, slow downloads using the MultiThreadedMapper.
But what is the best approach to the reduce ouput? I think I should write the raw binary image data into a sequence file, transfer those files to their eventual home, then write a small app to extract the individual images from the SequenceFile into individual JPGs and GIFs.
Or is there a better option to consider?
If you feel up to it (or maybe through some Googleing you can find an implementation), you could write a FileOutputFormat which wraps a FSDataOutputStream with a ZipOutputStream, giving you a Zip file for each reducer (and thus saving you the effort in writing seq file extraction program.
Don't be daunted by writing your own OutputFormat, it really isn't that difficult (and much easier than writing custom InputFormats which have to worry about splits). In fact here's a starting point - you just need to implement the write method:
// Key: Text (path of the file in the output zip)
// Value: BytesWritable - binary content of the image to save
public class ZipFileOutputFormat extends FileOutputFormat<Text, BytesWritable> {
#Override
public RecordWriter<Text, BytesWritable> getRecordWriter(
TaskAttemptContext job) throws IOException, InterruptedException {
Path file = getDefaultWorkFile(job, ".zip");
FileSystem fs = file.getFileSystem(job.getConfiguration());
return new ZipRecordWriter(fs.create(file, false));
}
public static class ZipRecordWriter extends
RecordWriter<Text, BytesWritable> {
protected ZipOutputStream zos;
public ZipRecordWriter(FSDataOutputStream os) {
zos = new ZipOutputStream(os);
}
#Override
public void write(Text key, BytesWritable value) throws IOException,
InterruptedException {
// TODO: create new ZipEntry & add to the ZipOutputStream (zos)
}
#Override
public void close(TaskAttemptContext context) throws IOException,
InterruptedException {
zos.close();
}
}
}
I am trying to read from cassandra and write the reducers output to multiple output files using MultipleOutputs api (Hadoop version 1.0.3). The file formats in my case are custom output formats extending FileOutputFormat. I have configured my job in a similar manner as shown in MultipleOutputs api.
However, when I run the job, I only get one output file named part-r-0000 which is in text output format. If job.setOutputFormatClass() is not set, by default it considers TextOutputFormat to be the format. Also it will only allow one of the two format classes to be initialized. It completely ignores the output formats I specified in MulitpleOutputs.addNamedOutput(job, "format1", MyCustomFileFormat1.class, Text.class, Text.class) and MulitpleOutputs.addNamedOutput(job, "format2", MyCustomFileFormat2.class, Text.class, Text.class). Is someone else facing similar problem or am I doing something wrong ?
I also tried to write a very simple MR program which reads from a text file and writes the output in 2 formats TextOutputFormat and SequenceFileOutputFormat as shown in the MultipleOutputs api. However, no luck there as well. I get only 1 output file in text output format.
Can someone help me with this ?
Job job = new Job(getConf(), "cfdefGen");
job.setJarByClass(CfdefGeneration.class);
//read input from cassandra column family
ConfigHelper.setInputColumnFamily(job.getConfiguration(), KEYSPACE, COLUMN_FAMILY);
job.setInputFormatClass(ColumnFamilyInputFormat.class);
job.getConfiguration().set("cassandra.consistencylevel.read", "QUORUM");
//thrift input job configurations
ConfigHelper.setInputRpcPort(job.getConfiguration(), "9160");
ConfigHelper.setInputInitialAddress(job.getConfiguration(), HOST);
ConfigHelper.setInputPartitioner(job.getConfiguration(), "RandomPartitioner");
SlicePredicate predicate = new SlicePredicate().setColumn_names(Arrays.asList(ByteBufferUtil.bytes("classification")));
//ConfigHelper.setRangeBatchSize(job.getConfiguration(), 2048);
ConfigHelper.setInputSlicePredicate(job.getConfiguration(), predicate);
//specification for mapper
job.setMapperClass(MyMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
//specifications for reducer (writing to files)
job.setReducerClass(ReducerToFileSystem.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
//job.setOutputFormatClass(MyCdbWriter1.class);
job.setNumReduceTasks(1);
//set output path for storing output files
Path filePath = new Path(OUTPUT_DIR);
FileSystem hdfs = FileSystem.get(getConf());
if(hdfs.exists(filePath)){
hdfs.delete(filePath, true);
}
MyCdbWriter1.setOutputPath(job, new Path(OUTPUT_DIR));
MultipleOutputs.addNamedOutput(job, "cdb1', MyCdbWriter1.class, Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "cdb2", MyCdbWriter2.class, Text.class, Text.class);
boolean success = job.waitForCompletion(true);
return success ? 0:1;
public static class ReducerToFileSystem extends Reducer<Text, Text, Text, Text>
{
private MultipleOutputs<Text, Text> mos;
public void setup(Context context){
mos = new MultipleOutputs<Text, Text>(context);
}
//public void reduce(Text key, Text value, Context context)
//throws IOException, InterruptedException (This was the mistake, changed the signature and it worked fine)
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException
{
//context.write(key, value);
mos.write("cdb1", key, value, OUTPUT_DIR+"/"+"cdb1");
mos.write("cdb2", key, value, OUTPUT_DIR+"/"+"cdb2");
context.progress();
}
public void cleanup(Context context) throws IOException, InterruptedException {
mos.close();
}
}
public class MyCdbWriter1<K, V> extends FileOutputFormat<K, V>
{
#Override
public RecordWriter<K, V> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException
{
}
public static void setOutputPath(Job job, Path outputDir) {
job.getConfiguration().set("mapred.output.dir", outputDir.toString());
}
protected static class CdbDataRecord<K, V> extends RecordWriter<K, V>
{
#override
write()
close()
}
}
I found my mistake after debugging that my reduce method is never called. I found that my function definition did not match API's definition, changed it from public void reduce(Text key, Text value, Context context) to public void reduce(Text key, Iterable<Text> values, Context context). I don't know why reduce method does not have #Override tag, it would have prevented my mistake.
I also encountered a similar issue - mine turned out to be that I was filtering all my records in the Map process so nothing was being passed to Reduce. With un-named multiple outputs in the reduce task, this still resulted in a _SUCCESS file and an empty part-r-00000 file.
Is there a way to use the relation name in MapReduce's Map and Reduce? I am trying to do Set difference using Hadoop's MapReduce.
Input: 2 files R and S containing list of terms. (Am going to use t to denote a term)
Objective: To find R - S, i.e. terms in R and not in S
Approach:
Mapper: Spits out t -> R or t -> S, depending on whether t comes from R or S. So, the map output has the t as the key and the file name as the value.
Reducer: If the value list for a t contains only R, then output t -> t.
Do I need to some how tag the terms with the filename? Or is there any other way?
Source code for something I did for Set Union (doesn't need file name anywhere in this case). Just wanted to use this as an example to illustrate the unavailability of filename in Mapper.
public class Union {
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter) throws IOException {
output.collect(value, value);
}
}
public static class Reduce extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException{
while (values.hasNext())
{
output.collect(key, values.next());
break;
}
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(Union.class);
conf.setJobName("Union");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(Map.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.set("mapred.job.queue.name", "myQueue");
conf.setNumReduceTasks(5);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}
As you can see I can't identify which key -> value pair (input to the Mapper) came from which file. Am I overlooking something simple here?
Thanks much.
I would implement your question just like you answered. That is just the way MapReduce was meant to be.
I guess your problem was actually writing n-times the same value into the HDFS?
EDIT:
Pasted from my Comment down there
Ah I got it ;) I'm not really familiar with the "old" API, but you can "query" your Reporter with:
reporter.getInputSplit();
This returns you an interface called InputSplit. This is easily castable to "FileSplit". And within FileSplit object you could obtain the Path with: "split.getPath()". And from the Path object you just need to call the getName() method.
So this snippet should work for you:
FileSplit fsplit = reporter.getInputSplit(); // maybe cast it down to FileSplit if needed..
String yourFileName = fsplit.getPath().getName();