A heterogeneous database comparison method and apparatus based on semantic vector mapping and block fingerprint verification

Abstract

This invention discloses a method and apparatus for comparing heterogeneous databases based on semantic vector mapping and block fingerprint verification. Specifically, it involves: first, using Transformer-based heterogeneous structure semantic mapping to transform the schema definition DDL into high-dimensional semantic vectors; then, using data entropy-based adaptive block fingerprinting to measure the change activity of data blocks and dynamically adjusting the verification strategy; next, employing a vectorized fuzzy equivalence determination mechanism to avoid false consistency differences caused by floating-point precision or text synonyms; and finally, using an eBPF-based kernel-level bypass monitoring and state reconstruction mechanism to achieve seamless verification in the production environment. This invention achieves automated structure alignment across relational and non-relational databases, balances real-time performance and accuracy, improves data verification throughput and timeliness, enables seamless verification in the production environment, and reduces performance overhead and labor costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of database management and data verification, and particularly relates to a heterogeneous database comparison method and device based on semantic vector mapping and block fingerprint verification. BACKGROUND

[0002] With the deepening of enterprise digital transformation, multi-modal databases (Multi-model Database) and hybrid cloud architecture have become mainstream. In the process of data migration, disaster recovery synchronization and DevOps release, the consistency verification of database structure and data faces great challenges. Traditional database comparison methods such as row-level comparison based on primary key sorting, full MD5 digest, pt-table-checksum, etc. have the following significant shortcomings:

[0003] 1. Heterogeneous structures are difficult to align. Traditional tools rely on strict field name matching. When the source is a relational database such as MySQL and the target is a document database such as MongoDB or a vector database, due to differences in naming conventions and nested structures, traditional tools cannot identify the corresponding relationship between "user_id" and "uid" or the internal fields of "json_data".

[0004] 2. Massive data performance bottleneck. In PB-level data scenarios, full scan calculation of hash values consumes a large amount of I / O and CPU resources, which can easily cause performance jitter or even block business in production environments.

[0005] 3. Lack of dynamic adaptability. Existing block verification usually uses a fixed step size. For "hot areas" with frequent data changes and "cold data areas" that remain unchanged for a long time, the same verification frequency is used, which is inefficient and wastes resources.

[0006] 4. High false positive rate. Binary inconsistencies caused by floating-point precision differences and character set encoding (UTF8 vs UTF8MB4) are often misjudged as data errors, lacking semantic-level equivalence judgment.

[0007] Therefore, there is an urgent need for an intelligent database comparison method that can understand data semantics, adaptively adjust verification granularity, and support heterogeneous environments. SUMMARY

[0008] The present application aims to provide a heterogeneous database comparison method and device based on semantic vector mapping and block fingerprint verification with low system load, high accuracy, and high comparison efficiency.

[0009] The technical solution for achieving the present application is a heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification, comprising the following steps:

[0010] Step 1, adopt the heterogeneous structure semantic mapping based on Transformer to convert Schema definition DDL into high-dimensional semantic vector;

[0011] Step 2, adaptive block fingerprint based on data entropy, use data entropy to measure the change activity of data block, dynamically adjust the verification strategy;

[0012] Step 3, adopt vector fuzzy equivalence judgment mechanism to avoid false consistency differences caused by floating point precision or text synonyms;

[0013] Step 4, adopt eBPF-based kernel-level bypass monitoring and state reconstruction mechanism to realize no-sense verification of production environment.

[0014] Further, step 1 adopts the heterogeneous structure semantic mapping based on Transformer to convert Schema definition DDL into high-dimensional semantic vector, as follows:

[0015] Step 1.1, set the source database table structure definition as , and the target database table structure definition as ; perform structure serialization to serialize the table name, field name, type, annotation and constraint condition of , into text description and ;

[0016] Step 1.2, perform vector embedding, use pre-trained code large model to extract feature vector , :

[0017]

[0018]

[0019] Step 1.3, perform semantic similarity matrix calculation, calculate the cosine similarity of the source field vector and the target field vector , and construct the mapping matrix :

[0020]

[0021] Step 1.4, model the field mapping problem as the maximum weight matching problem of bipartite graph, and lock the best mapping relationship through global optimization algorithm, as follows:

[0022] Step 1.4.1, construct a weighted bipartite graph: define the source database field set as the left node set , the target database field set is defined as the right node set , the similarity value in the matrix is the edge weight of the connection node and ;

[0023] Step 1.4.2, global optimization solution: Kuhn-Munkres algorithm is used to solve the bipartite graph, to find a mapping set , so that the set meets the following conditions:

[0024] (a) uniqueness constraint: any two mapping pairs in the set do not share the same u or v, that is, to ensure one-to-one field mapping;

[0025] (b) global maximization: the sum of the weights of all edges in the set reaches the maximum value;

[0026] Step 1.4.3, threshold decision and final mapping generation: after the algorithm outputs the optimal matching pair, the system introduces a confidence threshold for secondary verification: if the similarity of the matching pair is greater than the threshold, it is determined to be an effective mapping, and the corresponding field association is automatically established; if , it is determined that there is no corresponding field, and is marked as a source-end-only field New Field, and is marked as a target-end-missing field Missing Field.

[0027] Further, the adaptive block fingerprint based on data entropy described in step 2 uses data entropy to measure the change activity of the data block and dynamically adjusts the verification strategy, as follows:

[0028] Step 2.1, define the change frequency of the data block in the time window as , then the data entropy of the data block is defined as:

[0029]

[0030] where is the data distribution probability, is the weight coefficient, is the theoretical maximum change frequency;

[0031] Step 2.2, based on the value of , dynamically select the fingerprint calculation strategy:

[0032] 1) Hot data in high-entropy areas: Merkle Tree deep verification is used to subdivide data blocks into tiny slices, calculate root hash in real time, and locate specific changed rows;

[0033] 2) Low-entropy cold data: Sparse sampling hash is used to randomly extract 1% of the key rows in the block to calculate fingerprints. If they match, the full verification is skipped.

[0034] 3) Adaptive verification step size: The time interval for the next verification. Inversely proportional to entropy, an increase in entropy indicates that the data is becoming more active, thus shortening the verification time interval. The calculation method is as follows:

[0035]

[0036] in It is an adjustable dynamic attenuation coefficient.

[0037] Furthermore, the vectorized fuzzy equivalence determination mechanism described in step 3 avoids false consistency differences caused by floating-point precision or text synonyms, as detailed below:

[0038] Step 3.1: Set the values ​​of the difference field. and Mapped to vector space;

[0039] Step 3.2: Calculate the Euclidean distance ;

[0040] Step 3.3, if If it is determined to be semantically consistent, it will be marked as a warning rather than an error:

[0041] .

[0042] Furthermore, step 4 employs a kernel-level bypass monitoring and state reconstruction mechanism based on eBPF to achieve seamless verification of the production environment, as detailed below:

[0043] Step 4.1: Deploy an eBPF probe at the operating system kernel layer to capture kernel-mode data;

[0044] Step 4.2: The user-space agent asynchronously consumes the raw byte stream from the Ring Buffer and performs user-space protocol reassembly and parsing;

[0045] Step 4.3: Use schema dynamic evolution awareness for semantic splitting;

[0046] Step 4.4: Build a lightweight shadow index in the memory of the bypass verification server;

[0047] Step 4.5: Transform the verification process into comparing the shadow index with the target database.

[0048] Furthermore, step 4.1, which involves deploying an eBPF probe at the operating system kernel layer to capture kernel-mode data, is detailed as follows:

[0049] Step 4.1.1, Socket probe mounting: Use kprobe or tracepoint to mount to the key functions of the kernel network protocol stack to intercept all incoming and outgoing traffic on the specified port;

[0050] Step 4.1.2, Context Filtering: In kernel space, traffic is filtered directly based on the PID or a five-tuple consisting of source IP, source port, destination IP, destination port, and protocol. Only database-related write operation traffic is copied to the RingBuffer, while irrelevant traffic is discarded, thereby keeping CPU overhead below 1%.

[0051] Furthermore, the user-space proxy program described in step 4.2 asynchronously consumes the raw byte stream from the Ring Buffer and performs user-space protocol reassembly and parsing, as detailed below:

[0052] Step 4.2.1, TCP stream reassembly: To address the TCP packet fragmentation and out-of-order issues, the sliding window algorithm is used to reassemble fragmented network packets into complete application layer protocol data units (PDUs).

[0053] Step 4.2.2, L7 Protocol Decoding: The database communication protocol is located at the L7 layer of the OSI seven-layer model. The built-in multi-database protocol parser is used to parse out the specific SQL statements or Command instructions.

[0054] Furthermore, the semantic demultiplexing using schema dynamic evolution awareness described in step 4.3 is as follows:

[0055] Read-only operations do not change the database state. A semantic analyzer filters out read-only operations, extracts DML instructions and their parameters that cause data changes, and DDL that affects the database table structure, and generates a standardized change event stream. The SQL is divided into three flows: the first path is the identification path, which identifies and discards stateless read instructions; the second path is the data path, which extracts data manipulation instructions (DML), carries specific business data payloads, and sends them to the vector transformation engine; the third path is the control path, which extracts data definition instructions (DDL) and sends them to the schema knowledge base for real-time updates of field mapping tables.

[0056] Furthermore, step 4.4, which involves constructing a lightweight shadow index in the memory of the bypass verification server, is detailed as follows:

[0057] Step 4.4.1, Logic Replay: The verification device receives the change event stream generated in step 4.3 and simulates the change operation in the local embedded key-value store;

[0058] Step 4.4.2, Real-time fingerprint maintenance: During the replay process, the Merkle Tree root hash value of the affected data blocks is updated synchronously;

[0059] Step 4.4.3, State Snapshot: The shadow index reflects the theoretical state that the production database should have at a certain moment in real time.

[0060] Step 4.5 describes converting the verification process into comparing the shadow index with the target database, as follows:

[0061] Step 4.5.1, Snapshot Alignment: Obtain the GTID or Oplog timestamp of the target database and capture the shadow index snapshot at the corresponding moment;

[0062] Step 4.5.2, Hash Comparison: Compare the Merkle Tree root hash value calculated by the shadow index with the verification value of the target database. If the comparison results match, the verification passes; otherwise, the verification fails and an alarm is triggered.

[0063] A heterogeneous database comparison device based on semantic vector mapping and block fingerprint verification is provided. This device is used to implement the aforementioned heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification. The device includes an intelligent mapping module, an eBPF bypass capture module, a shadow index calculation engine, and a difference arbitration module, wherein:

[0064] The intelligent mapping module is responsible for the automatic alignment of heterogeneous database schemas and includes a metadata collection unit and an algorithm solving unit. The intelligent mapping module has a built-in quantized Transformer model for generating database DDL semantic vectors. It also has a built-in KM algorithm engine that can receive similarity matrix input, output the globally optimal field mapping table, and support manual correction feedback.

[0065] The eBPF bypass capture module is responsible for seamlessly acquiring data changes from the production environment. It includes kernel probes and a protocol parser. The kernel probes run in kernel space and are responsible for traffic interception and filtering. The protocol parser runs in user space and supports decoding of various database protocols, including MySQL, Oracle, PostgreSQL, and MongoDB, and outputs change events in a unified format.

[0066] The shadow index calculation engine is responsible for constructing the theoretical state of the data in the bypass, and has a built-in key-value store as the carrier of the shadow index; the engine includes a replay logic unit and a Merkle Tree maintenance unit for real-time calculation of data fingerprints.

[0067] The difference arbitration module is responsible for performing the final verification and comparison, and includes a snapshot alignment unit and a hash comparison unit; by coordinating the time level of the shadow engine and the target database, it ensures that the hash comparison is performed at the same logical point in time and generates a verification report;

[0068] The intelligent mapping module, eBPF bypass capture module, shadow index calculation engine, and difference arbitration module run on an independent verification server. This server is equipped with a CPU, an AI inference accelerator card, and a large-capacity memory. The CPU is used for protocol parsing and algorithm solving; the AI ​​inference accelerator card has a built-in quantized Transformer model for generating structural semantic vectors in milliseconds; and the large-capacity memory is used to maintain the shadow index. The server is connected to the source and target databases via a network.

[0069] Compared with existing technologies, the present invention has the following significant advantages: (1) Strong heterogeneous compatibility: AI semantic vector technology is used to realize the automatic structural alignment of cross-relational and non-relational databases, which improves the throughput and timeliness of data verification; (2) Extremely low performance loss: Based on the adaptive block strategy of data entropy, the network bandwidth occupation and memory consumption are reduced by an order of magnitude in the verification scenario of cross-WAN or cloud-edge collaboration; with the eBPF bypass monitoring technology of the present invention, the CPU loss of the source database is less than 1%, realizing the seamless verification in the production environment; (3) Low labor cost: Vectorized fuzzy judgment effectively filters out invalid difference alarms caused by format, precision and encoding, reducing the manual investigation cost of operation and maintenance personnel; (4) Balance between real-time performance and accuracy: Dynamically adjust the verification frequency to ensure that hot data differences can be discovered in seconds, while cold data does not occupy too many resources. Attached Figure Description

[0070] Figure 1 This is a flowchart of the heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to the present invention.

[0071] Figure 2 This is a schematic diagram of the heterogeneous structure semantic mapping process based on Transformer in this invention.

[0072] Figure 3 This is a schematic diagram of the adaptive block fingerprint verification process based on data entropy in this invention. Detailed Implementation

[0073] It is readily understood that, based on the technical solution of this invention, those skilled in the art can conceive of various embodiments of this invention without altering its essential spirit. Therefore, the following specific embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of this invention or as limitations or restrictions on its technical solution.

[0074] like Figure 1 As shown, this invention provides a heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification. Its main innovations include: Transformer-based Structure Semantic Mapping (SSM), Adaptive Block Fingerprinting (AEF) based on data entropy, vectorized fuzzy equivalence determination, and non-intrusive eBPF traffic bypass verification. The method comprises the following steps:

[0075] Step 1: Employ Transformer-based heterogeneous structure semantic mapping to transform the schema definition DDL into high-dimensional semantic vectors, such as... Figure 2 As shown, the details are as follows:

[0076] Step 1.1: To address the issues of inconsistent field names and different nested structure levels in heterogeneous databases, this invention does not rely on simple string matching, but instead transforms the Schema Definition DDL into a high-dimensional semantic vector; the source database table structure is defined as... The target database table structure is defined as follows: Perform structure serialization, serializing table names, field names, types, comments, and constraints into text descriptions. and ;

[0077] Step 1.2: Perform vector embedding, using a pre-trained code model, such as CodeBERT, to extract feature vectors:

[0078]

[0079]

[0080] Step 1.3: Calculate the semantic similarity matrix and the source field vector. With target field vector Cosine similarity, constructing a mapping matrix :

[0081]

[0082] Step 1.4: Obtain the semantic similarity matrix between the source field and the target field. Subsequently, a simple greedy strategy, which directly selects the maximum value of each row, is prone to "many-to-one" conflicts or local optima. This invention models the field mapping problem as a maximum weight matching problem in a bipartite graph, and locks the optimal mapping relationship through a global optimization algorithm, as follows:

[0083] Step 1.4.1: Construct a weighted bipartite graph: Define the source database field set as the left node set. The target database field set is defined as the right-hand node set. ,matrix similarity value That is, the connecting node and Edge weights;

[0084] Step 1.4.2, Conflict resolution mechanism for global optimization: The Kuhn-Munkres algorithm is used to solve the bipartite graph to find a mapping set. The set satisfies the following condition:

[0085] (a) Uniqueness constraint: No two mapping pairs in the set share the same u or v, that is, it guarantees a one-to-one mapping of fields;

[0086] (b) Global maximization: the sum of the weights of all edges in the set. Reaching the maximum value;

[0087] Mapping logic example: Assume the source table has fields `user_name` and `nickname`, and the target table has a field `uname`. The similarity between `user_name` and `uname` is 0.95, and the similarity between `nickname` and `uname` is 0.85. If a greedy strategy is used, both will compete for `uname`, leading to a conflict. The algorithm of this invention considers global benefits: if `nickname` maps to another field in the target table, such as `nick` with a similarity of 0.9, it will bring a higher total score. The algorithm will automatically determine that `user_name` corresponds to `uname`, and `nickname` corresponds to `nick`, thus achieving the globally optimal configuration.

[0088] Step 1.4.3, Threshold Decision and Final Mapping Generation: After the algorithm outputs the optimal matching pair, the system introduces a confidence threshold. ,For example Perform a second check: if a match is found similarity If so, it is considered a valid mapping, and the system automatically establishes an association between "user_name" and "uname"; if If no corresponding field is found, it is marked as such. New Field, a field unique to the source side This refers to the missing field on the target side.

[0089] Through the above steps, the present invention can automatically process complex... Within the matrix, the semantically logical field correspondences are accurately parsed, effectively solving the alignment problem caused by inconsistent naming conventions in heterogeneous databases.

[0090] Step 2: Adaptive Entropy Fingerprinting (AEF) based on data entropy. This method uses data entropy to measure the activity of data block changes and dynamically adjusts the verification strategy, such as... Figure 3 As shown, the details are as follows:

[0091] Step 2.1: To address the performance issues caused by full scans, this invention introduces the concept of "data entropy" to measure the activity level of data block changes and dynamically adjust the verification strategy. Define the data block. In the time window The frequency of changes within is Then the data entropy of this block Defined as:

[0092]

[0093] in For the data distribution probability, These are the weighting coefficients. The theoretical maximum change frequency set for the system;

[0094] Step 2.2, based on The system dynamically selects the fingerprint calculation strategy based on the value of the fingerprint.

[0095] 1) Hot data in high-entropy areas: Merkle Tree deep verification is used to subdivide data blocks into tiny slices, calculate root hash in real time, and quickly locate specific changed rows;

[0096] 2) Low-entropy cold data: Sparse sampling hash is used to randomly extract 1% of the key rows in the block to calculate fingerprints. If they match, the full verification is skipped.

[0097] 3) Adaptive verification step size: The time interval for the next verification. Inversely proportional to entropy, an increase in entropy indicates that the data is becoming more active, thus shortening the verification time interval. The calculation method is as follows:

[0098]

[0099] in It is an adjustable dynamic attenuation coefficient.

[0100] Step 3: Employ a vectorized fuzzy equivalence determination mechanism to avoid false consistency differences caused by floating-point precision or text synonyms, as detailed below:

[0101] To avoid false consistency discrepancies caused by floating-point precision, such as "1.00" and "1.0" or textual synonyms such as "Male" and "M", this invention introduces a vectorized judgment mechanism. When a regular hash comparison is inconsistent, a secondary judgment is initiated:

[0102] Step 3.1: Set the values ​​of the difference field. and Mapped to vector space;

[0103] Step 3.2: Calculate the Euclidean distance ;

[0104] Step 3.3, if If it is semantically consistent, it will be marked as a warning rather than an error.

[0105]

[0106] Step 4: Employ a kernel-level bypass monitoring and state reconstruction mechanism based on eBPF to achieve seamless verification of the production environment, as detailed below:

[0107] To address the issue that traditional verification methods require initiating SELECT or CHECKSUM queries on the production database, thus consuming I / O and CPU resources, this invention proposes a kernel-level bypass monitoring and state reconstruction mechanism based on eBPF. This mechanism runs completely independently of the database process, achieving "zero table locking and zero query" verification in the production environment.

[0108] Step 4.1: Deploy an eBPF probe at the operating system kernel layer to capture kernel-mode data, as detailed below:

[0109] Step 4.1.1, Socket probe mounting: Use kprobe or tracepoint to mount to key functions of the kernel network protocol stack, such as tcp_sendmsg and tcp_recvmsg, to intercept all incoming and outgoing traffic on specified ports, such as MySQL's 3306 and MongoDB's 27017.

[0110] Step 4.1.2, Context Filtering: In kernel space, traffic is filtered directly based on the PID or a five-tuple consisting of source IP, source port, destination IP, destination port, and protocol. Only database-related write operation traffic is copied to the RingBuffer, while irrelevant traffic is discarded, thereby keeping CPU overhead below 1%.

[0111] Step 4.2: The user-space agent asynchronously consumes the raw byte stream from the Ring Buffer, performs user-space protocol reassembly and parsing, as follows:

[0112] Step 4.2.1, TCP stream reassembly: To address the TCP packet fragmentation and out-of-order issues, the sliding window algorithm is used to reassemble fragmented network packets into complete application layer protocol data units (PDUs).

[0113] Step 4.2.2, L7 Protocol Decoding: The database communication protocol is located at the L7 layer of the OSI seven-layer model. It uses built-in multi-database protocol parsers, such as MySQL Binary Protocol and MongoDB Wire Protocol, to parse out specific SQL statements or Command instructions.

[0114] Step 4.3: Use schema dynamic evolution awareness for semantic traffic splitting, as detailed below:

[0115] After parsing the SQL, semantic splitting is also required. Since read-only operations do not change the database state, the semantic analyzer filters out read-only operations such as SELECT and extracts DML commands such as INSERT, UPDATE, and DELETE that cause data changes and their parameters, generating a standardized change event stream.

[0116] Since the vector module relies on DDL information to process DML, when an "INSERT INTO users (id, name)" statement arrives at the vector engine, the engine queries the schema knowledge base. The knowledge base's role is to determine, based on previously captured DDL or preset configurations, that the source-side id corresponds to the target-side uid, and the source-side name corresponds to the target-side username. Based on this rule, the vector engine converts the data into a unified format "{uid: value, username: value}", thereby achieving heterogeneous validation. Therefore, in addition to extracting DML, it is also necessary to extract DDL statements that affect the database table structure, such as ALTER TABLE and ADD COLUMN.

[0117] Therefore, SQL is divided into three paths: the first path is the identification path, which identifies and discards stateless read commands, such as SELECT, to reduce computational load; the second path is the data path, which extracts data manipulation commands (DML), carries specific business data payloads, and sends them to the vector transformation engine; the third path is the control path, which extracts data definition commands (DDL) and sends them to the schema knowledge base for real-time updates of the field mapping table.

[0118] Through the above steps, the present invention does not require downtime configuration. When the production database executes ALTER TABLE to add a field, the bypass module can automatically detect and update the verification rules without reporting errors or verification failures, thus enhancing the automation and robustness of the system.

[0119] Step 4.4: Build a lightweight shadow index in the memory of the bypass verification server, as follows:

[0120] Instead of querying data from the production database, the system builds a lightweight shadow index in the memory of the bypass verification server.

[0121] Step 4.4.1, Logic Replay: The verification device receives the change event stream generated in step 4.3 and simulates the execution of the change operation in a local embedded key-value store, such as RocksDB or In-memory SkipList;

[0122] Step 4.4.2, Real-time fingerprint maintenance: During the replay process, the Merkle Tree root hash value of the affected data blocks is updated synchronously; since the shadow index only stores the primary key and hash fingerprint, and does not store the full data, its space occupation is extremely small;

[0123] Step 4.4.3, State Snapshot: The shadow index reflects the theoretical state that the production database should have at a certain moment in real time.

[0124] Step 4.5: Transform the verification process into comparing the shadow index with the target database, as follows:

[0125] Step 4.5.1, Snapshot Alignment: Obtain the GTID or Oplog timestamp of the target database and capture the shadow index snapshot at the corresponding moment;

[0126] Step 4.5.2, Hash Comparison: Compare the Merkle Tree root hash value calculated by the shadow index with the verification value of the target database. If the comparison results match, the verification passes; otherwise, the verification fails, an alarm is triggered, and the process is retried.

[0127] The entire process does not require reading disk files from the production database, nor does it occupy the production database's memory buffer pool, completely eliminating the interference of verification operations on core business performance.

[0128] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0129] Example

[0130] This embodiment provides a heterogeneous database comparison device and method based on semantic vector mapping and block fingerprint verification, aiming to solve the problems of difficult field mapping and the impact of the verification process on production performance during heterogeneous database migration. Figure 1 As shown, the device includes an intelligent mapping module, an eBPF bypass capture module, a shadow index calculation engine, and a difference arbitration module;

[0131] The intelligent mapping module is responsible for the automatic alignment of heterogeneous database schemas and includes a metadata collection unit and an algorithm solving unit. The module has a built-in quantized Transformer model to generate database DDL semantic vectors. It also has a built-in KM algorithm engine that can receive similarity matrix input, output the globally optimal field mapping table MappingTable, and support manual correction feedback.

[0132] The eBPF bypass capture module is responsible for seamlessly acquiring data changes from the production environment. It includes kernel probes and a protocol parser. The kernel probes run in kernel space and are responsible for traffic interception and filtering. The protocol parser runs in user space and supports decoding of various mainstream database protocols such as MySQL, Oracle, PostgreSQL, and MongoDB, outputting change events in a unified format.

[0133] The shadow index calculation engine is responsible for constructing the theoretical state of the data in a bypass manner. It has a built-in high-performance key-value store, such as RocksDB, as the carrier of the shadow index; the engine includes a replay logic unit and a Merkle Tree maintenance unit, which are used to calculate data fingerprints in real time without persisting the full amount of data.

[0134] The difference arbitration module is responsible for performing the final verification and comparison, and includes a snapshot alignment unit and a hash comparison unit; by coordinating the time level of the shadow engine and the target database, it ensures that the hash comparison is performed at the same logical point in time and generates a verification report;

[0135] These modules run on a separate verification server, which is equipped with a high-performance CPU, an AI inference accelerator card, and a large amount of memory. The high-performance CPU is used for protocol parsing and algorithm solving; the AI ​​inference accelerator card has a built-in quantized Transformer model for generating structural semantic vectors in milliseconds; and the large amount of memory is used to maintain shadow indexes. The server is connected to the source and target databases via a network.

[0136] The method specifically includes the following steps:

[0137] Step 1: Employ Transformer-based heterogeneous structure semantic mapping to transform the schema definition DDL into high-dimensional semantic vectors, such as... Figure 2 As shown, the details are as follows:

[0138] Step 1.1: The system first connects to the source database and the target database, such as MySQL and MongoDB, and extracts the schema metadata of both, including table names, field names, data types, comments, etc.

[0139] Step 1.2: Use NLP technology, namely the BERT model, to perform semantic analysis on field names and annotations to generate high-dimensional feature vectors;

[0140] Step 1.3: Statistically analyze the data distribution characteristics of the fields, such as numerical range, null value rate, and uniqueness, and generate statistical feature vectors.

[0141] Step 1.4: Construct the source field set With target field set semantic similarity matrix The details are as follows:

[0142] Step 1.4.1, Modeling: Transform the field mapping problem into a maximum weight matching problem in a bipartite graph;

[0143] Step 1.4.2, Solution: Use the Kuhn-Munkres algorithm to solve the matrix. The algorithm performs a global optimization solution; while ensuring the "one-to-one" mapping constraint, it seeks to maximize the total similarity weight. The largest set of mappings;

[0144] Step 1.4.3, Decision: Perform threshold verification on the mapping pairs output by the algorithm, such as... Automatically create a username such as user_name (MySQL) The association relationship of uname(Mongo) is determined, and fields with no matching are marked.

[0145] Step 2: Adaptive block fingerprinting based on data entropy. Data entropy is used to measure the change activity of data blocks, and the verification strategy is dynamically adjusted, such as... Figure 3 As shown;

[0146] Step 3: Adopt a vectorized fuzzy equivalence determination mechanism to avoid false consistency differences caused by floating-point precision or text synonyms;

[0147] Step 4: Employ a kernel-level bypass monitoring and state reconstruction mechanism based on eBPF to achieve seamless verification of the production environment, as detailed below:

[0148] Step 4.1: Deploy an eBPF probe at the operating system kernel layer to capture kernel-mode data, mount it to kernel functions such as tcp_sendmsg, and filter out traffic from database ports such as 3306;

[0149] Step 4.2, Stream Reassembly: In user space, fragmented TCP packets are reassembled into complete database protocol data units;

[0150] Step 4.3, Event Extraction: Parse the protocol content, filter out SELECT queries, and extract only INSERT, UPDATE, DELETE operations and their parameters to generate a standardized "change event stream";

[0151] Step 4.4: Build a lightweight shadow index in the memory of the bypass verification server, as follows:

[0152] Step 4.4.1, Logic Replay: Consume the "Change Event Stream" and simulate the execution of the change operation in the shadow index;

[0153] Step 4.4.2, Fingerprint Calculation: Update the Merkle Tree root hash value of the affected data blocks in real time. This process is completely independent of the production database and does not consume the production database's I / O and memory resources;

[0154] Step 4.5: Transform the verification process into comparing the shadow index with the target database, as follows:

[0155] First, obtain the GTID or Oplog timestamp of the target database at the current moment; then, capture the shadow index snapshot at the corresponding moment and extract its Merkle Tree root hash value H1; next, call the target database interface to calculate the hash value H2 of the corresponding data; finally, compare H1 and H2. If they are inconsistent, locate the specific data shard for secondary precise verification and output a difference report.

[0156] The apparatus and method of the present invention can be widely applied in the following scenarios and have significant technical advantages:

[0157] Scenario 1: Migrating a financial system from Oracle to MySQL / TiDB

[0158] The core system of a certain digital currency trading system has extremely high stability requirements, and the verification program is not allowed to lock the source database or consume a large amount of I / O; moreover, there are significant differences in the field definitions between Oracle and MySQL, such as converting NUMBER to DECIMAL.

[0159] By utilizing eBPF bypass capture, full data verification was achieved during peak transaction periods with zero interference to the original core business. The Hungarian algorithm was used to automatically solve the field type mapping problem for thousands of tables, reducing configuration time from weeks to hours.

[0160] Scenario 2: Converting a relational database to a document-oriented database: MySQL to MongoDB

[0161] A data source consists of a flat two-dimensional table, while the target is a nested JSON document. This fundamental change in data structure renders traditional primary key-based validation tools ineffective.

[0162] This device uses an intelligent mapping module to identify a nested document structure in MongoDB corresponding to multiple related tables in MySQL, and simulates this structural transformation in the shadow index, thereby accurately verifying the consistency of data content after the table structure is reshaped.

[0163] Scenario 3: Cross-cloud real-time disaster recovery synchronization verification

[0164] Enterprises establishing real-time disaster recovery in a multi-cloud environment need to continuously monitor the consistency of primary and backup data to prevent silent data corruption caused by network packet loss or synchronization delays.

[0165] This device is deployed as a resident daemon that continuously consumes traffic captured by eBPF to build a real-time shadow fingerprint. Once an inconsistency between the shadow fingerprint and the backup database is detected, an alarm is immediately triggered, achieving millisecond-level detection of data tampering or loss.

[0166] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification, characterized in that, Includes the following steps: Step 1: Use Transformer-based heterogeneous structure semantic mapping to transform the schema definition DDL into a high-dimensional semantic vector; Step 2: Adaptive block fingerprinting based on data entropy, using data entropy to measure the change activity of data blocks and dynamically adjusting the verification strategy; Step 3: Adopt a vectorized fuzzy equivalence determination mechanism to avoid false consistency differences caused by floating-point precision or text synonyms; Step 4: Employ a kernel-level bypass monitoring and state reconstruction mechanism based on eBPF to achieve seamless verification of the production environment.

2. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 1, characterized in that, Step 1 describes the use of Transformer-based heterogeneous structure semantic mapping to transform the schema definition DDL into a high-dimensional semantic vector, as detailed below: Step 1.1: Define the source database table structure as follows. The target database table structure is defined as follows: Perform structure serialization, and... , The table name, field names, types, comments, and constraints are serialized into a text description. and ; Step 1.2: Perform vector embedding and extract feature vectors using a pre-trained large code model. , : Step 1.3: Calculate the semantic similarity matrix and the source field vector. With target field vector cosine similarity Construct a mapping matrix : Step 1.4: Model the field mapping problem as a maximum weight matching problem in a bipartite graph, and use a global optimization algorithm to lock in the optimal mapping relationship, as follows: Step 1.4.1: Construct a weighted bipartite graph: Define the source database field set as the left node set. The target database field set is defined as the right-hand node set. ,matrix similarity value That is, the connecting node and Edge weights; Step 1.4.2, Global Optimization Solution: The Kuhn-Munkres algorithm is used to solve the bipartite graph to find a mapping set. The set satisfies the following condition: (a) Uniqueness constraint: No two mapping pairs in the set share the same u or v, that is, it guarantees a one-to-one mapping of fields; (b) Global maximization: the sum of the weights of all edges in the set. Reaching the maximum value; Step 1.4.3, Threshold Decision and Final Mapping Generation: After the algorithm outputs the optimal matching pair, the system introduces a confidence threshold. Perform a second check: if a match is found similarity If so, it is determined to be a valid mapping, and the association of the corresponding fields is automatically established; if If no corresponding field is found, it is marked as such. New Field, a field unique to the source side This refers to the missing field on the target side.

3. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 1, characterized in that, Step 2 describes an adaptive block fingerprinting method based on data entropy. This method uses data entropy to measure the activity of data block changes and dynamically adjusts the verification strategy, as detailed below: Step 2.1: Define data blocks In the time window The frequency of changes within is Then the data entropy of this data block Defined as: in For the data distribution probability, These are the weighting coefficients. This represents the theoretical maximum change frequency. Step 2.2, based on The value is used to dynamically select the fingerprint calculation strategy: 1) Hot data in high-entropy areas: Merkle Tree deep verification is used to subdivide data blocks into tiny slices, calculate root hash in real time, and locate specific changed rows; 2) Low-entropy cold data: Sparse sampling hash is used to randomly extract 1% of the key rows in the block to calculate fingerprints. If they match, the full verification is skipped. 3) Adaptive verification step size: The time interval for the next verification. Inversely proportional to entropy, an increase in entropy indicates that the data is becoming more active, thus shortening the verification time interval. The calculation method is as follows: in It is an adjustable dynamic attenuation coefficient.

4. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 1, characterized in that, Step 3 describes the use of a vectorized fuzzy equivalence determination mechanism to avoid false consistency differences caused by floating-point precision or text synonyms, as detailed below: Step 3.1: Set the values ​​of the difference field. and Mapped to vector space; Step 3.2: Calculate the Euclidean distance ; Step 3.3, if If it is determined to be semantically consistent, it will be marked as a warning rather than an error: 。 5. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 1, characterized in that, Step 4 describes the use of a kernel-level bypass monitoring and state reconstruction mechanism based on eBPF to achieve seamless verification of the production environment, as detailed below: Step 4.1: Deploy an eBPF probe at the operating system kernel layer to capture kernel-mode data; Step 4.2: The user-space agent asynchronously consumes the raw byte stream from the Ring Buffer and performs user-space protocol reassembly and parsing; Step 4.3: Use schema dynamic evolution awareness for semantic splitting; Step 4.4: Build a lightweight shadow index in the memory of the bypass verification server; Step 4.5: Transform the verification process into comparing the shadow index with the target database.

6. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 5, characterized in that, Step 4.1, which involves deploying an eBPF probe at the operating system kernel layer to capture kernel-mode data, is detailed below: Step 4.1.1, Socket probe mounting: Use kprobe or tracepoint to mount to the key functions of the kernel network protocol stack to intercept all incoming and outgoing traffic on the specified port; Step 4.1.2, Context Filtering: In kernel space, traffic is filtered directly based on the PID or a five-tuple consisting of source IP, source port, destination IP, destination port, and protocol. Only database-related write operation traffic is copied to the Ring Buffer, while irrelevant traffic is discarded, thereby keeping CPU overhead below 1%.

7. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 6, characterized in that, Step 4.2 describes the user-space proxy program asynchronously consuming the raw byte stream from the Ring Buffer and performing user-space protocol reassembly and parsing, as detailed below: Step 4.2.1, TCP stream reassembly: To address the TCP packet fragmentation and out-of-order issues, the sliding window algorithm is used to reassemble fragmented network packets into complete application layer protocol data units (PDUs). Step 4.2.2, L7 Protocol Decoding: The database communication protocol is located at the L7 layer of the OSI seven-layer model. The built-in multi-database protocol parser is used to parse out the specific SQL statements or Command instructions.

8. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 7, characterized in that, Step 4.3, which describes semantic splitting using schema dynamic evolution awareness, is as follows: Read-only operations do not change the database state. A semantic analyzer filters out read-only operations, extracts DML instructions and their parameters that cause data changes, and DDL that affects the database table structure, and generates a standardized change event stream. The SQL is divided into three flows: the first path is the identification path, which identifies and discards stateless read instructions; the second path is the data path, which extracts data manipulation instructions (DML), carries specific business data payloads, and sends them to the vector transformation engine; the third path is the control path, which extracts data definition instructions (DDL) and sends them to the schema knowledge base for real-time updates of field mapping tables.

9. The heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification according to claim 8, characterized in that, Step 4.4 describes building a lightweight shadow index in the memory of the bypass verification server, as follows: Step 4.4.1, Logic Replay: The verification device receives the change event stream generated in step 4.3 and simulates the change operation in the local embedded key-value store; Step 4.4.2, Real-time fingerprint maintenance: During the replay process, the Merkle Tree root hash value of the affected data blocks is updated synchronously; Step 4.4.3, State Snapshot: The shadow index reflects the theoretical state that the production database should have at a certain moment in real time; Step 4.5 describes converting the verification process into comparing the shadow index with the target database, as follows: Step 4.5.1, Snapshot Alignment: Obtain the GTID or Oplog timestamp of the target database and capture the shadow index snapshot at the corresponding moment; Step 4.5.2, Hash Comparison: Compare the Merkle Tree root hash value calculated by the shadow index with the verification value of the target database. If the comparison results match, the verification passes; otherwise, the verification fails and an alarm is triggered.

10. A heterogeneous database comparison device based on semantic vector mapping and block fingerprint verification, characterized in that, This device is used to implement the heterogeneous database comparison method based on semantic vector mapping and block fingerprint verification as described in any one of claims 1 to 9. The device includes an intelligent mapping module, an eBPF bypass capture module, a shadow index calculation engine, and a difference arbitration module, wherein: The intelligent mapping module is responsible for the automatic alignment of heterogeneous database schemas and includes a metadata collection unit and an algorithm solving unit. The intelligent mapping module has a built-in quantized Transformer model to generate database DDL semantic vectors. It also has a built-in KM algorithm engine that can receive similarity matrix input, output the globally optimal field mapping table MappingTable, and support manual correction feedback. The eBPF bypass capture module is responsible for seamlessly acquiring data changes from the production environment. It includes kernel probes (KernelProbes) and a protocol parser (Protocol Parser). The kernel probes run in kernel space and are responsible for traffic interception and filtering. The protocol parser runs in user space and supports decoding of various database protocols, including MySQL, Oracle, PostgreSQL, and MongoDB, and outputs change events in a unified format. The shadow index calculation engine is responsible for constructing the theoretical state of the data in the bypass, and has a built-in key-value store as the carrier of the shadow index; the engine includes a replay logic unit and a Merkle Tree maintenance unit for real-time calculation of data fingerprints. The difference arbitration module is responsible for performing the final verification and comparison, and includes a snapshot alignment unit and a hash comparison unit; by coordinating the time level of the shadow engine and the target database, it ensures that the hash comparison is performed at the same logical point in time and generates a verification report; The intelligent mapping module, eBPF bypass capture module, shadow index calculation engine, and difference arbitration module run on an independent verification server. This server is equipped with a CPU, an AI inference accelerator card, and a large-capacity memory. The CPU is used for protocol parsing and algorithm solving; the AI ​​inference accelerator card has a built-in quantized Transformer model for generating structural semantic vectors in milliseconds; and the large-capacity memory is used to maintain the shadow index. The server is connected to the source and target databases via a network.

Images