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ICICLES: Self-tuning Samples for Approximate Query Answering By Venkatesh Ganti , Mong Li Lee, and Raghu Ramakrishna

ICICLES: Self-tuning Samples for Approximate Query Answering By Venkatesh Ganti , Mong Li Lee, and Raghu Ramakrishnan. Harikrishnan Karunakaran Sulabha Balan. CSE 6339 . Outline. Introduction Icicles Icicle Maintenance Icicle-Based Estimators Quality & Performance Conclusion.

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ICICLES: Self-tuning Samples for Approximate Query Answering By Venkatesh Ganti , Mong Li Lee, and Raghu Ramakrishna

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  1. ICICLES: Self-tuning Samples for Approximate Query AnsweringBy VenkateshGanti, Mong Li Lee, and RaghuRamakrishnan Harikrishnan Karunakaran Sulabha Balan • CSE 6339

  2. Outline • Introduction • Icicles • Icicle Maintenance • Icicle-Based Estimators • Quality & Performance • Conclusion

  3. Introduction • Analysis of data in data warehouses useful in decision support • Users of decision support systems want interactive systems OLAP – Online Analytical Processing • Aggregate Query Answering Systems (AQUA) developed to reduce response time to desirable levels • Tolerant of approximate results

  4. Approximate Querying • Various Approaches • Sampling-based • Histogram-based • Clustering • Probabilistic • Wavelet-based

  5. Uniform Random Sampling Sales S_sales 50% Sample scale factor SELECT SUM(sales) x 2 AS cnt FROM s_sales WHERE state = ‘TX’

  6. Biased Sampling Sales S_sales Sample relation for aggregation query workload regarding Texas branches

  7. Methodology • All tuples in a Uniform Random Sample are treated as equally important for answering queries • Sample needs to be tuned to contain tuples which are more relevant to answer queries in a workload • Need for a dynamic algorithm that changes the sample as and according to suit the queries being executed in the workload

  8. SELECT COUNT(*), AVG(LI Extendedprice), SUM(LI Extendedprice) FROM LI, C, O, S, N, R WHERE C Custkey=O Custkey AND O Orderkey=LI Orderkey AND LI Suppkey=S Suppkey AND C Nationkey=N Nationkey AND N Regionkey=R Regionkey AND R Name=North America AND O Orderdate01-01-1998 AND O Orderdate12-31-1998; Join Synopsis • Join of a Uniform Random Sample of a Fact Table with a set of accompanying Dimension Tables

  9. Need for Icicles • Any aggregate query on the fact table can be answered approximately using exactly one of a smaller number of synopses • Uniform Random Sample of Relation wastes memory • OLAP queries exhibit locality in their data access

  10. Icicles • Class of samples to capture data locality of aggregate queries of foreign key joins • Identify focus of a query workload and sample accordingly • Is a uniform random sample of a multiset of tuples L, which is the union of R and all sets of tuples that were required to answer queries in the workload (an extension of R) • Is a non-uniform sample of the original relation R

  11. Icicle L

  12. Icicle Maintanence Algorithm

  13. Icicle Maintanence Algorithm Algorithm is efficient due to • Uniform Random Sample of L ensures tuple’s selection in its icicle is proportional to it’s frequency • Incremental maintenance of icicle requires only the segment of R that satisfies the new query from the workload Reservoir Sampling Algorithm

  14. Icicle Maintenance Example SELECT average(*) FROM widget-tuners WHERE date.month = ‘April’

  15. Icicle-Based Estimators • In spite of unified sampling being used the result is a biased sample • Frequency Relation maintained over all tuples in relation • Different Estimation mechanisms for Average, Count and Sum

  16. Estimators • Average Average taken over set of distinct sample tuples that satisfy the query predicate of the average query is a pretty good estimate of the average • Count Sum of Expected Contributionsof all tuples in the sample that satisfy the given query • Sum Estimate is given by the product of the average and the count estimates

  17. Maintaining Frequency Relation • Frequency Attribute added to the Relation • Starting Frequency set to 1 for all tuples • Incremented each time tuple is used to answer a query • Frequencies of relevant tuples updated only when icicle updated with new query

  18. Quality Guarantees • When queries exhibit data locality then icicle is constituted of more tuples from frequently accessed subsets of the relation • Accuracy improves with increase in number of tuples used to compute it • Class consisting of queries ‘focused’ with respect to workload will obtain more accurate approximate answers from the icicle

  19. Quality Guarantees contd...

  20. Performance Evaluation SELECT COUNT(*), AVG(LI_Extendedprice), SUM(LI_Extendedprice) FROM LI, C, O, S, N, R WHERE C_Custkey=O_Custkey AND O_Orderkey=LI_Orderkey AND LI_Suppkey=S_Suppkey AND C_Nationkey = N_Nationkey AND N_Regionkey = R_Regionkey AND R Name = [region] AND O Orderdate >= Date[startdate] AND O Orderdate <= 12-31-1998 Qworkload : Template for generating workloads SELECT COUNT(*), AVG(LI_Extendedprice), SUM(LI_Extendedprice) FROM LICOS-icicle, N, R WHERE C_Nationkey = N_Nationkey AND N_Regionkey = R_Regionkey AND R Name = [region] AND O Orderdate >= Date[startdate] AND O Orderdate <= 12-31-1998 Template for obtaining approximate answers

  21. Performance Evaluation contd...

  22. Performance Evaluation contd... • The Error Plots for Comparison • Static uniform random sample on Join Synopsis • Icicle as it evolves with the workload • Icicle-Complete which is formed after entire workload has been executed once

  23. Performance Evaluation contd... Focused Queries

  24. Performance Evaluation contd... Mixed Workload

  25. Observations (focused) • Rapid decrease in relative error of query answers from icicles with queries focused on a set of core tuples • Icicle plot shows a convergence to the Icicle-Complete plot • Quick Convergence of Icicle plot towards Icicle-Complete means Icicle adapts fast

  26. Observations (mixed) • Improvement due to usage of icicles is not significant • Can be concluded that icicles are at worst as good as the static samples

  27. Conclusion • Icicles provide class of samples that adapt according to the characteristics of the workload • It can never be worse than the case of static sampling • It focuses on relatively small subsets in the relation

  28. Inferences • There is no significant gains in the case of Uniform Workload • There is a trade-off between accuracy and cost • Restricted to certain scenarios where the queries tend to be increasingly focused towards the workload.

  29. References • V. Ganti, M. Lee, and R. Ramakrishnan. ICICLES: Self-tuning Samples for Approximate Query Answering. VLDB Conference 2000. • S Acharya, PB Gibbons, V Poosala, S RamaswamyJoin synopses for approximate query answering. ACM SIGMOD Record 1999

  30. Thank You Questions?

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