CSE 232A Graduate Database Systems

Transcription

1 CSE 232A Graduate Database Systems Arun Kumar Topic 4: Query Optimization Chapters 12 and 15 of Cow Book Slide ACKs: Jignesh Patel, Paris Koutris 1

2 Lifecycle of a Query Query Result Query Database Server Select R.text from Report R, Weather W where W.image.rain() and W.city = R.city and W.date = R.date and R.text. matches( insurance claims ) Query Parser Optimizer Syntax Tree and Logical Query Plan Physical Query Plan Query Scheduler Segments Execute Operators Query.... Result 2

3 Recall the Netflix Schema Ratings RatingID Stars RateDate UID MID /27/ UID Name Age JoinDate 79 Alice 23 01/10/13 80 Bob 41 05/10/13 Users MID Name Year Director Movies 20 Inception 2010 Christopher Nolan 16 Avatar 2009 Jim Cameron 3

4 Example SQL Query RatingID Stars RateDate UID MID UID Name Age JoinDate MID Name Year Director SELECT FROM WHERE M.Year, COUNT(*) AS NumBest Ratings R, Movies M R.MID = M.MID AND R.Stars = 5 GROUP BY M.Year ORDER BY NumBest DESC Suppose, we also have a B+Tree Index on Ratings (Stars) 4

5 Result Table Logical Query Plan SORT On NumBest GROUP BY AGGREGATE M.Year, COUNT(*) JOIN R.MID = M.MID Called Logical Operators From extended RA Each one has alternate physical implementations SELECT R.stars = 5 SELECT No predicate Ratings Table Movies Table 5

6 Result Table Physical Query Plan External Merge-Sort In-mem quicksort; B=50 Sort-based Aggregate Index-Nested Loop Join Called Physical Operators Specifies exact algorithm/code to run for each logical operator, with all parameters (if any) Indexed Access Use Index on Stars File Scan Read heapfile Aka Query Evaluation Plan Ratings Table Movies Table 6

7 Result Table Physical Query Plan External Merge-Sort In-mem quicksort; B=50 Hash-based Aggregate Hash Join This is also a correct PQP for the given LQP! Q: Which PQP is faster? This is a key job of the RDBMS Query Optimizer! File Scan Read Index leaf pages File Scan Read heapfile Ratings Table Movies Table 7

8 So, what is query optimization and how does it work? 8

9 Meet Query Optimization Basic Idea: Goal (Ideal): A given LQP could have several possible PQPs with very different runtime performance Get the optimal (fastest) PQP for a given LQP Goal (Realistic): Fine, just avoid the clearly awful PQPs! Jeff Naughton Query optimization is a metaphor for life itself! It is often hard to even know what an optimal plan would be, but it is feasible to avoid many obviously bad plans! 9

10 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 10

11 Overview of Query Optimizer SQL Query Logical Query Plan Parser Plan Enumerator Optimizer Plan Cost Estimator Catalog Physical Query Plan (Optimized) To Scheduler/Executor 11

12 System Catalog Set of pre-defined relations for metadata about DB (schema) For each Relation: Relation name, File name File structure (heap file vs. clustered B+ tree, etc.) Attribute names and types; Integrity constraints; Indexes For each Index: Index name, Structure (B+ tree vs. hash, etc.); Index key For each View: View name, and View definition 12

13 Statistics in the System Catalog RDBMS periodically collects stats about DB (instance) For each Table R: Cardinality, i.e., number of tuples, NTuples (R) Size, i.e., number of pages, NPages (R), or just N R For each Index X: Cardinality, i.e., number of distinct keys IKeys (X) Size, i.e., number of pages IPages (X) (for a B+ tree, this is the number of leaf pages only) Height (for tree indexes) IHeight (X) Min and max keys in index ILow (X), IHigh (X) 13

14 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 14

15 Result Table Concept: Pipelining External Merge-Sort In-mem quicksort; B=50 Hash-based Aggregate Q: Does the hash-based aggregate have to wait till the entire output of the upstream hash join is available? File Scan Read Index leaf pages Movies Table Hash Join File Scan Read heapfile RatingsTable No! We can pipeline the output of the join pass on a join output tuple as soon as it is obtained! 15

16 Concept: Pipelining Basic Idea: Benefits: Do not force downstream physical operators to wait till the entire output is available Display output to the user incrementally CPU Parallelism in multi-core systems! File Scan Tuples Hash Join Hash-based Aggregate 16

17 Concept: Pipelining Crucial for PQPs with workflow of many phy. ops. Common feature of almost all RDBMSs Works for many operators: SCAN, HASH JOIN, etc. Q: Are all physical operators amenable to pipelining? No! Some may stall the pipeline: Blocking Op A blocking op. requires its output to be Materialized as a temporary table Usually, any phy. op. involving sorting is blocking! 17

18 Result Table Blocking Op External Merge-Sort In-mem quicksort; B=50 Hash-based Aggregate Sort-Merge Join This phy. op. is blocking because we need to sort Movies and sort Ratings (materialize the output) before we can start any aggregate computations! File Scan Read heapfile File Scan Read heapfile Movies Table RatingsTable 18

19 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 19

20 Mechanism: Iterator Interface Software API to process PQP; makes pipelining easy to impl. Enables us to abstract away individual phy. op. impl. details Three main functions in usage interface of each phy. op.: Open(): GetNext(): Close(): Initialize the phy. op. state, get arguments Allocate input and output buffers Ask the phy. op. impl. to deliver next output tuple; pass it on; if blocking, wait Clear phy. op. state, free up space 20

21 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 21

22 Overview of Query Optimizer SQL Query Logical Query Plan Parser Plan Enumerator Optimizer Plan Cost Estimator Catalog Physical Query Plan (Optimized) To Scheduler/Executor 22

23 Enumerating Alternative PQPs Plan Enumerator explores various PQPs for a given LQP Challenge: Space of plans is huge! How to make it feasible? RDBMS Plan Enumerator has Rules to help determine what plans to enumerate, and also consults Cost models Two main sources of Rules for enumerating plans: Logical: Algebraic Rewrites: Use relational algebra equivalence to rewrite LQP itself! Physical: Choosing Phy. Op. Impl.: Use different phy. op. impl. for a given log. op. in LQP 23

24 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 24

25 Algebraic Rewrite Rules Rewrite a given RA query in to another that is equivalent (a logical property) but might be faster (a physical property) RA operators have some formal properties we can exploit We will cover only a few rewrite rules: Single-operator Rewrites Unary operators Binary operators Cross-operator Rewrites 25

26 Unary Operator Rewrites Key unary operators in RA: Commutativity of p 1 ( p2 (R)) = p2 ( p1 (R)) Cascading of p 1 ( p2 (... p n (R)...)) = p1^p 2^ ^p n (R) Cascading of A i A i+1 8i =1...(n 1) A1 ( A2 (... An (R)...)) = A1 (R) Q: Why are cascading rewrites beneficial? 26

27 Binary Operator Rewrites Key binary operator in RA: Commutativity of Associativity of././ R./ S = S./ R./ (R./ S)./ T = R./ (S./ T ) Q: Why are these properties beneficial? Q: What other binary operators in RA satisfy these? 27

28 Cross-Operator Rewrites Commuting and A B p(a)( B (R)) = B ( p(a) (R)) Combining and Pushing the select./ Commuting with and p(r S) =R./ p S p(a)(r./ S) = p(a)(r S) = p(a) (R)./ S p(a) (R) S A R. A (R S) = A\R. (R) A\S. (S) B A A (R./ p(b) S)= A\R. (R)./ p(b) A\S. (S) 28

29 Review Question 29

30 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 30

31 Choosing Phy. Op. Impl. Given a (rewritten) LQP, pick phy. op. impl. for each log. op. Recall various RA op. impl. with their I/O (and CPU costs)./ File scan vs Indexed (B+ Tree vs Hash) Hashing-based vs Sorting-based vs Indexed BNLJ vs INLJ vs SMJ vs HJ etc. Q: With algebraic rewrites?! 3 options 3 options 4 options = 36 PQPs! 31

32 Phy. Op. Impl.: Other Factors Are the indexes clustered or unclustered? Are there multiple matching indexes? Use multiple? Are index-only access paths possible for some ops? Are there interesting orderings among the inputs? Would sorted outputs benefit downstream ops? Estimation of cardinality of intermediate results! How best to reorder multi-table joins? Query optimizers are complex beasts! Still a hard, open research problem! 32

33 Phy. Op. Impl.: Join Orderings Since joins are associative, exponential number of orderings! Left Deep tree Right Deep tree Bushy tree Almost all RDBMSs consider only left deep join trees Enables easy pipelining! Why? Interesting orderings idea from System R optimizer paper Dynamic program to combine enumeration and costing Access Path Selection in a Relational Database Management System SIGMOD 79 33

34 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 34

35 Overview of Query Optimizer SQL Query Logical Query Plan Parser Plan Enumerator Optimizer Plan Cost Estimator Catalog Physical Query Plan (Optimized) To Scheduler/Executor 35

36 Costing PQPs For each PQP considered by the Plan Enumerator, the Plan Cost Estimator computes Cost of the PQP Weighted sum of I/O cost and CPU cost (Distributed RDBMSs also include Network cost) Challenge: Given a PQP, compute overall cost Issues to consider: Pipelining vs. blocking ops; cannot simply add costs! Cardinality estimation for intermediate tables! Q: What statistics does the catalog have to help? 36

37 Costing PQPs Most RDBMSs use various heuristics to make costing tractable; so, it is approximate! Example: Complex predicates Not enough info! But, most RDBMSs use the independence heuristic! Selectivity of conjunction = Product of selectivities Thus, 0.05 * 0.1 = 0.005, i.e., 0.5% 37

38 Query Optimization: Summary Plan Enumerator and Cost Estimator work in lock step: Rules determine what PQPs are enumerated Logical: Algebraic rewrites of LQP Physical: Op. Impl. and ordering alternatives Cost models and heuristics help cost the PQPs Still an active research area! Parametric Q.O., Multi-objective Q.O., Multi-objective parametric Q.O., Multiple Q.O., Online/Adaptive Q.O., Dynamic re-optimization, etc. 38

39 Review Question RatingID Stars RateDate UID MID 10m pages Page size 8KB; Buffer memory 4GB; 8B for each field SELECT COUNT(DISTINCT UID) FROM Ratings Propose an efficient physical plan and compute its I/O cost. Q: What if there was an unclustered B+ tree index on UID? (RecordID pointers can be assumed to be 8B too) 39

40 Review Question RatingID Stars RateDate UID MID MID Name Year Director Page size 8KB; Buffer memory 4GB 10m pages 100k pages SELECT AVG(Stars) FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Director = Christopher Nolan AND R.UID = 1234; Propose an efficient physical plan that does not materialize any intermediate data (fully pipelined) and compute its I/O cost. 40

41 Query Optimization Overview of Query Optimizer Physical Query Plan (PQP) Concept: Pipelining Mechanism: Iterator Interface Enumerating Alternative PQPs Logical: Algebraic Rewrites Physical: Choosing Phy. Op. Impl. Costing PQPs Materialized Views 41

42 <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> Introducing Materialized Views A View is a virtual table created with an SQL query A Materialized View is a physically instantiated/stored view Example: RatingID Stars RateDate UID MID UID Name Age JoinDate MID Name Year Director SELECT AVG(Stars) FROM Ratings R, Movies M, Users U WHERE R.MID = M.MID AND R.UID = U.UID M.Director = Christopher Nolan AND U.Age >= 20 AND U.Age < 30; AVG (Stars)(R./ Director= Christopher Nolan (M)./ 20appleAge<30 (U)) Requires file scans of R, M, and U and, say, hash joins 42

43 <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit 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sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> Materialized Views Example Example: RatingID Stars RateDate UID MID UID Name Age JoinDate MID Name Year Director AVG (Stars)(R./ Director= Christopher Nolan (M)./ 20appleAge<30 (U)) CREATE MATERIALIZED VIEW NolanRatings AS SELECT RatingID, Stars, UID, MID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Director = Christopher Nolan ; Creates a subset of R with ratings for only Nolan s movies V RatingID,Stars,UID,MID (R./ Director= Christopher Nolan (M)) 43

44 <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit sha1_base64="4hc4knyhfktkx2fu4hrzurw2dfy=">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</latexit> <latexit 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sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">aaacznicbzhbaxnbfmzn16pt1da2iidehgyhhvj2rdclunoc7kfsq0kl2zc+nbxnhs7oldnvw8ky/phephvxz3a2jaitdwz+fn97zjtv0kjjr1h0pqgfrdx89hh1rfxk6bp1jfbzzyezprxyf0yze5gcqyu19kmswovciuspwvp06qjxz6/romn0f5oxomphqmumbzcxxu16wbofgyg15oynhrxxsq40k1sd+ry9pe7t8c/ednu83/djca+uefemj6m5iyk8cxkaw5+xnrqoynj6/exlb+1ozv1ttdfd+/wjuadrnbp7srs7bnei/whr/d7es+iwzz2o29+sirfljpqeauegcvtqqajluiisw0npsabxbvmcetsqoxtvi5hq/sore54z648mvld/nqggd26ep76z2dvd9rrxf96wpozdqjk6kam1ul0okxunw5vm+wqrizp7aggl35wlgvgq5h+m5uoi7z75pgxe78eep73phbwu41hl22yhdvnm3rid9ogdsj4t7eewfmwgw8hpcd18eb68bq2d5cww+6dc/gvzi7i/</latexit> <latexit sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> <latexit sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> <latexit sha1_base64="zkjpkjwfaz7r6dtmfiabaesjtz0=">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</latexit> <latexit sha1_base64="zkjpkjwfaz7r6dtmfiabaesjtz0=">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</latexit> <latexit sha1_base64="zkjpkjwfaz7r6dtmfiabaesjtz0=">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</latexit> <latexit sha1_base64="zkjpkjwfaz7r6dtmfiabaesjtz0=">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</latexit> Materialized Views Example Example: RatingID Stars RateDate UID MID UID Name Age JoinDate MID Name Year Director AVG (Stars)(R./ Director= Christopher Nolan (M)./ 20appleAge<30 (U)) Given the materialized view V, RDBMS optimizer can automatically rewrite to use V to avoid scans of R and M V RatingID,Stars,UID,MID (R./ Director= Christopher Nolan (M)) AVG (Stars)(V./ 20appleAge<30 (U)) Likely much faster since V is likely much smaller than R, but this depends on data statistics; leave it to optimizer! Q: How did DBA know to materialize a view for Nolan ratings? 44

45 <latexit sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> <latexit sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> <latexit sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> <latexit sha1_base64="g7fjnxzvcmjeorqmazkc0to5ksk=">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</latexit> Materialized View Maintenance Example: RatingID Stars RateDate UID MID UID Name Age JoinDate MID Name Year Director We are given this materialized view V over R and M V RatingID,Stars,UID,MID (R./ Director= Christopher Nolan (M)) Q: What if new ratings are inserted to R for Nolan s movies? RDBMS will automatically trigger updates to V Such updates are called Materialized View Maintenance 2 alternatives: Recompute whole view from scratch vs Incremental View Maintenance (IVM) 45

46 <latexit sha1_base64="mddam1hjpkcdqhsi1agpf+zdvfa=">aaab73icbzbnswmxeizn/az1q+rrs7ai9vj2rdclunsdxxbsb7rlyabznjsbxznzozt+cs8efphq3/hmvzft96ctlwqe3pkhm2+qsghqdb+dldw19y3n3fz+e2d3b79wcngwcaozr7nyxrovumolulyoaivvjzrtkjc8gqxvp/xme9dgxoobrwn3i9pxihsmorvadxjnaqw7s26h6jbdmcgyebkuivo1w/jq9gkwrlwhk9sytucm6i+prsekn+q7qeejzupa522likbc+opzvhnyap0ecwntn0iyc39pjglkzcgkbgdecwawa1pzv1o7xfdkhwuvpmgvm38uppjgtkbhk57qnkecwabmc7sryqoqkumbud6g4c2evayn87jnuxzrrnxkcetgge6gbb5cqgxuoqp1ycdhgv7hzxl0xpx352peuujkm0fwr87nd9wkjom=</latexit> <latexit sha1_base64="mddam1hjpkcdqhsi1agpf+zdvfa=">aaab73icbzbnswmxeizn/az1q+rrs7ai9vj2rdclunsdxxbsb7rlyabznjsbxznzozt+cs8efphq3/hmvzft96ctlwqe3pkhm2+qsghqdb+dldw19y3n3fz+e2d3b79wcngwcaozr7nyxrovumolulyoaivvjzrtkjc8gqxvp/xme9dgxoobrwn3i9pxihsmorvadxjnaqw7s26h6jbdmcgyebkuivo1w/jq9gkwrlwhk9sytucm6i+prsekn+q7qeejzupa522likbc+opzvhnyap0ecwntn0iyc39pjglkzcgkbgdecwawa1pzv1o7xfdkhwuvpmgvm38uppjgtkbhk57qnkecwabmc7sryqoqkumbud6g4c2evayn87jnuxzrrnxkcetgge6gbb5cqgxuoqp1ycdhgv7hzxl0xpx352peuujkm0fwr87nd9wkjom=</latexit> <latexit sha1_base64="mddam1hjpkcdqhsi1agpf+zdvfa=">aaab73icbzbnswmxeizn/az1q+rrs7ai9vj2rdclunsdxxbsb7rlyabznjsbxznzozt+cs8efphq3/hmvzft96ctlwqe3pkhm2+qsghqdb+dldw19y3n3fz+e2d3b79wcngwcaozr7nyxrovumolulyoaivvjzrtkjc8gqxvp/xme9dgxoobrwn3i9pxihsmorvadxjnaqw7s26h6jbdmcgyebkuivo1w/jq9gkwrlwhk9sytucm6i+prsekn+q7qeejzupa522likbc+opzvhnyap0ecwntn0iyc39pjglkzcgkbgdecwawa1pzv1o7xfdkhwuvpmgvm38uppjgtkbhk57qnkecwabmc7sryqoqkumbud6g4c2evayn87jnuxzrrnxkcetgge6gbb5cqgxuoqp1ycdhgv7hzxl0xpx352peuujkm0fwr87nd9wkjom=</latexit> <latexit sha1_base64="mddam1hjpkcdqhsi1agpf+zdvfa=">aaab73icbzbnswmxeizn/az1q+rrs7ai9vj2rdclunsdxxbsb7rlyabznjsbxznzozt+cs8efphq3/hmvzft96ctlwqe3pkhm2+qsghqdb+dldw19y3n3fz+e2d3b79wcngwcaozr7nyxrovumolulyoaivvjzrtkjc8gqxvp/xme9dgxoobrwn3i9pxihsmorvadxjnaqw7s26h6jbdmcgyebkuivo1w/jq9gkwrlwhk9sytucm6i+prsekn+q7qeejzupa522likbc+opzvhnyap0ecwntn0iyc39pjglkzcgkbgdecwawa1pzv1o7xfdkhwuvpmgvm38uppjgtkbhk57qnkecwabmc7sryqoqkumbud6g4c2evayn87jnuxzrrnxkcetgge6gbb5cqgxuoqp1ycdhgv7hzxl0xpx352peuujkm0fwr87nd9wkjom=</latexit> <latexit sha1_base64="v/tj7tuf+7hciryxbktlde8r42w=">aaab8xicbzdlsgmxfizpvnz6q7p0eyzsuikziuhgkorczqv2gu1qmmmmdc1khiqjlkfv4cafim59g3e+jwk7c239ifdxn3piob8fc66n43yjldw19y3n3fz+e2d3b79wcnjuuaioa9birkrte80el6xhubgshstgql+wlj+6ndzbt0xphskhm46zf5kb5agnxfjrsvnc17heviud9qpfp+lmhjfbzaaimwq9wle3h9ekznjqqbtuue5svjqow6lgk3w30swmdeqgrgnrkpbpl51tpmgn1unjifl2synn7u+jliraj0pfdobedpvibwr+v+skjrjyui7jxdbj5x8ficamwtpzcz8rro0ywybucbsrpkoicdu2plwnwv08erma5xxxcv2iwl3j4sjbmzxagvy4hcrcqw0aqehcm7zcg9lobb2jj3nrcspmjucp0ocpmrao5q==</latexit> <latexit sha1_base64="v/tj7tuf+7hciryxbktlde8r42w=">aaab8xicbzdlsgmxfizpvnz6q7p0eyzsuikziuhgkorczqv2gu1qmmmmdc1khiqjlkfv4cafim59g3e+jwk7c239ifdxn3piob8fc66n43yjldw19y3n3fz+e2d3b79wcnjuuaioa9birkrte80el6xhubgshstgql+wlj+6ndzbt0xphskhm46zf5kb5agnxfjrsvnc17heviud9qpfp+lmhjfbzaaimwq9wle3h9ekznjqqbtuue5svjqow6lgk3w30swmdeqgrgnrkpbpl51tpmgn1unjifl2synn7u+jliraj0pfdobedpvibwr+v+skjrjyui7jxdbj5x8ficamwtpzcz8rro0ywybucbsrpkoicdu2plwnwv08erma5xxxcv2iwl3j4sjbmzxagvy4hcrcqw0aqehcm7zcg9lobb2jj3nrcspmjucp0ocpmrao5q==</latexit> <latexit sha1_base64="v/tj7tuf+7hciryxbktlde8r42w=">aaab8xicbzdlsgmxfizpvnz6q7p0eyzsuikziuhgkorczqv2gu1qmmmmdc1khiqjlkfv4cafim59g3e+jwk7c239ifdxn3piob8fc66n43yjldw19y3n3fz+e2d3b79wcnjuuaioa9birkrte80el6xhubgshstgql+wlj+6ndzbt0xphskhm46zf5kb5agnxfjrsvnc17heviud9qpfp+lmhjfbzaaimwq9wle3h9ekznjqqbtuue5svjqow6lgk3w30swmdeqgrgnrkpbpl51tpmgn1unjifl2synn7u+jliraj0pfdobedpvibwr+v+skjrjyui7jxdbj5x8ficamwtpzcz8rro0ywybucbsrpkoicdu2plwnwv08erma5xxxcv2iwl3j4sjbmzxagvy4hcrcqw0aqehcm7zcg9lobb2jj3nrcspmjucp0ocpmrao5q==</latexit> <latexit sha1_base64="v/tj7tuf+7hciryxbktlde8r42w=">aaab8xicbzdlsgmxfizpvnz6q7p0eyzsuikziuhgkorczqv2gu1qmmmmdc1khiqjlkfv4cafim59g3e+jwk7c239ifdxn3piob8fc66n43yjldw19y3n3fz+e2d3b79wcnjuuaioa9birkrte80el6xhubgshstgql+wlj+6ndzbt0xphskhm46zf5kb5agnxfjrsvnc17heviud9qpfp+lmhjfbzaaimwq9wle3h9ekznjqqbtuue5svjqow6lgk3w30swmdeqgrgnrkpbpl51tpmgn1unjifl2synn7u+jliraj0pfdobedpvibwr+v+skjrjyui7jxdbj5x8ficamwtpzcz8rro0ywybucbsrpkoicdu2plwnwv08erma5xxxcv2iwl3j4sjbmzxagvy4hcrcqw0aqehcm7zcg9lobb2jj3nrcspmjucp0ocpmrao5q==</latexit> Incremental View Maintenance (IVM) Basic Idea: Recomputing V from scratch may be an overkill Try to incrementally update parts that change V = Q(D) V 0 = Q(D 0 ) D can be the outcome of inserts and/or deletes to D Q can be a unary query or involve multiple tables Computing V may require inserts and/or deletes to V; realized as algebraic rewrite rules at LQP level Whether or not IVM of V is feasible and/or efficient depends on form of Q, nature of updates to D, data statistics, etc. We will focus only on inserts to D triggering inserts to V 46

47 <latexit sha1_base64="1xebhzmtxpv7c0w2mlvbbllfea8=">aaab/xicbzdlssnafiynxmu9xcvozwarxzvebn0irv24rmveoallmj1ph04mywyi1fb8ftcufhhre7jzbzy2wwjrdwmf/zmhc+ypes6udpxva2fxaxlltbbwxn/y3nq2d3ybkk4lhtqnesxbavhamyc6zppdk5faoobdmxhcj+vnb5ckxejedxpwi9itlgsuagn17p3amb7enezrnmhedxbnck1jl5yymxgebzehespv7dhfxjemaqrcu06uartoov2msm0oh1hrsxukha5id9ogbyla+dnk+he+mk4xh7e0t2g8cx9pzcrsahgfpjmiuq9ma2pzv1o71egfnzgrpboens4ku451jmdr4c6tqdufgibumnmrpn0icdumskijwz398jw0tsuu4buzuuuqj6oadtahokeuokcvdiuqqi4oektp6bw9wu/wi/vufuxbf6x8zg/9kfx5a4ghk1c=</latexit> <latexit sha1_base64="1xebhzmtxpv7c0w2mlvbbllfea8=">aaab/xicbzdlssnafiynxmu9xcvozwarxzvebn0irv24rmveoallmj1ph04mywyi1fb8ftcufhhre7jzbzy2wwjrdwmf/zmhc+ypes6udpxva2fxaxlltbbwxn/y3nq2d3ybkk4lhtqnesxbavhamyc6zppdk5faoobdmxhcj+vnb5ckxejedxpwi9itlgsuagn17p3amb7enezrnmhedxbnck1jl5yymxgebzehespv7dhfxjemaqrcu06uartoov2msm0oh1hrsxukha5id9ogbyla+dnk+he+mk4xh7e0t2g8cx9pzcrsahgfpjmiuq9ma2pzv1o71egfnzgrpboens4ku451jmdr4c6tqdufgibumnmrpn0icdumskijwz398jw0tsuu4buzuuuqj6oadtahokeuokcvdiuqqi4oektp6bw9wu/wi/vufuxbf6x8zg/9kfx5a4ghk1c=</latexit> <latexit sha1_base64="1xebhzmtxpv7c0w2mlvbbllfea8=">aaab/xicbzdlssnafiynxmu9xcvozwarxzvebn0irv24rmveoallmj1ph04mywyi1fb8ftcufhhre7jzbzy2wwjrdwmf/zmhc+ypes6udpxva2fxaxlltbbwxn/y3nq2d3ybkk4lhtqnesxbavhamyc6zppdk5faoobdmxhcj+vnb5ckxejedxpwi9itlgsuagn17p3amb7enezrnmhedxbnck1jl5yymxgebzehespv7dhfxjemaqrcu06uartoov2msm0oh1hrsxukha5id9ogbyla+dnk+he+mk4xh7e0t2g8cx9pzcrsahgfpjmiuq9ma2pzv1o71egfnzgrpboens4ku451jmdr4c6tqdufgibumnmrpn0icdumskijwz398jw0tsuu4buzuuuqj6oadtahokeuokcvdiuqqi4oektp6bw9wu/wi/vufuxbf6x8zg/9kfx5a4ghk1c=</latexit> <latexit sha1_base64="1xebhzmtxpv7c0w2mlvbbllfea8=">aaab/xicbzdlssnafiynxmu9xcvozwarxzvebn0irv24rmveoallmj1ph04mywyi1fb8ftcufhhre7jzbzy2wwjrdwmf/zmhc+ypes6udpxva2fxaxlltbbwxn/y3nq2d3ybkk4lhtqnesxbavhamyc6zppdk5faoobdmxhcj+vnb5ckxejedxpwi9itlgsuagn17p3amb7enezrnmhedxbnck1jl5yymxgebzehespv7dhfxjemaqrcu06uartoov2msm0oh1hrsxukha5id9ogbyla+dnk+he+mk4xh7e0t2g8cx9pzcrsahgfpjmiuq9ma2pzv1o71egfnzgrpboens4ku451jmdr4c6tqdufgibumnmrpn0icdumskijwz398jw0tsuu4buzuuuqj6oadtahokeuokcvdiuqqi4oektp6bw9wu/wi/vufuxbf6x8zg/9kfx5a4ghk1c=</latexit> <latexit sha1_base64="gryid+fzwj3q7fiqbhmff6psnz8=">aaacgnicbvdnssnagnz4w+tf1koxxslws0le0itq1imhd1vswmhc2gw37dpnnuxuhbl6hf58fs8efpemxnwbn20o2jqwmmx8h/vnbamjulnwtze3v7c4tfxaka+urw9smlvbjuspwksjoeoihsbjgi1ju1hfsdsrbeubi61gcjh7rqcijoxxnromxitql6yhxuhpytdt5wceqqe6oe2gm1dfjzdqu5u1bl8nob+61lemqu4lyqrb20pfrfg1aww4s+ycvecbhm9+ul2o04jecjmkzce2euvlscikgrmv3vssboeb6pgopjgkipsycbqr3ndkf4zc6bcrofz/b2qokniybxoyp1xoe7n4n9djvxjqztroukvippkotbluhoy9ws4voj4baokwoppwiptiikx0m2vdgj0dezy4rzvb85vjsv28qkmedseeqaibnia6uain0aqypijn8arejcfjxxg3piajc0axswp+wpj6azgan/o=</latexit> <latexit sha1_base64="gryid+fzwj3q7fiqbhmff6psnz8=">aaacgnicbvdnssnagnz4w+tf1koxxslws0le0itq1imhd1vswmhc2gw37dpnnuxuhbl6hf58fs8efpemxnwbn20o2jqwmmx8h/vnbamjulnwtze3v7c4tfxaka+urw9smlvbjuspwksjoeoihsbjgi1ju1hfsdsrbeubi61gcjh7rqcijoxxnromxitql6yhxuhpytdt5wceqqe6oe2gm1dfjzdqu5u1bl8nob+61lemqu4lyqrb20pfrfg1aww4s+ycvecbhm9+ul2o04jecjmkzce2euvlscikgrmv3vssboeb6pgopjgkipsycbqr3ndkf4zc6bcrofz/b2qokniybxoyp1xoe7n4n9djvxjqztroukvippkotbluhoy9ws4voj4baokwoppwiptiikx0m2vdgj0dezy4rzvb85vjsv28qkmedseeqaibnia6uain0aqypijn8arejcfjxxg3piajc0axswp+wpj6azgan/o=</latexit> <latexit sha1_base64="gryid+fzwj3q7fiqbhmff6psnz8=">aaacgnicbvdnssnagnz4w+tf1koxxslws0le0itq1imhd1vswmhc2gw37dpnnuxuhbl6hf58fs8efpemxnwbn20o2jqwmmx8h/vnbamjulnwtze3v7c4tfxaka+urw9smlvbjuspwksjoeoihsbjgi1ju1hfsdsrbeubi61gcjh7rqcijoxxnromxitql6yhxuhpytdt5wceqqe6oe2gm1dfjzdqu5u1bl8nob+61lemqu4lyqrb20pfrfg1aww4s+ycvecbhm9+ul2o04jecjmkzce2euvlscikgrmv3vssboeb6pgopjgkipsycbqr3ndkf4zc6bcrofz/b2qokniybxoyp1xoe7n4n9djvxjqztroukvippkotbluhoy9ws4voj4baokwoppwiptiikx0m2vdgj0dezy4rzvb85vjsv28qkmedseeqaibnia6uain0aqypijn8arejcfjxxg3piajc0axswp+wpj6azgan/o=</latexit> <latexit sha1_base64="gryid+fzwj3q7fiqbhmff6psnz8=">aaacgnicbvdnssnagnz4w+tf1koxxslws0le0itq1imhd1vswmhc2gw37dpnnuxuhbl6hf58fs8efpemxnwbn20o2jqwmmx8h/vnbamjulnwtze3v7c4tfxaka+urw9smlvbjuspwksjoeoihsbjgi1ju1hfsdsrbeubi61gcjh7rqcijoxxnromxitql6yhxuhpytdt5wceqqe6oe2gm1dfjzdqu5u1bl8nob+61lemqu4lyqrb20pfrfg1aww4s+ycvecbhm9+ul2o04jecjmkzce2euvlscikgrmv3vssboeb6pgopjgkipsycbqr3ndkf4zc6bcrofz/b2qokniybxoyp1xoe7n4n9djvxjqztroukvippkotbluhoy9ws4voj4baokwoppwiptiikx0m2vdgj0dezy4rzvb85vjsv28qkmedseeqaibnia6uain0aqypijn8arejcfjxxg3piajc0axswp+wpj6azgan/o=</latexit> <latexit 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sha1_base64="ta0eqpjhe96xtp4szsgjuabpdtg=">aaacinicbvdlssnafj34tr6qlt0mfqgcsckcuvoxcefcxvalkevmoomdm0myuvfkyle48vfcufduledhoh0s6upawogce5lzt5bkydb1p52r0bhxicmp6dlm7nz8qnlxqw6stdney4lm9fuahksr8xoklpwq1rxuipllchvu9s/vudyiis+wk/kmgigwowcavmqv9+rulzxe0dq5p34esker9xxgjcd8iipobiziy0goqtfrrfv8vvwuujtud/qv8qakqgy4bzxf/xbcmsvjzbkmaxhuis0cnaomevhym8ntylcq8yalmshumnnvxikuwavnw0tbfyptqcmboshjoiqwk92o5rfxff/zghmgu81cxgmgpgb9j8jmukxoty/afpozlb1lgglhs1j2axoy2lzltgtv98l/sx1r07p8bluyfzioy4qskfvsjr7zifvkmjysgmhkgtyrf/lqpdrpzpvz0r8dcqy7y+qhnk9voz+kta==</latexit> <latexit sha1_base64="ta0eqpjhe96xtp4szsgjuabpdtg=">aaacinicbvdlssnafj34tr6qlt0mfqgcsckcuvoxcefcxvalkevmoomdm0myuvfkyle48vfcufduledhoh0s6upawogce5lzt5bkydb1p52r0bhxicmp6dlm7nz8qnlxqw6stdney4lm9fuahksr8xoklpwq1rxuipllchvu9s/vudyiis+wk/kmgigwowcavmqv9+rulzxe0dq5p34esker9xxgjcd8iipobiziy0goqtfrrfv8vvwuujtud/qv8qakqgy4bzxf/xbcmsvjzbkmaxhuis0cnaomevhym8ntylcq8yalmshumnnvxikuwavnw0tbfyptqcmboshjoiqwk92o5rfxff/zghmgu81cxgmgpgb9j8jmukxoty/afpozlb1lgglhs1j2axoy2lzltgtv98l/sx1r07p8bluyfzioy4qskfvsjr7zifvkmjysgmhkgtyrf/lqpdrpzpvz0r8dcqy7y+qhnk9voz+kta==</latexit> Incremental View Maintenance (IVM) Unary IVM for insertions: R 0 = R [ R Newly inserted tuples Group By Agg: V AggList,Agg(Y )(R) Feasibility of IVM Depends on Agg() function! Rewrite rules exist for SUM, COUNT, and MIN/MAX over bags AVG not possible in general; needs deeper system changes V 0 = AggList,SUM (Y ) (V [ AggList,SUM (Y ) R) V 0 = AggList,SUM (Y ) (V [ AggList,COUNT (Y ) R) V 0 = AggList,MIN (Y ) (V [ AggList,MIN (Y ) R) 48