⭐ INTRODUCTION TO PARALLEL SYSTEMS – PARALLEL DATABASES
Modern organizations generate enormous amounts of data (GB → TB → PB).
To process this data quickly and efficiently, traditional single-processor database systems are not sufficient.
This leads to the need for parallel systems and parallel databases, where multiple processors work simultaneously to speed up database operations.
Parallel systems apply the concept of parallel processing to databases so that queries, transactions, indexing, sorting, and analytics can be executed much faster.
⭐ WHAT IS A PARALLEL SYSTEM?
A Parallel System is a computer system consisting of:
- Multiple processors/CPUs
- Multiple memory units
- Multiple disks/storage devices
- High-speed intercommunication network
These processors work at the same time (in parallel) on different parts of a problem → leading to high speed and performance.
Parallel processing is used in:
- Databases
- Scientific computing
- Real-time analytics
- Big data systems
- Artificial Intelligence (AI)
⭐ NEED FOR PARALLEL SYSTEMS IN DATABASES
Database workloads today involve:
- Millions of records
- Complex queries (joins, aggregation, sorting)
- Real-time analytics
- Large data warehouses
- Heavy transaction load (banking, telecom, e-commerce)
Sequential processing becomes slow and inefficient.
Parallel processing offers solutions to all these limitations.
✔ 1. High Performance
Tasks are divided across multiple processors → faster query response time.
✔ 2. High Throughput
More queries can be executed simultaneously.
✔ 3. Large Data Handling
Can store & process terabytes or petabytes of data efficiently.
✔ 4. Scalability
Add more processors/nodes to improve performance.
✔ 5. High Availability
If one processor fails, others continue working → system still runs.
✔ 6. Supports OLAP & Data Mining
Parallelism speeds up data warehousing and analytical workloads.
⭐ WHAT ARE PARALLEL DATABASES?
A Parallel Database is a database system designed to run on multiple processors/machines (nodes) simultaneously, executing database operations in parallel.
Parallel databases use:
- Multiple CPUs
- Multiple disk drives
- Partitioned data
- Parallel algorithms
- High-speed networks
They divide data and queries into smaller units and process them concurrently for fast results.
⭐ MAIN GOALS OF PARALLEL DATABASES
✔ 1. Improve Query Performance
Parallel execution of joins, sorting, grouping, searching.
✔ 2. High Throughput
Allows many users/queries to run without slowing down.
✔ 3. Scalability (Horizontal Scaling)
Add more machines instead of upgrading a single machine.
✔ 4. Load Balancing
Distributes workload across nodes evenly.
✔ 5. Fault Tolerance
Failure of one node does not stop the entire system.
✔ 6. Efficient Data Processing in Warehouses
Ideal for OLAP, ETL, business intelligence, and analytical workloads.
⭐ HOW PARALLELISM IS ACHIEVED IN DATABASES?
Parallel databases use three levels of parallelism:
1. Inter-Query Parallelism
Multiple queries execute in parallel on different processors.
Example:
User A runs SELECT query while User B runs UPDATE — both run simultaneously.
2. Intra-Query Parallelism
A single query is broken into multiple tasks executed in parallel.
Example:
A large join is processed by multiple CPUs at the same time.
3. Intra-Operation Parallelism
Each operation of a query (scan, join, sort) is parallelized.
Example:
Parallel table scan, parallel hash join, parallel sorting.
⭐ PARALLELISM USING SYSTEM ARCHITECTURE
Parallel databases rely on different architectures:
- Shared Memory Systems
- Shared Disk Systems
- Shared Nothing Systems (MPP)
- Hybrid Architectures
Shared-Nothing (Massively Parallel Processing – MPP) is the most scalable and used in modern big data systems (Redshift, BigQuery, Teradata).
⭐ FEATURES OF PARALLEL DATABASES
✔ Data partitioning (range, hash, round-robin)
✔ Parallel query optimization
✔ Parallel joins and sorting
✔ Fault tolerance and recovery
✔ Load balancing
✔ High throughput
✔ Distributed storage
⭐ ADVANTAGES OF PARALLEL DATABASES
- Extremely fast query processing
- Handles massive datasets
- Supports concurrent users
- Fault-tolerant
- Easily scalable
- Efficient resource utilization
⭐ DISADVANTAGES / CHALLENGES
- Complex to implement
- High cost (hardware + networking)
- Difficult debugging and optimization
- Data skew (imbalanced data distribution)
- Communication overhead
- Requires advanced DBAs
⭐ Perfect 5–6 Mark Short Answer
Parallel Systems consist of multiple processors working simultaneously to execute tasks efficiently.
Parallel Databases apply this concept to database operations by dividing data and queries into smaller tasks processed across multiple CPUs or nodes.
This leads to faster query performance, improved throughput, high scalability, and better management of large datasets.
Parallel databases achieve parallelism through techniques such as intra-query, inter-query, and intra-operation parallelism and typically use architectures like shared-memory, shared-disk, and shared-nothing systems.