Parallel Databases: Advantages, Disadvantages, Shared Memory & Share Disk

Parallel Databases enhance processing and input/output speeds by leveraging multiple CPUs and disks in a parallel manner. The goal of a Parallel Database system is to optimize performance through the parallel execution of various tasks, including data loading, index creation, and query evaluation. Unlike serial processing, where steps are executed sequentially, parallel processing performs multiple operations simultaneously.

Databases bring advantages to organizations of all sizes as they enhance information management. A database employs a server, a specialized program overseeing user requests. For large user bases and extensive record processing, organizations adopt the parallel database approach. These databases are known for their speed, flexibility, and reliability.

Architecture of Parallel Databases

There exist three primary architectures for constructing parallel DBMS:

  1. Shared Memory
  2. Shared Disk System
  3. Shared Nothing System

Shared Memory System:

This configuration involves linking multiple processors through a network and granting them access to a shared memory region.


  1. Resembles traditional machines and is programmer-friendly.
  2. Low overhead.
  3. Leverages OS services to utilize additional CPUs.


  1. Prone to bottleneck issues.
  2. Expensive to construct.
  3. Less responsive to partitioning.

Shared Disk System:

Each processor possesses its own main memory and direct access to all disks through an interconnected network.


  1. Resembles traditional machines and is programmer-friendly.
  2. Low overhead.
  3. Leverages OS services to utilize additional CPUs.


  1. Increased interference.
  2. Greater network bandwidth usage.
  3. Less responsive to partitioning.

Shared Nothing:

Here, each processor maintains local main memory and disk space. Processors cannot access each other’s storage, and communication occurs solely through a network connection. Each processor has its own mass storage and main memory.


  1. Enables linear scale-up and speed-up.
  2. Benefits from effective partitioning.
  3. Cost-effective to build.


  1. Complex programming.
  2. Addition of new nodes requires reorganization.

Parallel Query Evaluation

A relational query execution plan comprises a graph/tree of relational algebra operators that can execute in parallel. Operators consuming the output of another operator exhibit pipelined parallelism.

Data Partitioning:

Large databases are horizontally partitioned across multiple disks, allowing parallel I/O operations. Partitioning methods include:

  1. Round Robin Partitioning: Assigns the 1st tuple to processor i mod n, suitable for queries accessing the entire relation.
  2. Hash Partitioning: Applies a hash function to tuples to determine processors, maintaining even data distribution.
  3. Range Partitioning: Sorts tuples and assigns ranges to processors, potentially causing data skew.

Advantages of Parallel Databases:

  1. Operates on multiple computers simultaneously.
  2. Offers high performance, speed, reliability, and capacity.

Disadvantages of Parallel Databases:

  1. Costly implementation.
  2. Complex management of parallel operations.
  3. Requires substantial resources for support and maintenance.


  1. A parallel database is designed to run on multiple computers simultaneously, improving performance.
  2. The three architectures for databases are Shared Memory, Shared Disk System, and Shared Nothing System.
  3. Data can be partitioned using round-robin, Hash, and Range Partitioning methods.

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