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On-the-Fly Sharing for Streamed Aggregation Sailesh Krishnamurthy, Chung Wu, and Michael J. Franklin Presented by: Joshua Lee and Mingrui Wei Material.

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Presentation on theme: "On-the-Fly Sharing for Streamed Aggregation Sailesh Krishnamurthy, Chung Wu, and Michael J. Franklin Presented by: Joshua Lee and Mingrui Wei Material."— Presentation transcript:

1 On-the-Fly Sharing for Streamed Aggregation Sailesh Krishnamurthy, Chung Wu, and Michael J. Franklin Presented by: Joshua Lee and Mingrui Wei Material is partially referenced from [1]

2 Agenda Motivation Contributions Shared Time Slice (STS) Shared Data Fragments (SDF) Shared Data Shards (SDS) Experimental Evaluation Conclusion

3 Motivation Naive approach leads to scalability and performance problems Static optimization costs too much and does not fit in Dynamic Environments

4 Contributions Shared Time Slices Shared Data Fragments Shared Data Shards On-the-fly MQO Performance study

5 Shared Time Slice (STS) To share Windows, there are TWO approaches Paned Paired

6 STS, continued Sharing sliced window Combining multiple sliced windows

7 STS, continued To share, or not to share On-the-fly sliced window composition

8 Shared Data Fragments (SDF) Shared processing for aggregate queries with the same window, but different predicates For instance, a set of queries Q, all like Query 3, where each Q i has a window with Range=5min and Slide=5min, but different WHERE clauses

9 SDF: Motivation T is the set of input tuples in one window p i (T) is the set of tuples in T that satisfies p i A i (T) is the aggregate result over p i (T) Unshared approach evaluates each p i (T) and then calculates each A i (T) as the answer to Q i

10 SDF: Fragments Defined SDF approach is to define fragments F i as disjoint subsets over T The example, at left, shows that F 5 consists of those tuples that satisfy p 1 and p 3

11 SDF: Conceptual View A partial aggregation, G, is applied to each F i Each A i (T) is formed by a final aggregation, H, on a set of G(F i ) aggregations For instance, in the previous example: A 1 (T) = A(p 3 (T) = H{G(F 1 ),G(F 3 ),G(F 5 ),G(F 7 )}

12 SDF: Implementation Each tuple is augmented with a signature indicating which predicates it satisfies, identifying the fragment in which the tuple belongs (it also identifies the queries to which the fragment belongs) The Fragment Manager dynamically aggregates all tuples with identical signatures A final aggregation is performed on the Fragment Aggregates using the signatures to route to each query

13 SDF: Cost Comparison The unshared approach has no partial aggregation step and each tuple is subjected to as many aggregations as the number of queries it satisfies The SDF approach requires a partial aggregation operation for each tuple, as well as a final aggregation per fragment for each query of which the fragment is a part

14 Shared Data Shards (SDS) Both STS and SDF partition the input, form partial aggregates over the partitions, and then final aggregates SDS first slices the input, then fragments the slices Partial aggregates are calculated on the fragmented slices Final aggregates are calculated using sets of the partial aggregates

15 Experimental Evaluation A performance study using trading data from NYSE and NASDAQ was performed They looked at STS vs. unshared with (same predicates, different windows) They looked at SDF vs. unshared with (different predicates, same windows) They looked at SDS vs. unshared with (different predicates, different windows)

16 Conclusion Paired beats Paned Shared Data Fragments beats Unshared Shared Data Shards beats Unshared Slice

17 References 1. Krishnamurthy, S., Wu, C., and Franklin, M On-the-fly sharing for streamed aggregation. In Proceedings of the 2006 ACM SIGMOD international Conference on Management of Data (Chicago, IL, USA, June , 2006). SIGMOD '06. ACM Press, New York, NY,


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