Hadoop Overview kdd2011
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In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
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Hadoop Overview kdd2011
- 1. Modeling with Hadoop Vijay K. Narayanan Principal Scientist, Yahoo! Labs, Yahoo! Milind Bhandarkar Chief Architect, Greenplum Labs, EMC2
- 2. Session 1: Overview of Hadoop • Motivation • Hadoop • Map-Reduce • Distributed File System • Next Generation MapReduce • Q & A 2
- 3. Session 2: Modeling with Hadoop • Types of learning in MapReduce • Algorithms in MapReduce framework • Data parallel algorithms • Sequential algorithms • Challenges and Enhancements 3
- 4. Session 3: Hands On Exercise • Spin-up Single Node Hadoop cluster in a Virtual Machine • Write a regression trainer • Train model on a dataset 4
- 5. Overview of Apache Hadoop
- 6. Hadoop At Yahoo! (Some Statistics) • 40,000 + machines in 20+ clusters • Largest cluster is 4,000 machines • 170 Petabytes of storage • 1000+ users • 1,000,000+ jobs/month 6
- 9. Who Uses Hadoop ?
- 10. Why Hadoop ? 10
- 11. Big Datasets (Data-Rich Computing theme proposal. J. Campbell, et al, 2007)
- 12. Cost Per Gigabyte (http://www.mkomo.com/cost-per-gigabyte)
- 13. Storage Trends (Graph by Adam Leventhal, ACM Queue, Dec 2009)
- 14. Motivating Examples 14
- 16. Search Assist • Insight: Related concepts appear close together in text corpus • Input: Web pages • 1 Billion Pages, 10K bytes each • 10 TB of input data • Output: List(word, List(related words)) 16
- 17. Search Assist // Input: List(URL, Text) foreach URL in Input : Words = Tokenize(Text(URL)); foreach word in Tokens : Insert (word, Next(word, Tokens)) in Pairs; Insert (word, Previous(word, Tokens)) in Pairs; // Result: Pairs = List (word, RelatedWord) Group Pairs by word; // Result: List (word, List(RelatedWords) foreach word in Pairs : Count RelatedWords in GroupedPairs; // Result: List (word, List(RelatedWords, count)) foreach word in CountedPairs : Sort Pairs(word, *) descending by count; choose Top 5 Pairs; // Result: List (word, Top5(RelatedWords)) 17
- 18. People You May Know
- 19. People You May Know • Insight: You might also know Joe Smith if a lot of folks you know, know Joe Smith • if you don t know Joe Smith already • Numbers: • 100 MM users • Average connections per user is 100 19
- 20. People You May Know // Input: List(UserName, List(Connections)) foreach u in UserList : // 100 MM foreach x in Connections(u) : // 100 foreach y in Connections(x) : // 100 if (y not in Connections(u)) : Count(u, y)++; // 1 Trillion Iterations Sort (u,y) in descending order of Count(u,y); Choose Top 3 y; Store (u, {y0, y1, y2}) for serving; 20
- 21. Performance • 101 Random accesses for each user • Assume 1 ms per random access • 100 ms per user • 100 MM users • 100 days on a single machine 21
- 22. MapReduce Paradigm 22
- 23. Map Reduce • Primitives in Lisp ( Other functional languages) 1970s • Google Paper 2004 • http://labs.google.com/papers/ mapreduce.html 23
- 24. Map Output_List = Map (Input_List) Square (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) = (1, 4, 9, 16, 25, 36,49, 64, 81, 100) 24
- 25. Reduce Output_Element = Reduce (Input_List) Sum (1, 4, 9, 16, 25, 36,49, 64, 81, 100) = 385 25
- 26. Parallelism • Map is inherently parallel • Each list element processed independently • Reduce is inherently sequential • Unless processing multiple lists • Grouping to produce multiple lists 26
- 27. Search Assist Map // Input: http://hadoop.apache.org Pairs = Tokenize_And_Pair ( Text ( Input ) ) Output = { (apache, hadoop) (hadoop, mapreduce) (hadoop, streaming) (hadoop, pig) (apache, pig) (hadoop, DFS) (streaming, commandline) (hadoop, java) (DFS, namenode) (datanode, block) (replication, default)... } 27
- 28. Search Assist Reduce // Input: GroupedList (word, GroupedList(words)) CountedPairs = CountOccurrences (word, RelatedWords) Output = { (hadoop, apache, 7) (hadoop, DFS, 3) (hadoop, streaming, 4) (hadoop, mapreduce, 9) ... } 28
- 29. Issues with Large Data • Map Parallelism: Chunking input data • Reduce Parallelism: Grouping related data • Dealing with failures load imbalance 29
- 31. Apache Hadoop • January 2006: Subproject of Lucene • January 2008: Top-level Apache project • Stable Version: 0.20.203 • Latest Version: 0.22 (Coming soon) 31
- 32. Apache Hadoop • Reliable, Performant Distributed file system • MapReduce Programming framework • Ecosystem: HBase, Hive, Pig, Howl, Oozie, Zookeeper, Chukwa, Mahout, Cascading, Scribe, Cassandra, Hypertable, Voldemort, Azkaban, Sqoop, Flume, Avro ... 32
- 33. Problem: Bandwidth to Data • Scan 100TB Datasets on 1000 node cluster • Remote storage @ 10MB/s = 165 mins • Local storage @ 50-200MB/s = 33-8 mins • Moving computation is more efficient than moving data • Need visibility into data placement 33
- 34. Problem: Scaling Reliably • Failure is not an option, it s a rule ! • 1000 nodes, MTBF 1 day • 4000 disks, 8000 cores, 25 switches, 1000 NICs, 2000 DIMMS (16TB RAM) • Need fault tolerant store with reasonable availability guarantees • Handle hardware faults transparently 34
- 35. Hadoop Goals • Scalable: Petabytes (10 15 Bytes) of data on thousands on nodes • Economical: Commodity components only • Reliable • Engineering reliability into every application is expensive 35
- 36. Hadoop MapReduce 36
- 37. Think MapReduce • Record = (Key, Value) • Key : Comparable, Serializable • Value: Serializable • Input, Map, Shuffle, Reduce, Output 37
- 38. Seems Familiar ? cat /var/log/auth.log* | grep session opened | cut -d -f10 | sort | uniq -c ~/userlist 38
- 39. Map • Input: (Key , Value ) 1 1 • Output: List(Key , Value ) 2 2 • Projections, Filtering, Transformation 39
- 40. Shuffle • Input: List(Key , Value ) 2 2 • Output • Sort(Partition(List(Key , List(Value )))) 2 2 • Provided by Hadoop 40
- 41. Reduce • Input: List(Key , List(Value )) 2 2 • Output: List(Key , Value ) 3 3 • Aggregation 41
- 42. Hadoop Streaming • Hadoop is written in Java • Java MapReduce code is native • What about Non-Java Programmers ? • Perl, Python, Shell, R • grep, sed, awk, uniq as Mappers/Reducers • Text Input and Output 42
- 43. Hadoop Streaming • Thin Java wrapper for Map Reduce Tasks • Forks actual Mapper Reducer • IPC via stdin, stdout, stderr • Key.toString() t Value.toString() n • Slower than Java programs • Allows for quick prototyping / debugging 43
- 44. Hadoop Streaming $ bin/hadoop jar hadoop-streaming.jar -input in-files -output out-dir -mapper mapper.sh -reducer reducer.sh # mapper.sh sed -e 's/ /n/g' | grep . # reducer.sh uniq -c | awk '{print $2 t $1}' 44
- 45. Hadoop Distributed File System (HDFS) 45
- 46. HDFS • Data is organized into files and directories • Files are divided into uniform sized blocks (default 128MB) and distributed across cluster nodes • HDFS exposes block placement so that computation can be migrated to data 46
- 47. HDFS • Blocks are replicated (default 3) to handle hardware failure • Replication for performance and fault tolerance (Rack-Aware placement) • HDFS keeps checksums of data for corruption detection and recovery 47
- 48. HDFS • Master-Worker Architecture • Single NameNode • Many (Thousands) DataNodes 48
- 49. HDFS Master (NameNode) • Manages filesystem namespace • File metadata (i.e. inode ) • Mapping inode to list of blocks + locations • Authorization Authentication • Checkpoint journal namespace changes 49
- 50. Namenode • Mapping of datanode to list of blocks • Monitor datanode health • Replicate missing blocks • Keeps ALL namespace in memory • 60M objects (File/Block) in 16GB 50
- 51. Datanodes • Handle block storage on multiple volumes block integrity • Clients access the blocks directly from data nodes • Periodically send heartbeats and block reports to Namenode • Blocks are stored as underlying OS s files 51
- 53. Next Generation MapReduce 53
- 54. MapReduce Today (Courtesy: Arun Murthy, Hortonworks)
- 55. Why ? • Scalability Limitations today • Maximum cluster size: 4000 nodes • Maximum Concurrent tasks: 40,000 • Job Tracker SPOF • Fixed map and reduce containers (slots) • Punishes pleasantly parallel apps 55
- 56. Why ? (contd) • MapReduce is not suitable for every application • Fine-Grained Iterative applications • HaLoop: Hadoop in a Loop • Message passing applications • Graph Processing 56
- 57. Requirements • Need scalable cluster resources manager • Separate scheduling from resource management • Multi-Lingual Communication Protocols 57
- 58. Bottom Line • @techmilind #mrng (MapReduce, Next Gen) is in reality, #rmng (Resource Manager, Next Gen) • Expect different programming paradigms to be implemented • Including MPI (soon) 58
- 59. Architecture (Courtesy: Arun Murthy, Hortonworks)
- 60. The New World • Resource Manager • Allocates resources (containers) to applications • Node Manager • Manages containers on nodes • Application Master • Specific to paradigm e.g. MapReduce application master, MPI application master etc 60
- 61. Container • In current terminology: A Task Slot • Slice of the node s hardware resources • #of cores, virtual memory, disk size, disk and network bandwidth etc • Currently, only memory usage is sliced 61
- 62. Questions ? 62