Centralized Query Optimization
Centralized query optimization focuses on improving query execution in a single database system, where all data is stored and processed on a single node. The optimization process involves indexing, join ordering, caching, and minimizing CPU and disk I/O time.
Distributed Query Optimization
Distributed query optimization is applied in distributed database systems, where data is fragmented and stored across multiple nodes. It involves additional considerations such as data fragmentation, replication, network communication, and minimizing data transfer costs.
Key Differences Between Centralized and Distributed Query Optimization
| Aspect | Centralized Query Optimization | Distributed Query Optimization |
|---|---|---|
| Complexity | Simple, as all data resides in one location. | More complex due to data fragmentation and network constraints. |
| Cost Estimation | Based on local CPU, I/O, and memory costs. | Includes network latency, data transfer costs, and remote processing. |
| Execution Strategy | Focuses on indexing, join order, and caching. | Uses data locality, join site selection, and minimizing data movement. |
| Data Access Optimization | All data is stored in a single location, making access straightforward. | Data may be fragmented or replicated across multiple nodes, requiring additional coordination. |
| Goal | Minimize CPU and disk I/O time. | Minimize communication overhead and balance load across nodes. |
| Join Processing Optimization | Uses simple join algorithms like nested loop and hash join. | Requires distributed join strategies like semi-join and bloom join. |
| Optimization Approach | Uses heuristic or cost-based optimization. | Uses heuristic, cost-based, or adaptive optimization to account for dynamic data placement. |
| Response Time | Faster, as there is no network overhead. | Slower due to inter-node communication delays. |
| Fault Tolerance | Lower, as a single failure affects the entire system. | Higher, as data is distributed and replicated across nodes. |
Incorporating Communication Overhead and Data Transfer Costs in a Distributed Cost Model
In a distributed query optimization model, query execution costs are estimated while considering network-related overheads.
Factors Affecting Query Costs in Distributed Systems
- Communication Overhead
- Includes message exchange, synchronization, and network latency.
- Optimized by reducing the number of messages exchanged and batching requests.
- Data Transfer Costs
- Estimated based on data size × transfer rate.
- Reduced using techniques like semi-joins, Bloom filters, and predicate pushdown to filter data before transmission.
- Join Processing Overhead
- Evaluates broadcast joins, partitioned joins, and shuffled joins to minimize data movement.
- Prefers local joins and efficient algorithms like hash joins to improve performance.
- Load Balancing & Node Selection
- Queries are distributed based on node capacity and proximity to minimize execution time.
- Balancing the query load prevents bottlenecks and improves overall system efficiency.
Hence, centralized query optimization is simpler and faster, as it operates within a single node, whereas distributed query optimization is more complex due to network and data fragmentation challenges. A well-designed distributed cost model is crucial for reducing communication overhead and improving query execution efficiency across multiple nodes.