package com.hadooparchitecturebook;
import java.io.IOException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import org.apache.commons.lang.ObjectUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.joda.time.DateTime;
import org.joda.time.format.DateTimeFormat;
import org.joda.time.format.DateTimeFormatter;
/**
* This is the main class that is responsible for doing sessionization in MapReduce
*/
public class MRSessionize {
private static final String LOG_RECORD_REGEX =
"(\\d+.\\d+.\\d+.\\d+).*\\[(.*) \\].*GET (\\S*).*\\d+ \\d+ (\\S+) \\\"(.*)\\\"";
private static final Pattern logRecordPattern = Pattern.compile(LOG_RECORD_REGEX);
private static final String TIMESTAMP_PATTERN = "dd/MMM/yyyy:HH:mm:ss";
private static final DateTimeFormatter TIMESTAMP_FORMATTER = DateTimeFormat.forPattern(TIMESTAMP_PATTERN);
// In our sessionization algorithm, a session expires in 30 minutes. Another click from the same user after 30 or
// more minutes is a part of a new session
private static final int SESSION_TIMEOUT_IN_MINUTES = 30;
private static final int SESSION_TIMEOUT_IN_MS = SESSION_TIMEOUT_IN_MINUTES * 60 * 1000;
/**
* Mapper class used in Sessionization
*/
public static class SessionizeMapper
extends Mapper<Object, Text, IpTimestampKey, Text> {
private Matcher logRecordMatcher;
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
logRecordMatcher = logRecordPattern.matcher(value.toString());
// We only emit something out if the record matches with our regex. Otherwise, we assume the record is
// busted and simply ignore it
if (logRecordMatcher.matches()) {
String ip = logRecordMatcher.group(1);
DateTime timestamp = DateTime.parse(logRecordMatcher.group(2), TIMESTAMP_FORMATTER);
Long unixTimestamp = timestamp.getMillis();
IpTimestampKey outputKey = new IpTimestampKey(ip, unixTimestamp);
context.write(outputKey, value);
}
}
}
// TODO: NEED TO BUNDLE JODA TIME IN ASSEMBLY
/**
* Reducer class used in Sessionization
*/
public static class SessionizeReducer
extends Reducer<IpTimestampKey, Text, IpTimestampKey, Text> {
private Text result = new Text();
public void reduce(IpTimestampKey key, Iterable<Text> values,
Context context
) throws IOException, InterruptedException {
// The sessionId generated here is per day, per IP. So, any queries
// that will be done as if this session ID were global, would require
// a combination of the day in question and IP as well.
String sessionId = null;
Long lastTimeStamp = null;
for (Text value : values) {
String logRecord = value.toString();
// If this is the first record for this user or it's been more than the timeout since
// the last click from this user, let's increment the session ID.
if (lastTimeStamp == null || (key.getUnixTimestamp() - lastTimeStamp > SESSION_TIMEOUT_IN_MS)) {
sessionId = key.getIp() + "+" + key.getUnixTimestamp();
}
lastTimeStamp = key.getUnixTimestamp();
result.set(logRecord + " " + sessionId);
// Since we only care about printing out the entire record in the result, with session ID appended
// at the end, we just emit out "null" for the key
context.write(null, result);
}
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: MRSessionize <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "MapReduce Sessionization");
job.setJarByClass(MRSessionize.class);
job.setMapperClass(SessionizeMapper.class);
job.setReducerClass(SessionizeReducer.class);
// WARNING: do NOT set the Combiner class
// from the same IP in one place before we can do sessionization
// Also, our reducer doesn't return the same key,value types it takes
// It can't be used on the result of a previous reducer
job.setMapOutputKeyClass(IpTimestampKey.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(Text.class);
// We need these for secondary sorting.
// We need to shuffle the records (between Map and Reduce phases) by using IP address as key, since that is
// the field we are using for determining uniqueness of users. However, when the records arrive to the reducers,
// we would like them to be sorted in ascending order of their timestamps. This concept is known as secondary
// sorting since we are "secondarily" sorting the records by another key (timestamp, in our case) in addition
// to the shuffle key (also called the "partition" key).
// So, to get some terminology straight.
// Natural key (aka Shuffle key or Partition key) is the key we use to shuffle. IP address in our case
// Secondary Sorting Key is the key we use to sort within each partition that gets sent to the user. Timestamp
// in our case.
// Together, the natural key and secondary sorting key form what we call the composite key. This key is called
// IpTimestampKey in our example.
// For secondary sorting, even though we are partitioning and shuffling by only the natural key, the map output
// key and the reduce input key is the composite key. We, however, use a custom partitioner and custom grouping
// comparator that only uses the natural key part of the composite key to partition and group respectively (both
// happen during the shuffle phase).
// However, we have a different sort comparator which also gets used in the shuffle phase but determines how
// the records are sorted when they enter the reduce phase. This custom sort comparator in our case will make use
// of the entire composite key.
// We found http://vangjee.wordpress.com/2012/03/20/secondary-sorting-aka-sorting-values-in-hadoops-mapreduce-programming-paradigm/
// to be very helpful, if you'd like to read more on the subject.
job.setPartitionerClass(NaturalKeyPartitioner.class);
job.setGroupingComparatorClass(NaturalKeyComparator.class);
job.setSortComparatorClass(CompositeKeyComparator.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
