Definition of terms Definition of terms Explain business conditions driving distributed databases Explain business conditions driving distributed databases Describe salient characteristics of distributed database environments Describe salient characteristics of distributed database environments Explain advantages and risks of distributed databases Explain advantages and risks of distributed databases Explain strategies and options for distributed database design Explain strategies and options for distributed database design Discuss synchronous and asynchronous data replication and partitioning Discuss synchronous and asynchronous data replication and partitioning

Distributed Database: A single logical database spread physically across computers in multiple locations that are connected by a data communications link Distributed Database: A single logical database spread physically across computers in multiple locations that are connected by a data communications link Decentralized Database: A collection of independent databases on non-networked computers Decentralized Database: A collection of independent databases on non-networked computers They are NOT the same thing

Shares the database logical processing among two or more independent sites that are connected through a network Shares the database logical processing among two or more independent sites that are connected through a network Distributed database different with distributed processing Distributed database different with distributed processing

DATABASE SERVER

Distributed processing Distributed processing – Does not require distributed database – May be based on a single database on single computer – Copies or parts of database processing functions must be distributed to all data storage sites Distributed database Distributed database – Requires distributed processing Both Both – Require a network to connect components

Business unit autonomy and distribution Business unit autonomy and distribution Data sharing Data sharing Data communication reliability and costs Data communication reliability and costs Multiple application vendors Multiple application vendors Database recovery Database recovery Transaction and analytic processing Transaction and analytic processing

Distributed database environments

Homogeneous Homogeneous – same DBMS at each node Heterogeneous Heterogeneous – different DBMSs at different nodes

Easy to manage, difficult to enforce Easy to manage, difficult to enforce – Autonomous independent DBMSs: update data independent DBMSs: update data – Non-autonomous Central/master DBMS coordinates database access & updates across the nodes Central/master DBMS coordinates database access & updates across the nodes

Identical DBMSs Homogeneous Distributed Database Environment Data is distributed across all the nodes Same DBMS at each node All data is managed by the distributed DBMS (no exclusively local data) All access is through one, global schema The global schema is the union of all the local schema

Difficult to manage, preferred by independent organizations Difficult to manage, preferred by independent organizations – Systems supports some or all functionality of one logical database supports some or all functionality of one logical database Two types: Two types: – Full DBMS Functionality–all distributed DB functions – Partial-Multi database–some distributed DB functions » Federated–supports local databases for unique data requests Loose Integration–local dbs have their own schemas Tight Integration–local dbs use common schema » Unfederated–requires all access to go through a central, coordinating module – Gateways simple paths are created to other databases without the benefits of one logical database simple paths are created to other databases without the benefits of one logical database

Non-identical DBMSs Data distributed across all the nodes Data distributed across all the nodes Different DBMSs may be used at each node Different DBMSs may be used at each node Local access is done using the local DBMS and schema Local access is done using the local DBMS and schema Remote access is done using the global schema Remote access is done using the global schema

Major objective: provide ease of access to data for users at many different locations. Major objective: provide ease of access to data for users at many different locations. Distributed Database System must provide: Distributed Database System must provide: – Location Transparency User does not have to know the location of the data User does not have to know the location of the data Data requests automatically forwarded to appropriate sites Data requests automatically forwarded to appropriate sites – Local Autonomy Local site can operate with its database when network connections fail Local site can operate with its database when network connections fail Each site controls its own data, security, logging, recovery Each site controls its own data, security, logging, recovery

Synchronous Distributed Database Synchronous Distributed Database – All copies of the same data are always identical – Data updates are immediately applied to all copies throughout network – Good for data integrity – High overhead  slow response times Asynchronous Distributed Database Asynchronous Distributed Database – Some data inconsistency is tolerated – Data update propagation is delayed – Lower data integrity – Less overhead  faster response time

Increased reliability/availability Increased reliability/availability Local control over data Local control over data Modular growth Modular growth Lower communication costs Lower communication costs Faster response for certain queries Faster response for certain queries

Software cost and complexity Software cost and complexity Processing overhead Processing overhead Data integrity exposure Data integrity exposure Slower response for certain queries Slower response for certain queries

FOUR basic strategies for distributing databases: FOUR basic strategies for distributing databases: – Data replication Copies of data distributed to different sites Copies of data distributed to different sites – Horizontal partitioning Different rows of a table distributed to different sites Different rows of a table distributed to different sites – Vertical partitioning Different columns of a table distributed to different sites Different columns of a table distributed to different sites – Combinations of the above

Customer relation for a bank Customer relation for a bank

Copies of data distributed to different sites Copies of data distributed to different sites Advantages: Advantages: – Reliability One site fails, a copy can be found at another site One site fails, a copy can be found at another site – Fast response Process queries locally Process queries locally – May avoid complicated distributed transaction integrity routines (if replicated data is refreshed at scheduled intervals) – Decouples nodes (transactions proceed even if some nodes are down) – Reduced network traffic at prime time (if updates can be delayed)

Disadvantages: Disadvantages: – Additional requirements for storage space – Additional time for update operations – Complexity and cost of updating – Integrity exposure of getting incorrect data if replicated data is not updated simultaneously Therefore, better when used for non-volatile (read-only) data

Push Replication Push Replication – updating site sends changes to other sites Pull Replication Pull Replication – receiving sites control when update messages will be processed

Snapshot Replication Snapshot Replication – Changes periodically sent to master site – Master collects updates in log – Full or differential (incremental) snapshots – Dynamic vs. shared update ownership Near Real-Time Replication Near Real-Time Replication – Broadcast update orders without requiring confirmation – Done through use of triggers – Update messages stored in message queue until processed by receiving site

Data timeliness Data timeliness – Applications that can tolerate out-of-date data DBMS capabilities DBMS capabilities – if DBMS cannot support multi-node queries, replication may be necessary Performance implications Performance implications – refreshing may cause performance problems for busy nodes Network heterogeneity Network heterogeneity – complicates replication Communications network capabilities Communications network capabilities – complete refreshes place heavy demand on telecommunications

Different rows of a table at different sites Different rows of a table at different sites Each partition has a unique row but each row has all columns Each partition has a unique row but each row has all columns

Horizontal partitions of Customer relation Horizontal partitions of Customer relation

Advantages Advantages – Data stored close to where it is used  efficiency – Local access optimization  better performance – Only relevant data is available  security – Unions across partitions  ease of query Disadvantages Disadvantages – Accessing data across partitions  inconsistent access speed – No data replication  backup vulnerability

Different columns of a table at different sites Different columns of a table at different sites Each vertical partition has a unique columns Each vertical partition has a unique columns Partitions must have one same key column to make sure we can combine it back. Partitions must have one same key column to make sure we can combine it back.

Advantages and disadvantages are the same as for horizontal partitioning except that combining data across partitions is more difficult because it requires joins (instead of unions) Advantages and disadvantages are the same as for horizontal partitioning except that combining data across partitions is more difficult because it requires joins (instead of unions)

Some data stored centrally, other data are replicated at the various sites. Some data stored centrally, other data are replicated at the various sites. Both horizontal and vertical partitions may be desirable for data distribution. Both horizontal and vertical partitions may be desirable for data distribution. Overriding Principle: Overriding Principle: – Data should be stored at the sites where they will be accessed most frequently.

Examples: Examples: – Engineering parts, Accounting and Customer data are each centralized at different locations – Standard parts data are partitioned (horizontally) among the three locations. – The Standard Price List is replicated at all three locations

1. Centralized database, distributed access 2. Replication with periodic snapshot update 3. Replication with near real-time synchronization of updates 4. Partitioned into segments at different sites, one logical database & DDBMS 5. Partitioned into independent, nonintegrated segments across multiple computers & database software

Organizational Forces: Funding, autonomy of units, security Organizational Forces: Funding, autonomy of units, security Frequency and locality or clustering of reference to data Frequency and locality or clustering of reference to data – Data should be located close to the applications that use those data Growth and expansion needs Growth and expansion needs Technological capabilities Technological capabilities – Costs of managing complex technologies Need for reliable service Need for reliable service