Pipelined Chunk Fetching for Federated Database Performance
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Solution Overview
Problem
Database management systems face challenges in optimizing performance and resource usage when handling complex and voluminous queries, particularly when accessing data stored in remote systems, which often requires full materialization of database tables, consuming significant memory and processing resources.
Innovation Solution
Implementing a pipelined execution of partial fetches, where a local database system requests and receives chunks of a database table from a remote system, allowing the remote system to materialize only the necessary chunks rather than the entire table, and enabling the local system to form a local copy of the database table without full materialization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the remote database system materializes the entire database table to provide data to the local system, then the local system可以获得完整的本地副本，但远程系统的内存和处理器资源消耗会大幅增加
Solution Approach 1:
将完整的数据库表分割成多个数据块（chunk），每次只传输和内存映射一个数据块到本地系统，而不是传输整个表。通过分段处理，大幅减少了每次传输的数据量和远程系统的内存负担，同时保证了完整数据的逐步获取
Solution Approach 2:
在本地系统预先创建目标数据库表的空结构（包括元数据、数据类型、约束等），然后在接收到数据块后立即将其映射到预定义的位置。这种预先准备的方式使得数据接收和整合过程更加高效，无需等待完整数据传输后再进行处理
2Reliability
If the local database system requests the entire database table from the remote system, then it can form a complete local copy, but the data transfer time and network bandwidth consumption increase
Solution Approach 1:
将完整的数据表分割成多个较小的数据块，通过异步方式逐步接收和映射到本地。这种分段传输方式允许本地系统在数据到达的同时就开始处理，而不是等待完整数据传输完成，从而显著减少了有效数据处理的时间延迟
Solution Approach 2:
通过异步数据接收和内存映射机制，使本地系统能够在数据传输过程中持续进行数据处理操作，而不是处于等待状态。数据传输和数据使用过程重叠进行，消除了空闲等待时间，实现了有用操作的连续性
3Productivity
If the remote system materializes large chunks of data, then the local system can obtain data faster, but the memory consumption at the remote system increases significantly
Solution Approach 1:
通过控制每个数据块的大小，在传输效率和内存消耗之间找到平衡点。数据块大小可以根据远程系统的可用内存和网络的传输能力进行动态调整，使得每次传输的数据量既不会过大导致内存不足，也不会过小降低传输效率
Solution Approach 2:
根据系统状态动态调整数据块的大小和传输参数。当远程系统资源充足时可以使用较大的数据块提高吞吐量，当资源紧张时减小数据块大小以降低内存压力，实现了系统性能的资源适应性优化
Data Source
AI summary
In some implementations, there is provided a method including receiving a request to provide a local database system with smart data access to a database table stored at a remote database system; executing, by the local database system, a series of one or more fetches, each of which obtains a chunk of the database table stored at the remote database system, such that a corresponding result set for each fetch causes the remote database system to fetch and materialize a corresponding chunk of the database table rather than the database table in its entirety; and reading, by the local database system, a first chunk obtained from the database table stored at the remote database system to form, at least in part, the local copy at the local database system.