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![Map-Reduce Execution Engine
(Example: Color Count)
14
Shuffle & Sorting
based on k
Reduce
Reduce
Reduce
Map
Map
Map
Map
Input blocks on
HDFS
Produces (k, v)
( , 1)
Parse-hash
Parse-hash
Parse-hash
Parse-hash
Consumes(k, [v])
( , [1,1,1,1,1,1..])
Produces(k’, v’)
( , 100)
Users only provide the“Map”and“Reduce”functions
www.kellytechno.com](https://image.slidesharecdn.com/hadoop-trainting-in-hyderabadkellytechnologies-160111055252/75/Hadoop-trainting-in-hyderabad-kelly-technologies-14-2048.jpg)
![Example 2: Color Count
20
Shuffle & Sorting
based on k
Reduce
Reduce
Reduce
Map
Map
Map
Map
Input blocks on
HDFS
Produces (k, v)
( , 1)
Parse-hash
Parse-hash
Parse-hash
Parse-hash
Consumes(k, [v])
( , [1,1,1,1,1,1..])
Produces(k’, v’)
( , 100)
Job: Count the number of each color in a data set
Part0003
Part0002
Part0001
That’s the output file, it has 3
parts on probably 3 different
machines
www.kellytechno.com](https://image.slidesharecdn.com/hadoop-trainting-in-hyderabadkellytechnologies-160111055252/75/Hadoop-trainting-in-hyderabad-kelly-technologies-20-2048.jpg)
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- 1. Hadoop/MapReduce Computing Paradigm 1 Special Topicsin DBs Large-Scale Data Management
- 2. Large-Scale Data Analytics 2 MapReduce computing paradigm (E.g., Hadoop) vs.Traditional database systems Database vs. Many enterprises are turning to Hadoop Especially applications generating big data Web applications, social networks, scientific applications www.kellytechno.com
- 3. Why Hadoop isable to compete? 3 Scalability (petabytes of data, thousands of machines) Database vs. Flexibility in accepting all data formats (no schema) Commodity inexpensive hardware Efficient and simple fault-tolerant mechanism Performance (tons of indexing, tuning, data organization tech.) Features: - Provenance tracking -Annotation management - …. www.kellytechno.com
- 4. What is Hadoop 4 Hadoop is a software framework for distributed processing of large datasets across large clusters of computers Large datasets Terabytes or petabytes of data Large clusters hundreds or thousands of nodes Hadoop is open-source implementation for Google MapReduce Hadoop is based on a simple programming model called MapReduce Hadoop is based on a simple data model, any data will fit www.kellytechno.com
- 5. What is Hadoop(Cont’d) 5 Hadoop framework consists on two main layers Distributed file system (HDFS) Execution engine (MapReduce) www.kellytechno.com
- 6. Hadoop Master/Slave Architecture 6 Hadoop is designed as a master-slave shared-nothing architecture Master node (single node) Many slave nodes www.kellytechno.com
- 7. Design Principles ofHadoop 7 Need to process big data Need to parallelize computation across thousands of nodes Commodity hardware Large number of low-end cheap machines working in parallel to solve a computing problem This is in contrast to Parallel DBs Small number of high-end expensive machines www.kellytechno.com
- 8. Design Principles ofHadoop 8 Automatic parallelization & distribution Hidden from the end-user Fault tolerance and automatic recovery Nodes/tasks will fail and will recover automatically Clean and simple programming abstraction Users only provide two functions “map” and “reduce” www.kellytechno.com
- 9. How Uses MapReduce/Hadoop 9 Google: Inventors of MapReduce computing paradigm Yahoo: Developing Hadoop open-source of MapReduce IBM, Microsoft, Oracle Facebook,Amazon,AOL, NetFlex Many others + universities and research labs www.kellytechno.com
- 10. Hadoop: How itWorks 10 www.kellytechno.com
- 11. Hadoop Architecture 11 Master node(single node) Many slave nodes • Distributed file system (HDFS) • Execution engine (MapReduce) www.kellytechno.com
- 12. Hadoop Distributed FileSystem (HDFS) 12 Centralized namenode - Maintains metadata info about files Many datanode (1000s) - Store the actual data - Files are divided into blocks - Each block is replicated N times (Default = 3) File F 1 2 3 4 5 Blocks (64 MB) www.kellytechno.com
- 13. Main Properties ofHDFS 13 Large: A HDFS instance may consist of thousands of server machines, each storing part of the file system’s data Replication: Each data block is replicated many times (default is 3) Failure: Failure is the norm rather than exception Fault Tolerance: Detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS Namenode is consistently checking Datanodes www.kellytechno.com
- 14. Map-Reduce Execution Engine (Example:Color Count) 14 Shuffle & Sorting based on k Reduce Reduce Reduce Map Map Map Map Input blocks on HDFS Produces (k, v) ( , 1) Parse-hash Parse-hash Parse-hash Parse-hash Consumes(k, [v]) ( , [1,1,1,1,1,1..]) Produces(k’, v’) ( , 100) Users only provide the“Map”and“Reduce”functions www.kellytechno.com
- 15. Properties of MapReduceEngine 15 JobTracker is the master node (runs with the namenode) Receives the user’s job Decides on how many tasks will run (number of mappers) Decides on where to run each mapper (concept of locality) • This file has 5 Blocks run 5 map tasks • Where to run the task reading block “1” • Try to run it on Node 1 or Node 3 Node 1 Node 2 Node 3 www.kellytechno.com
- 16. Properties of MapReduceEngine (Cont’d) 16 TaskTracker is the slave node (runs on each datanode) Receives the task from JobTracker Runs the task until completion (either map or reduce task) Always in communication with the JobTracker reporting progress Reduce Reduce Reduce Map Map Map Map Parse-hash Parse-hash Parse-hash Parse-hash In this example,1 map-reduce job consists of 4 map tasks and 3 reduce tasks www.kellytechno.com
- 17. Key-Value Pairs 17 Mappersand Reducers are users’ code (provided functions) Just need to obey the Key-Value pairs interface Mappers: Consume <key, value> pairs Produce <key, value> pairs Reducers: Consume <key, <list of values>> Produce <key, value> Shuffling and Sorting: Hidden phase between mappers and reducers Groups all similar keys from all mappers, sorts and passes them to a certain reducer in the form of <key, <list of values>> www.kellytechno.com
- 18. MapReduce Phases 18 Deciding onwhat will be the key and what will be the value developer’s responsibility www.kellytechno.com
- 19. Example 1: WordCount 19 Job: Count the occurrences of each word in a data set Map Tasks Reduce Tasks www.kellytechno.com
- 20. Example 2: ColorCount 20 Shuffle & Sorting based on k Reduce Reduce Reduce Map Map Map Map Input blocks on HDFS Produces (k, v) ( , 1) Parse-hash Parse-hash Parse-hash Parse-hash Consumes(k, [v]) ( , [1,1,1,1,1,1..]) Produces(k’, v’) ( , 100) Job: Count the number of each color in a data set Part0003 Part0002 Part0001 That’s the output file, it has 3 parts on probably 3 different machines www.kellytechno.com
- 21. Example 3: ColorFilter 21 Job: Select only the blue and the green colors Input blocks on HDFS Map Map Map Map Produces (k, v) ( , 1) Write to HDFS Write to HDFS Write to HDFS Write to HDFS • Each map task will select only the blue or green colors • No need for reduce phase Part0001 Part0002 Part0003 Part0004 That’s the output file, it has 4 parts on probably 4 different machines www.kellytechno.com
- 22. Bigger Picture: Hadoopvs. Other Systems 22 Distributed Databases Hadoop Computing Model - Notion of transactions - Transaction is the unit of work - ACID properties, Concurrency control - Notion of jobs - Job is the unit of work - No concurrency control Data Model - Structured data with known schema - Read/Write mode - Any data will fit in any format - (un)(semi)structured - ReadOnly mode Cost Model - Expensive servers - Cheap commodity machines FaultTolerance - Failures are rare - Recovery mechanisms - Failures are common over thousands of machines - Simple yet efficient fault tolerance Key Characteristics - Efficiency, optimizations, fine-tuning - Scalability, flexibility, fault tolerance • Cloud Computing • A computing model where any computing infrastructure can run on the cloud • Hardware & Software are provided as remote services • Elastic: grows and shrinks based on the user’s demand • Example:Amazon EC2 www.kellytechno.com
- 23.