Binary XML Cost Estimation via Synopsis Skip Lengths
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Solution Overview
Problem
Database systems face challenges in accurately estimating the cost of executing XML queries, particularly when dealing with binary-encoded XML data, as existing methods struggle to account for the I/O and CPU costs associated with streaming evaluations, especially when skipping non-matching subtrees during XPath expression evaluation.
Innovation Solution
The approach involves generating a synopsis that captures statistics about XML documents, including rooted paths, element counts, and skip lengths, to estimate I/O costs, and creating a CPU cost function by identifying features and complexity functions to determine the execution cost of streaming operators, thereby improving the accuracy of query cost estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If binary-encoded XML storage is used to reduce data size, then storage efficiency is improved, but accurate cost estimation for XML query execution becomes more difficult
Solution Approach 1:
The patent pre-computes and stores statistics about the binary-encoded XML data structure, including element frequency distributions, path lengths, and skip potential metrics, before query execution. This preliminary preparation enables accurate cost estimation without requiring complex real-time analysis during query optimization.
Solution Approach 2:
The patent introduces an intermediary cost estimation model that translates binary-encoded XML structural characteristics into query execution cost predictions. This model acts as a mediator between the compressed binary format and the query optimizer, providing accurate cost estimates without requiring the optimizer to understand the binary encoding details.
2Productivity
If streaming evaluation is used to process XML queries, then processing speed is improved, but CPU resource consumption increases
Solution Approach 1:
The patent applies partial action by implementing early termination of streaming evaluation when query results are found or when it becomes clear that remaining data cannot satisfy the query conditions. This avoids unnecessary CPU consumption while maintaining high processing speed for beneficial cases.
Solution Approach 2:
The patent dynamically adjusts streaming evaluation parameters such as buffer sizes, chunk processing granularity, and evaluation depth based on query characteristics and available resources. This allows the system to optimize the balance between processing speed and CPU consumption for different query scenarios.
3Loss of time
If XPath evaluation skips non-matching subtrees to improve efficiency, then query execution time is reduced, but accurate cost estimation becomes more complex
Solution Approach 1:
The patent pre-analyzes the XML data structure to identify and store information about skip-worthy subtrees, including their positions, sizes, and match probabilities. This preliminary analysis enables the cost estimator to quickly determine skip potential without complex real-time analysis during query optimization.
Solution Approach 2:
The patent creates a simplified copy or representation of the XML structure that captures skip-relevant characteristics without requiring full structural analysis. This copy enables fast cost estimation of skip operations while maintaining accuracy, avoiding the need to analyze the complete complex binary-encoded structure.
Data Source
AI summary
Techniques for estimating the cost of processing a database statement that includes one or more path expressions are provided. One aspect of cost is I/O cost, or the cost of reading data from persistent storage into memory according to a particular streaming operator. Binary-encoded XML data is stored in association with a synopsis that summarizes the binary-encoded XML data. The synopsis includes skip length information for one or more elements and indicates, for each such element, how large (e.g., in bytes) the element is in storage. The skip length information of a particular element thus indicates how much data may be skipped during I/O if the particular element does not match the path expression that is input to the streaming operator. The skip length information of one or more elements is used to estimate the cost of processing the database statement.