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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…
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“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
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k1 v1 k2 v2 k3 v3 k4 v4 k5 v5 k6 v6 map map map map a 1 b 2 c 3 c 6 a 5 c 2 b 7 c 8 combine combine combine combine a 1 b 2 c 9 a 5 c 2 b 7 c 8 partition partition partition partition Shuffle and Sort: aggregate values by keys a 1 5 b 2 7 c 2 9 8 reduce reduce reduce r1 s1 r2 s2 r3 s3
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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
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Preserving State Mapper object Reducer object one object per task state state configure API initialization hook configure one call per input key-value pair map reduce one call per intermediate key close API cleanup hook close
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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!
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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
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Shuffle and Sort Mapper intermediate files (on disk) merged spills (on disk) Combiner Reducer circular buffer (in memory) Combiner other reducers spills (on disk) other mappers
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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
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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
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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
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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?
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