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Database Engine Design a.k.a. Research@ DSL

Database Engine Design a.k.a. Research@ DSL . Jayant Haritsa. Database Management Systems (DBMS). Efficient and convenient mechanisms for storing, querying and maintenance of enterprise data Cornerstone of computer industry Uses more than 80 percent of computers worldwide

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Database Engine Design a.k.a. Research@ DSL

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  1. Database Engine Designa.k.a. Research@ DSL Jayant Haritsa

  2. Database Management Systems (DBMS) • Efficient and convenient mechanisms for storing, querying and maintenance of enterprise data • Cornerstone of computer industry • Uses more than 80 percent of computers worldwide • Employs more than 70 percent of computer professionals • Largest monetary sector of computer business

  3. DBMS FEATURES • Handle data of arbitrary size • Income-Tax records are in Petabytes (1015) • Self-contained • contains both data and meta-data • Program-Data insulation • application s/w not affected by storage changes SR No | Name | Address | Hostel | GPA SR No | Name | Address | GPA | Hostel

  4. DBMS FEATURES (contd) • Declarative Access • state what you want, not how to get it • On-the-Fly Questions • ask new questions without writing new programs • PEACE OF MIND • changes to the database are guaranteed to be immune to subsequent system failures Sri Sri Ravishankar of the Information World

  5. Current Database Systems • Commercial • IBM DB2 / Oracle / Microsoft SQL Server / Sybase • Public-domain • PostgreSQL / MySQL / Berkeley DB

  6. DBMS Myths  • Databases? Isn’t that the boring part of accounting? • Hazaar dumb Cobol programming! • Maha-bore - almost as dull as watching Rahul Dravid bat! • High-tech name for data entry! • Will only get job with TCS! • ...

  7. DBMS Realities  • Design of database engines has lots of really, really interesting intellectual problems with practical impact • theory, algorithms, data structures, experiments, prototypes • Turing awards • 1981: Edgar Codd (relational data model) • 1999: Jim Gray (transaction model) • Ullman, Silberschatz, Papadimitrou, … • Rajaraman, Patnaik, Balakrishnan, Jacob/Govindarajan …

  8. Database Systems Lab(DSL) Established 1995

  9. Research Topics • Real-TimeDatabase Systems • Distributed TransactionManagement • OODBMS • Web Databases • Data Mining • XML Databases • Biological Databases • Query Optimization • Multilingual Databases • Music Databases 1995-2000 2000-2005 Last few years

  10. MIDDLEWARE OO Models Mining XML Research Trajectory CORE DB TECHNOLOGY TransactionProcessing AccessMethods Query Processing

  11. Research Techniques • Theory • real-time, data mining, query optimization • Simulation studies • real-time, distributed, web dbms • Empirical evaluation • data mining, biological, multilingual dbms, query optimization • Prototype development • OODBMS (Flexible Manufacturing [MIDAS], VLSI [DIAS], Bio-diversity [Oshadhi,Bodhi] ) • XML (Storage [LegoDB], Compression [XGrind] ) • Query Optimization (Clustering [Plastic], Visualization [Picasso] ) • Multilingual Databases (Cross-lingual SQL [Mira] )

  12. SPINE: Putting Backbone into Genomic Sequence Indexing

  13. A T CT$ ATTACT$ GTTAATTACT$ A TA TTACT$ CT$ $ 3 4 9 0 7 ATTACT$ 8 CT$ ATTACT$ CT$ 2 6 5 1 Standard Genomic Index: Suffix Tree [Weiner 1973] Vertically-compressed trie of suffixes augmented with links 0 1 2 3 4 5 6 7 8 9 Data =‘GTTAATTACT$’ Suffix Links (xW → W) Tree Edges Search forQuery =‘TTA’ 5 1

  14. Locate all Maximal Matching Substrings[Chang & Lawler 1990] • For each position in query sequence Q, locate all longest matching substrings of length ≥in the indexed data sequence D • Example: D = ‘GTTAATTACT$’Q = ‘CTAATGA’ and = 3 Result: { TAAT:<2,1>AAT:<3,2>}

  15. Maximal Substring Searchwith Suffix Tree Index 0 1 2 3 4 5 6 7 8 9 D =‘GTTAATTACT$’ Q =‘CTAATGA’  =3 A T CT$ ATTACT$ GTTAATTACT$ A TA TTACT$ CT$ $ 3 3 4 9 0 7 ATTACT$ 8 CT$ ATTACT$ CT$ 2 6 2 1 5

  16. Features of Suffix Tree Index • Accurate retrieval • no false negatives (unlike BLAST) • LinearTime Complexity for both Constructionand Search! • because of Suffix-links • Widely used • More than 40-50 applications over biological sequences [Gusfield, 2002] • MUMmer [Celera Genomics], AVID, …

  17. Crippling Limitation • Viable only for sequences that are short enough for their associated suffix tree to fit completely in main memory …[Baeza-Yates and Navarro, 2000] • Best that has been built so far is for sequences of ~ 10 Mbp (Human Genome is 300 times longer!)

  18. Difficulties in Supporting Suffix Trees on Long Sequences - 1 Space overheads are enormous • Order(s) of magnitude larger than data! • Human Genome can be easily stored in main memory (~1 GB) but the index couldbe of the order of 10-100 GB • Disk-resident suffix trees for long sequences

  19. Difficulties in Supporting Suffix Trees on Long Sequences - 2 Tree Construction on Disk is Very Slow • Due to disk thrashing from random seeks The active suffix creeps through the text like a caterpillar … corresponding active node swings through the tree like a butterfly[Giegerich and Kurtz, 1995]

  20. Difficulties in Supporting Suffix Trees on Long Sequences - 3 Searching on Disk is Very Slow • Unbalanced Tree Structure • Shape of tree depends onsequence stochastic properties • “Multi-directional” traversals causes disk thrashing • Tree-Edge  “Vertical Walk-Down” • Suffix-Link  “Horizontal Jump-Across” Suffix Tree Search • Edge + Link mesh • Two phase Search • Locate • Report ≥  Combination of Batman and Spiderman !

  21. The SPINE* IndexA Horizontally-Compacted Trie Index [*SequenceProcessingINdexing Engine]

  22. 0 0 0 A 0 C(0) 1 1 C 2 2 1 A(1) C 1 2 3 3 A 4 4 1(2) C 2 A 5 5 6 C 7 SPINE Index Structure D = ‘ACCACAC’ Complete horizontal compaction into single linear chain!! • Nodes • Forward Edges • Vertebras (Backbone) • Ribs / Ext-Ribs • Backward Edges • Links Root node Link Rib Extension rib Vertebra

  23. 0 A 1 C (0) C 2 C A (1) 3 A 4 Structural Advantages of SPINE w.r.t. Suffix Trees • Number of nodes is equal to length of string, whereas in suffix tree can go up to double. • Entire data sequence explicitly embedded in index  throw away the data! • On-line incremental algorithm (by definition) • do not need to possess entire data sequence in advance • Node creation order andlogical order are the same prefix-partitionable D =‘ACCA’

  24. 0 A 1 C (0) C 2 C A (1) 3 A 4 Advantages of SPINE (contd) • Each node represents a set of suffixes whereas in suffix tree each node represents only a single suffix • Number of suffixes processed for construction and searching is smaller • Easy to develop buffering strategies forpersistent implementations

  25. SPINE Performance Summary Data SetsEcoli: 3.5 Mbp Celegans: 15.5 Mbp HC 21: 28.5 Mbp HC19: 57.5 Mbp Suffix Tree (MUMmer - Celera Genomics) • Spine Space • ~ 2/3 of Suffix Tree • Spine Time • Construction: ~ 1/2 of Suffix Tree • Searching: ~ 1/2 of Suffix Tree

  26. SPINE Summary • First index based on horizontal (inter-path) compaction of the trie • Collapses into a single linear structure • Improved features and performance w.r.t. suffix trees, the classical index • Prefix-partitionable (first index to have this property) • Easily amenable to persistent disk implementation • Retains linear time/space complexity • Better construction speed and capacity • Better search response times

  27. Full details at http://dsl.serc.iisc.ernet.in Questions?

  28. END PRESENTATION

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