Columnar Database Histogram Query Optimization
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Solution Overview
Problem
Current database management systems face inefficiencies in data storage and retrieval due to increasing data volumes, despite advancements like columnar databases, which still require optimizations to reduce complexity and storage requirements while improving query processing efficiency.
Innovation Solution
Implementing a height-balanced histogram for columnar databases to determine which data blocks need to be read during query processing, using probabilistic data structures and bucket range sizes to selectively access data, thereby reducing unnecessary read operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If columnar database tables are used to reduce access operations, then query efficiency is improved, but data storage complexity increases
Solution Approach 1:
The patent segments data storage into columnar units and introduces histogram-based segmentation to divide columns into buckets with different access frequencies. This allows the system to manage complexity by organizing data into manageable segments that can be accessed independently based on query patterns.
Solution Approach 2:
The patent implements preliminary action by pre-computing histograms that capture the access frequency distribution of column data before queries are executed. These pre-computed statistics enable the system to make informed decisions about data block access patterns, reducing the need for complex real-time analysis during query execution.
2Quantity of substance
If more data is stored and managed, then information capacity increases, but maintenance cost increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting data block access patterns based on histogram-derived access frequency parameters. The system transforms raw data into histogram statistics that reveal access patterns, then uses these parameters to optimize which data blocks are accessed and how frequently, reducing unnecessary I/O operations and maintenance costs as data volume grows.
3Productivity
If histogram-based selective access is implemented, then read operations are minimized, but computational overhead increases
Solution Approach 1:
The patent implements partial action by computing histograms selectively based on actual query workloads rather than maintaining complete histograms for all data uniformly. The system performs histogram computation and analysis only on portions of data that are actually accessed or likely to be accessed, avoiding the computational overhead of processing entire datasets when only partial data is needed.
Data Source
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AI summary
A probabilistic data structure is generated for efficient query processing using a histogram for unsorted data in a column of a columnar database. A bucket range size is determined for multiples buckets of a histogram of a column in a columnar database table. In at least some embodiments, the histogram may be a height-balanced histogram. A probabilistic data structure is generated to indicate for which particular buckets in the histogram there is a data value stored in the data block. When an indication of a query directed to the column for select data is received, the probabilistic data structure for each of the data blocks storing data for the column may be examined to determine particular ones of the data blocks which do not need to be read in order to service the query for the select data.