CS MapReduce. Vitaly Shmatikov

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1 CS 5450 MapReduce Vitaly Shmatikov

2 BackRub (Google), 1997 slide 2

3 NEC Earth Simulator, 2002 slide 3

4 Conventional HPC Machine ucompute nodes High-end processors Lots of RAM unetwork Specialized Very high performance ustorage server RAID disk array slide 4

5 HPC Programming Model uprograms described at a very low level Detailed control of processing and communication urely on small number of software packages Written by specialists Limits classes of problems and solution methods slide 5

6 Typical HPC Operation u Characteristics Long-lived processes Make use of spatial locality Hold all data in memory (no disk access) High-bandwidth communication u Strengths High utilitization of resources Effective for many scientific applications u Weaknesses Requires careful tuning of applications to resources Intolerant of any variability slide 6

7 HPC Fault Tolerance wasted u Checkpoint Periodically store state of all processes Significant I/O traffic u Restore after failure Reset state to last checkpoint All intervening computation wasted u Performance scaling Very sensitive to the number of failing components slide 7

8 Google Data Center, 2016 Dozens of these! slide 8

9 Ideal Cluster Programming Model uapplications written in terms of high-level operations on the data uruntime system controls scheduling, load balancing slide 9

10 MapReduce, 2004 slide 10

11 MapReduce Programming Model u Map computation across many data objects For example, webpages u Aggregate results in many different ways u System deals with resource allocation and availability slide 11

12 Programming in Lisp Computing the sum of squares (map square ( )) Processes each record sequentially and independently Output: ( ) (reduce + ( )) Computes (+ 16 (+ 9 (+ 4 1) ) ) Output: 30 slide 12

13 Application: Word Count SELECT count(word) FROM data GROUP BY word slide 13

14 Partial Aggregation 1. In parallel, each worker computes word counts from individual files 2. Collect results, wait until all finished 3. Merge intermediate output 4. Compute word count on merged intermediates slide 14

15 Map Process individual records to generate (key, value) pairs Key Value Welcome Everyone Hello Everyone Welcome 1 Everyone 1 Hello 1 Everyone 1 slide 15

16 Parallel Map Process individual records to generate (key, value) pairs in parallel Key Value Welcome Everyone Welcome 1 Everyone 1 map task 1 Hello Everyone Hello 1 Everyone 1 map task 2 Process large number of records in parallel slide 16

17 Reduce Merge all intermediate values per key Key Value Key Value Welcome 1 Everyone 1 Hello 1 Everyone 1 Everyone 2 Welcome 1 Hello 1 slide 17

18 Partitioning Merge all intermediate values in parallel: partition keys, assign each key to one Reduce Key Value Key Value Welcome 1 Everyone 1 Hello 1 Everyone 1 Reduce task 1 Reduce task 2 Everyone 2 Welcome 1 Hello 1 slide 18

19 MapReduce API map(key, value) -> list(<k, v >) Applies function to (key, value) pair and produces set of intermediate pairs reduce(key, list<value>) -> <k, v > Applies aggregation function to values Outputs result slide 19

20 MapReduce WordCount map(key, value): for each word w in value: EmitIntermediate(w, "1"); reduce(key, list(values): int result = 0; for each v in values: result += ParseInt(v); Emit(AsString(result)); slide 20

21 Optimizations combine(list<key, value>) -> list<k,v> Perform partial aggregation on each mapper node <the, 1>, <the, 1>, <the, 1> à <the, 3> reduce() should be commutative and associative partition(key, int) -> int Need to aggregate intermediate values with same key Given n partitions, map key to partition 0 i < n Typically via hash(key) mod n - but not always! slide 21

22 Putting It Together map combine partition reduce slide 22

23 Syncronization Barrier slide 23

24 App: grep u Input: large set of files u Output: lines that match pattern u Map: Output a line if it matches the supplied pattern u Reduce: Copy the intermediate data to output slide 24

25 App: Reverse Web-Link Graph u Input: web graph, i.e., tuples (A, B) where page A links to page B u Output: for each page, list of pages that link to it u Map: For each (source, target), output (target, source) u Reduce: Output (target, list(source)) slide 25

26 App: URL Access Frequency u Input: log of accessed URLs (e.g., from a proxy) u Output: for each URL, % of total accesses for that URL u Map: For each URL, output (URL, 1) u Multiple reducers: Output (URL, urlcount) Chain another MapReduce... u Map: For each (URL, urlcount), output (1, (URL, urlcount)) u Reduce: Compute TotalCount, output multiple (URL, urlcount/totalcount) slide 26

27 App: Sorting u Input: series of values u Output: sorted values u Map: For each value, output (value, _) u Partitioning: Partition keys across reducers based on ranges Take data distribution into accounts to balance reducer tasks u Reduce: Copy the intermediate data to output slide 27

28 MapReduce Features u Characteristics Computation broken up into many short-lived tasks (mapping, reducing) Disk storage to hold intermediate results u Strengths Very flexible placement, scheduling, load balancing Can access large datasets u Weaknesses Higher overhead Lower raw performance slide 28

29 MapReduce Execution slide 29

30 Fault Tolerance in MapReduce u Map worker writes intermediate output to local disk, separated by partitioning; once completed, tells master node u Reduce worker told of location of map task outputs, pulls their partition s data from each mapper, executes function across data u Note: All-to-all shuffle between mappers and reducers Written to disk ( materialized ) before each stage slide 30

31 Fault Tolerance in MapReduce umaster node monitors state of system If master fails, job aborts umap worker failure In-progress and completed tasks marked as idle Reduce workers notified when map task is reexecuted on another map worker ureducer worker failure In-progress tasks are reset and re-executed Completed tasks had been written to global file system slide 31

32 Stragglers ustraggler = task that takes long time to execute Bugs, flaky hardware, poor partitioning ufor slow map tasks, execute in parallel on second map worker as backup, race to complete task uwhen done with most tasks, reschedule any remaining executing tasks Keep track of redundant executions Significantly reduces overall run time slide 32

33 Straggler Mitigation slide 33

34 Goodbye MapReduce slide 34

35 Modern Data Processing Big driver: machine learning slide 35

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