What is a Parallel Database System?
A Parallel Database System combines database management and parallel processing to deliver high-performance and high-availability database servers. It uses multiple processors and disks to execute operations concurrently, making tasks faster and more efficient.
What is Parallel Processing in Parallel DBMS?
Parallel processing divides a large task into many smaller tasks and executes them concurrently on several nodes. This approach speeds up the completion of the larger task. Nodes can be separate processors on different machines or multiple processors on a single machine.
What are the Key Elements of Parallel Processing?
- Speedup: Faster query execution.
- Scaleup: Handles larger workloads efficiently.
- Synchronization: Ensures tasks are executed in the correct order.
- Locking: Prevents data conflicts.
- Messaging: Enables communication between nodes.
What are the Key Characteristics of a Parallel Processing System?
- Concurrency: Each processor can perform tasks simultaneously.
- Synchronization: Tasks may need to be coordinated.
- Resource Sharing: Nodes usually share resources like data, disks, and other devices.
What are the Main Types of Parallel Architectures?
- Shared Memory: Multiple CPUs share a single memory and disk.
- Shared Disk: Each CPU has its own memory but shares the same disk.
- Shared Nothing: Each CPU has its own memory and disk, with no sharing of resources.
What is the Difference Between Parallel and Distributed Databases?
Parallel Databases: Run across multiple processors, designed for parallel operations, and data resides in a single location.
Distributed Databases: Data is physically distributed across different sites, and each site has its own local database system.
What are the Advantages of Using a Parallel Database System?
- Performance Improvement: Increased performance by using multiple CPUs and disks.
- High Availability: Failure of one node doesn't affect the entire system.
- Proper Resource Utilization: CPUs are never idle due to parallel execution.
- Increased Reliability: Execution can continue with another node if one fails.
What are the Disadvantages of Parallel DBMS?
- High Cost: Requires many processors and storage devices.
- Complex Management: Difficult to configure and maintain.
- Resource Upgrades: Keeping up with technology can be expensive.
- Difficult to Update: Software updates and maintenance can take time.
What are the Challenges of Implementing Parallel Processing?
- Task Structuring: Ensuring tasks can be executed in parallel.
- Sequencing: Maintaining the correct order of tasks that must be executed serially.
- Synchronization Overhead: Managing the time and resources needed for synchronization.
What is Synchronization in Parallel Processing?
It is the process of coordinating tasks running in parallel to ensure correct results. It involves managing the timing and order of tasks to avoid conflicts and ensure efficient execution.
What are the Types of Data Partitioning in I/O Parallelism?
- Round-robin: Distributes data evenly across partitions.
- Hash: Uses a hash function to determine the partition.
- Range: Distributes data based on value intervals.
- Schema: Different tables are placed on different disks.
What is Query Optimization in Parallel DBMS?
Query Optimization is the process of producing an execution plan for a query that minimizes cost. It involves assessing different execution plans and selecting the most efficient one based on factors like disk access, CPU time, and communication time.
What are the Components of Parallel DBMS Architecture?
- Session Manager: Manages connections between client processes and other subsystems.
- Transaction Manager: Handles query compilation and execution.
- Data Manager: Provides low-level functions needed to run queries in parallel.
What is Shared Memory Architecture?
Multiple CPUs share a single memory and disk array, connected by a fast interconnect.
Advantages: Simplicity, high-speed access, efficient communication.
Disadvantages: Limited scalability, high cost, bus blocking with many CPUs.
What is Shared Disk Architecture?
Multiple CPUs with their own memory share access to the same disk storage.
Advantages: No bottleneck, easier load balancing, better fault tolerance, high extensibility.
Disadvantages: Interference and contention, scalability issues, higher complexity.
What is Inter-query Parallelism?
Runs multiple queries at the same time to improve throughput.
Example: A bank processes multiple customer transactions simultaneously.
What is Inter-operation Parallelism?
Involves executing different operations within a query expression simultaneously.
How it Works: Operations like scan, join, and sort are performed in parallel across multiple processors, reducing query execution time.
What is Skew in Parallel DBMS?
Refers to the uneven distribution of data across disks.
How it Affects Performance: Causes imbalance, leading to delays and inefficient resource use.