Distributed Database Design

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Distributed Database Design
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What is to be Designed

• In designing a distributed database the same
  issues are faced as for a centralized database
  plus in addition
• Fragmentation (how relations are to be logically
  divided up if at all)
• Allocation (how relations/fragments are to be
  assigned to sites)
• Replication (if and where relations/fragments
  should be replicated)

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Fragmentation

• There are two basic ways in which a relation can
  be fragmented
• Horizontally (each fragment contains a subset of
  the tuples/rows of the relation)
• Vertically (each fragment contains a subset of
  the attributes/columns of the relation)

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Hybrid (Mixed) Fragmentation
Relation A
Fragment H1
Fragment H2V2
Fragment H2V1H1
Fragment H2V1H2
Fragment H2V1H3
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Horizontal Fragmentation

• Horizontal fragments are defined according to a
  condition or predicate that the tuples in it
  satisfy
• For example the team relation of the sports club
  database might be horizontally fragmented
  according to the values of the attribute club.

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Vertical Fragmentation

• Vertical fragments contain a subset of the
  attributes of the original relation
• Note however an important point Every vertical
  fragment should contain all of the prime
  attribute(s) - the primary key - of the original
  relation
• Why

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Correctness of Fragmentation

• A fragmentation if it is to be correct should
  obey certain conditions or rules
• Completeness If it is in the original relation
  then it is also in one of the fragments
• Reconstruction The original relation can be
  correctly rebuilt from the fragments
• Disjointness Data in one fragment should not
  also be present in another except for the
  primary key attributes in vertical fragmentation

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Derived Horizontal Fragmentation

• When a horizontal fragmentation is defined by a
  predicate that is directly applicable to the
  tuples of the relation it is a primary
  horizontal fragmentation
• When the fragmentation is defined by a predicate
  that is applied to the tuples in a different
  relation it is a derived horizontal fragmentation

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Semi-Joins Derived Horizontal Fragmentation

• A derived horizontal fragmentation can be
  formally specified using a semi-join
• A semi-join is a natural join that retains the
  attributes of only one of the participating
  relations
• It is denoted by the symbol
• For example

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Degree of Fragmentation

• What determines how far we should go in
  fragmenting relations
• The answer lies not in the data but in the
  applications (i.e. queries) that access it
• We want to fragment to get the best possible
  result for all applications/queries but what
  does best possible mean

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Factors Affecting Fragmentation

• The two primary factors affecting how we fragment
  are
• The database structure (global conceptual schema)
• The application characteristics (predicates used
  locations and frequencies of use)
• Other important aspects are the network
  characteristics and computer processing power

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Applications Simple Predicates

• Applications (think of them as queries) that
  access a database often select out a subset of
  the data according to a predicate a Boolean
  expression
• A simple predicate pj is one of the form
• pj Ai Value
• where Ai is an attribute is a comparison
  operator and Value is a domain value

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Horizontal Fragmentation Minterm Predicates

• In designing horizontal fragmentation we need to
  identify the chunks of tuples in the database
  that are accessed as a unit by applications/querie
  s
• Each chunk is defined by a minterm predicate
  which is a combination of simple predicates
• These chunks are then candidates for horizontal
  fragments

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Minterm Predicates

• It is sometimes (often) possible to eliminate
  some minterm predicates based on our
  understanding of the database semantics
• This occurs when one minterm predicate implies
  another

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Minterm Predicates (cont)

• As well as knowing what the minterm predicates
  are it is important to know (typically) how many
  tuples would be selected by each and
• How frequently the tuples selected by each are
  accessed (this is closely related to query access
  frequency)

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Derived Horizontal Fragmentation Again

• It often happens that a relation can have
  alternate and conflicting possible derived
  fragmentations
• This may occur when it is related to two (or
  more) parent relations through foreign keys

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Choosing a Derived Fragmentation

• Given two (or more) possible derived
  fragmentations how to choose between them
• The two factors are
• Application usage (the more frequently accessed
  minterm is generally preferable)
• Join characteristics (the design that gives
  better overall join performance is preferable)

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Join Characteristics

• The performance of a join in a distributed
  database benefits when
• The relations or fragments to be joined are
  small (i.e. have relatively few tuples)
• The join can be processed in a genuinely
  distributed fashion

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Join Graphs

• A join graph is a pictorial way of showing which
  fragments of two relations participating in a
  join might produce non-empty results
• Each fragment is represented by a node and those
  fragments that produce a non-empty join result
  are connected by an edge (line)

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A Simple Join Graph
T1
PF1
T2
PF2
Fragments of team
Fragments of plays_for
T3
PF3
T4
PF4
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A Total Join Graph
P1
PF1
P2
PF2
Fragments of player
Fragments of plays_for
P3
PF3
P4
PF4
P5
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A Partitioned Join Graph
P1
PF1
P2
PF2
Fragments of player
Fragments of plays_for
P3
PF3
P4
PF4
P5
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Vertical Fragmentation

• Analogously to horizontal fragmentation the
  purpose of vertical fragmentation is to group
  together attributes into chunks that are
  usually accessed together by user
  applications/queries
• Vertical fragmentation is more complex to design
  than horizontal fragmentation

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Approaches to Vertical Fragmentation

• There are two basic approaches to vertical
  fragmentation
• Splitting originally designed relations
• Grouping individual attributes from scratch
• The first is preferred since
• The original relations (should) be well designed
• Appropriate fragments are likely to be closer to
  relation size than individual attribute size

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Application Information for Vertical Fragmentation

• The application information needed for vertical
  fragmentation design is
• Which applications/queries use which attributes
• The frequencies with which the various
  applications/queries are executed
• The first of these can be captured in an
  attribute usage matrix then use of the second
  results in a attribute affinity matrix

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The Player Relation

• Suppose we wish to determine how the
  relationplayer(Name Height Gender Address
  Weight DOB Telephone)should be vertically
  fragmented
• The queries that access player are shown on the
  next

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Queries on Player

• q1 SELECT Name DOB Address Telephone FROM
  player WHERE Gender
• q2 SELECT avg(Height) avg(Weight) FROM player
  WHERE Gender
• q3 SELECT Name Height Weight DOB FROM player
  WHERE Name LIKE
• q4 SELECT Name Address Telephone FROM player
  WHERE Name

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Attribute Usage Matrix

• This is a matrix that has attributes along one
  axis and queries along the other
• It contains a 1 in positions for which the
  relevant query accesses the attribute of
  interest and 0 otherwise
• The matrix for player and queries q1 to q4 is
  shown on the next

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Attribute Usage Matrix
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Attribute Affinity Matrix

• This is a matrix that has the attributes of the
  relation we are dealing with on both axes
• To construct the matrix we need to know the
  frequency of accesses (across all sites) to each
  query
• Then we can work out how often (relatively) each
  pair of attributes are accessed together

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Query Characteristics

• Suppose there are three sites accessing the
  player relation with these relative
  frequencies

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Attribute Affinity Matrix
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Attribute Clustering

• The next step is to group together the
  attribute(s) that have an affinity for each other
  this results in a clustered affinity matrix
• There is a formal so-called bond energy
  algorithm for this (given in Özsu Valduriez)
  but we present an informal version that is easier
  to understand although it is not of course
  suitable for realistically complex situations

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Clustered Attribute Affinity Matrix
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Hybrid Fragmentation

• The nature of the applications/queries may
  require a more complex fragmentation then just a
  simple horizontal or vertical one
• The technique of fragmentation is however
  basically identical (e.g. apply the horizontal
  fragmentation technique followed by the vertical
  fragmentation method to one or more of the
  horizontal fragments)

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Allocation

• The general problem of determining the optimal
  site(s) to allocate the various fragments to is
  impossibly complex
• Clearly each fragment has to be stored at one
  site at least however there is also the choice
  of replicating it at one or more other sites as
  well

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Aim of Allocation

• The fundamental aim of allocation is to minimize
  some cost function that may include
• Inter-site communication costs
• Processing costs
• Data retrieval and updating costs
• Data storage costs

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Factors Affecting Allocation

• Factors that will influence the final allocation
  include
• Expected fragment sizes
• Query selectivity
• Ratio of retrievals to updates for each fragment
• The originating site(s) of queries/updates
• Site storage and processing capabilities
• Inter-site network communication capacities