Map-Reduce and Apache Hadoop - Nakov at BGUG Spring Conference - June 2016 http://www.nakov.com/?p=4474&preview=true

1. Map-Reduce and Apache
Hadoop for Big Data
Computations
Svetlin Nakov
Training and Inspiration
Software University
http://softuni.bg
BGOUG Seminar, Borovets, 4-June-2016

2. 2
1. Big Data
2. Map-Reduce
 What is Map-Reduce?
 How It Works?
 Mappers and Reducers
 Examples
3. Apache Hadoop
Table of Contents

3. Big Data
What is Big Data Processing?

4. 4
 Big data == process very large data sets (terabytes / petabytes)
 So large or complex, that traditional data processing is inadequate
 Usually stored and processed by distributed databases
 Often related to analysis of very large data sets
 Typical components of big data systems
 Distributed databases (like Cassandra, HBase and Hive)
 Distributed processing frameworks (like Apache Hadoop)
 Distributed processing systems (like Map-Reduce)
 Distributed file systems (like HDFS)
Big Data

5. Map-Reduce

6. 6
 Map-Reduce is a distributed processing framework
 Computational model for processing huge data-sets (terabytes)
 Using parallel processing on large clusters (thousands of nodes)
 Relying on distributed infrastructure
 Like Apache Hadoop or MongoDB cluster
 The input and output data is stored in a distributed file system (or
distributed database)
 The framework takes care of scheduling, executing and monitoring
tasks, and re-executes the failed tasks
What is Map-Reduce?

7. 7
 How map-reduce works?
1. Splits the input data-set into independent chunks
2. Process each chunk by "map" tasks in parallel manner
 The "map" function groups the input data into key-value pairs
 Equal keys are processed by the same "reduce" node
3. Outputs of the "map" tasks are
 Sorted, grouped by key, then sent as input to the "reduce" tasks
4. The "reduce" tasks
 Aggregate the results per each key and produces the final output
Map-Reduce: How It Works?

8. 8
 The map-reduce process is a sequence of transformations,
executed on several nodes in parallel
 Map: groups input chunks of data to key-value pairs
 E.g. splits documents(id, chunk-content) into words(word, count)
 Combine: sorts and combines all values by the same key
 E.g. produce a list of counts for each word
 Reduce: combines (aggregates) all values for certain key
The Map-Reduce Process
(input) <key1, val1>  map  <key2, val2>  combine 
<key2, list<val2>>  reduce  <key3, val3> (output)

9. 9
 We have a very large set of documents (e.g. 200 terabytes)
 We want to count how many times each word occurs
 Input: set of documents {key + content}
 Mapper:
 Extract the words from each document (words are used as keys)
 Transforms documents {key + content}  word-count-pairs {word, count}
 Reducer:
 Sums the counts for each word
 Transforms {word, list<count>}  word-count-pairs {word, count}
Example: Counting Words

10. 10
Counting Words: Mapper and Reducer
public void map(Object offset, Text docText, Context context)
throws IOException, InterruptedException {
String[] words = docText.toString().toLowerCase().split("W+");
for (String word : words)
context.write(new Text(word), new IntWritable(1));
}
public void reduce(Text word, Iterable<IntWritable> counts,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable count : counts)
sum += count.get();
context.write(word, new IntWritable(sum));
}

11. Word Count in Apache Hadoop
Live Demo

12. 12
 We are given a CSV file holding real estate sales data:
 Estate address, city, ZIP code, state, # beds, # baths, square foots,
sale date price and GPS coordinates (latitude + longitude)
 Find all cities that have sales in price range [100 000 … 200 000]
 As side effect, find the sum of all sales by city
Example: Extract Data from CSV Report

13. 13
Process CSV Report – How It Works?
SELECT
city,
SUM(price)
FROM
Sales
GROUP BY
city
chunkingchunking
map map
map
city sum(price)
SACRAMENTO 625140
LINCOLN 843620
RIO LINDA 348500
reduce
reduce
reduce

14. 14
Process CSV Report: Mapper and Reducer
public void map(Object offset, Text inputCSVLine, Context context)
throws IOException, InterruptedException {
String[] fields = inputCSVLine.toString().split(",");
String city = fields[1];
int price = Integer.parseInt(fields[9]);
if (price > 100000 && price < 200000)
context.write(new Text(city), new LongWritable(price);
}
public void reduce(Text city, Iterable<LongWritable> prices,
Context context) throws IOException, InterruptedException {
long sum = 0;
for (LongWritable val : prices)
sum += val.get();
context.write(city, new LongWritable(sum));
}

15. Processing CSV Report
in Apache Hadoop
Live Demo

16. Apache Hadoop
Distributed Processing Framework

17. 17
 Apache Hadoop project develops open-source software for reliable,
scalable, distributed computing
 Hadoop Distributed File System (HDFS) – a distributed file system that
transparently moves data across Hadoop cluster nodes
 Hadoop MapReduce – the map-reduce framework
 HBase – a scalable, distributed database for large tables
 Hive – SQL-like query for large datasets
 Pig – a high-level data-flow language for parallel computation
 Hadoop is driven by big players like IBM, Microsoft, Facebook,
VMware, LinkedIn, Yahoo, Cloudera, Intel, Twitter, Hortonworks, …
Apache Hadoop

18. 18
Hadoop Ecosystem
HDFS Storage
Redundant (3 copies)
For large files – large blocks
64 MB or 128 MB / block
Can scale to 1000s of nodes
MapReduce API
Batch (Job) processing
Distributed and localized to
clusters
Auto-parallelizable for huge
amounts of data
Fault-tolerant (auto retries)
Adds high availability and
more
Hadoop Libraries
Pig
Hive
HBase
Others

19. 19
Hadoop Cluster HDFS (Physical) Storage
Name Node
Data Node
1
Data Node
2
Data Node
3
Secondary
Name Node
• Contains web site to view cluster
information
• V2 Hadoop uses multiple Name Nodes
for HA
One Name Node
• 3 copies of each node by default
Many Data Nodes
• Using common Linux shell commands
• Block size is 64 or 128 MB
Work with data in HDFS

20.  Tips:
 sudo means "run as administrator" (super user)
 Some distributions use hadoop dfs rather than hadoop fs
Common Hadoop Shell Commands
hadoop fs –cat file:///file2
hadoop fs –mkdir /user/hadoop/dir1 /user/hadoop/dir2
hadoop fs –copyFromLocal <fromDir> <toDir>
hadoop fs –put <localfile> hdfs://nn.example.com/hadoopfile
sudo hadoop jar <jarFileName> <class> <fromDir> <toDir>
hadoop fs –ls /user/hadoop/dir1
hadoop fs –cat hdfs://nn1.example.com/file1
hadoop fs –get /user/hadoop/file <localfile>

21. Hadoop Shell Commands
Live Demo

22. 22
 Apache Hadoop MapReduce
 The world's leading implementation of the map-reduce
computational model
 Provides parallelized (scalable) computing
 For processing very large data sets
 Fault tolerant
 Runs on commodity of hardware
 Implemented in many cloud platforms: Amazon EMR, Azure
HDInsight, Google Cloud, Cloudera, Rackspace, HP Cloud, …
Hadoop MapReduce

23. 23
Hadoop Map-Reduce Pipeline

24. 24
 Download and install Java and Hadoop
 http://hadoop.apache.org/releases.html
 You will need to install Java first
 Download a pre-installed Hadoop virtual machine (VM)
 Hortonworks Sandbox
 Cloudera QuickStart VM
 You can use Hadoop in the cloud / local emulator
 E.g. Azure HDInsight Emulator
Hadoop: Getting Started

25. Playing with Apache Hadoop
Live Demo

26. 26
 Big data == processing huge datasets that are too big for
processing on a single machine
 Use a cluster of computing nodes
 Map-reduce == computational paradigm
for parallel data processing of huge data-sets
 Data is chunked, then mapped into groups,
then groups are processed and the results are aggregated
 Highly scalable, can process petabytes of data
 Apache Hadoop – industry's leading Map-Reduce framework
Summary

27. ?
Map-Reduce
https://softuni.bg/trainings/1194/Algorithms-September-2015

28. License
 This course (slides, examples, labs, videos, homework, etc.)
is licensed under the "Creative Commons Attribution-
NonCommercial-ShareAlike 4.0 International" license
28
 Attribution: this work may contain portions from
 "Fundamentals of Computer Programming with C#" book by Svetlin Nakov & Co. under CC-BY-SA license
 "Data Structures and Algorithms" course by Telerik Academy under CC-BY-NC-SA license

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