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![Hadoop
• Open Source (ASL2) implementation of
Google's MapReduce[1] and Google
DFS (Distributed File System) [2]
(~from 2006 Lucene subproject)
[1] MapReduce: Simplified Data Processing on Large Scale Clusters by
Jeffrey Dean and Sanjay Ghemawat, Google labs, 2004
[2] The Google File System by Sanjay Ghemawat, Howard Gobioff, Shun-Tak
Leung, 2003](https://image.slidesharecdn.com/hadoopmahouthbase-120504080317-phpapp01/85/An-Introduction-to-Apache-Hadoop-Mahout-and-HBase-4-320.jpg)
![Hadoop
• Open Source (ASL2) implementation of
Google's MapReduce[1] and Google
DFS (Distributed File System) [2]
(~from 2006 Lucene subproject)
[1] MapReduce: Simplified Data Processing on Large Scale Clusters by
Jeffrey Dean and Sanjay Ghemawat, Google labs, 2004
[2] The Google File System by Sanjay Ghemawat, Howard Gobioff, Shun-Tak
Leung, 2003](https://image.slidesharecdn.com/hadoopmahouthbase-120504080317-phpapp01/75/An-Introduction-to-Apache-Hadoop-Mahout-and-HBase-4-2048.jpg)
Uploaded byLukas Vlcek
An Introduction to Apache Hadoop, Mahout and HBase
Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It implements the MapReduce programming model pioneered by Google and a distributed file system (HDFS). Mahout builds machine learning libraries on top of Hadoop. HBase is a non-relational distributed database modeled after Google's BigTable that provides random access and real-time read/write capabilities. These projects are used by many large companies for large-scale data processing and analytics tasks.
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An Introduction to Apache Hadoop, Mahout and HBase
- 1. An Introduction toHadoop, Mahout & HBase Lukáš Vlček, JBUG Brno, May 2012
- 4. Hadoop • Open Source(ASL2) implementation of Google's MapReduce[1] and Google DFS (Distributed File System) [2] (~from 2006 Lucene subproject) [1] MapReduce: Simplified Data Processing on Large Scale Clusters by Jeffrey Dean and Sanjay Ghemawat, Google labs, 2004 [2] The Google File System by Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung, 2003
- 5. Hadoop • MapReduce Simple programming model for data processing putting parallel large-scale data analysis into hands of masses. • HDFS A filesystem designed for storing large files with streaming data access patterns, running on clusters of commodity hardware. • (Common, + other related projects...)
- 6. MapReduce programming model map (k1,v1) → list(k2,v2) reduce (k2,list(v2)) → list(v3)
- 7. MapReduce “Hello World” • Counting the number of occurrences of each word in collection of documents.
- 8. MapReduce “Hello World” map(k1,v1) → list(k2,v2) map(key, value){ // key: document name // value: document content for each word w in value { emitIntermediate(w,1) } }
- 9. MapReduce “Hello World” reduce(k2,list(v2)) → list(v3) reduce(key, values){ // key: a word // value: a list of counts int result = 0; for each word v in values { result += v; } emit(result); }
- 10. MapReduce – benefits •The model is easy to use Steep learning curve. • Many problems are expressible as MapReduce computations • MapReduce scales to large clusters Such model makes it easy to parallelize and distribute your computation to thousands of machines!
- 11. MapReduce – downsides •The model is easy to use People tend to try the simplest approach first. • Many problems are expressible as MapReduce computations MapReduce may not be the best model for you. • MapReduce scales to large clusters It is so easy to overload the cluster with simple code.
- 12. More elaborated examples •Distributed PageRank • Distributed Dijkstra's algorithm (almost) Lectures to Google software engineering interns, Summer 2007 http://code.google.com/edu/submissions/mapreduce-minilecture/listing.html
- 13. Google PageRank today? •Aug, 2009: Google moved away from MapReduce back-end indexing system onto a new search architecture, a.k.a. Caffeine. http://en.wikipedia.org/wiki/Google_Search#Google_Caffeine
- 14. Google Maps? • “Inparticular, for large road networks it would be prohibitive to precompute and store shortest paths between all pairs of nodes.” Engineering Fast Route Planning Algorithms, by Peter Sanders and Dominik Schultes, 2007 http://algo2.iti.kit.edu/documents/routeplanning/weaOverview.pdf
- 15. Demand for Real-Timedata • MapReduce batch oriented processing of (large) data does not fit well into growing demand for real-time data. • Hybrid approach? Ted Dunning on Twitter's Storm: http://info.mapr.com/ted-storm-2012-03.html http://www.youtube.com/channel/UCDbTR_Z_k- EZ4e3JpG9zmhg
- 19. Mahout • The goalis to build open source scalable machine learning libraries. Started by: Isabel Drost, Grant Ingersoll, Karl Wettin Map-Reduce for Machine Learning on Multicore, 2006 http://www.cs.stanford.edu/people/ang//papers/nips06- mapreducemulticore.pdf
- 20. Implemented Algorithms • Classification •Clustering • Pattern Mining • Regression • Dimension Reduction • Evolutionary Algorithms • Recommenders / Collaborative Filtering • Vector Similarity • ...
- 21. Back to Hadoop HDFS
- 22. HDFS • Very largefiles Up to GB and PT. • Streaming data access Write once, read many times. Optimized for high data throughput. Read involves large portion of the data. • Commodity HW Clusters made of cheap and low reliable machines. Chance of failure of individual machine is high.
- 23. HDFS – don't! •Low-latency data access HBase is better for low-latency access. • Lots of small files NameNode memory limit. • Multiple writes and file modifications Single file writer. Write at the end of file.
- 24. HDFS: High Availability •Currently being added to the trunk http://www.cloudera.com/blog/2012/03/high-availability-for- the-hadoop-distributed-file-system-hdfs/
- 25. HDFS: Security &File Appends • Finally available as well, but probably in different branches. http://www.cloudera.com/blog/2012/01/an-update-on- apache-hadoop-1-0/ http://www.cloudera.com/blog/2009/07/file-appends-in- hdfs/
- 26. Improved POSIX support •Available from third party vendors (for example MapR M3 or M5 edition)
- 29. HBase • Non-relational, autore-balancing, fault tolerant distributed database • Modeled after Google BigTable • Initial prototype in 2007 • Canonical use case webtable Bigtable: A Distributed Storage System for Structured Data, many authors, Google labs (2006)
- 30. HBase Conceptual view Copyright © 2011, Lars George. All rights reserved.
- 31. HBase • Basic operations: Get, Put, Scan, Delete • A {row, column, version} identify a cell • Allows run Hadoop's MapReduce jobs • Optimized for high throughput
- 32. Use Case: Real-timeHBase Analytics • Nice use case for real-time analysis by Sematext http://blog.sematext.com/2012/04/22/hbase-real-time- analytics-rollbacks-via-append-based-updates/ http://blog.sematext.com/2012/04/27/hbase-real-time- analytics-rollbacks-via-append-based-updates-part-2/
- 33. Use Case: MessagingPlatform • Facebook implemented messaging system using HBase http://www.facebook.com/notes/facebook-engineering/the- underlying-technology-of-messages/454991608919
- 34. Hadoop, Mahout and/orHBase is used by... • Amazon (A9), Adobe, Ebay, Facebook, Google (university program), IBM, Infochimps, Krugle, Last.fm, LinkedIn, Microsoft, Rackspace, RapLeaf, Spotify, StumbleUpon, Twitter, Yahoo! … many more!
- 35. More Resources • Hadoop:http://hadoop.apache.org/ • Mahout: http://mahout.apache.org/ • HBase: http://hbase.apache.org/book/book.html
- 36.
- 37. Photo Sources • http://www.flickr.com/photos/renwest/4909849477/ By renwest, CC BY-NC-SA 2.0 • http://www.flickr.com/photos/asianartsandiego/4838273718/ By Asian Curator at The San Diego Museum of Art, CC BY-NC-ND 2.0 • http://www.flickr.com/photos/zeepack/2932405424/ By ZeePack, CC BY-ND 2.0 • http://www.flickr.com/photos/16516252@N00/3132303565/ By blueboy1478, CC BY- ND 2.0
- 38. Backup Slides: Anatomyof MapReduce Execution • http://code.google.com/edu/parallel/map reduce-tutorial.html#MRExec
- 39. Backup Slides: HDFS Architecture • http://hadoop.apache.org/common/docs /current/hdfs_design.html