V iew Maintenance

1

VView iew MaintenanceMaintenance

Based on several papers in view maintenance, most notably:Based on several papers in view maintenance, most notably:

A.Gupta and I.S. Mumick. A.Gupta and I.S. Mumick.

Maintenance of Materialized Views: Problems, Techniques, and ApplicationMaintenance of Materialized Views: Problems, Techniques, and Application . . In Bulletin of Technical Committee on Data Engineering 1995In Bulletin of Technical Committee on Data Engineering 1995

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OutlineOutline

1.1. Introduction to viewsIntroduction to views

2.2. The problem of materialized view maintenanceThe problem of materialized view maintenance

3.3. The idea behind incremental view maintenanceThe idea behind incremental view maintenance

4.4. Dimensions – the problem spaceDimensions – the problem space

5.5. View maintenance using full informationView maintenance using full information The counting algorithmThe counting algorithm

6.6. View maintenance using partial informationView maintenance using partial information Self-maintenanceSelf-maintenance

7.7. Applications of materialized viewsApplications of materialized views

8.8. Open problemsOpen problems

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OutlineOutline







7.7. Applications of incremental view maintenanceApplications of incremental view maintenance


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What is a view? What is a view?

A view is a derived relation defined in terms of A view is a derived relation defined in terms of base (stored) relations.base (stored) relations.

Example: Example: Flight is table of available direct flights. Flight is table of available direct flights. We need a view of flights with one intermediate stop.We need a view of flights with one intermediate stop.

1. Introduction to views

CREATE TABLE Flight ( from CHAR(20), to CHAR(20),);

CREATE VIEW Conn(src, dest) AS SELECT F1.from, F2.to FROM Flight F1, Flight F2 WHERE F1.to = F2.from;

5

Views are treated as base tables in regard of querying Views are treated as base tables in regard of querying


From To

Worcester Boston

Boston New York

Boston Seattle

Worcester PhiladelphiaNew York Las VegasPhiladelphia Seattle

From ToWorcester Boston

Boston New YorkBoston SeattleWorcester PhiladelphiaNew York Las VegasPhiladelphia

Seattle

Src Dest

Worcester New York

Worcester Seattle

Boston Las Vegas

The base relationThe base relationFlightFlight

The viewThe viewConnConn

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Motivation: Why views? Motivation: Why views?

Logical data independence Logical data independence If the conceptual schema changes, the changes can be “masked” If the conceptual schema changes, the changes can be “masked”

through the views in the external schemathrough the views in the external schema

Security Security Not everybody may see everythingNot everybody may see everything

““Relations” tailored to user’s needs Relations” tailored to user’s needs

And many more reasons and applications … discussed laterAnd many more reasons and applications … discussed later


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How will the following query be answered? How will the following query be answered?

Two options:Two options: Computing on demand Computing on demand

• query modification, composing of the user query and the view query query modification, composing of the user query and the view query

View materialization View materialization • The view Conn would be materialized, its content would be stored in The view Conn would be materialized, its content would be stored in

the databasethe database

View materialization vs. computing on demandView materialization vs. computing on demand


SELECT * FROM Conn C WHERE C.src = “Worcester”SELECT * FROM Conn C WHERE C.src = “Worcester”

SELECT * FROM (SELECT F1.from, F2.to FROM Flight F1, Flight F2 WHERE F1.to = F2.from ) AS C

WHERE C.src = “Worcester”

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How will the following query be answered? How will the following query be answered?

Two options:Two options: Computing on demand Computing on demand

• query modification, composing of the user query and the view query query modification, composing of the user query and the view query

View materialization View materialization • The view Conn would be materialized, its content would be stored in The view Conn would be materialized, its content would be stored in

the databasethe database



SELECT * FROM Conn C WHERE C.src = “Worcester”SELECT * FROM Conn C WHERE C.src = “Worcester”

SELECT * FROM (SELECT F1.from, F2.to FROM Flight F1, Flight F2 WHERE F1.to = F2.from ) AS C

WHERE C.src = “Worcester”

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Trade-offs? Trade-offs?


Queries can be answered fasterQueries can be answered faster Indexes can be build over a materialized view to even more Indexes can be build over a materialized view to even more

speed up the processing of the queries defined over the viewspeed up the processing of the queries defined over the view

The view requires additional storage spaceThe view requires additional storage space The consistency of the view has to be maintainedThe consistency of the view has to be maintained

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OutlineOutline







7.7. ApplicationsApplications


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Definition of the problem of materialized view Definition of the problem of materialized view maintenancemaintenance

What is materialized view maintenance ?What is materialized view maintenance ? When the base relations are modified, When the base relations are modified, the view (often) becomes inconsistent. the view (often) becomes inconsistent. Updating the view to make it consistent Updating the view to make it consistent is called view maintenance (refreshing).is called view maintenance (refreshing).

2. The problem of view maintenance2. The problem of view maintenance

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View maintenance policies ( When ? )View maintenance policies ( When ? )

Immediate view maintenanceImmediate view maintenance The view is refreshed within the same transaction that updates The view is refreshed within the same transaction that updates

the underlying tablesthe underlying tables+ the view is always up to date+ the view is always up to date

- slows down the transaction - slows down the transaction

Deferred view maintenanceDeferred view maintenance LazyLazy – the view is refreshed when query over it has to be – the view is refreshed when query over it has to be

evaluatedevaluated- slows down the queries- slows down the queries

PeriodicPeriodic - the view is refreshed periodically, e.g., once a day - the view is refreshed periodically, e.g., once a day• Such views are called snapshotsSuch views are called snapshots

ForcedForced – the view is refreshed after a certain number of changes – the view is refreshed after a certain number of changes have been made to the underlying tableshave been made to the underlying tables


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Methods of view maintenance (How ?)Methods of view maintenance (How ?)

Recomputation Recomputation recompute to view from scratchrecompute to view from scratch

Incremental view maintenanceIncremental view maintenance compute the changes to the view in response to the changes to compute the changes to the view in response to the changes to

the base relationthe base relation add/delete some tuples in the existing materialized viewadd/delete some tuples in the existing materialized view

HeuristicsHeuristics: Incremental view maintenance is usually : Incremental view maintenance is usually cheaper then recomputationcheaper then recomputation


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OutlineOutline









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The idea behind incremental view maintenance The idea behind incremental view maintenance

Example: Flight is table of available direct flights. We need a Example: Flight is table of available direct flights. We need a view of flights with one intermediate stopview of flights with one intermediate stop

)2F1F( : Dest) Conn(Src,

)Flight,2F(

)Flight,1F(

string) : tostring, :(fromFlight

from.2Fto.1Fto.2F,from.1F

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)Flight,2F(

)Flight,1F(



From To

Worcester Boston

Boston New York

Boston Seattle

Worcester Philadelphia

New York Las Vegas

Philadelphia Seattle

Src Dest

Worcester New York

Worcester Seattle

Boston Las Vegas

From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


F1

F2 The view ConnThe view Conn

3. The idea behind view maintenance3. The idea behind view maintenance

The base relationThe base relation

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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New YorkSrc Dest

Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York

Philadelphia New York

F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York


)Flight,2F(

)Flight,1F(



Inserted Inserted tupletuple

New tuples New tuples in the viewin the view


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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York


)Flight,2F(

)Flight,1F(




New tuples New tuples in the viewin the view


19

From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York


)Flight,2F(

)Flight,1F(



F1F1

F1F1

F1F1=F1+=F1+ F1 F1


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))2F1F()2F1F()2F1F((Conn

)2F1F(Conn

))2F2F()1F1F((ConnConn

)2F1F(Conn2F2F2F

1F1F1FConnConnConn

from.2Fto.1Ffrom.2Fto.1F





The differentiation equation The differentiation equation


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)2F1F(Conn))2F2F()1F1F((ConnConn





From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York


22







From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York

Join


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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York

Join








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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York

Join=








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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas



Worcester New York

Worcester Seattle

Boston Las Vegas

Seattle Las Vegas

Boston New York


F1



From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Seattle New York

Savings!


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Insertions Insertions The observed example was for insertions into the base relationThe observed example was for insertions into the base relation

DeletionsDeletions If tuples are deleted from a base relation, the tuples that need to If tuples are deleted from a base relation, the tuples that need to

be deleted from the view are computed similarly using delta’sbe deleted from the view are computed similarly using delta’s

Updates Updates May be treated separately or may be modeled as deletions May be treated separately or may be modeled as deletions

followed by insertsfollowed by inserts

Multiple relations involvedMultiple relations involved The deltas are similarly computedThe deltas are similarly computed


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OutlineOutline









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DimensionsDimensions

Amount of Information

Type of Modification

Expressiveness of View

Definition Language

Insertions

Other Views

BaseRelations

IntegrityConstraints

MaterializedView

.

.

.

DeletionsUpdates Sets of each

Group UpdatesChange view definition

RecursionDifference

Outer-JoinsChronicle Algebra

Conjunctive

Queries

DuplicatesArithmetic

AggregationSubqueries

Union

….

4. Dimensions4. Dimensions

+ Instance dimension

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Information Dimension Information Dimension

Information dimension Information dimension Information available for view maintenance Information available for view maintenance (other than (other than view definition and the modificationview definition and the modification, , which which are always assumed availableare always assumed available))

E.g., E.g., • base relations,base relations,• the materialized view, the materialized view, • other views, other views, • integrity constraints…integrity constraints…

Don’t we have all these information?Don’t we have all these information?

• Sometimes we don’t, Sometimes we don’t, • Sometimes it is expensive to access them Sometimes it is expensive to access them (base relations when views are physically not close to data) (base relations when views are physically not close to data)

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Dimensions – information & modificationDimensions – information & modification

Example:Example:


Information

Modification

Materialized view Conn only

Plus, base relation Flight

Key info only:

(from, to) is key in Flight

Insert

Delete

Update

CREATE TABLE Flight ( from CHAR(20), to CHAR(20), price REAL, company CHAR(20) );

CREATE VIEW CheapF(src, dest) AS SELECT DISTINCT F.from, F.to FROM Flight F WHERE F.price < 400;

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Example:Example:


Information

Modification

The materialized

view Conn only

The base relation Flight

only

Key info only:


Insert

Delete

Update



Can we maintain the view when tuples are inserted into base relation if only information available is the materialized view (its content)

Reminder: The view definition and the modification are always available

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Example:Example:

Information

Modification

The materialized

view Conn only

The base relation Flight

only

Key info only:


Insert Delete Update




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OutlineOutline









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View maintenance using full informationView maintenance using full information

‘ ‘Classical’ view maintenance algorithms assume : Classical’ view maintenance algorithms assume : Full Information: the base relations, the materialized view, keys,…Full Information: the base relations, the materialized view, keys,… All database and modification instancesAll database and modification instances Modification: inserts, deletes, updates ( as insert-delete )Modification: inserts, deletes, updates ( as insert-delete ) Language: Focus on efficient techniques for maintaining PSJ views Language: Focus on efficient techniques for maintaining PSJ views

expressed in subset of view definition language (SQL)expressed in subset of view definition language (SQL)

Classification along language dimension:Classification along language dimension: Nonrecursive viewsNonrecursive views

• The counting algorithmThe counting algorithm Outer-join viewsOuter-join views Recursive viewsRecursive views ……

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The Counting Algorithm - MotivationThe Counting Algorithm - Motivation

5. View Main. using full information5. View Main. using full information

))2F1F()2F1F()2F1F((

Conn

from.2Fto.1F

from.2Fto.1F

from.2Fto.1F

to.2F,from.1F

From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Src Dest

Worcester New York

Worcester Seattle

Boston Las Vegas

From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


F1

F2The view ConnThe view Conn


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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


Src Dest

Worcester New York

Worcester Seattle

Boston Las Vegas

From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


F1

F2The view ConnThe view Conn


Shall we delete theShall we delete the (Worcester, Seattle) (Worcester, Seattle) tuple from the view?tuple from the view?


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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


F1

F2


No, becauseNo, becauseit can still be derived fromit can still be derived from

the remaining tuplesthe remaining tuples

Src Dest

Worcester New York

Worcester Seattle

Boston Las Vegas

The view ConnThe view Conn


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The Counting AlgorithmThe Counting Algorithm

We need to know We need to know

if there are more derivations of one tuple in the viewif there are more derivations of one tuple in the view

Main idea: Main idea: Keep count of number of derivations for each tuple in viewKeep count of number of derivations for each tuple in view A tuple is removed from view only if its count is zeroA tuple is removed from view only if its count is zero


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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


F1

F2


Src Dest Count

Worcester New York 1

Worcester Seattle 2

Boston Las Vegas 1



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From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


From To

Worcester Boston

Boston New York

Boston Seattle


New York Las Vegas


F1

F2


Src Dest Count


Worcester Seattle 2 1

Boston Las Vegas 1



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From To

Worcester Boston

Boston New York


New York Las Vegas


From To

Worcester Boston

Boston New York


New York Las Vegas


F1

F2


Src Dest Count


Worcester Seattle 1 0

Boston Las Vegas 1



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What if we have aggregation? What if we have aggregation?


CREATE TABLE Flight ( from CHAR(20), to CHAR(20), price REAL );

CREATE TABLE Flight ( from CHAR(20), to CHAR(20), price REAL );

CREATE VIEW CheapFlight(src, dest, minPrice) AS SELECT F.from, F.to, MIN (F.price) FROM Flight F GROUP BY F.from, F.to;

CREATE VIEW CheapFlight(src, dest, minPrice) AS SELECT F.from, F.to, MIN (F.price) FROM Flight F GROUP BY F.from, F.to;

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From To Price

Boston New York 200

Chicago Philadelphia 350

Boston New York 300

New York Las Vegas 330


Boston New York 250


Src Dest MinPrice Count

Boston New York 200 1

Chicago Philadelphia 350 1

New York Las Vegas 330 1

The view CheapFlightThe view CheapFlight

The Counting Algorithm - AggregationThe Counting Algorithm - Aggregation


The inserted tuple does not affect the view

The inserted tuple does not affect the view

Potentially affected tuplePotentially affected tuple

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From To Price

Boston New York 200


Boston New York 300



Boston New York 180



Boston New York 200 180 1





One tuple in the viewhas to be updated

One tuple in the viewhas to be updated



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From To Price

Boston New York 200


Boston New York 300



Boston New York 180



Boston New York 180 200 1





One tuple in the view has to be recomputed

One tuple in the view has to be recomputed

DeletedDeletedtupletuple


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When a change to the base relation occurs:When a change to the base relation occurs:

Identifies the tuples that may be affectedIdentifies the tuples that may be affected

Whenever possible incrementally computes new values of Whenever possible incrementally computes new values of affected tuples by only looking at materialized view and affected tuples by only looking at materialized view and modification. modification.

Other aggregation functions that may be computed this way: Other aggregation functions that may be computed this way: COUNT, SUM, MIN, MAXCOUNT, SUM, MIN, MAX

Some other aggregation functions like AVERAGE and VARIANCE Some other aggregation functions like AVERAGE and VARIANCE can be decomposed into incrementally computable functionscan be decomposed into incrementally computable functions


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The Counting Algorithm – Multiple relations and views over viewsThe Counting Algorithm – Multiple relations and views over views

Handles views over multiple relations, handles views over views Handles views over multiple relations, handles views over views (by first updating the views lower in the hierarchy)(by first updating the views lower in the hierarchy)


Base relation 1Base relation 1

Materialized view 1Materialized view 1

Base relation 2Base relation 2 Base relation 3Base relation 3

Materialized view 2Materialized view 2

II

I II I

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The Counting Algorithm - SummaryThe Counting Algorithm - Summary

Keeps track of the number of derivation of each tuple – tuples with Keeps track of the number of derivation of each tuple – tuples with count zero are deleted from the viewcount zero are deleted from the view

Handles updates as difference of positive and negative countsHandles updates as difference of positive and negative counts

Handles views over multiple relations, handles views over viewsHandles views over multiple relations, handles views over views

Language limitations: SPJ views, UNION, negation, aggregationLanguage limitations: SPJ views, UNION, negation, aggregation

Works for both set and duplicate semanticsWorks for both set and duplicate semantics


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OutlineOutline









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View maintenance using partial informationView maintenance using partial information

Views Views maymay be maintainable using partial information be maintainable using partial information May depend on the modification: insert, delete or updateMay depend on the modification: insert, delete or update

Goals:Goals: Check whether the view can be maintained Check whether the view can be maintained How to maintain the viewHow to maintain the view

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Using no Information: Query Independent of UpdateUsing no Information: Query Independent of Update

Some modifications to the base tables may be irrelevant to Some modifications to the base tables may be irrelevant to the view - leave it unchangedthe view - leave it unchanged

Determine if the modification is irrelevant using:Determine if the modification is irrelevant using: The view definitionThe view definition The modificationThe modification

Recognizing irrelevant modifications prevents unnecessary Recognizing irrelevant modifications prevents unnecessary delta-computationsdelta-computations

6. View Main. using partial information6. View Main. using partial information

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Query Independent of Update - ExampleQuery Independent of Update - Example


CREATE TABLE Flight ( from CHAR(20), to CHAR(20), price REAL, company CHAR(20) )

CREATE TABLE RCompany( name CHAR(20), rating INTEGER )

CREATE VIEW GoodFlights(src, dest) AS SELECT F.from, F.to FROM Flight F, RCompany CWHERE F.company = C.name

AND C.rating > 5 AND F.price < 400

Which of these modifications would be irrelevant? Which of these modifications would be irrelevant?

INSERT INTO RCompany VALUES (“Swissair”, 10)INSERT INTO RCompany VALUES (“Swissair”, 10)

INSERT INTO RCompany VALUES (“EnronAir”, 2)INSERT INTO RCompany VALUES (“EnronAir”, 2)

DELETE FROM Flight F WHERE F.from=“Boston” AND PRICE< 350DELETE FROM Flight F WHERE F.from=“Boston” AND PRICE< 350

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Query Independent of Update - ExampleQuery Independent of Update - Example



CREATE TABLE RCompany( name CHAR(20), rating INTEGER )

Which of these modifications would be irrelevant? Which of these modifications would be irrelevant?

INSERT INTO RCompany VALUES (“Swissair”, 10)INSERT INTO RCompany VALUES (“Swissair”, 10)

INSERT INTO RCompany VALUES (“EnronAir”, 2)INSERT INTO RCompany VALUES (“EnronAir”, 2)

DELETE FROM Flight F WHERE F.from=“Boston” AND PRICE< 350DELETE FROM Flight F WHERE F.from=“Boston” AND PRICE< 350



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Self-MaintenanceSelf-Maintenance

Self-maintainable views are views that can be maintained Self-maintainable views are views that can be maintained using only materialized view (self) and key constraints using only materialized view (self) and key constraints

A view may be self-maintainable in respect to some A view may be self-maintainable in respect to some modification types (insert, delete, update) modification types (insert, delete, update) but not in respect to all of thembut not in respect to all of them


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Self-MaintenanceSelf-Maintenance

Distinguished Attribute – Appears in the SELECT clause in Distinguished Attribute – Appears in the SELECT clause in the view definitionthe view definition

Exposed Attribute – Used in a predicate in the view definitionExposed Attribute – Used in a predicate in the view definition

DistinguishedDistinguishedExposedExposed

CREATE TABLE Flight ( from CHAR(20), to CHAR(20), price REAL, company CHAR(20), duration REAL, PRIMARY KEY (from, to) )

CREATE TABLE RCompany( name CHAR(20), rating INTEGER PRIMARY KEY (name) )

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Self-Maintenance - insertSelf-Maintenance - insert

SPJ view that takes join of two or more distinct relations is SPJ view that takes join of two or more distinct relations is notnot self maintainable in respect to insertions self maintainable in respect to insertions


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Self-Maintenance - updateSelf-Maintenance - update

SPJ view is self-maintainable in respect to updates on non-SPJ view is self-maintainable in respect to updates on non-exposed attributes when the key attributes are distinguished. exposed attributes when the key attributes are distinguished.




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Self-Maintenance - updateSelf-Maintenance - update




Update of Update of duration does duration does not affect the not affect the

viewview

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OutlineOutline









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Applications of materialized viewsApplications of materialized views

Query speed-upQuery speed-up

Integrity constraint checkingIntegrity constraint checking

Query optimizationQuery optimization

Data warehousingData warehousing

Chronicle systems (~ continuous queries)Chronicle systems (~ continuous queries)

Mobile systemsMobile systems

Data visualization Data visualization

………………

62

Query speed-upQuery speed-up

Queries are answered faster if their answers are Queries are answered faster if their answers are precomputed (materialized)precomputed (materialized)

How does the keeping of precomputed query result How does the keeping of precomputed query result (materialized view) correlate to the: (materialized view) correlate to the: Frequency of the query ?Frequency of the query ? Frequency of the updates to the base relations?Frequency of the updates to the base relations?

7. Applications7. Applications

63


Static integrity constraints can be modeled as Static integrity constraints can be modeled as materialized views that are required to be empty materialized views that are required to be empty

Example: Example:



CREATE ASSERTION NoMonopolyCHECK ( NOT EXISTS ( SELECT F.company

FROM Flight FGROUP BY F.companyHAVING COUNT(*) > 100 ) )

64


Example: Example:





CREATE VIEW Monopolies AS SELECT F.company FROM Flight F GROUP BY F.company HAVING COUNT(*) > 100

CREATE ASSERTION NoMonopolyCHECK ( NOT EXISTS ( SELECT *

FROM Monopolies ) )

Can be Can be modeled asmodeled as

65


Example: Example:







FROM Monopolies ) )

What happens What happens on updates on updates

to the base relation?to the base relation?

66


Example: Example:







FROM Monopolies ) )

How can we do How can we do better?better?

67


Example: Example:







FROM Monopolies ) )

Which additional view Which additional view shall we materialize? shall we materialize?

68


Materialize an additional view Materialize an additional view for maintaining Monopolies:for maintaining Monopolies:






FROM Monopolies ) )

CREATE VIEW Helper (name, total) AS SELECT F.company, COUNT(*) FROM Flight F GROUP BY F.company

CREATE VIEW Monopolies (name) AS SELECT F.company FROM Flight F GROUP BY F.company HAVING COUNT(*) > 100

CREATE VIEW Monopolies(name) AS SELECT H.company FROM Helper H WHERE H.total > 100

69

Query optimizationQuery optimization

Materialized views can be used even for answering Materialized views can be used even for answering queries that do not explicitly contain the views in their queries that do not explicitly contain the views in their definition definition

Cashed results can be seen as temporarily materialized Cashed results can be seen as temporarily materialized views and can also by used for faster query views and can also by used for faster query optimizationoptimization

Problems:Problems: Recognizing which views may be utilized for processing the Recognizing which views may be utilized for processing the

queryquery Finding which views and/or relations should be used for Finding which views and/or relations should be used for

achieving lowest cost of the query evaluation achieving lowest cost of the query evaluation


70

Query optimization – the Microsoft SQL Server approachQuery optimization – the Microsoft SQL Server approach

Approach:Approach: Generate all possible rewritings of the queryGenerate all possible rewritings of the query View matching: Determine subexpressions that may be View matching: Determine subexpressions that may be

computed from materialized views computed from materialized views Estimate their costsEstimate their costs Choose the one with the lowest costChoose the one with the lowest cost

Indexes the view definitions using a special index to Indexes the view definitions using a special index to speed up the view matchingspeed up the view matching

Language limitation: Select-Project-Join-GroupByLanguage limitation: Select-Project-Join-GroupBy


71

OutlineOutline









72

Open problemsOpen problems

7. Open problems7. Open problems

Amount of Information

Type of Modification

Expressiveness of View

Definition Language

Insertions

Other Views

BaseRelations

IntegrityConstraints

MaterializedView

.

.

.

DeletionsUpdates Sets of each

Group UpdatesChange view definition

RecursionDifference

Outer-JoinsChronicle Algebra

Conjunctive

Queries

DuplicatesArithmetic

AggregationSubqueries

Union

….

+ Instance dimension

73

Open problemsOpen problems

7. Open problems7. Open problems

Great portion of the problem space is still unsolved or Great portion of the problem space is still unsolved or may be solved more efficiently , so many open may be solved more efficiently , so many open questions remainquestions remain

Other issues:Other issues: When to perform the maintenance?When to perform the maintenance? How to efficiently select additional views to be maintained?How to efficiently select additional views to be maintained? …………....

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ReferencesReferences A.Gupta, I.S.Mumick. A.Gupta, I.S.Mumick. Maintenance of Materialized Views: Problems, Techniques, Maintenance of Materialized Views: Problems, Techniques,

and Applicationand Application. In Bulletin of the Technical Committee on Data engineering 1995. In Bulletin of the Technical Committee on Data engineering 1995 Most of this presentationMost of this presentation

R.Ramakrishnan, J.Gehrke. R.Ramakrishnan, J.Gehrke. Database Management SystemsDatabase Management Systems, McGraw-Hill 2000, McGraw-Hill 2000 Introduction to viewsIntroduction to views

A.Gupta, I.S. Mumick, V.S. Subrahmanian. A.Gupta, I.S. Mumick, V.S. Subrahmanian. Maintaining Views IncrementallyMaintaining Views Incrementally. In . In SIGMOD 1995SIGMOD 1995 The counting algorithmThe counting algorithm

J.A. Blakeley, P.Larson, and F.Tompa. J.A. Blakeley, P.Larson, and F.Tompa. Efficiently Updating Materialized ViewsEfficiently Updating Materialized Views. In . In SIGMOD 1986SIGMOD 1986 Query independent of updateQuery independent of update

K.Ross, D.Srivastava, S.Sudarshan. K.Ross, D.Srivastava, S.Sudarshan. Materialized View Maintenance and Integrity Materialized View Maintenance and Integrity Constraint Checking: Trading Space for TimeConstraint Checking: Trading Space for Time. In SIGMOD 96.. In SIGMOD 96. Applications: Integrity constraint checkingApplications: Integrity constraint checking

J Goldstein, P. Larson. J Goldstein, P. Larson. Optimizing Queries Using Materialized Views: A practical Optimizing Queries Using Materialized Views: A practical Scalable SolutionScalable Solution. In SIGMOD 2001. In SIGMOD 2001 Applications: Query optimization – the Microsoft SQL Server approachApplications: Query optimization – the Microsoft SQL Server approach

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