Efficient Storage and Temporal Query Evaluation of Hierarchical Data Archiving Systems

Transcription

1 Efficient Storage and Temporal Query Evaluation of Hierarchical Data Archiving Systems Hui (Wendy) Wang, Ruilin Liu Stevens Institute of Technology, New Jersey, USA Dimitri Theodoratos, Xiaoying Wu New Jersey Institute of Technology, New, Jersey, USA

2 Scientific Data in XML extensible Markup Language (XML): A mark-up language XML for scientific data Describe data structure and give simple processing instructions (e.g., XSIL and XDMF) Provide common data format that is an open, universal standard (e.g, CML, MathML, GML) Swiss-Prot Dataset 2

3 Updates on Scientific Data in XML Scientific databases are continuously updated Necessity of maintaining all versions in the archive Problem: as increasing volumes of these data being accumulated, the archives can reach a critical mass. 3

4 An Example of Multiple XML Dataset Versions Four consecutive instances of an extract of the Swiss-Prot Dataset 4

5 Challenges (1) how to store successive versions of XML databases in an archiving database in a cost-effective way. (2) how to evaluate queries efficiently on the archiving database. 5

6 Our Contributions A novel compact and updateable storage scheme for XML archiving databases. A simple yet expressive query language for XML archiving databases. Optimization of query evaluation over the compact storage. 6

7 Outline Preliminaries Compact storage Temporal query language Query optimization Experiments Conclusion 7

8 Compact Storage Scheme (I) Merge versions Store multiple occurrences of the same node only once in the archiving database A. Each node p in A is associated with a timestamp set which contains the timestamps of the instances of p. 8

9 An Example of Merging root 0 Entry 1 Species 2 Features 3 Descr 4 Rattus norvegicus Domain 5 Descr 6 100KDA protein Versions root 0 Entry 1 Species 2 Features 3 Descr 4 Ref 7 Domain 5 Author 8 Rattus norvegicus 100KDA protein PRO-RICH Version 1 Version 2 root 0 [1-2] [1-2] Entry 1 Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] [1-2] [2] Rattus norvegicus Domain 5 [1-2] Descr 6 [1] PRO-RICH 100KDA protein Author 8 [2] Nene V Nene V Monotone property on timestamps of the nodes on the same path 9

10 Compact Storage Scheme (II) Timestamp set compaction The timestamp label of the parent node only preserves the timestamps that do not appear on any of its children. Lp = ts(p) cchild(p) ts(c) 10

11 An Example of Timestamp Set root 0 Compaction root 0 Entry 1 Species 2 Features 3 Descr 4 Rattus norvegicus Domain 5 Descr 6 100KDA protein Entry 1 Species 2 Features 3 Descr 4 Ref 7 Domain 5 Author 8 Rattus norvegicus 100KDA protein PRO-RICH Version 1 Version 2 Nene V root 0 [1-2] [1-2] Entry 1 Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] [1-2] [1-2] [1-2] [2] Domain 5 Author 8 Rattus norvegicus [1-2] [2] 100KDA protein Descr [2] [2] 6 Nene V [1] [1] PRO-RICH 11

12 An Example of Compact root 0 Entry 1 Storage root 0 Entry 1 Species 2 Features 3 Descr 4 Rattus norvegicus Domain 5 Descr 6 PRO-RICH 100KDA protein Species 2 Features 3 Descr 4 Ref 7 Domain 5 Author 8 Rattus norvegicus 100KDA protein Nene V root 0 Entry 1 Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] Domain 5 Author [2] 8 100KDA protein [2] Descr 6 Nene V [1] Rattus norvegicus PRO-RICH 12

13 Efficient Updates Incremental timestamp label computation When insert a new instance D k at timestamp k, root 0 add the newly inserted trees T 1,..., T m in D k to the archive, For leaf nodes in D k, update the timestamp labels of their corresponding archive nodes by adding timestamp k. root 0 Entry 1 Entry 1 Features 3 Descr 9 Domain 5 Version 3 Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] Domain 5 100KDA Author [2] 8 protein [2] Descr 6 Nene V [1] Rattus norvegicus PRO-RICH Archiving Dataset before Update Descr 9 13

14 Efficient Updates Incremental timestamp label computation When insert a new instance D k at timestamp k, root 0 add the newly inserted trees T 1,..., T m in D k to the archive, update the archive nodes of the leaf nodes in D k root by adding the timestamp k. 0 Entry 1 Entry 1 Species 2 Features 3 Descr 4 [1-2] [1-2] Ref 7 Descr 9 [3] Features 3 Descr 9 Domain 5 Version 3 Rattus norvegicus Domain 5 [2-3] Descr 6 [1] PRO-RICH 100KDA protein Author 8 [2] Nene V Archiving Dataset after Update 14

15 Comparison with Related Work Compact storage [1] (Top-down approach) Remove timestamps at children if they are the same as those of the parent node Compared with our solution (bottom-up approach) w.r.t. # of updated timestamp labels when inserting a new instance D k into the archiving database A TD: # of nodes in D k whose corresponding nodes in A have timestamp labels + new nodes in D k BU: # of leaf nodes in D k 15

16 An Example of TD V.S. BU root 0 [1-4] Entry 1 Species[2-3] 2 Features 3 Descr [2-4] 4 [3-4] Ref 7 [2-4] Rattus norvegicus Domain 5 [3-4] Descr [4] 6 PRO-RICH 100KDA protein Author 8 [2] Nene V TD approach: Archiving Dataset before Update root 0 Entry 1 Species 2 Features 3 Descr 4 Ref 7 [2-3] [2] [3-4] [3-4] Domain 5 100KDA Author [3] 8 protein [2] Descr 6 Nene V [4] Rattus norvegicus PRO-RICH BU approach: Archiving Dataset before Update 16

17 An Example of TD V.S. BU root 0 [1-5] Entry 1 Species[2-3] 2 Features 3 Descr [2-5] 4 [3-4] Ref 7 [2-4] root 0 Entry 1 Features 3 Domain 5 Descr 6 Version 5 Rattus norvegicus Domain 5 [3-5] Descr [4-5] 6 PRO-RICH root 0 Entry 1 100KDA protein Author 8 [2] Nene V TD approach: Archiving Dataset after Update Species 2 Features 3 Descr 4 Ref 7 [2-3] [2] [3-4] [3-4] Domain 5 100KDA Author [3] 8 protein [2] Descr 6 [4-5] Rattus norvegicus PRO-RICH Nene V BU approach: Archiving Dataset after Update 17

18 Outline Compact storage Temporal query language Query optimization Experiments Conclusion 18

19 Temporal Query Language Types: Snapshot history trace Temporal constraints: includes(t), overlaps(t a, t b ), before(t)/after(t), contains(t a, t b )/is_contained(t a, t b ), meets(t a, t b ) Temporal queries: XML structural queries + temporal constraints on query nodes 19

20 Archivin g databas e Evaluation of Temporal Constraints over Compressed Timestamps root 0 [1-2] Entry 1 Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] [1-2] Domain 5 [1-2][2] Author 8 100KDA protein [2] Descr[1] 6 Rattus norvegicus Nene V Answer Descr 4 PRO-RICH 100KDA protein root Query Entry include(2) Domain overlaps(1,3) Descr* contain(1,2) 20

21 Temporal Evaluation Annotations DC (Descendant Check) LC (Local Check) NC (No Check) Archive root 0 [1-2] Entry 1 Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] [1-2] Domain 5 [1-2][2] Author 8 100KDA protein [2] Descr 6 Nene V [1] Rattus norvegicus PRO-RICH Query root Entry DC include(2) LC Domain Descr* overlaps(1,3) contain(1,2) 21

22 Cost Model Cost model of temporal evaluation annotations where T q is the number of nodes of the tree rooted at q in a query. root 0 Query Domain Entry overlaps(1,3) [1-2] Entry 1 root include(2) Descr* contain(1,2) NC(0) DC(2) LC(1) Archive Species 2 Features 3 Descr 4 Ref 7 [1-2] [1-2] [1-2] Domain 5 [1-2][2] Author 8 100KDA protein [2] Descr 6 [1] Rattus norvegicus PRO-RICH Nene V 22

23 Outline Preliminaries Compact storage Temporal query language Query optimization Experiments Conclusion 23

24 Optimization Problem DC is expensive (recursive check) DC can be replaced by LC/NC in some cases Goal: Replace as many DCs as possible with LCs/NCs 24

25 An Example of Optimization root Entry include(2) DC Descr* contain(1,4) DC Query Q root Database Schema Entry include(2) DC NC Descr* contain(1,4) DC LC After optimization 25

26 Inference Rules We use inference rules to find redundant temporal annotations Inference rules: P 1,, P k R if the premises P 1,, P k are true, then the conclusion R is also true. Types of inference rules Without database schema With presence of database schema 26

27 Inference Rules: No Database Schema AD(ancestor-descendant) Rule: Q = p//q q t p TR(transitivity) Rule: p t q and q t r p t r 27

28 Inference Rules: with Database Schema SP(SinglePath) Rule: Q = p//q, SinglePath(p, q ) p = t q DC(descendant) Rule: Q = p//q, Q = p//r, SinglePath(p, r ) q t r DE(derived) Rule: Q = p//q, Q = p//r, SinglePath(p, r ), SinglePath(q, r ) r t q 28

29 Temporal Constraint Graph Query root Temporal constraint graph Entry Features include(2) overlaps(1,3) contain(1,4) Descr* contain(3,4) Species An edge from p to q indicates an inferred p t q relationship 29

30 Temporal Constraint Consumption We check temporal constraint consumption on the temporal constraint graph Consuming temporal constraint on p Consumed temporal constraint on q include(t) p t q includes(t) overlaps(t 1, t 2 ), t [t 1, t 2 ] contains(t 1, t 2 ) includes(t 3 ), t 3 [t 1, t 2 ] contains(t 3, t 4 ), t 3 t 1, t 4 t 2 overlaps(t 3, t 4 ), t 1 < t 3 < t 2 or t 1 < t 4 < t 2 q t p is_contained(t 1, t 2 ) is_contained(t 3, t 4 ), t 3 t 1, t 4 t 2 before(t) after(t) before(t 1 ), t 1 t after(t 1 ), t 1 t q = t p meets(t 1, t 2 ) meets(t 1, t 2 ) 30

31 An Example of Query Optimization Consuming temporal constraint on p (Descr) contains(1, 4) Consumed temporal constraint on q (Entry) Descr t Entry includes(2) root Entry include(2) DC Features overlaps(1,3) DC NC Descr* contain(1,4) DC Species contain(3,4) DC LC LC NC 31

32 Outline Preliminaries Compact storage Temporal query language Query optimization Experiments Conclusion 32

33 Experiment Setup Hardware Intel Core 2 CPU 2.40 GHz processor, 4.00 GB of RAM Software OS: Windows 7 The algorithms were implemented in Java JDOM engine: parse the XML databases Wutka DTD parser: parse the XML DTD Oracle Berkeley DB XML engine: query evaluation 33

34 Datasets Synthetic dataset using the IBM XML generator on the DTD of the XMark benchmark Real dataset Treebank dataset Dataset Size # of elements Max. depth Avg. depth Treebank 22.3MB Xmark 14.6MB

35 Versions For Xmark and Treebank datasets, we created 50 and 20 consecutive database instances respectively. Each instance was generated from the previous one by first deleting and then inserting (sub)trees. The (inserted and deleted) trees take 10% of the nodes of the database instance. 35

36 Experiment Three storage approaches: The naive approach (NA): keeps the timestamp sets as un-compacted The top-down (TD) approach ([1]): eliminates the timestamps of the children nodes that are identical to the parent. Our bottom-up (BU) approach: eliminates the timestamps from the parent nodes that are repeated on children. 36

37 Experiment: Archiving Archiving Time Overhead Compaction ratio Dataset Top-down Bottom-up XMark (shallow&fat) XMark (deep&thin) 2.15% 2.72% 3.09% 2.75% Total number of timestamps (space overhead) 37

38 Experiment: Update Cost Summary of archiving overhead Both TD and BU can reduce the number of timestamps in the archive. The difference between TD and BU regarding the number of timetstamps is not significant. BU always has much better update cost than TD. 38

39 Experiment: Query Optimization Temporal Constraint Evaluation Optimization Our optimization can bring significant performance improvement with negligible overhead 39

40 Outline Preliminaries Compact storage Temporal query language Query optimization Experiments Conclusion 40

41 Conclusion We proposed an efficient XML archiving database system that consists of A novel compact and updateable storage scheme A simple yet expressive query language for XML archiving databases Optimization of query evaluation over the compact storage 41

42 Future Work Consider additional temporal constraints Consider unrestricted database schemas that may contain cycles Design efficient optimization algorithms that can work in this broader framework 42

43 Thank you! 43

44 Reference [1] P. Buneman, S. Khanna, K. Tajima, and W.-C. Tan. Archiving scientific data. In ACM Transactions on Database Systems, [2] A. P. Chapman, H. Jagadish, and P. Ramanan. Efficient provenance storage. In SIGMOD, [3] S. Chawathe and H. Garcia-molina. Meaningful change detection in structured data. In SIGMOD, [4] P. T. Jayant and J. R. Haritsa. Xgrind: A query-friendly xml compressor. In ICDE, [5] H. Liefke and D. Suciu. XMill: an efficient compressor for XML data. In SIGMOD, [6] H. M uller, P. Buneman, and I. Koltsidas. Xarch: Archiving scientific and reference data. In SIGMOD, [7] F. Rizzolo and A. A. Vaisman. Temporal xml: modeling, indexing, and query processing. The VLDB Journal, 17: , August [8] F. Wang and C. Zaniolo. Temporal queries in XML document archives and web warehouses. In TIME-ICTL, [9] F. Wang and C. Zaniolo. Temporal queries and version management in XML-based docu-ment archives. Data Knowl. Eng., 65: , May

45 Outline Preliminaries Compact storage Temporal query language Query optimization Experiments Conclusion 45

46 XML database XML database: tree-structured, ID-based. 46

47 Archiving XML database XML instances: XML database at certain time point Each instance is associate with a timestamp (version number) Archiving Database: multiple instances are merged into one database 47

48 Updating XML database Update operations: insertion deletion A sequence of update operations can be modeled as a set of deletions followed by a set of insertions. root 0 root 0 Entry 1 Entry 1 Species 2 Features 3 Descr 4 Species 2 Features 3 Descr 4 Ref 7 Domain 5 Rattus norvegicus 100KDA protein Descr 6 Domain 5 Rattus norvegicus 100KDA protein Descr 6 Author 8 Nene V PRO-RICH PRO-RICH 48

49 A Piece of Related Work P. Buneman, et al. Archiving scientific data. TODS, Solution: Merge instances Remove timestamps if same as parent node (Top-down) Weakness: Inefficient updates Inefficient query 49

50 Witness Graph Definition Given a query Q, a witness graph for Q is a graph WQ such that a)the nodes of W Q correspond to the nodes of Q b)there is an edge from node p to node q in W Q, iff p is a witness node of q in Q. Example Construct the witness graph from the temporal constraint graph after temporal annotation consumption (next page) 50