This document summarizes research on optimizing parallel group-by queries using MapReduce. It presents the MapReduce and MapCombineReduce models, discusses cost estimation, and evaluates experiments comparing the two models. The optimized MapCombineReduce model reduces network communication costs by using a combiner to pre-aggregate data locally on worker nodes before transferring results. Experiments show MapCombineReduce provides better speed-up and scalability for queries with reasonable selectivity.

1. Parallelizing Multiple Group-by
queries using MapReduce:
optimization and cost estimation
Jie Pan · Frédéric Magoulès ·
Yann Le Biannic · Christophe Favart
B99705024 林劭軒
B99705021 李奕德
R00725051 郗昀彥
§ Ecole Centrale Paris · † SAP Research
§ §
† †
Telecommunication Systems 2013

2. Outline
• MapReduce and Optimized MapReduce
• Cost Estimation
• Experiments and Evaluation

3. MapReduce
Data
MapDi MapDi MapDi MapDi
Master Node
Worker Nodes

4. MapReduce
Data
MapDi MapDi MapDi MapDi
Di Map
Master Node
Worker Nodes

5. MapReduce
Data
MapDi MapDi MapDi MapDi
Di Map
Master Node
Worker Nodes
serialize :: structured objects → byte stream
de-serialize :: byte stream → structured objects

6. MapReduce
Data
MapDi MapDi MapDi MapDi
Di IiMap
Master Node
Worker Nodes

7. MapReduce
Data
MapDi MapDi MapDi MapDi
Di IiMap
DiDiDiIi
Master Node
Worker Nodes

8. Data
MapDi MapDi MapDi MapDi
Di IiMap
Reducer
Result
DiDiDiIi
Master Node
Worker Nodes
MapReduce

9. Motivation
• Data Analysis (Business Intelligence)
• Task with Predicates
• High Selectivity => High Communication Cost
•
• Goal: Reduce the Volume of Intermediate Data
DiDiDiIi
Master NodeWorker Nodes
Selectivity =
#Data
#Data Satisfying Predicates

10. Data
MapDi MapDi MapDi MapDi
Di IiMap
signal
Master Node
Worker Nodes
MapCombineReduce (1/2)

11. MapCombineReduce (2/2)
Data
MapDi MapDi MapDi MapDi
IiCombiner
Master Node
Worker Nodes
CombinerCombiner
CombinerCombiner

12. MapCombineReduce (2/2)
Data
MapDi MapDi MapDi MapDi
Ai IiCombiner
Master Node
Worker Nodes
CombinerCombiner
CombinerCombiner

13. Data
Reducer
Result
MapDi MapDi MapDi MapDi
DiDiDiAi
Ai IiCombiner
Master Node
Worker Nodes
CombinerCombiner
CombinerCombiner
MapCombineReduce (2/2)

14. Cost Estimation

15. Notations – general

16. Cost
min ∑ Cst + Cw + Ccl + Ccmm
Data
MapDi MapDi MapDi MapDi
Di IiMap
Reducer
Result
DiDiDiIi
Master Node
Worker Nodes

17. Initial Build (1/4)
Creating a mapping
Serialize Data
Forall mappers
Network Factor
Mapper’s Data Transfer Cost
Result Transfer Cost

18. Initial Build (2/4)
De-serialize Data Serialize Result
Fragment
Load to Memory
Filter Cost

19. Initial Build (3/4)
De-serialize All Result
Selected Data
Aggregation Cost

20. Initial Build (4/4)
• sizem = 0
• Cmpg * nbm is constant

21. Optimized Build (1/6)
Nodes to be Combined
Size of Combiner’s Object
Does Not Change

22. Optimized Build (2/6)
Does Not Change
Does Not Serialize Result

23. Optimized Build (3/6)
Serialize Intermediate Result

24. Optimized Build (4/6)
De-serialize Intermediate Result

25. Optimized Build (5/6)
• Network Factor * (Start to Map +
Worker to Combiner +
Reduce Phare)

26. Optimized Build (6/6)
• sizem = 0
• sizec = 0
• Cmpg * nbm is constant

27. Compare
The factors has changed!!

28. Experiments and Evaluation

29. Experiments Environment (1/2)
• Running the experience over
• 9 sites geographically distributed in France
• featuring 5000 processors
• 1 cluster situated in the Sophia site
• IBM eServer 325
• Total number of nodes in this cluster: 49
[1] https://www.grid5000.fr/
[1]

30. Experiments Environment (2/2)
• Each node is composed of
• 2 CPUs of AMD Opteron 246
• 1 MB of cache, 2 GB of memory
• network: 2xGigabit Ethernet
• Java 1.6, GridGain 2.1.1

31. Dataset
• Dataset: 640000 records
• Each record contains 15 columns
• partition with 5 different fragment sizes
• 1000, 2000, 4000, 8000 and 16000
• with selectivity = 0.0106, 0.099 and 0.185

32. Experiments
• Run a sequential test on
• 1 machine
• Launch the parallel tests in GridGain on
• 5, 10, 15 and 20 machines

33. Results - Query Selectivity 0.0106

34. Results - Query Selectivity 0.099

35. Results - Query Selectivity 0.185

36. Result
• When the selectivity is bigger, the optimized version’s
speeds-up better than the initial version.
• When the query’s selectivity is small, only a small
amount of data need to be transferred over network.
• When the query’s selectivity is big, then the
communication cost becomes dominant.

37. Scalability
• use several datasets having the same columns
• composed of 640000, 1280000, 1920000 and 2560000 records
• Fragment: 16000
• Run the queries with the same selectivity

38. Conclusion
• MapReduce Model
• MapCombineReduce Model
• The combiner: pre-aggregator which aggregates over worker
node
• Reduce the amount of intermediate data transferred over network
• Cost estimation
• Experimental results
• Better speed-up and scalability for a reasonable selectivity