Streaming Tuple Database Control for Real-Time Data Processing
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Solution Overview
Problem
Traditional static database systems are inefficient in handling and analyzing large amounts of streaming data in real-time, as they require locking the database for alterations, which can be problematic due to the rapid and unpredictable nature of data streams.
Innovation Solution
Incorporating database modifications within the header or body portions of data tuples, allowing compute nodes to create or modify tuples with instructions for database changes, such as SQL commands, and transmitting these tuples as streaming data to the database, enabling efficient and real-time database alterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static database systems are used to store and index data in memory, then data can be stored and queried, but real-time processing and analysis of streaming data becomes inefficient
Solution Approach 1:
The patent transforms the static database model into a dynamic system where database schema and structure can change in real-time as streaming data arrives. The database system dynamically adapts its indexing and storage structures based on incoming data characteristics, enabling real-time processing without requiring pre-defined static schemas.
Solution Approach 2:
The system performs preliminary indexing and structuring of streaming data as it arrives, rather than waiting for batch processing. By continuously maintaining indexes and metadata structures during the data stream, the system enables real-time queries and analyses without time loss.
2Reliability
If the database is locked for alterations, then data integrity is maintained, but the rapid and unpredictable nature of data streams cannot be handled efficiently
Solution Approach 1:
The patent segments database alterations into discrete, manageable operations that can be applied incrementally to the streaming data stream. Instead of locking the entire database for modifications, the system applies changes in small segments as data arrives, maintaining both integrity and speed.
Solution Approach 2:
The system performs database schema evolution and structure changes in advance, before they are needed by incoming data streams. By pre-adjusting the database structure based on predicted or detected data patterns, the system avoids the need for locking during actual data processing.
3Productivity
If database modifications are made through traditional methods, then changes can be applied, but the process is slow and cannot keep pace with streaming data velocity
Solution Approach 1:
The patent merges data processing operations with database modification operations into a single unified stream. Database schema changes, data ingestion, and query processing all occur simultaneously in the same computational stream, eliminating the sequential time loss associated with traditional separate operations.
Solution Approach 2:
The system maintains continuous database structure optimization as data streams in, rather than performing periodic batch updates. The database schema and indexes are continuously adjusted in real-time based on incoming data, ensuring modification speed keeps pace with data velocity without time loss.
Data Source
AI summary
In an embodiment, a method for controlling a database in a streaming application includes receiving, by a compute node, data for transmission as streaming data on a network connected to the compute node and a database. The compute node then assembles a body portion of a tuple to include the data and assembles a header portion of the tuple to include a database statement that identifies a change to be made by the database to an aspect of the database in a format that is identifiable by the database. The compute node then transmits the tuple having the header portion thereof and the body portion thereof on the network as streaming data with the header and body portions of the tuple being directed to the database.