2. MAP REDUCE BASICS CHAPTER 2

3. Basics Divide and conquer – Partition large problem into smaller subproblems – Worker work on subproblems in parallel Threads in a core, cores in multi-core processor, multiple processor in a machine, machines in a cluster

4. History CPUs Single CPU – inserted into single CPU socket on a motherboard Didn’t work well but: – Tried to add other CPU sockets, so multiple CPUs – Need additional hardware to connect them to RAM, resources – Lots of overhead

5. Hyper-threading Parallel computation to PCs – 2000 Pentium 4 – single CPU core but HT – Appears as 2 logical CPUs – MIMD – But are sharing execution – Need OS to take advantage of it Logical core is the number of physical cores times the number of threads that can run on a machine

6. Multi-core Additional cores added Single CPU socket, all cores on same chip so less latency – If dual core, CPU has 2 central processing units – Can run 2 processes at same time – Since all on same chip No extra hardware needed to connect to RAM, etc. faster communication

7. vCPU Virtual Processor VM assigned to a vCPU, share of a physical CPU assigned to a VM vCPU is a series of time slots on logical processors Adding more vCPUs can increase wait time

8. How to increase vCPUs If a vCPU is presented to OS as single core CPU in single socket, limits number of vCPUs Typically OS restrict number of physical CPUs not logical CPUs Multicore vCPUs - VMware introduced virtual sockets and cores per socket, e.g. 2 virtual sockets and 4 virtual cores per socket, allows 8vCPUs Or can use hyperthreads to increase vCPUs

9. Multiple threads on a VM If 2 hyper threads each on 4 cores, VM thinks have 8 cores If want all 8 threads at once?

10. Each thread at 50% utilization Each physical CPU at 100% Avoid creating a single VM with more vCPUs than physical cores If need more, split up VM

11. Basics MR – abstraction that hides system-level details from programmer Move code to data – Spread data across disks – DFS manages storage

12. Topics Functional programming MapReduce Distributed file system

13. Functional Programming Roots MapReduce = functional programming plus 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 – Computation is evaluation of mathematical functions – Avoids state and mutable data – Emphasizes application of functions instead of changes in state

14. Functional Programming Roots 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 – Theoretical foundation provided by lambda calculus a formal system for function definition Exemplified by LISP, Scheme

15. Overview of Lisp Functions written in prefix notation where operators precede operands (+ 1 2)  3 (* 3 4)  12 (sqrt ( + (* 3 3) (* 4 4)))  5 (define x 3)  x (* x 5)  15

16. Functions Functions = lambda (anonymous) expressions bound to variables Example expressed with lambda:(+ 1 2)  3 λxλy.x+y Above expression is equivalent to: Once defined, function can be applied: (define (foo x y) (sqrt (+ (* x x) (* y y)))) (define foo (lambda (x y) (sqrt (+ (* x x) (* y y))))) (foo 3 4)  5

17. Functional Programming Roots Map and Fold Two important concepts in functional programming – Map: do something to everything in a list – Fold: combine results of a list in some way

18. Functional Programming Map Higher order functions – accept other functions as arguments – Map Takes a function f and its argument, which is a list applies to all elements in list Returns a list as result Lists are primitive data types – [1 2 3 4 5] – [[a 1] [b 2] [c 3]]

19. Map/Fold in Action Simple map example: (map (lambda (x) (* x x)) [1 2 3 4 5])  [1 4 9 16 25]

20. Functional Programming Reduce – Fold Takes function g, which has 2 arguments: an initial value and a list. The g applied to initial value and 1 st item in list Result stored in intermediate variable Intermediate variable and next item in list 2 nd application of g, etc. Fold returns final value of intermediate variable

22. Map/Fold in Action Simple map example: Fold examples: Write Sum of squares: (map (lambda (x) (* x x)) [1 2 3 4 5])  [1 4 9 16 25] (fold + 0 [1 2 3 4 5])  15 (fold * 1 [1 2 3 4 5])  120 (define (sum-of-squares v) // where v is a list (fold + 0 (map (lambda (x) (* x x)) v))) (sum-of-squares [1 2 3 4 5])  55

23. Functional Programming Roots Use map/fold in combination Map – transformation of dataset Fold- aggregation operation Can apply map in parallel Fold – more restrictions, elements must be brought together – Many applications do not require g be applied to all elements of list, fold aggregations in parallel

24. MapReduce - Functional Programming Roots Input to function, apply function Function emits output – Can use output as input to next stage

25. MapReduce Map in MapReduce is same as in functional programming Reduce corresponds to fold 2 stages: – User specified computation applied over all input, can occur in parallel, return intermediate output – Output aggregated by another user-specified computation

26. Mappers/Reducers Key-value pair (k,v) – basic data structure in MR Keys, values – int, strings, etc., user defined – e.g. (k – URLs, v – HTML content) – e.g. (k – node ids, v – adjacency lists of nodes) Map: (k1, v1) -> [(k2, v2)] Reduce: (k2, [v2]) -> [(k3, v2)] Where […] denotes a list Notice output of Map, input to Reduce different

27. General Flow Apply mapper to every input key-value pair stored in DFS Generate arbitrary number of intermediate (k,v) Group by operation on intermediate keys within mapper (really a sort? But called a shuffle)) Distribute intermediate results by key – not across reducers but across the network (really a shuffle? But called a sort) Aggregate intermediate results Generate final output to DFS – one file per reducer Map Reduce

28. What function is implemented? 9

29. Another Example: unigram (word count) (docid, doc) on DFS, doc is text Mapper tokenizes (docid, doc), emits (k,v) for every word – (word, 1) Execution framework all same keys brought together in reducer Reducer – sums all counts (of 1) for word Each reduce writes to one file Words within file sorted, file same # words Can use output as input to another MR

30. Hadoop libraries for MapReduce

32. Combine - Bandwidth Optimization Issue: Can be a large number of key-value pairs – Example – word count (word, 1) – If copy across network intermediate data > input

33. Combine - Bandwidth Optimization Solution: use Combiner functions – allow local aggregation (after mapper) before shuffle sort Word Count - Aggregate (count each word locally) – intermediate = # unique words – Executed on same machine as mapper – no output from other mappers – Results in a “mini-reduce” right after the map phase – (k,v) of same type as input/output – If operation associative and commutative, reduce can be same as combiner – Reduces key-value pairs to save bandwidth

34. Partitioners – Load Balance Issue: Intermediate results can all be on one reducer Solution: use Partitioner functions – divide up intermediate key space and assign (k,v) to reducers – Specifies task to which copy (k,v) – Reducer processes keys in sorted order – Partitioner applies function to key – Hopefully same number of each to each reducer But may be- Zipfian

35. MapReduce Programmers specify two functions: map (k, v) → * reduce (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 Where n is the number of reducers – Allows reduce operations for different keys in parallel

37. Its not just Map and Reduce Apply mapper to every input key-value pair stored in DFS Generate arbitrary number of intermediate (k,v) Aggregate locally Assign to reducers Group by operation on intermediate keys Distribute intermediate results by key not across reducers Aggregate intermediate results Generate final output to DFS – one file per reducer Map Reduce Combine Partition

38. Execution Framework MapReduce program (job) contains Code for mappers Combiners (optional) Partitioners (optional) Code for reducers Configuration parameters (where is input, store output) – Execution framework takes care of everything else – Developer submits job to submission node of cluster (jobtracker)

39. 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? MapReduce takes care of all this

40. Execution Framework of MapReduce Scheduling – Job divided into tasks (certain block of (k,v) pairs) – Can have 1000s jobs need to be assigned – May exceed number that can run concurrently – Task queue – Coordination among tasks from different jobs

41. Execution Framework Synchronization – Concurrently running processes join up – Intermediate (k,v) grouped by key, copy intermediate data over network, shuffle/sort Number of copy operations (M mappers, R reducers) Worst case? – M X R copy operations Each mapper may send intermediate results to every reducer – Reduce computation cannot start until all mappers finished, (k,v) shuffled/sorted Differs from functional programming – But can copy intermediate (k,v) over network to reducer when mapper finishes

42. Execution Framework of MapReduce Speculative execution Map phase only as fast as? – slowest map task Problem: Stragglers, flaky hardware Solution: Use speculative execution: – Exact copy of same task on different machine – Uses result of fastest task in attempt to finish – Better for map or reduce? – Can improve running time by 44% (Google) – Doesn’t help if skewed distribution of values

43. Execution Framework Data/code co-location – Execute near data – If not possible must stream data Try to keep within same rack

44. Execution Framework Error/fault handling – The norm – Disk failures, RAM errors, datacenter outages – Software errors – Corrupted data

45. Map Reduce Implementations: – Google has a proprietary implementation in C++ – Hadoop is an open source implementation in Java (lead by Yahoo now Apache) Hadoop is a framework for storage and large scale processing of data sets on clusters of commodity hardware Walmart uses Hadoop for storage (although they originally broke it – wouldn’t scale as needed) P&G uses HIVE – For data analytics built on top of Hadoop

46. Differences in MapReduce Implementations Hadoop (Apache) vs. Google – Google – program can specify 2ndary sort, can’t change key in reducer – Hadoop - Values arbitrarily ordered, can change key in reducer Hadoop – Programmer can specify number of map tasks, but framework makes final decision – In reduce, programmer specified number of tasks is used

47. Hadoop Careful using external resources – e.g. bottleneck querying SQL DB Mappers can emit arbitrary number of intermediate (k,v), can be of different type Reduce can emit arbitrary number of final (k,v) and can be of different type than intermediate (k,v) Different from functional programming, can have side effects (state change internal – may cause problems, external may write to files) MapReduce can have no reduce, but must have mapper – Can just pass identity function to reducer – May not have any input compute pi

48. Other Sources Other source can serve as source/destination for data from MapReduce – Google – BigTable – Hbase – BigTable clone – Hadoop – integrated RDB with parallel processing, can write to DB tables

49. File Systems – GFS vs DFS Distributed File System (DFS) – In HPC, storage distinct from computation – NAS (network attached storage) and SAN are common Separate, dedicated nodes for storage – Fetch, load, process, write – Bottleneck Higher performance networks $$ (10G Ethernet), special purpose interconnects $$$ (InfiniBand) – $$ increases non-linearly – In GFS Computation and storage not distinct components

50. Hadoop Distributed File System - HDFS GFS supports proprietary MapReduce HDFS – supports Hadoop Don’t have to run GFS on DFS, but misses advantages Difference in GFS and HDFS vs. DFS: – Adapted to large data processing – divide user data into chunks/blocks – LARGE (was) – Replicate these across the local disk nodes in cluster – Master-slave architecture

51. HDFS vs GFS (Google File System) Difference in HDFS: – Master-slave architecture GFS: Master (master), slave (chunkserver) HDFS: master (namenode), slave (datanode) – Master – namespace (metadata, directory structure, file to block mapping, location of blocks, access permission) – Slaves – manage actual data blocks – Client contacts namespace, gets data from slaves, 3 copies of each block, etc. – Block is 64 MB – Initially Files were immutable – once closed cannot be modified

53. HDFS Namenode – Namespace management – Coordinate file operations Lazy garbage collection – Maintain file system health Heartbeats, under-replication, balancing Supports subset of POSIX API, pushed to application No Security

55. Hadoop Cluster Architecture HDFS namenode runs daemon Job submission node runs jobtracker – point of contact run MapReduce – Monitors progress of MapReduce jobs, coordinates Mappers and reducers Slaves run tasktracker – Runs users code, datanode daemon, serve HDFS data – Send heartbeat messages to jobtracker

56. Hadoop Cluster Architecture Number of reduce tasks depends on reducers specified by programmer Number of map tasks depends on – Hint from programmer – Number of input files – Number of HDFS data blocks of files

57. Hadoop Cluster Architecture Map tasks assigned – (k,v) called input split Input splits computed automatically Aligned on HDFS boundaries so associated with single block, simplifies scheduling Data locality, if not stream across network (same rack if possible)

58. How can we use MapReduce to solve problems? Refresh your memory on Dijkstra’s algorithm

60. Hadoop Cluster Architecture Mappers in Hadoop – Javaobjects with a MAP method – Mapper object instantiated for every map task by tasktracker – Life cycle – instantiation, hook in API for program specified code Mappers can load state, static data sources, dictionaries, etc. – After initialization: MAP method called by framework on all (k,v) in input split – Method calls within same Java object, can preserve state across multiple (k,v) in same task – Can run programmer specified termination code

61. Hadoop Cluster Architecture Reducers in Hadoop – Execution similar to that of mappers Instantiation, initialization, framework calls REDUCE method with intermediate key and iterator over all key values Intermediate keys in sorted order Can preserve state across multiple intermediate keys

62. CAP Theorem Consistency, availability, partition tolerance Cannot satisfy all 3 Partitioning unavoidable in large data systems, must trade off availability and consistency – If master fails, system is unavailable so consistent! – If multiple masters, more available, but inconsistent Workaround to single namenode – Warm standby namenode – Hadoop community working on it