Please help me how to create UserInputFormat Class in mapreduce to produce key value pair based upon my need.I need to store the first character of the string as a key and the entire string as valuw. how to achieve it
public static class UserInputFormat extends Mapper<Object, Text, Text, Text>{ //define datatype of key:value = Text:Text
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
String raw_String = value.toString();
if (raw_String.length() > 0)
{
Text key_str = new Text(raw_String.substring(0, 1)); //get the first char of raw_String as key
context.write(key_str, value); //key is the first character and value is the entire string
}
}
}
I think the above is what you need. It is a map task, will receive a string as input, and will output a pair of key:value -> [1stchar]:[entire string].
If not please make your question clearer.
Related
I am trying to analyze the social network data which contains follower and followee pairs. I want to find the top 10 users who have the most followees using MapReduce.
I made pairs of userID and number_of_followee with one MapReduce step.
With this data, however, I am not sure how to sort them in distributed systems.
I am not sure how priority queue can be used in either of Mappers and Reducers since they have the distributed data.
Can someone explain me how I can use data structures to sort the massive data?
Thank you very much.
If you have big input file (files) of format user_id = number_of_followers, simple map-reduce algorithm to find top N users is:
each mapper processes its own input and finds top N users in its file, writes them to a single reducer
single reducer receives number_of_mappers * N rows and finds top N users among them
To Sort the data in descending order, you need another mapreduce job. The Mapper would emit "number of followers" as key and twitter handle as value.
class SortingMap extends Map<LongWritable, Text, LongWritable, Text> {
private Text value = new Text();
private LongWritable key = new LongWritable(0);
#Overwrite
public void map(LongWritable key, Text value, Context context) throws IOException {
String line = value.toString();
// Assuming that the input data is "TweeterId <number of follower>" separated by tab
String tokens[] = value.split(Pattern.quote("\t"));
if(tokens.length > 1) {
key.set(Long.parseLong(tokens[1]));
value.set(tokens[0]);
context.write(key, value);
}
}
}
For reducer, use IdentityReducer<K,V>
// SortedComparator Class
public class DescendingOrderKeyComparator extends WritableComparator {
#Override
public int compare(WritableComparable w1, WritableComparable w2) {
return -1 * w1.compareTo(w2);
}
}
In the Driver Class, set SortedComparator
job.setSortComparatorClass(DescendingOrderKeyComparator.class);
In one of my class im using HashMap.Im calling that class inside my mapper. So now each mapper has its own HashMap. Now can i use all the HashMaps into a single reducer? Actually my HashMap contains Key as my filename and value is the Set.So each HashMap contains a filename and a Set. Now i want to use all the HashMap caontaining same filename and want to club all the values(Sets) and then write that HashMap into my Hdfs file
Yes you can do that. If your mapper is giving an output in the form of hashmap then you can use Hadoop's MapWritable as your value of mapper.
For e.g.
public class MyMapper extends Mapper<LongWritable, Text, Text, MapWritable>
you have to convert your Hashmap into MapWritable format:
MapWritable mapWritable = new MapWritable();
for (Map.Entry<String,String> entry : yourHashMap.entrySet()) {
if(null != entry.getKey() && null != entry.getValue()){
mapWritable.put(new Text(entry.getKey()),new Text(entry.getValue()));
}
}
Then provide the mapwritable to your context:
ctx.write(new Text("my_key",mapWritable);
For Reducer class you have take MapWritable as your input value
public class MyReducer extends Reducer<Text, MapWritable, Text, Text>
public void reduce(Text key, Iterable<MapWritable> values, Context ctx) throws IOException, InterruptedException
Then iterate through the map and extract the values the way you want. For e.g:
for (MapWritable entry : values) {
for (Entry<Writable, Writable> extractData: entry.entrySet()) {
//your logic for the data will go here.
}
}
I'm trying to run a simple map reduce operation a TSV dataset and I'm a bit confused about what goes wrong when I'm trying a simple map operation. Following is my modification of the sample Word Count problem of the map class.
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
private Text node = new Text();
public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
String line = value.toString();
String tokens[] = line.split('t');
node.set(tokens[1]);
int weight = Integer.parseInt(tokens[2]);
output.collect(node, new Writable(weight));
}
}
The input can be visualized as a TSV file having three columns. I get an error method.java.lang.String.split being not applicable for the above code in the line where the line is split into tokens. Any ideas where I may be going wrong?
String tokens[] = line.split('t');
Change it to
String tokens[] = line.split('\t');
String tokens[] = line.split('t');
It should be:
String tokens[] = line.split("\t");
Using single-quotes is for char type and would raise an exception.
I need to implement a functionality using map reduce.
Requirement is mentioned below.
Input for the mapper is a file containing two columns productId , Salescount
Reducers output , sum of salescount
Requirement is I need to calculate salescount / sum(salescount).
For this I am planing to use nested map reduce.
But for the second mapper I need to use first reducers output and first map's input.
How Can I implement this. Or is there any alternate way ?
Regards
Vinu
You can use ChainMapper and ChainReducer to PIPE Mappers and Reducers the way you want. Please have a look at here
The following will be similar to the code snippet you would need to implement
JobConf mapBConf = new JobConf(false);
JobConf reduceConf = new JobConf(false);
ChainMapper.addMapper(conf, FirstMapper.class, FirstMapperInputKey.class, FirstMapperInputValue.class,
FirstMapperOutputKey.class, FirstMapperOutputValue.class, false, mapBConf);
ChainReducer.setReducer(conf, FirstReducer.class, FirstMapperOutputKey.class, FirstMapperOutputValue.class,
FirstReducerOutputKey.class, FirstReducerOutputValue.class, true, reduceConf);
ChainReducer.addMapper(conf, SecondMapper.class, FirstReducerOutputKey.class, FirstReducerOutputValue.class,
SecondMapperOutputKey.class, SecondMapperOutputValue.class, false, null);
ChainReducer.setReducer(conf, SecondReducer.class, SecondMapperOutputKey.class, SecondMapperOutputValue.class, SecondReducerOutputKey.class, SecondReducerOutputValue.class, true, reduceConf);
or if you don't want to use multiple Mappers and Reducers you can do the following
public static class ProductIndexerMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable> {
private static Text productId = new Text();
private static LongWritable salesCount = new LongWritable();
#Override
public void map(LongWritable key, Text value,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException {
String[] values = value.toString().split("\t");
productId.set(values[0]);
salesCount.set(Long.parseLong(values[1]));
output.collect(productId, salesCount);
}
}
public static class ProductIndexerReducer extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable> {
private static LongWritable productWritable = new LongWritable();
#Override
public void reduce(Text key, Iterator<LongWritable> values,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException {
List<LongWritable> items = new ArrayList<LongWritable>();
long total = 0;
LongWritable item = null;
while(values.hasNext()) {
item = values.next();
total += item.get();
items.add(item);
}
Iterator<LongWritable> newValues = items.iterator();
while(newValues.hasNext()) {
productWritable.set(newValues.next().get()/total);
output.collect(key, productWritable);
}
}
}
`
With the usecase in hand, I believe we don't need two different mappers/mapreduce jobs to achieve this. (As an extension to the answer given in above comments)
Lets assume you have a very large input file split into multiple blocks in HDFS. When you trigger a MapReduce job with this file as input, multiple mappers(equal to the number of input blocks) will start execution in parallel.
In your mapper implementation, read each line from input and write the productId as key and the saleCount as value to context. This data is passed to the Reducer.
We know that, in a MR job all the data with the same key is passed to the same reducer. Now, in your reducer implementation you can calculate the sum of all saleCounts for a particular productId.
Note: I'm not sure about the value 'salescount' in your numerator.
Assuming that its the count of number of occurrences of a particular product, please use a counter to add and get the total sales count in the same for loop where you are calculating the SUM(saleCount). So, we have
totalCount -> Count of number of occurrences of a product
sumSaleCount -> Sum of saleCount value for each product.
Now, you can directly divide the above values: totalCount/sumSaleCount.
Hope this helps! Please let me know if you have a different use case in mind.
Suppose I have a tab delimited file containing user activity data formatted like this:
timestamp user_id page_id action_id
I want to write a hadoop job to count user actions on each page, so the output file should look like this:
user_id page_id number_of_actions
I need something like composite key here - it would contain user_id and page_id. Is there any generic way to do this with hadoop? I couldn't find anything helpful. So far I'm emitting key like this in mapper:
context.write(new Text(user_id + "\t" + page_id), one);
It works, but I feel that it's not the best solution.
Just compose your own Writable. In your example a solution could look like this:
public class UserPageWritable implements WritableComparable<UserPageWritable> {
private String userId;
private String pageId;
#Override
public void readFields(DataInput in) throws IOException {
userId = in.readUTF();
pageId = in.readUTF();
}
#Override
public void write(DataOutput out) throws IOException {
out.writeUTF(userId);
out.writeUTF(pageId);
}
#Override
public int compareTo(UserPageWritable o) {
return ComparisonChain.start().compare(userId, o.userId)
.compare(pageId, o.pageId).result();
}
}
Although I think your IDs could be a long, here you have the String version. Basically just the normal serialization over the Writable interface, note that it needs the default constructor so you should always provide one.
The compareTo logic tells obviously how to sort the dataset and also tells the reducer what elements are equal so they can be grouped.
ComparisionChain is a nice util of Guava.
Don't forget to override equals and hashcode! The partitioner will determine the reducer by the hashcode of the key.
You could write your own class that implements Writable and WritableComparable that would compare your two fields.
Pierre-Luc Bertrand