Querying Large Databases Rukmini Kaushik

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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