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Problem-solving on large-scale clusters: theory and applications Lecture 3: Bringing it all together.

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Presentation on theme: "Problem-solving on large-scale clusters: theory and applications Lecture 3: Bringing it all together."— Presentation transcript:

1 Problem-solving on large-scale clusters: theory and applications Lecture 3: Bringing it all together

2 Today’s Outline Course directions, projects, and feedback Quiz 2 Context / Where we are –Why do we care about fold() and map() ? –Why do we care about parallelization and data dependencies? MapReduce architecture from 10,000 feet

3 Context and Review Data dependencies determine whether a problem can be formulated in MapReduce The properties of fold() and map() determine how to formulate a problem in MapReduce How do you parallelize fold() ? map() ?

4 MapReduce Introduction MapReduce is both a programming model and a clustered computing system –A specific way of formulating a problem, which yields good parallelizability –A system which takes a MapReduce-formulated problem and executes it on a large cluster Hides implementation details, such as hardware failures, grouping and sorting, scheduling … Previous lectures have focused on MapReduce- the-problem-formulation Today will mostly focus on MapReduce-the- system

5 MR Problem Formulation: Formal Definition MapReduce: mapreduce f m f r l = map (reducePerKey f r ) (group (map f m l)) reducePerKey f r (k,v_list) = (k, (foldl (f r k) [] v_list)) –Assume map here is actually concatMap. –Argument l is a list of documents –The result of first map is a list of key-value pairs –The function f r takes 3 arguments key, context, current. With currying, this allows for locking the value of “key” for each list during the fold. MapReduce maps a fold over the sorted result of a map!

6 MR System Overview (1 of 2) Map: –Preprocesses a set of files to generate intermediate key-value pairs –As parallelized as you want Group: –Partitions intermediate key-value pairs by unique key, generating a list of all associated values Reduce: –For each key, iterates over value list –Performs computation that requires context between iterations –Parallelizable amongst different keys, but not within one key

7 MR System Overview (2 of 2) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

8 Example: MapReduce DocInfo (1 of 2) MapReduce: mapreduce f m f r l = map (reducePerKey f r ) (group (map f m l)) reducePerKey f r (k,v_list) = (k, (foldl (f r k) [] v_list) Pseudocode for f m f m contents = concat [ [(“spaces”, (count_spaces contents))], (map (emit “raw”) (split contents)), (map (emit “scrub”) (scrub (split contents)))] emit label value = (label, (value, 1))

9 Example: MapReduce DocInfo (2 of 2) MapReduce: mapreduce f m f r l = map (reducePerKey f r ) (group (map f m l)) reducePerKey f r (k,v_list) = (k, (foldl (f r k) [] v_list) Pseudocode for f r f r ‘spaces’ count (total:xs) = (total+count:xs) f r ‘raw’ (word,count) (result) = (update_result (word,count) result) f r ‘scrub’ (word,count) (result) = (update_result (word,count) result)

10 Group Exercise Formulate the following as map reduces: 1.Find the set of unique words in a document a)Input: a bunch of words b)Output: all the unique words (no repeats) 2.Calculate per-employee taxes a)Input: a list of (employee, salary, month) tuples b)Output: a list of (employee, taxes due) pairs 3.Randomly reorder sentences a)Input: a bunch of documents b)Output: all sentences in random order (may include duplicates) 4.Compute the minesweeper grid/map a)Input: coordinates for the location of mines b)Output: coordinate/value pairs for all non-zero cells Can you think generalized techniques for decomposing problems?

11 MapReduce Parallelization: Execution Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

12 MapReduce Parallelization: Pipelining Finely granular tasks: many more map tasks than machines –Better dynamic load balancing –Minimizes time for fault recovery –Can pipeline the shuffling/grouping while maps are still running Example: 2000 machines -> 200,000 map reduce tasks Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

13 Example: MR DocInfo, revisited Do MapReduce DocInfo in 2 passes (instead of 1), performing all the work in the “group” step Map1: 1.Tokenize document 2.For each token output: a)(“raw: ”,1) b)(“scrubbed: ”, 1) Reduce1: 1.For each key, ignore value list and output (key,1) Map2: 1.Tokenize document 2.For each token “type:value”, output (type,1) Reduce 2: 1.For each key, output (key, (sum values))

14 Example: MR DocInfo, revisited Of the 2 DocInfo MapReduce implementations, which is better? Define “better”. What resources are you considering? Dev time? CPU? Network? Disk? Complexity? Reusability? Mapper Reducer GFS Key: Connections are network links GFS is a cluster of storage machines

15 HaDoop-as-MapReduce mapreduce f m f r l = map (reducePerKey f r ) (group (map f m l)) reducePerKey f r (k,v_list) = (k, (foldl (f r k) [] v_list) Hadoop: 1.The f m and f r are function objects (classes) 2.Class for f m implements the Mapper interface Map(WritableComparable key, Writable value, OutputCollector output, Reporter reporter) 3.Class for f r implements the Reducer interface reduce(WritableComparable key, Iterator values, OutputCollector output, Reporter reporter) Hadoop takes the generated class files and manages running them

16 Bonus Materials: MR Runtime The following slides illustrate an example run of MapReduce on a Google cluster A sample job from the indexing pipeline, processes ~900 GB of crawled pages

17 MR Runtime (1 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

18 MR Runtime (2 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

19 MR Runtime (3 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

20 MR Runtime (4 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

21 MR Runtime (5 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

22 MR Runtime (6 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

23 MR Runtime (7 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

24 MR Runtime (8 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation

25 MR Runtime (9 of 9) Shamelessly stolen from Jeff Dean’s OSDI ‘04 presentation


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