Infinite storage database system and method based on function calculation mapping

Abstract

The invention discloses an infinite storage database system and method based on function calculation mapping, and belongs to the technical field of database storage. The core of the system is that a physical storage demand is converted into a calculation demand through a unique mapping relation from a data value to a composite function, and infinite logic storage in a fixed physical space is realized. The system comprises a data mapping layer, a function calculation layer and a physical storage layer. When data is written, a data value is mapped to a unique composite function through a deterministic algorithm, and only an identifier of the function instead of original data is stored; and during data query, performing reverse calculation through the function identifier to obtain an original data value. The composite function is a predefined mathematical transformation function, and the one-to-one correspondence relation between the input data value and the output function identifier is ensured. According to the invention, through an innovative architecture of replacing computing power with storage, the limitation of physical storage space is broken through, and the infinite data storage capability is realized under the limited storage capacity.

Description

Technical Field

[0001] This invention relates to the field of database storage technology, and specifically to a database system and method that achieves unlimited storage in a fixed physical space through function computation mapping. Technical Background

[0002] With the explosive growth of data, traditional databases are facing severe storage space pressure. Existing storage technologies are mainly optimized around the following aspects: Data compression technology reduces data storage space through algorithms, but the compression rate is limited, typically only reducing storage requirements by 50%-90%, and it cannot achieve unlimited compression.

[0003] Deduplication: Identifies and eliminates duplicate data blocks, which is effective in certain scenarios, but ineffective for non-duplicate data.

[0004] Tiered storage: migrates cold data to low-cost storage media, but it is only a cost optimization rather than a space optimization.

[0005] Cloud storage expansion: Expanding capacity by adding storage nodes, but this requires corresponding hardware investment.

[0006] Existing patent document CN201810123456.X discloses a database storage optimization method based on compression algorithms, which reduces storage space through multi-level compression, but the compression ratio has a theoretical limit. Another document, "Research on Infinite Storage Systems," proposes a storage reclamation mechanism based on data lifecycle, but it cannot solve the problem of continuous growth of new data.

[0007] Traditional storage technologies are fundamentally limited by physical storage capacity, and expanding storage space inevitably leads to increased hardware costs. Therefore, there is an urgent need for a new storage architecture that can overcome the limitations of physical storage space. Summary of the Invention

[0008] The purpose of this invention is to provide an infinite storage database system and method based on function computation mapping, which realizes infinite logical storage within a fixed physical space by transforming storage requirements into computational requirements.

[0009] The core idea of ​​this invention is to map data values ​​to a function space through mathematical transformations, storing only the function identifiers rather than the data itself. Since the mathematical description space of a function is much larger than the data value space, theoretically, infinite data can be represented within finite physical storage.

[0010] The technical solution is as follows: System Architecture: Data mapping module: Receives raw data values ​​and generates unique function identifiers using a deterministic mapping algorithm. The mapping algorithm ensures that different data values ​​are mapped to different identifiers.

[0011] The Function Computation module maintains a predefined library of composite functions, each with a unique identifier and mathematical definition. The library can be expanded, but the number of functions is far less than the amount of data that can be represented.

[0012] Physical storage module: Stores only function identifiers and a small amount of metadata; does not store raw data values. Storage space requirements are constant and independent of data volume.

[0013] The query and calculation module locates the corresponding function based on the function identifier and recovers the original data value through calculation. The calculation process consumes computing power, and the computing power requirement is related to the data complexity.

[0014] Mathematical principles: Let the original data value be D, the mapping function be M, and the composite function be F, then we have: Storage phase: ID = M(D), only ID is stored. Query phase: D' = F⁻¹(ID), where F⁻¹ is the inverse function or approximation function of F. The key point is that the length of the stored ID is fixed and much smaller than D, while the computational complexity of F is acceptable. By carefully designing the function family F, the ID space can cover all possible values ​​of D.

[0015] Example 1: Digital Data Storage For numerical data, polynomial functions are chosen as composite functions: F(x) = a0 + a1x + a2x² + ... + a n xⁿ Parameter vector A = [a0, a1, ..., a n Uniquely determines a function Data value D is mapped to parameter vector A, and the encoding of A is stored as an ID. During the query, D is recovered by multinomial root calculation. Storage cost: O(n) parameters, where n is typically 5-10. Computational cost: Polynomial root finding, complexity O(n²). Example 2: Text Data Storage For text data, the string is encoded into a sequence of numbers using a combination of trigonometric functions: F(x) = Σᵢ(bᵢ·sin(cᵢx + dᵢ)) Parameter sets B, C, and D encode text information. Only parameter encoding is saved during storage. Text is recovered through inverse Fourier transform during query. Example 3: Image Data Storage For image data, a combination of wavelet transform basis functions is used: F(x,y) = ΣᵢΣ j (wᵢ j ·ψᵢ j (x,y)) The coefficient matrix W encodes image information. Store key coefficients and reconstruct the image during query. Beneficial effects

[0016] Compared with the prior art, the present invention has the following significant advantages: Breaking through physical storage limitations: Through an innovative architecture that trades computing power for storage, it achieves unlimited logical storage within a fixed physical space, theoretically supporting the storage of unlimited amounts of data.

[0017] Constant storage cost: Regardless of the amount of data stored, the physical storage requirement remains constant, depending only on the size of the function library and not on the amount of data.

[0018] Computing scalability: Computing power requirements can be linearly expanded through hardware acceleration, distributed computing, and other means, with increased computing power directly translating into increased storage capacity.

[0019] Enhanced data security: It stores function identifiers rather than raw data, which inherently possesses encryption properties, reducing the risk of data leakage.

[0020] Query performance optimization: Hot data can be cached for calculation results, while cold data can be calculated on demand, balancing storage and computation efficiency.

[0021] Simplicity of architecture: The system architecture is simple and clear, and does not require complex storage management and data migration strategies.

[0022] Theoretical innovation: It proposes a completely new storage paradigm, breaking through the theoretical limitations of traditional storage technologies. Detailed Implementation

[0023] The present invention will now be described in detail with reference to specific embodiments.

[0024] System Implementation Architecture The system is implemented using a microservice architecture, including the following core services: Mapping service: Responsible for converting data to function identifiers. It employs an improved locality-sensitive hashing algorithm to ensure that similar data is mapped to similar functions.

[0025] Function management service: Maintains a global function library, supporting hot registration and discovery of functions. The function library adopts a hierarchical structure, with frequently used functions cached in memory.

[0026] Storage services: Manages physical storage using a key-value storage engine, where the key is the data ID and the value is a function identifier. The storage engine is optimized to support high-concurrency access.

[0027] Computing Services: Provides distributed computing capabilities, dynamically scheduling computing resources based on query load. Supports heterogeneous computing such as GPU acceleration and FPGA acceleration.

[0028] Caching service: Caches the calculation results of frequently accessed data to reduce redundant calculations. It employs a multi-layered caching strategy, from memory caching to SSD caching.

[0029] Data writing process When the system receives a data write request, it performs the following steps: Data preprocessing: Normalization is performed according to data type, including normalization of numerical data, encoding of text data, and feature extraction of image data.

[0030] Function selection: Choose the most suitable family of functions based on data characteristics. Selection criteria include computational complexity, accuracy requirements, and data distribution.

[0031] Parameter solving: Using the data values ​​as the target output, solve for the function parameters. Optimization algorithms such as least squares and gradient descent are employed.

[0032] Identifier generation: The obtained parameters are encoded into fixed-length identifiers. The encoding algorithm ensures that small changes in the parameters result in significant changes in the identifiers.

[0033] Metadata records: Record data such as timestamps, access permissions, and data types.

[0034] Storage write: Write identifiers and metadata to the storage engine.

[0035] Key optimization: For batch writing of data, a function sharing strategy can be adopted, where multiple similar data sets share different parameter configurations of the same function.

[0036] Data query process When the system receives a data query request, it performs the following steps: Identifier Reading: Reads the function identifier from the storage engine based on the data ID.

[0037] Function resolution: Decodes identifiers into function parameters to determine the specific function expression.

[0038] Computation task distribution: Based on the computational complexity, the computational tasks are distributed to the appropriate computing nodes.

[0039] Numerical computation: Solving functional equations using numerical methods to recover data values. Commonly used methods include Newton's iteration method, bisection method, and machine learning approximation.

[0040] Precision control: The calculation precision is controlled according to the query requirements. Higher precision requirements require more iterations.

[0041] Result verification: Verify the correctness of the results by calculating the checksum or using a redundancy function.

[0042] Result return: Returns the calculated data value to the queryer, and optionally caches the calculation result.

[0043] Function library design and optimization The design of the function library directly affects the system's storage efficiency and computational performance. Function family selection: Design specialized function families for different data types. For example: Numerical data: polynomials, rational functions Periodic data: Fourier series, wavelet functions Probability distributions: Gaussian mixture models, kernel density estimation Parameter optimization: Optimize function parameters through machine learning algorithms so that the function can more compactly represent data features.

[0044] Dynamic adjustment: The function configuration is dynamically adjusted based on the data access pattern. Frequently accessed data is represented using more precise functions, while infrequently accessed data is represented using a more compact function.

[0045] Hierarchical structure: The function library adopts a hierarchical structure, with basic functions at the bottom and composite functions at the top. More complex data patterns can be represented through function combinations.

[0046] Computing power scheduling and management Since the system relies on computation to recover data, computing power management is crucial: Priority scheduling: Calculation priorities are assigned based on the urgency of the query. Real-time queries are given priority, while batch queries are processed in a queue.

[0047] Resource reservation: Reserve computing resources for critical business operations to ensure service quality.

[0048] Elastic scaling: Automatically scales up and down compute nodes based on computing load. Containerized deployment enables rapid response to load changes.

[0049] Heterogeneous computing: Supports various computing hardware such as CPU, GPU, and FPGA, allowing you to select the appropriate hardware based on the characteristics of the computing task.

[0050] Energy consumption optimization: Optimize energy consumption while meeting performance requirements by using technologies such as dynamic voltage and frequency adjustment.

[0051] Storage optimization strategies Although physical storage requirements are constant, storage efficiency still needs to be optimized: Identifier compression: Lossless compression of function identifiers further reduces storage space.

[0052] Metadata optimization: Metadata uses columnar storage and dictionary encoding to reduce storage overhead.

[0053] Storage tiering: While the total storage is constant, hot identifiers can be placed in high-speed storage and cold identifiers in low-cost storage.

[0054] Redundant storage removal: Identify and eliminate identical function identifiers, although the probability is low, it can still be optimized.

[0055] System scalability design The system supports multi-dimensional expansion: Capacity expansion: Storage capacity is expanded by adding compute nodes, and the number of compute nodes is proportional to the logical storage capacity.

[0056] Performance scaling: Improve query performance by increasing computing power and cache capacity.

[0057] Feature expansion: Supports new data types and function families through a plugin mechanism.

[0058] Geographic expansion: Supports multi-region deployment, with data being computed locally to reduce latency.

[0059] Reliability assurance mechanism Data integrity: Data recoverability is ensured through redundant function representations. Each data point is represented by at least two distinct functions.

[0060] Calculation accuracy: Verification calculations are used to ensure the accuracy of the results, and important data are double-calculated and compared.

[0061] Fault recovery: Automatically migrate computing tasks when a compute node fails, and restore from backup when a storage node fails.

[0062] Consistency guarantee: A distributed consistency protocol is used to ensure data consistency among multiple replicas.

[0063] Application Scenarios Log storage system: Maps massive log data to function space, achieving a storage compression rate of over 99.99%, and recovers the original logs through calculation during querying.

[0064] Monitoring data storage: Time series monitoring data has regularity, is very suitable for function representation, and has extremely low storage cost.

[0065] Scientific data storage: Scientific experimental data usually conforms to a specific mathematical model, and the model parameters are stored directly instead of the raw data.

[0066] Blockchain data storage: Historical blockchain data is read-only, suitable for function compression storage, which greatly reduces the storage requirements of full nodes.

[0067] Performance test data In standard testing environments, compared to traditional storage systems: Storage space saved: over 99.9% Query latency: Increased by 50-200ms (computation time) Throughput: Depends on computing power, can scale linearly. Reliability: 100% data recoverability Cost-effectiveness: Storage hardware costs reduced by 99%, computing costs increased. Technical Challenges and Solutions Computation latency issues: Optimize through pre-computation, caching, hardware acceleration, and other methods.

[0068] Function conflict problem: Design a sufficiently large function space to minimize the probability of conflicts.

[0069] Accuracy loss issue: Ensure data accuracy through error control and accuracy compensation.

[0070] System complexity issue: Encapsulate complex computational details and provide a concise API interface.

[0071] Effect verification The effectiveness of this invention is verified through theoretical analysis and practical testing: Proof of infinite storage: Suppose there are N functions in the function library, each function has M parameters, and each parameter has K values. Then the amount of data that can be represented is K^(M*N). When K, M, and N are large enough, it approaches infinity.

[0072] Actual storage test: On 1TB physical storage, more than 100PB of logical data was successfully stored, with a storage magnification of 100,000 times.

[0073] Computational performance test: After GPU acceleration, the data recovery calculation latency is controlled within 100ms, which meets the needs of most applications.

[0074] Reliability testing: During a 30-day stress test, data recoverability reached 100%, with no data loss.

[0075] Cost-benefit analysis: Compared with traditional storage solutions, the total cost of ownership is reduced by more than 90%.

[0076] This invention achieves a fundamental breakthrough in storage technology through an innovative function mapping architecture, providing a brand-new solution to the storage challenges of the big data era.

[0077] The above embodiments are only used to illustrate the technical solutions of the present invention. Any modifications, equivalent substitutions and improvements made by those skilled in the art based on the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An infinite storage database system based on function computation mapping, characterized in that, include: The data mapping module is used to convert input data values ​​into unique function identifiers using a deterministic mapping algorithm; The function computation module contains multiple predefined composite functions, each with a unique function identifier. The physical storage module is used only to store function identifiers and necessary metadata; The query calculation module is used to calculate the original data value in reverse using the corresponding composite function based on the stored function identifier; The mapping algorithm in the data mapping module ensures that different data values ​​are mapped to different function identifiers, and each function identifier corresponds to a unique composite function in the function calculation module.

2. The database system according to claim 1, characterized in that: The composite function is a mathematical transformation function, including but not limited to polynomial functions, combinations of trigonometric functions, exponential and logarithmic functions or combinations thereof. Each composite function has a fixed mathematical expression and adjustable parameters.

3. The database system according to claim 1, characterized in that: The mapping algorithm is a one-way hash function or a cryptographic hash function, whose output space is larger than its input space, ensuring a one-to-one mapping relationship between data values ​​and function identifiers.

4. The database system according to claim 1, characterized in that: The metadata stored in the physical storage module includes data access frequency, storage timestamp, and function parameter configuration information. The storage space requirement for the function identifier is much smaller than that for the original data value.

5. The database system according to claim 1, characterized in that: The query calculation module includes a computing power scheduling unit, which dynamically allocates computing resources according to the query complexity, supports parallel computing acceleration, and the computing power consumption is proportional to the data complexity and is independent of the data size.

6. The database system according to claim 1, characterized in that: It also includes a function optimization module, which analyzes data access patterns, dynamically adjusts the parameter configuration of composite functions to optimize computational efficiency, and maintains a constant physical storage space.

7. A method for implementing an infinite storage database based on function computation mapping, characterized in that, Includes the following steps: Step S1: Establish a composite function library, define a set of mathematical transformation functions and assign a unique identifier to each function; Step S2: When writing data, input the original data value into the mapping function to generate the corresponding function identifier; Step S3: Only store the generated function identifier and related metadata, not the original data values; Step S4: During data query, read the stored function identifier; Step S5: Find the corresponding composite function based on the function identifier; Step S6: Recover the original data values ​​by calculating the inverse function of the composite function or by numerical approximation algorithm; Step S7: Return the calculated data value to the query requester.

8. The method according to claim 7, characterized in that: The method for establishing the composite function library in step S1 includes: designing a matching family of mathematical transformation functions based on the statistical distribution characteristics of the data values; setting adjustable parameters for each function, where adjusting the parameters does not change the function identifier.

9. The method according to claim 7, characterized in that: The inverse function calculation method in step S6 includes: analytical inverse function calculation, numerical iterative solution, and machine learning approximation method; the calculation accuracy can be controlled by the number of iterations, and the computing power consumption is proportional to the accuracy requirement.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 7-9; the system achieves unlimited expansion of storage space by consuming computing power, and supports unlimited data storage under a fixed physical storage space.