Low-Latency Query Processor Using Dynamic Intermediary Nodes
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Solution Overview
Problem
Current database systems, particularly federated database systems, face limitations in handling massive data volumes and dynamic network loads, leading to high latency and performance bottlenecks due to fixed data partitioning and limited scalability.
Innovation Solution
A low-latency query processor is implemented using a relationship histogram table to map object relationships in user queries to relevant component databases, with an intermediary module dynamically assigning nodes to connect with databases based on a configured query plan, enabling dynamic adjustment and optimization of data processing according to network load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If federation schemes are used to achieve greater capacity, then scalability is improved, but latency and performance are worsened due to fixed data partitioning and inability to dynamically adjust database access
Solution Approach 1:
The patent implements dynamic query plans that can be generated and executed at runtime based on current system state and data distribution. The query optimizer dynamically determines the best execution plan by evaluating multiple possible access paths and selecting the optimal one, allowing the system to adapt to changing conditions rather than relying on fixed partitioning schemes.
Solution Approach 2:
The system changes the parameter of query execution by using dynamic query plans with different access paths, join strategies, and data retrieval methods. The query optimizer can select different parameters (access methods, join algorithms, data locations) based on current data distribution and system state, transforming a static federation approach into a dynamic one.
2Quantity of substance
If data is horizontally partitioned across multiple component databases, then scalability is improved, but flexibility to dynamically adjust database access is worsened
Solution Approach 1:
The patent introduces dynamic query plans that can be generated at runtime to adapt to the current data distribution across horizontally partitioned databases. The system dynamically determines which databases to access and how to access them, providing flexibility despite horizontal partitioning.
Solution Approach 2:
The query optimizer acts as an intermediary between the user query and the horizontally partitioned databases. It receives the query, analyzes the current data distribution, and generates an optimized access plan that coordinates access across multiple databases, thereby providing the flexibility needed to work with horizontal partitioning.
3Measurement precision
If federation keys are assigned to map data queries to component databases, then data location is improved, but the amount of data that can be accommodated is limited
Solution Approach 1:
The patent segments the query processing task across multiple databases by dynamically determining which segment (database) contains the relevant data. Instead of relying on a single federation key mapping, the system can divide the query into multiple operations across different database segments, thereby accommodating larger total data capacity.
Solution Approach 2:
The system adds another dimension to data location by using dynamic query plans that consider multiple databases and access paths simultaneously. Rather than a single-dimensional federation key mapping, the query optimizer explores a multi-dimensional space of possible access paths across horizontally partitioned databases, increasing total accommodatable data capacity.
4Device complexity
If fixed data partitioning is used in federated database systems, then system complexity is reduced, but performance and latency are worsened under varying network load
Solution Approach 1:
The patent introduces dynamic query plans that adapt to varying network load and system conditions. The query optimizer dynamically selects execution strategies based on current performance characteristics, allowing the system to maintain good performance across different load conditions without requiring complex manual configuration.
Solution Approach 2:
The system uses feedback from data distribution analysis and performance monitoring to dynamically adjust query execution plans. The query optimizer receives feedback about the actual data distribution and system state, then uses this information to generate optimized access plans, creating a closed-loop control system that adapts to changing conditions.
Data Source
AI summary
Techniques for implementing a low-latency query processor accommodating an arbitrary number of data rows with no column indexing. In an aspect, data is stored across a plurality of component databases, with no requirement to strictly allocate data to partitions based on row keys. A histogram table is provided to map object relationships identified in a user query to the component databases where relevant data is stored. A server processing the user query communicates with component databases via an intermediary module. The intermediary module may include intermediary nodes dynamically assigned to connect to the component databases to retrieve and process the queried data.