The document discusses the implementation of efficient processing for rank-aware top-k join queries within the Map/Reduce model. It highlights the need for rapid data analysis and scalability due to exponential data growth while addressing weaknesses in current methods, including load balancing and early termination techniques. Experimental results compare three algorithms for improving performance in processing these queries, demonstrating the advantages of the proposed methods.

1. EFFICIENT PROCESSING OF RANK-AWARE
QUERIES IN MAP/REDUCE
OIKONOMAKIS SPYRIDON
SOF TWARE / ENGINEER AT PEOPLEPERHOUR

2. Need for a new model
 Exponential data growth
 Need for analysis, utilization and scalability of more and more
data
 Need for parallel processing
 Need to reduce reading time and data recovery
 Need for convenience in terms of programmer
 Cost

3. What is the Map/Reduce?
Distributed data processing programming model
and runtime environment that operates in a large
number of clusters of machines with parallel
processing

4. Is the Map/Reduce model reliable?

5. Map/Reduce

6. Weaknesses in Top-K Join Queries
What is the Top-K Join?
Weaknesses
 Read all the data for the recovery of K results
 Non-equitable distribution of workload per Reducer

7. Goals of the experiment
 Implementation of Top-K Join queries in
Map/Reduce model in an efficient manner
 Troubleshooting shown in Map / Reduce with:
 Early Termination
 Load Balancing

8. Design
 Comparison of three algorithms (1 default and 2 new)
 Naive
 EarlyTermination (using bounds)
 EarlyTermination & LoadBalancing (using bounds and Longest
Processing Time)
 Pre-Elaboration
 Production of two data tables with Join attributes
 Statistics for the data in the form of histograms
 Elaboration
 Calculating bounds of histograms for each table
 Run Map/Reduce

9. Design(2)

10. Early Termination
Check Bounds EarlyTermRecordReader
Send Data
Send Data
HDFS
Generated Sorted
Data
Histograms
EarlyTermInputFormat
Mapper
Reducers
Process

11. Early Termination & Load Balancing
EarlyTermRecordReader
Check
Bounds
Send Data
Send Data
HDFS
Generated Sorted
Data
Histograms
EarlyTermInputFormat
Mapper
Reducer
CustomPartitioner
Reducer Reducer

12. Experiment (1)
Parameters Values
Data Distribution: Zipfian
Number of data: 1.000.000 / table
Number of reducers: 10, 6
Number of K results: 10
Data skew: 0, 0.5, 1
Number of Joining Attributes: 10
Max value for data: 10000
Sorting: By score
Histograms: 10 bins
Cluster: 8 machines

13. Experiment Part – Comparison of algorithms (2)
0:50:24
0:43:12
0:36:00
0:28:48
0:21:36
0:14:24
0:07:12
0:00:00
0 0.5 1
Running time
Skew
REDUCERS = 10
Naive
Early Termination
Early Termination & Load
Balancing

14. Experiment Part – Comparison of algorithms (3)
2500000
2000000
1500000
1000000
500000
0
0 0.5 1
Number of records
Skew
REDUCERS = 10
Naive
Early termination
Early termination & Load Balancing

15. Experiment Part – Comparison of algorithms (4)
0:17:17
0:14:24
0:11:31
0:08:38
0:05:46
0:02:53
0:00:00
6 10
Running time
Number of Reducers
REDUCERS = 6
Early Termination
Early Termination & Load Balancing

16. Conclusion
By using the techniques proposed: :
 Early Termination
 Load Balancing
is possible to implement rank aware queries (Top-K) in
Map / Reduce efficiently and solving disadvantages of
the model Map / Reduce

17. Questions
????
Thank you.