Skip to content
Home » Distributed Query Processing

Distributed Query Processing

⭐ DISTRIBUTED QUERY PROCESSING (Detailed Explanation)

Distributed Query Processing (DQP) refers to the techniques and algorithms used to execute queries in a distributed database system, where data is stored across multiple sites connected by a network.

The goal is to process a query as efficiently as possible, minimizing communication cost, reducing response time, and ensuring correct results across all sites.

⭐ WHY IS DISTRIBUTED QUERY PROCESSING NEEDED?

In distributed databases:

  • Data is fragmented (horizontal/vertical)
  • Data may be replicated
  • Data resides at different geographical locations
  • Network communication is costly
  • Processing capabilities vary at each site

Traditional single-site query processing cannot be applied directly.

DQP handles these complexities.

⭐ GOALS OF DISTRIBUTED QUERY PROCESSING

  1. Minimize communication cost
    (Most expensive operation in distributed systems)
  2. Minimize local processing cost
    (Use local processors efficiently)
  3. Optimize overall response time
  4. Perform site selection
    (Choose best site to perform operations)
  5. Ensure correctness
    (Equivalent to centralized execution)
  6. Maximize parallelism
    (Perform operations at multiple sites simultaneously)

⭐ STEPS IN DISTRIBUTED QUERY PROCESSING

DQP involves the following steps:

⭐ 1. Query Decomposition

Convert the user query into a high-level relational algebra expression.

Tasks:

  • Syntactic analysis
  • Semantic analysis
  • Query rewriting
  • Checking if relations/fragments exist

Output → Global Relational Algebra Tree

⭐ 2. Data Localization (Localization of Subqueries)

Identify:

  • Which fragments are needed?
  • Which sites store the required fragments?
  • How to recombine fragments?

The global query is transformed into fragment queries.

Involves:

  • Fragment substitution
  • Replica selection
  • Determining sites for data access

Output → Queries executed at different sites.

⭐ 3. Global Query Optimization

(VERY IMPORTANT for exams)

Optimizer chooses the best execution strategy.

Optimization considers:

✔ Communication cost
✔ Local processing cost
✔ Size of intermediate results
✔ Join strategies
✔ Fragment locations
✔ Parallelism opportunities

The most expensive cost = sending data between sites.

⭐ 4. Distributed Execution

Tasks executed at different sites:

  • Local scans
  • Local joins
  • Local selections
  • Data transfer operations
  • Final assembly of results

Execution is done in parallel where possible.

⭐ QUERY PROCESSING STRATEGIES

Distributed queries are executed using different strategies:

⭐ 1. Query Shipping

  • Query is sent to the site where data is located.
  • Processing done at data location.

✔ Advantages:

  • Reduces data transfer
  • Efficient for selective queries

⭐ 2. Data Shipping

  • Data is brought from remote sites to the coordinating site.
  • Query is executed locally.

✔ Used When:

  • Query processing site is more powerful
  • Data sets are small

⭐ 3. Hybrid Shipping

Combination of query and data shipping
Used in most distributed DBMS systems.

⭐ DISTRIBUTED QUERY OPTIMIZATION TECHNIQUES

Optimization focuses mainly on reducing communication cost.

✔ Factors considered:

  • Location of fragments
  • Size of data
  • Selectivity of operations
  • Network bandwidth
  • Join ordering
  • Parallelism

⭐ COST MODEL IN DISTRIBUTED QUERY PROCESSING

Three types of costs:

  1. Local Processing Cost
    (CPU + disk I/O)
  2. Communication Cost
    (data transfer between sites)
  3. Synchronization Cost
    (coordination overhead)

Communication Cost is the dominant factor.

⭐ DISTRIBUTED JOIN STRATEGIES

(Very important)

✔ 1. Semi-Join

Send only matching attributes, not full tuples.

Reduces data transfer significantly.

✔ 2. Bloom Join

Use Bloom filters to reduce transferred data.

✔ 3. Repartition Join

Hash-partition both tables across sites → parallel joins.

✔ 4. Broadcast Join

Send small table to all sites where large table is stored.

✔ 5. Fragment-and-Replicate Join

Used when data is fragmented but replicated.

⭐ PARALLEL EXECUTION IN DISTRIBUTED QUERIES

Distributed systems exploit:

✔ Inter-Query Parallelism
✔ Intra-Query Parallelism
✔ Intra-Operation Parallelism
✔ Inter-Operation Parallelism

This accelerates joins, scans, and aggregations.

⭐ CHALLENGES IN DISTRIBUTED QUERY PROCESSING

✔ 1. Heterogeneous systems

Different DBMS and schemas.

✔ 2. Network failures

Queries must survive communication breakdowns.

✔ 3. Replication conflicts

Choosing the correct replica for reading/writing.

✔ 4. Fragmentation complexity

Handling horizontal/vertical/hybrid fragments.

✔ 5. Optimization complexity

Cost estimation is harder due to multi-site uncertainty.

⭐ ADVANTAGES OF DISTRIBUTED QUERY PROCESSING

✔ Better response time
✔ Improves scalability
✔ Reduces network traffic
✔ Supports local autonomy
✔ Uses parallelism across sites
✔ Efficient handling of large distributed databases

⭐ EXAMPLE (For MCA Exams)

Suppose a database is fragmented:

  • Employee1 stored at Site A
  • Employee2 stored at Site B

Query:

SELECT Name, Dept
FROM Employees
WHERE Salary > 50000;

Steps:

  1. Query sent to both sites
  2. Each site performs local selection
  3. Only filtered results sent back
  4. Coordinator merges and returns final output

This reduces communication cost and speeds up processing.

⭐ Perfect 5–6 Mark Short Answer

Distributed Query Processing refers to the techniques used to evaluate queries in distributed databases, where data is stored across multiple sites. It involves query decomposition, data localization, global optimization, and distributed execution. The goal is to minimize communication cost, reduce response time, and utilize parallelism. Techniques like semi-join, data shipping, query shipping, and distributed join algorithms ensure efficient execution of distributed queries while maintaining correctness and performance.