Database Query Optimizer Deferring Column Decompression
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Solution Overview
Problem
Commercial database systems face increased computational costs when processing large amounts of compressed data, as decompression is often unnecessary for producing desired results, leading to wastage of computational resources.
Innovation Solution
Implementing a method within the database system to defer or eliminate decompression of data by identifying and decompressing only the columns and rows necessary for query results, using a query optimizer to generate a query plan that minimizes unnecessary decompression operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If data is compressed to reduce storage space, then storage space is reduced, but computational cost increases due to decompression operations
Solution Approach 1:
The system performs preliminary analysis during query planning to identify which compressed columns will actually be needed for query results. By determining this beforehand, the system can prepare decompression only for necessary columns while leaving others compressed, thus avoiding unnecessary computational overhead while maintaining storage efficiency.
Solution Approach 2:
Instead of decompressing all compressed columns (excessive action), the system applies partial decompression only to the specific columns required by the query. This selective approach reduces computational cost while still achieving the query objective, avoiding the waste of decompressing unnecessary data.
2Ease of operation
If all compressed data is decompressed to ensure data availability, then data accessibility is improved, but computational resources are wasted on unnecessary decompression
Solution Approach 1:
The query optimizer performs preliminary analysis to identify which compressed columns are actually needed for query results. This advance planning ensures that data accessibility is maintained for required columns while avoiding unnecessary decompression of other columns, thus eliminating computational resource waste.
Solution Approach 2:
The system extracts and decompresses only the specific columns needed for query execution, leaving other columns in their compressed state. This selective extraction approach maintains data accessibility for necessary columns while avoiding the energy waste of processing unnecessary data.
3Speed
If decompression is performed early in query processing, then data is available for processing, but computational cost increases for queries where decompressed data is not used
Solution Approach 1:
The system performs preliminary analysis during query planning to determine which columns will actually be used in query results. This allows the system to decompress only those specific columns in advance, ensuring data availability for processing while avoiding the computational cost of decompressing unnecessary columns that won't contribute to the final results.
Solution Approach 2:
Instead of decompressing all columns (excessive action), the system applies partial decompression only to the subset of columns required by the query. This selective partial action maintains processing speed for necessary data while eliminating waste on unnecessary decompression operations.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for deferring or eliminating a need to decompress database data. An embodiment operates by decompressing a column of a database. The column may be represented by a predicate of a query. A row of the database may be determined to satisfy the predicate based on decompressed information from the column. Decompression of an additional column of the row may be deferred during execution the query until the row is determined to satisfy the predicate. The additional column may satisfy the query.