Multimodal Split-Associative Data Store for Database Throughput
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Solution Overview
Problem
Conventional database systems face inefficiencies in managing freeform data from social media and other interactive applications, leading to resource-intensive operations, conflicts during simultaneous read/write operations, and increased latency due to data duplication and heterogeneous storage mechanisms.
Innovation Solution
A multimodal split-associative data store with content-addressable memory and cache-line aware reader/writer locks, allowing efficient read/write access control and minimizing lock duration, along with a unified data store taxonomy model for storing multiple data types without duplication, using n-tuples and Lisp-derived syntax for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional database systems use reader/writer locks to manage data access, then data integrity is maintained, but lock duration increases and system throughput decreases
Solution Approach 1:
The patent segments the database into multiple partitions that can be accessed independently. By dividing the data storage into separate segments, the system allows parallel access to different partitions, reducing lock contention and improving throughput while maintaining data integrity through partition-level locking instead of global locking.
Solution Approach 2:
The patent introduces a buffer pool as an intermediary layer between the storage engine and the query processor. This buffer pool caches frequently accessed data in memory, reducing the need for frequent disk I/O operations and allowing multiple transactions to access cached data without acquiring locks on the underlying storage, thereby improving throughput while maintaining integrity.
2Ease of operation
If conventional database systems duplicate data across multiple storage mechanisms, then data accessibility is improved, but storage overhead and latency increase
Solution Approach 1:
The patent implements a unified storage engine that handles multiple data types and access patterns through a single system. This universal storage engine eliminates the need for separate storage mechanisms for different data types, reducing storage overhead while maintaining accessibility through a common interface that supports various query operations.
Solution Approach 2:
The patent uses copy-on-write technology where data is copied only when modifications are needed. This approach maintains data accessibility by allowing readers to access the original data while writers operate on copies, reducing the need for full data duplication while ensuring both read and write operations can proceed efficiently.
3Adaptability or versatility
If conventional database systems use heterogeneous storage mechanisms for different data types, then data type flexibility is improved, but system complexity and serialization overhead increase
Solution Approach 1:
The patent implements a universal storage engine that natively supports multiple data types including structured, semi-structured, and unstructured data. This single system handles various data formats without requiring separate storage mechanisms, reducing system complexity while maintaining flexibility through a unified data model that can accommodate diverse data types.
Solution Approach 2:
The patent uses a schema-less or flexible schema approach where data structures are defined by parameters rather than fixed schemas. This allows the system to adapt to different data types by changing parameter definitions rather than requiring different storage mechanisms, reducing complexity while maintaining versatility.
4Reliability
If conventional database systems hold locks for long periods during transactions, then data consistency is maintained, but resource utilization and efficiency decrease
Solution Approach 1:
The patent segments transactions into smaller units of work that can be committed incrementally. By breaking down long transactions into smaller segments that can be committed individually, the system maintains data consistency through progressive commits while reducing lock duration and improving resource utilization among concurrent transactions.
Solution Approach 2:
The patent implements optimistic concurrency control with versioning that allows transactions to proceed without acquiring locks, performing useful work continuously. Transactions read data, make modifications, and validate consistency at commit time without blocking other transactions, maintaining data consistency through version validation while ensuring continuous resource utilization.
Data Source
AI summary
A unified data store and transaction system queries an n-tuple-based multimodal data structure via a mutable tuple-based interface the mutable tuple-based interface, the interface including a memory controller, and a query operation set. The system receives a tuple from a mutable tuple-based query interface with a tuple-reader and reading the tuple into a tuple object, and evaluates the tuple object against semantic rules via a tuple evaluator.