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Querying Large Databases

Researching efficient algorithms and software architectures for query engines, including query execution engine, sorting, hashing, aggregation, duplicate removal, complex query execution plan, parallel query execution mechanism, parallel algorithms, query optimization, and tuning query performance.

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Querying Large Databases

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  1. Querying Large Databases Rukmini Kaushik

  2. Purpose • Research for efficient algorithms and software architectures of query engines.

  3. Query Execution Engine Architecture • Query processing algorithms – physical algebra • Data Model – logical algebra

  4. Sorting & Hashing • Both are memory intensive. • Memory Concerns - Merge Efficiency & memory management. - Hash table overflow

  5. Aggregation and Duplicate Removal • Aggregation Concept Describes a set of objects with one value. • Algorithms Three Types - Nested Loops - Sorting - Hashing

  6. Aggregation & Duplicate Removal • Nested Loops - Easiest of the three - Doesn’t work well for large inputs • Sorting - Sort for common elements which results in a simple duplicate removal. - Should remove duplicates as early as possible.

  7. Aggregation & Duplicate Removal • Hashing - Hash on group attributes. - Can perform duplicate removal when creating hash table. • Algorithm Analysis Sorting and hashing functions are logarithmic with input size

  8. Complex Query Execution Plan • Purpose - To schedule a query with several operations optimally • Ideas - Right-deep plans - Left-deep plans

  9. Complex Query Execution Plan • Prediction - Use a decision tree of sub-plans - Done by using choose-plan operators • Major Concern - Optimal resource allocation

  10. Parallel Query Execution Mechanism • Goal Obtain speed-up & scale-up • Speed-up - Uses extra hardware for constant size problem - Linear speed-up is optimal - Can be expressed as parallel efficiency

  11. Parallel Query Execution Mechanism • Scale-up - Uses same resources with altered problem size - Can be expressed as parallel efficiency.

  12. Parallel Query Execution Mechanism • Parallel Vs Distributed Systems • Distributed - Locally Autonomous - Also uses Parallelism

  13. Parallel Query Execution Mechanism • Parallel - One center of control - Three types Shared memory Shared Disk Distributed Memory

  14. Parallel Query Execution Mechanism • Three forms of parallelism - Inter Query: Servicing multiple requests at the same time - Inter Operator: Pipelining - Intra Operator: Execute a single operator in multiple processors

  15. Parallel Query Execution Mechanism • Implementation Bracket Models Operator Models • Bracket Model Goal: Generic process template that receives and sends data and performs one operation at a time

  16. Parallel Query Execution Mechanism Number of inputs is limited to two Can be run in parallel by having many templates in the system running simultaneously. • Operator Model Goal: Insert parallel operators in an ordered plan

  17. Parallel Query Execution Mechanism • Uses the exchange operator • Exchange operator - Does not manipulate data - Provides capabilities for parallel query processing - Changes a complex query into a single process

  18. Parallel Algorithms • Idea: More focus on algorithms and parallel execution • Parallel selections and updates - Disk input and output should be made parallel - Selection: Maintain indices near stored data - Updates: Use keys for partitioning attributes

  19. Parallel Algorithms • Parallel Sorting: -classified by - number of parallel inputs - number of parallel outputs - Algorithms consists of local sort and a data exchange step

  20. Parallel Algorithms - Major Concern - Deadlock can be avoided by using range partitioning - having a sufficient size data exchange buffer - using a modified sort algorithm

  21. Query Optimization • Uses the differences between logical and physical aspects • Must keep track of the properties of the inputs • Cost models focus on throughput measures

  22. Tuning query performance • Focus - Guidelines for improving query performance • Guidelines for three points of view - implementor and vendor - database administrator - application programmer

  23. Tuning Query Performance • Implementor System should support indexing and clustering Query optimizer should be reliable and accurate • Administrator Ensure usage of system facilities

  24. Tuning Query Performance carefully choose physical database design provide available and efficient processing resources • Application Programmer Provide high level queries

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