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⭐ INTER-OPERATION & INTRA-OPERATION PARALLELISM

(Parallel Databases – Detailed Discussion)

Parallel databases achieve high performance by dividing tasks among multiple processors.
Two fundamental types of intra-query parallelism are:

1. Intra-Operation Parallelism
2. Inter-Operation Parallelism

These techniques improve the response time of a single large query by executing different parts in parallel.

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⭐ 1. INTRA-OPERATION PARALLELISM

✔ Definition

Intra-Operation Parallelism means performing a single database operation (such as scan, join, sort, aggregation) in parallel using multiple processors.

Here, one operation of the query is broken into smaller tasks, each handled by different CPUs/disks.

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⭐ Why Use Intra-Operation Parallelism?

✔ Greatly speeds up heavy operations on large datasets
✔ Speeds up table scans, joins, sorting, grouping
✔ Necessary for OLAP, Data Warehousing, Big Data
✔ Utilizes multiple processors efficiently

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⭐ Types of Intra-Operation Parallelism

There are three main types:

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⭐ A. Partitioned (Parallel) Scanning

A table is divided horizontally into partitions across multiple disks or nodes.

Each processor scans one partition:

CPU1 → Partition 1  
CPU2 → Partition 2  
CPU3 → Partition 3  
CPU4 → Partition 4

Result: Faster table scan (4× speedup for 4 CPUs)

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⭐ B. Parallel Sorting

Sorting large data is time-consuming, so DBMS divides:

1. Break data into chunks
2. Each CPU sorts its chunk
3. Sorted chunks merged in parallel

Used in parallel merge sort and external sorting algorithms.

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⭐ C. Parallel Join

Joins are among the most expensive operations.
Parallel joins include:

1. Parallel Hash Join
   • Build hash table on partitions
   • Probe in parallel
2. Parallel Nested Loop Join
   • Blocks distributed among processors
3. Parallel Merge Join
   • Sort partitions
   • Merge simultaneously

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⭐ D. Parallel Aggregation

Aggregation functions (SUM, AVG, COUNT, GROUP BY) can be executed locally:

CPU1 → SUM on Partition 1  
CPU2 → SUM on Partition 2  
CPU3 → SUM on Partition 3  

Final result = merge of local aggregates.

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⭐ Benefits of Intra-Operation Parallelism

• Speeds up operations on large tables
• Balances load across processors
• Achieves almost linear scale with more CPUs
• Reduces total query response time

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⭐ 2. INTER-OPERATION PARALLELISM

(Also called Pipeline Parallelism)

✔ Definition

Inter-Operation Parallelism means executing different operations of the same query plan simultaneously.

Each operator of the query (scan, join, sort) runs in a pipeline, producing output tuples as soon as they are available, without waiting for the previous operation to finish completely.

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⭐ How It Works?

A query execution plan consists of multiple operators:

Example:

σ (Salary > 50000)
         |
     Hash Join
         |
     Table Scan

With pipeline parallelism:

• Table Scan starts reading rows
• Hash Join starts processing these rows immediately
• Selection starts filtering as soon as join outputs tuples

All three operators overlap in time.

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⭐ Types of Inter-Operation Parallelism

There are two levels:

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⭐ A. Independent Parallelism

Two operations that do not depend on each other run simultaneously.

Example:

• Query 1: Scan Table A
• Query 2: Scan Table B

Both scans can run in parallel.

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⭐ B. Pipeline (Producer–Consumer) Parallelism

The output of one operator is streamed directly into the next operator.

This reduces the total execution time significantly.

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⭐ Benefits of Inter-Operation Parallelism

✔ Reduces overall execution time of a query
✔ Improves throughput
✔ Allows pipeline execution (continuous flow of data)
✔ Efficient for multi-step queries (scan → join → sort → aggregate)

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⭐ Difference Between Intra-Operation & Inter-Operation Parallelism

Feature	Intra-Operation Parallelism	Inter-Operation Parallelism
What is parallelized?	A single operation	Multiple operators of a query
Example	Parallel join, parallel scan	Scan, join, sort processed together
Goal	Speed up heavy operations	Reduce total query time
Type	Partition-level	Pipeline-level
Used in	OLAP, large table operations	Complex query plans
Complexity	High	Medium

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⭐ Example (MCA Exam Style)

Query:

SELECT Dept, AVG(Salary)
FROM Employees
GROUP BY Dept;

Using Intra-Operation Parallelism:

• Table partitioned into 4 parts
• Each CPU calculates local averages
• Results merged into global average

Using Inter-Operation Parallelism:

• Scan → handle tuples
• GroupBy → starts processing early
• Merge results in a pipeline

Result: Faster execution

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⭐ Perfect 5–6 Mark Short Answer

Intra-Operation Parallelism breaks a single database operation (such as scan, join, sort) into parallel tasks executed on multiple processors. It speeds up heavy operations and supports large analytical queries.

Inter-Operation Parallelism (pipeline parallelism) executes different operations of a query plan simultaneously. Operators such as scan, join, and selection run in a pipeline, reducing total execution time.

Both help improve query performance in parallel database systems.

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