Joining Tables from Different Data Sources
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Solution Overview
Problem
Current systems face challenges in efficiently joining tables from different data sources, leading to inefficient data retrieval and processing of queries across multiple databases.
Innovation Solution
The method involves receiving a query with a join operation, identifying and indexing relevant columns, and retrieving data in parallel to create a new result set by executing the join operation on indexed columns, optimizing data retrieval by only fetching necessary columns and rows, and using a system with processors and memories to perform these operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is retrieved from multiple data sources to join tables, then the completeness of query results is improved, but the data retrieval time and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by identifying and indexing columns before the actual join operation. The query optimization module analyzes the join query in advance to determine which columns need to be retrieved and indexes them, so that during execution only necessary data is fetched from multiple data sources, reducing retrieval time while maintaining result completeness
Solution Approach 2:
The system segments the data retrieval process by separating the indexing of join columns from the retrieval of other columns. The query optimization module divides the query into distinct phases: first indexing the join columns from multiple data sources, then retrieving remaining columns only after the join is established, thereby reducing overall data retrieval time
2Manufacturing precision
If all columns are retrieved from tables to ensure complete join operation, then the accuracy of join results is improved, but the data transmission volume and processing overhead increase
Solution Approach 1:
The system extracts only the essential join columns from tables at multiple data sources before performing the join operation. The query optimization module identifies which columns are needed for the join condition and retrieves only those columns first, transmitting minimal data while ensuring join accuracy. Other non-join columns are retrieved only after the join is established
Solution Approach 2:
The system performs partial action by retrieving only the necessary join columns initially rather than all columns. This partial retrieval is sufficient to perform the join operation accurately, and additional columns are fetched only if needed, avoiding unnecessary data transmission while maintaining join result accuracy
3Reliability
If data from multiple data sources is joined using traditional sequential methods, then the correctness of the join operation is maintained, but the query processing efficiency decreases
Solution Approach 1:
The system performs preliminary indexing of join columns from multiple data sources before executing the join operation. The query optimization module prepares the data by creating indexes on the join columns in advance, which enables faster and more efficient join execution while maintaining correctness, as the indexing structure ensures accurate matching of rows across data sources
Solution Approach 2:
The system implements continuous useful action through pipelined processing where data retrieval, indexing, and join operations overlap in time. While data is being retrieved from data sources, the system simultaneously indexes the join columns, and the join operation begins as soon as sufficient indexed data is available, eliminating idle waiting time and improving overall query processing efficiency without sacrificing join correctness
Data Source
AI summary
Methods and apparatus for joining two or more tables are disclosed. A query including a join query is received, where the join query requests that a new table be generated from data obtained from two or more tables, where the two or more tables are stored at two or more different data sources. Data from the tables is retrieved corresponding to the query. A new table is then generated from the retrieved data.