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Ges de Des Gra cel Mape n Hop Francieli ZANON BOITO francieli.zanon-boito@inria.fr November 2018 Refce Slides by Thomas Ropars Coursera

  1. Ges���� de D���ées à Gra��� Éc�e�l� Map�����e �n� H��o�p Francieli ZANON BOITO francieli.zanon-boito@inria.fr November 2018

  2. Ref����ce� ● Slides by Thomas Ropars ● Coursera – Big Data, University of California San Diego ● The lecture notes of V. Leroy ● Designing Data-Intensive Applications by Martin Kleppmann ● Mining of Massive Datasets by Leskovec et al. 2 of 61

  3. In �o��y's ��as� ● The MapReduce paradigm for big data processing, and its most popular implementation (Apache Hadoop) ● Main ideas and how it works ● In the TP: put it to practice 3 of 61

  4. His���y ● First publications ○ "The Google File System", S. Ghemawat et al. 2003 ○ "MapReduce: simplified data processing on large clusters", D. Jeffrey and S. Ghemawat 2004 ● Used to implement several tasks: ○ Building the indexing system for Google Search ○ Extracting properties of web pages ○ Graph processing, etc Google does not use MapReduce anymore * ● ○ The amount of data they handle increased too much ○ Moved on to more efficient technologies * https://www.datacenterknowledge.com/archives/2014/06/25/google-dumps-mapreduce-favor-new-hyper-scale-analytics-system 4 of 61

  5. His���y ● Apache Hadoop: open source MapReduce framework ○ Implemented by people working at Yahoo!, released in 2006 ● Now it is a full ecosystem, used by many companies Notably, Facebook * ○ ○ HDFS @ Yahoo!: 600PB on 35K servers ** ○ Criteo (42k cores, 150PB, 300k jobs per day) *** * https://dzone.com/articles/how-is-facebook-deploying-big-data ** http://yahoohadoop.tumblr.com/post/138739227316/hadoop-turns-10 *** http://labs.criteo.com/about-us/ 5 of 61

  6. Ma�n ��e�� ● A distributed computing execution framework ● Data represented as key-value pairs ● A distributed file system ● Two main operations on data: Map and Reduce 6 of 61

  7. Map ��� R�du�� ● The Map operation ○ Transformation operation ○ A function is applied to each element of the input set ○ map( f )[ x 0 , ..., x n ] = [ f (x 0 ), ..., f (x n ) ] ○ map( ∗ 2)[2, 3, 6] = [4, 6, 12] ● The Reduce operation ○ Aggregation operation (fold) ○ reduce( f )[ x 0 , ..., x n ] = [ f ( x 0 , f ( x 1 , ..., f (x n-1 , x n ))) ] ○ reduce(+)[2, 3, 6] = (2 + (3 + 6)) = 11 ○ In MapReduce, Reduce is applied to all the elements with the same key 7 of 61

  8. Wh� i� �t �� �op����? Cod� ���e (ex���t), be����t �o ��l ● “Simple” to program and execute ○ Handles distribution of data and the computation ○ Detects failure and automatically takes corrective actions ● Scale to large number of nodes ○ Data parallelism (as opposed to task parallelism): running the same task on different data pieces in parallel ○ Move the computation instead of the data ■ Distributed file system is central ■ Execute tasks where their data is 8 of 61

  9. Wh� i� �t �� �op����? ● Fault tolerance ○ Data replication by the distributed file system ○ Intermediate results are written to disk ○ Failed tasks are re-executed on other nodes ○ Tasks can be executed multiple times in parallel to deal with stragglers (slow nodes) 9 of 61

  10. Age��� ● Introduction ● A first MapReduce program ● Apache Hadoop ○ MapReduce ○ HDFS ○ Yarn ● Combiners 10 of 61

  11. A fi��t M�����uc� ���g�a�: wo�� c����er ● We want to count the occurrences of words in a text ● Input: A set of lines, each line is a pair < line number, line content > ● Output: A set of pairs < word, number of occurrences > < "aaa", 2 > < 1, "aaa bb ccc" > < "bb", 3 > < 2, "aaa bb" > < "ccc", 1 > 11 of 61

  12. 1, "aaa bb ccc" map(key, value): 2, "bb bb d" for each word in value: 3, "d aaa bb" output pair(word, 1) 4, "d" 12 of 61

  13. 1, "aaa bb ccc" map(key, value): "aaa", 1 for each word in value: "bb", 1 "ccc", 1 output pair(word, 1) 13 of 61

  14. 1, "aaa bb ccc" map(key, value): "aaa", 1 2, "bb bb d" for each word in value: "bb", 1 "ccc", 1 output pair(word, 1) "bb", 1 "bb", 1 "d", 1 14 of 61

  15. 1, "aaa bb ccc" map(key, value): "aaa", 1 2, "bb bb d" for each word in value: "bb", 1 3, "d aaa bb" "ccc", 1 output pair(word, 1) 4, "d" "bb", 1 "bb", 1 "d", 1 "d", 1 "aaa", 1 "bb", 1 "d", 1 15 of 61

  16. 1, "aaa bb ccc" map(key, value): "aaa", 1 2, "bb bb d" for each word in value: "bb", 1 3, "d aaa bb" "ccc", 1 output pair(word, 1) 4, "d" "bb", 1 "bb", 1 reduce(key, values): "d", 1 result = 0 "d", 1 for value in values: "aaa", 1 "bb", 1 result += value "d", 1 output pair(key, result) 16 of 61

  17. 1, "aaa bb ccc" map(key, value): "aaa", 1 2, "bb bb d" for each word in value: 3, "d aaa bb" output pair(word, 1) 4, "d" reduce(key, values): "aaa", [1,1] "aaa", 2 result = 0 for value in values: "aaa", 1 result += value output pair(key, result) 17 of 61

  18. 1, "aaa bb ccc" map(key, value): "aaa", 1 2, "bb bb d" for each word in value: "bb", 1 3, "d aaa bb" output pair(word, 1) 4, "d" "bb", 1 "bb", 1 reduce(key, values): "bb", "aaa", 2 [1,1,1,1] result = 0 "bb", 4 for value in values: "aaa", 1 "bb", 1 result += value output pair(key, result) 18 of 61

  19. 1, "aaa bb ccc" map(key, value): "aaa", 1 2, "bb bb d" for each word in value: "bb", 1 3, "d aaa bb" "ccc", 1 output pair(word, 1) 4, "d" "bb", 1 "bb", 1 reduce(key, values): "d", 1 "aaa", 2 result = 0 "d", 1 "bb", 4 for value in values: "aaa", 1 "ccc", 1 "bb", 1 result += value "d", 3 "d", 1 output pair(key, result) 19 of 61

  20. 1, "aaa bb ccc" "aaa", 1 2, "bb bb d" "bb", 1 But we generate a lot of intermediate 3, "d aaa bb" "ccc", 1 data! 4, "d" "bb", 1 Why not keep a centralized counter "bb", 1 per word? "d", 1 "d", 1 "aaa", 2 That's the price we pay for scalability! "aaa", 1 "bb", 4 "bb", 1 "ccc", 1 Let's see how it works. "d", 1 "d", 3 20 of 61

  21. Age��� ● Introduction ● A first MapReduce program ● Apache Hadoop ○ MapReduce ○ HDFS ○ Yarn ● Combiners 21 of 61

  22. Map�����e ● The developer defines: ○ map and reduce functions to manipulate key-value pairs ○ key and value types (map output needs to match reduce input) ● The map function will be executed once per input pair ● The reduce function will be executed once per existing key (with all the values associated with that key) 22 of 61

  23. We start with the input separated in blocks and distributed over the nodes Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 23 of 61

  24. We have one map task per input block (each task executes the map function multiple times) In the same node to avoid data movement! Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 24 of 61

  25. Now we have the Shuffle & Sort phase First sort each map task output by key Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 25 of 61

  26. Send the pairs to the adequate reduce task (hashing) The number of reduce tasks is configurable Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 26 of 61

  27. Combine the pairs that have the same key Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 27 of 61

  28. Run the reduce tasks Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 28 of 61

  29. Now we have (unsorted) output that is distributed over some nodes Figure from https://www.supinfo.com/articles/single/2807-introduction-to-the-mapreduce-life-cycle 29 of 61

  30. H��S ● Distributed file system for shared-nothing infrastructures ● Main goals: scalability and fault tolerance, optimized for throughput ● It is not POSIX-compliant ○ Sequential read and writes only ○ Write-once-read-many file access (supports append and truncate) 30 of 61

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