DBMS Topics
HubUnit 1 — Introduction to DBMS & Entity-Relationship ModelAdvantages and Disadvantages of DBMSAggregationCharacteristics of DBMSData IndependenceData ModelsDatabase Users and AdministratorsDBMS LanguagesER DiagramsEntity-Relationship ER ModelIntroduction to DBMSKeys in DBMSRelationships and CardinalitySchema and InstanceSpecialization and GeneralizationUnit 1 Summary — Introduction to DBMS & ER ModelThree-Level Architecture ANSI/SPARC ArchitectureWeak Entity SetUnit 2 — Relational Model & SQLConstraints in SQLCursorsDCL and TCL CommandsDDL CommandsDML CommandsDomain Relational Calculus DRCFunctions and Stored ProceduresJoins in SQLNested Queries SubqueriesRelational AlgebraRelational CalculusRelational ModelSQL IntroductionUnit 2 Summary — Relational Model & SQLTriggers in SQLTuple Relational Calculus TRCViews in SQLArmstrong's Axioms & Closure of AttributesBoyce-Codd Normal Form BCNFClosure of AttributesDependency PreservationFifth Normal Form 5NFFirst Normal Form 1NFFourth Normal Form 4NFFunctional DependencyLossless DecompositionMultivalued DependencyNormal Forms — OverviewNormalization — IntroductionSecond Normal Form 2NFUnit 3 Summary — NormalizationThird Normal Form 3NFUnit 4 — Transaction Management & Concurrency ControlACID PropertiesCheckpointsConcurrency Control & SerializabilityDeadlock in DBMSLock-Based Protocols & Two-Phase LockingLog-Based RecoveryRecovery SystemSerializabilityShadow PagingUnit 4 Summary — Transaction Management & Concurrency ControlTimestamp-Based ProtocolTransaction ManagementTransaction StatesTwo-Phase Locking 2PLUnit 5 — Storage & IndexingB+ TreeB-TreeCost EstimationDynamic HashingFile OrganizationHashing TechniquesIndexing — IntroductionMulti-Level IndexingQuery OptimizationQuery ProcessingSingle-Level IndexingStatic HashingUnit 5 Summary — Storage & IndexingUnit 6 — Advanced Topics in DBMSBig Data and DBMSCloud DatabaseData MiningData WarehouseDatabase SecurityDistributed DatabaseDistributed Query ProcessingDistributed DBMS Architecture, Fragmentation & ReplicationMongoDB — IntroductionNoSQL DatabaseReplication in Distributed DatabasesUnit 6 Summary — Advanced Topics in DBMSViva Voce — DBMSImportant Viva Questions — DBMSInterview Questions — DBMSViva Answers — DBMSDBMS Multiple Choice Questions MCQsDBMS Lab Programs
Distributed Database
Last Updated : 21 May, 2026
A distributed database is a collection of multiple, logically interrelated databases distributed over a computer network. The data is stored at different sites nodes/loca
Unit 6252 words14 headingsTables includedExamples included
What is a Distributed Database?
A distributed database is a collection of multiple, logically interrelated databases distributed over a computer network. The data is stored at different sites (nodes/locations), but the system presents it to users as a single unified database.
| Site A | Site B | Site C | ||
|---|---|---|---|---|
| Delhi | Mumbai | Chennai | ||
| [EmpTable] | [OrdTable] | [ProdTable |
Key Characteristics
1. Distribution Transparency
Users should not need to know where data is physically stored.
| Location Transparency | User queries without knowing where data is |
| Replication Transparency | User unaware of multiple copies |
| Fragmentation Transparency | User unaware of data splitting |
2. Heterogeneity
Different sites may run different DBMS software (Oracle at Site A, MySQL at Site B). A distributed DBMS must handle this.
3. Local Autonomy
Each site can still operate independently when the network is unavailable.
Advantages of Distributed Databases
| Advantage | Description |
|---|---|
| Reliability | Failure of one site doesn't bring down entire system |
| Availability | Data replicated → can serve requests even during partial failures |
| Performance | Local queries execute faster at the nearest site |
| Scalability | Add more sites as data/load grows |
| Departmental ownership | Each department controls its own data |
| Reduced communication cost | Local queries don't travel over network |
Disadvantages
| Disadvantage | Description |
|---|---|
| Complexity | Much harder to manage, tune, and debug |
| Higher cost | Network infrastructure + distributed DBMS software |
| Security risks | More attack surfaces across network |
| Concurrency control | Global transactions across sites are complex |
| Consistency challenges | CAP theorem applies — cannot guarantee all three properties |
CAP Theorem
In a distributed system, you can guarantee at most two of:
| Consistency | Every read returns the most recent write |
| Availability | Every request receives a response |
| Partition Tolerance | System continues despite network partitions |
| CA Systems | Traditional RDBMS (single node) |
| CP Systems | MongoDB (with default settings), HBase, ZooKeeper |
| AP Systems | Cassandra, CouchDB, DynamoDB |
DDBMS Architecture Models
1. Client-Server Architecture
Clients (thin) → request data
Servers (thick) → store and process data
┌────────┐ ┌────────┐
│Client 1│────►│Server A│ ← Primary data server
└────────┘ └────────┘
┌────────┐ │
│Client 2│ │ replication
└────────┘ ┌───▼────┐
┌────────┐ │Server B│ ← Replica/secondary server
│Client 3│────►└────────┘
└────────┘
2. Peer-to-Peer Architecture
| Node 1 | Node 2 | |
|---|---|---|
| Node 3 | ←→ | Node 4 |
3. Federated Database
Multiple autonomous databases cooperate without full integration.
Distributed Transactions
Transactions that span multiple sites require coordination:
Two-Phase Commit (2PC) Protocol
| COORDINATOR | ───────────────► | Site A |
|---|---|---|
| ◄─ VOTE YES ──── | ||
| ───────────────► | Site B | |
| ◄─ VOTE YES ──── |
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for Distributed Database.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this DBMS topic.
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