CS6030 Cloud Computing. Acknowledgements. Today s Topics. Intro to Cloud Computing 10/20/15. Ajay Gupta, WMU-CS. WiSe Lab

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1 CS6030 Cloud Computing Ajay Gupta B239, CEAS Computer Science Department Western Michigan University Acknowledgements I have liberally borrowed these slides and material from a number of sources including Web MIT, Harvard, UMD, UCSD, UW, Clarkson,... Amazon, Google, IBM, Apache, ManjraSoft, CloudBook,... Thanks to original authors including Dyer, Lin, Dean, Buyya, Ghemawat, Fanelli, Bisciglia, Kimball, Michels-Slettvet, If I have missed any, its purely unintentional. My sincere appreciation to those authors and their creative mind. 2 Today s Topics Functional programming MapReduce Distributed file system 3 1

2 Functional Programming MapReduce = functional programming meets distributed processing on steroids Not a new idea dates back to the 50 s (or even 30 s) What is functional programming? Computation as application of functions Theoretical foundation provided by lambda calculus How is it different? Traditional notions of data and instructions are not applicable Data flows are implicit in program Different orders of execution are possible Exemplified by LISP and ML 4 Overview of Lisp Lisp Lost In Silly Parentheses One may focus on a particular dialect: Scheme Lists are primitive data types '( ) '((a 1) (b 2) (c 3)) Functions written in prefix notation (+ 1 2) 3 (* 3 4) 12 (sqrt (+ (* 3 3) (* 4 4))) 5 (define x 3) x (* x 5) 15 5 Functions Functions = lambda expressions bound to variables (define foo (lambda (x y) (sqrt (+ (* x x) (* y y))))) Syntactic sugar for defining functions Above expressions is equivalent to: (define (foo x y) (sqrt (+ (* x x) (* y y)))) Once defined, function can be applied: (foo 3 4) 5 6 2

3 Other Features In Scheme, everything is an s-expression No distinction between data and code Easy to write self-modifying code Higher-order functions Functions that take other functions as arguments (define (bar f x) (f (f x))) Doesn t matter what f is, just apply it twice. (define (baz x) (* x x)) (bar baz 2) 16 7 Recursion is your friend Simple factorial example (define (factorial n) (if (= n 1) 1 (* n (factorial (- n 1))))) (factorial 6) 720 Even iteration is written with recursive calls! (define (factorial- iter n) (define (aux n top product) (if (= n top) (* n product) (aux (+ n 1) top (* n product)))) (aux 1 n 1)) (factorial- iter 6) Lisp MapReduce? What does this have to do with MapReduce? After all, Lisp is about processing lists Two important concepts in functional programming Map: do something to everything in a list Fold: combine results of a list in some way 9 3

4 Map Map is a higher-order function How map works: Function is applied to every element in a list Result is a new list f f f f f 10 Fold Fold is also a higher-order function How fold works: Accumulator set to initial value Function applied to list element and the accumulator Result stored in the accumulator Repeated for every item in the list Result is the final value in the accumulator Initial value f f f f f final value 11 Map/Fold in Action Simple map example: (map (lambda (x) (* x x)) '( )) '( ) Fold examples: (fold + 0 '( )) 15 (fold * 1 '( )) 120 Sum of squares: (define (sum- of- squares v) (fold + 0 (map (lambda (x) (* x x)) v))) (sum- of- squares '( ))

5 Lisp MapReduce Let s assume a long list of records: imagine if... We can parallelize map operations We have a mechanism for bringing map results back together in the fold operation That s MapReduce! (and Hadoop) Observations: No limit to map parallelization since maps are independent We can reorder folding if the fold function is commutative and associative 13 Typical Problem Iterate over a large number of records Extract something of interest from each Shuffle and sort intermediate results Aggregate intermediate results Generate final output Map Reduce Key idea: provide an abstraction at the point of these two operations 14 Start MapReduce Programmers specify two functions: map (k, v) <k, v >* reduce (k, v ) <k, v >* All v with the same k are reduced together Usually, programmers also specify: partition (k, number of partitions ) partition for k Often a simple hash of the key, e.g. hash(k ) mod n Allows reduce operations for different keys in parallel Implementations: Google has a proprietary implementation in C++ Hadoop is an open source implementation in Java (lead by Yahoo) 15 5

6 It s just divide and conquer! Data Store Initial kv pairs Initial kv pairs Initial kv pairs Initial kv pairs map map map map k 1, values k 3, values k 1, values k 1, values k 1, values k 3, values k 3, values k 3, values k 2, values k 2, values k 2, values k 2, values Barrier: aggregate values by keys k 1, values k 2, values k 3, values reduc e reduc e reduc e final k 1 values final k 2 values final k 3 values 16 Recall these problems? How do we assign work units to workers? What if we have more work units than workers? What if workers need to share partial results? How do we aggregate partial results? How do we know all the workers have finished? What if workers die? 17 MapReduce Runtime Handles scheduling Assigns workers to map and reduce tasks Handles data distribution Moves the process to the data Handles synchronization Gathers, sorts, and shuffles intermediate data Handles faults Detects worker failures and restarts Everything happens on top of a distributed FS (later) 18 6

7 Hello World : Word Count Map(String input_key, String input_values): // input_key: document name // input_values: document contents for each word w in input_values: EmitIntermediate(w, "1"); Reduce(String key, Iterator intermediate_values): // key: a word, same for input and output // intermediate_values: a list of counts int result = 0; for each v in intermediate_values: result += ParseInt(v); Emit(AsString(result)); Source: Dean and Ghemawat (OSDI 2004) Bandwidth Optimization Issue: large number of key-value pairs Solution: use Combiner functions Executed on same machine as mapper Results in a mini-reduce right after the map phase Reduces key-value pairs to save bandwidth 21 7

8 Skew Problem Issue: reduce is only as fast as the slowest map Solution: redundantly execute map operations, use results of first to finish Addresses hardware problems... But not issues related to inherent distribution of data 22 How do we get data to the workers? NAS Compute Nodes SAN What s the problem here? 23 Distributed File System Don t move data to workers Move workers to the data! Store data on the local disks for nodes in the cluster Start up the workers on the node that has the data local Why? Not enough RAM to hold all the data in memory Disk access is slow, disk throughput is good A distributed file system is the answer GFS (Google File System) HDFS for Hadoop 24 8

9 GFS: Assumptions Commodity hardware over exotic hardware High component failure rates Inexpensive commodity components fail all the time Modest number of HUGE files Files are write-once, mostly appended to Perhaps concurrently Large streaming reads over random access High sustained throughput over low latency GFS slides adapted from material by Dean et al. 25 GFS: Design Decisions Files stored as chunks Fixed size (64MB) Reliability through replication Each chunk replicated across 3+ chunkservers Single master to coordinate access, keep metadata Simple centralized management No data caching Little benefit due to large data sets, streaming reads Simplify the API Push some of the issues onto the client Source: Ghemawat et al. (SOSP 2003) 9

10 GFS Single Master We know this is a: Single point of failure Scalability bottleneck GFS solutions: Shadow masters Minimize master involvement Never move data through it, use only for metadata (and cache metadata at clients) Large chunk size Master delegates authority to primary replicas in data mutations (chunk leases) Simple, and good enough! 28 GFS Master s Responsibilities (1/2) Metadata storage Namespace management/locking Periodic communication with chunkservers Give instructions, collect state, track cluster health Chunk creation, re-replication, rebalancing Balance space utilization and access speed Spread replicas across racks to reduce correlated failures Re-replicate data if redundancy falls below threshold Rebalance data to smooth out storage and request load 29 GFS Master s Responsibilities (2/2) Garbage Collection Simpler, more reliable than traditional file delete Master logs the deletion, renames the file to a hidden name Lazily garbage collects hidden files Stale replica deletion Detect stale replicas using chunk version numbers 30 10

11 GFS : Metadata Global metadata is stored on the master File and chunk namespaces Mapping from files to chunks Locations of each chunk s replicas All in memory (64 bytes / chunk) Fast Easily accessible Master has an operation log for persistent logging of critical metadata updates Persistent on local disk Replicated Checkpoints for faster recovery 31 GFS: Mutations Mutation = write or append Must be done for all replicas Goal: minimize master involvement Lease mechanism: Master picks one replica as primary; gives it a lease for mutations Primary defines a serial order of mutations All replicas follow this order Data flow decoupled from control flow 32 Parallelization Problems How do we assign work units to workers? What if we have more work units than workers? What if workers need to share partial results? How do we aggregate partial results? How do we know all the workers have finished? How is MapReduce different? What if workers die? 33 11

12 From Theory to Practice 1. Scp data to cluster 2. Move data into HDFS 3. Develop code locally You 4. Submit MapReduce job 4a. Go back to Step 3 Hadoop Cluster 5. Move data out of HDFS 6. Scp data from cluster 34 On Amazon: With EC2 0. Allocate Hadoop cluster 1. Scp data to cluster 2. Move data into HDFS 3. Develop code locally EC2 You 4. Submit MapReduce job 4a. Go back to Step 3 Your Hadoop Cluster Uh oh. Where did the data go? 5. Move data out of HDFS 6. Scp data from cluster 7. Clean up! 35 On Amazon: EC2 and S3 Copy from S3 to HDFS EC2 (The Cloud) S3 (Persistent Store) Your Hadoop Cluster Copy from HDFS to S

13 Next time More on MapReduce Synchronization and coordinating partial results Use of complex keys and values Examples 37 Questions? 38 13

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