MapReduce Algorithm Design - Parallel Reduce Operations
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The document discusses the design and execution of the MapReduce algorithm, detailing the roles of mappers and reducers, data partitioning, and optimization techniques like combiners and in-mapper combining. It highlights the importance of local aggregation to enhance performance by reducing communication overhead during the shuffle and sort phases, and presents various strategies for efficiently aggregating partial counts from the mappers. Additionally, it covers examples such as the term co-occurrence matrix and analyzes trade-offs in algorithm design, emphasizing synchronization and state preservation in MapReduce operations.
MapReduce Algorithm Design - Parallel Reduce Operations
- 2. MapReduce: Recap • Programmers must specify: map (k, v) → <k’, v’>* reduce (k’, v’) → <k’, v’>* – All values with the same key are reduced together • Optionally, also: partition (k’, number of partitions) → partition for k’ – Often a simple hash of the key, e.g., hash(k’) mod n – Divides up key space for parallel reduce operations combine (k’, v’) → <k’, v’>* – Mini-reducers that run in memory after the map phase – Used as an optimization to reduce network traffic • The execution framework handles everything else…
- 3. “Everything Else” • The execution framework handles everything else… – Scheduling: assigns workers to map and reduce tasks – “Data distribution”: moves processes to data – Synchronization: gathers, sorts, and shuffles intermediate data – Errors and faults: detects worker failures and restarts • Limited control over data and execution flow – All algorithms must expressed in m, r, c, p • You don’t know: – Where mappers and reducers run – When a mapper or reducer begins or finishes – Which input a particular mapper is processing – Which intermediate key a particular reducer is processing
- 4. combine combine combine combine b a 1 2 c 9 a c 5 2 b c 7 8 partition partition partition partition map map map map k1 k2 k3 k4 k5 k6 v1 v2 v3 v4 v5 v6 b a 1 2 c c 3 6 a c 5 2 b c 7 8 Shuffle and Sort: aggregate values by keys reduce reduce reduce a 1 5 b 2 7 c 2 9 8 r1 s1 r2 s2 r3 s3
- 5. Tools for Synchronization • Cleverly-constructed data structures – Bring partial results together • Sort order of intermediate keys – Control order in which reducers process keys • Partitioner – Control which reducer processes which keys • Preserving state in mappers and reducers – Capture dependencies across multiple keys and values
- 6. Preserving State Mapper object configure map close state one object per task Reducer object configure reduce close state one call per input key-value pair one call per intermediate key API initialization hook API cleanup hook
- 7. Scalable Hadoop Algorithms: Themes • Avoid object creation – Inherently costly operation – Garbage collection • Avoid buffering – Limited heap size – Works for small datasets, but won’t scale!
- 8. Importance of Local Aggregation • Ideal scaling characteristics: – Twice the data, twice the running time – Twice the resources, half the running time • Why can’t we achieve this? – Synchronization requires communication – Communication kills performance • Thus… avoid communication! – Reduce intermediate data via local aggregation – Combiners can help
- 9. Shuffle and Sort Mapper Reducer other mappers other reducers circular buffer (in memory) spills (on disk) merged spills (on disk) intermediate files (on disk) Combiner Combiner
- 10. Word Count: Baseline What’s the impact of combiners?
- 11. Word Count: Version 1 Are combiners still needed?
- 12. Word Count: Version 2 Are combiners still needed?
- 13. Design Pattern for Local Aggregation • “In-mapper combining” – Fold the functionality of the combiner into the mapper by preserving state across multiple map calls • Advantages – Speed – Why is this faster than actual combiners? • Disadvantages – Explicit memory management required – Potential for order-dependent bugs
- 14. Combiner Design • Combiners and reducers share same method signature – Sometimes, reducers can serve as combiners – Often, not… • Remember: combiner are optional optimizations – Should not affect algorithm correctness – May be run 0, 1, or multiple times • Example: find average of all integers associated with the same key
- 15. Computing the Mean: Version 1 Why can’t we use reducer as combiner?
- 16. Computing the Mean: Version 2 Why doesn’t this work?
- 17. Computing the Mean: Version 3 Fixed?
- 18. Computing the Mean: Version 4 Are combiners still needed? What if the S & C are too large?
- 19. Algorithm Design: Running Example • Term co-occurrence matrix for a text collection – M = N x N matrix (N = vocabulary size) – Mij: number of times i and j co-occur in some context (for concreteness, let’s say context = sentence) • Why? – Distributional profiles as a way of measuring semantic distance – Semantic distance useful for many language processing tasks
- 20. MapReduce: Large Counting Problems • Term co-occurrence matrix for a text collection = specific instance of a large counting problem – A large event space (number of terms) – A large number of observations (the collection itself) – Goal: keep track of interesting statistics about the events • Basic approach – Mappers generate partial counts – Reducers aggregate partial counts How do we aggregate partial counts efficiently?
- 21. First Try: “Pairs” • Each mapper takes a sentence: – Generate all co-occurring term pairs – For all pairs, emit (a, b) → count • Reducers sum up counts associated with these pairs • Use combiners!
- 23. “Pairs” Analysis • Advantages – Easy to implement, easy to understand • Disadvantages – Lots of pairs to sort and shuffle around (upper bound?) – Not many opportunities for combiners to work
- 24. Another Try: “Stripes” Idea: group together pairs into an associative array Each mapper takes a sentence: Generate all co-occurring term pairs For each term, emit a → { b: countb, c: countc, d: countd … } Reducers perform element-wise sum of associative arrays (a, b) → 1 (a, c) → 2 (a, d) → 5 (a, e) → 3 (a, f) → 2 a → { b: 1, c: 2, d: 5, e: 3, f: 2 } a → { b: 1, d: 5, e: 3 } a → { b: 1, c: 2, d: 2, f: 2 } a → { b: 2, c: 2, d: 7, e: 3, f: 2 } +
- 26. “Stripes” Analysis • Advantages – Far less sorting and shuffling of key-value pairs – Can make better use of combiners • Disadvantages – More difficult to implement – Underlying object more heavyweight – Fundamental limitation in terms of size of event space
- 27. Cluster size: 38 cores Data Source: Associated Press Worldstream (APW) of the English Gigaword Corpus (v3), which contains 2.27 million documents (1.8 GB compressed, 5.7 GB uncompressed)
- 28. Questions • Can you combine “Stripes” approach with in- mapper combiner? • What if the stripes are too large?
- 29. Relative Frequencies • How do we estimate relative frequencies from counts? • Why do we want to do this? • How do we do this with MapReduce? ' ) ' , ( count ) , ( count ) ( count ) , ( count ) | ( B B A B A A B A A B f
- 30. f(B|A): “Stripes” • Easy! – One pass to compute (a, *) – Another pass to directly compute f(B|A) a → {b1:3, b2 :12, b3 :7, b4 :1, … }
- 31. f(B|A): “Pairs” For this to work: Must emit extra (a, *) for every bn in mapper Must make sure all a’s get sent to same reducer (use partitioner) Must make sure (a, *) comes first (define sort order) Must hold state in reducer across different key-value pairs (a, b1) → 3 (a, b2) → 12 (a, b3) → 7 (a, b4) → 1 … (a, *) → 32 (a, b1) → 3 / 32 (a, b2) → 12 / 32 (a, b3) → 7 / 32 (a, b4) → 1 / 32 … Reducer holds this value in memory
- 32. “Order Inversion” • Common design pattern – Computing relative frequencies requires marginal counts – But marginal cannot be computed until you see all counts – Buffering is a bad idea! – Trick: getting the marginal counts to arrive at the reducer before the joint counts • Optimizations – Apply in-memory combining pattern to accumulate marginal counts – Should we apply combiners?
- 33. Synchronization: Pairs vs. Stripes • Approach 1: turn synchronization into an ordering problem – Sort keys into correct order of computation – Partition key space so that each reducer gets the appropriate set of partial results – Hold state in reducer across multiple key-value pairs to perform computation – Illustrated by the “pairs” approach • Approach 2: construct data structures that bring partial results together – Each reducer receives all the data it needs to complete the computation – Illustrated by the “stripes” approach
- 34. Recap: Tools for Synchronization • Cleverly-constructed data structures – Bring data together • Sort order of intermediate keys – Control order in which reducers process keys • Partitioner – Control which reducer processes which keys • Preserving state in mappers and reducers – Capture dependencies across multiple keys and values
- 35. Issues and Tradeoffs • Number of key-value pairs – Object creation overhead – Time for sorting and shuffling pairs across the network • Size of each key-value pair – De/serialization overhead • Local aggregation – Opportunities to perform local aggregation varies – Combiners make a big difference – Combiners vs. in-mapper combining – RAM vs. disk vs. network