Method for building data link of real-time data into lake based on debezium, flink and hudi

Abstract

The application relates to the technical field of data structure, and discloses a method for building a data middle platform real-time data link into a lake based on Debezium, Flink and Hudi, which executes a lock-free incremental snapshot to capture source end data through deployment of a collection component, disassembles a primary key change event into a logical deletion event and an addition event, introduces a message queue to perform data buffering and automatic registration of a mode, converts the event into a binary message body and outputs the binary message body to a business theme physical partition, performs state processing and dynamic primary key adaptation based on a stream computing engine, generates a unified primary key identifier, configures a data-in-lake matching rule and performs read-time merging table storage management, writes target data entities into a disk based on a version arbitration mechanism, builds a full-link monitoring module and an abnormality self-recovery mechanism, measures telemetry indexes, performs abnormality grading and classification, and triggers retry and link state rollback. The method can effectively process a source end primary key change scene, and guarantees data consistency and system recoverability.

Description

Technical Field

[0001] This invention relates to the field of data structure technology, specifically a method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi. Background Technology

[0002] As data middleware platforms and lake warehouse integrated architectures continue to evolve, enterprises are increasingly demanding real-time dashboards and real-time risk control with response times down to seconds or minutes. Throughout this development, whether it's the shift from traditional commercial software to open-source frameworks or the evolution from single tools to platform-based services, the core technical aspect of project construction is the real-time and accurate synchronization of incremental data from downstream business databases (such as Oracle, PostgreSQL, SQL Server, and MySQL) to data lakes (such as Huawei Cloud MRS platform, StarRing TDH platform, and AWS S3 platform). This process involves Change Data Capture (CDC), which aims to non-intrusively read and parse the underlying logs of the source database to extract atomic events of data changes in real time and synchronize them to downstream systems, supporting highly timely data flow and lake ingestion operations.

[0003] Currently, the existing methods and mainstream technologies for real-time database data acquisition in this field mainly fall into five categories: The first category is commercial software, such as Oracle GoldenGate, which possesses enterprise-level data replication capabilities, supports heterogeneous databases, and is commonly used in scenarios with extremely high consistency requirements, such as finance and telecommunications. The second category is cloud vendor services, such as Huawei Cloud MRSCDL. This type of managed CDC service based on Kafka Connect is mainly used for data integration within the cloud ecosystem. The third category is open-source frameworks, such as Flink CDC, which is based on streaming processing technology, emphasizes end-to-end real-time processing, and is widely used in the construction of real-time data warehouses and data lakes. The fourth and fifth categories are mainly open-source tools, such as Canal and Maxwell, which focus on parsing MySQL Binlog logs and lightweight JSON format conversion, respectively, suitable for simple MySQL data synchronization scenarios. In traditional development projects, the industry typically uses the above-mentioned product categories combined with big data platforms to carry out data ingestion into data lakes.

[0004] While existing technologies can achieve basic data synchronization, they still have shortcomings when dealing with complex and dynamic business scenarios. Specifically, current real-time data ingestion methods often overlook data flow errors caused by primary key changes in the source business database. This problem arises because when primary key updates occur in the actual production database, the data identification and synchronization mechanisms of traditional change data capture tools are inadequate. This leads to the data lake's inability to accurately identify and match updated data with the corresponding primary key, easily causing data duplication and a mixed arrangement of old and new data. This causes data deviation within the lake, compromising the accuracy and integrity of downstream data and interfering with the enterprise's needs for real-time data analysis and business decision-making. Furthermore, in handling multi-source, heterogeneous, high-concurrency scenarios, existing systems are not yet perfect in backpressure control and multi-component collaboration mechanisms, resulting in data within the lake failing to accurately map the actual business state. To address these issues, existing technologies have not yet formed a complete and universal solution. Most solutions rely on temporary batch initialization of data and subsequent data cleaning, which requires manual intervention and consumes significant human resources. Therefore, there is an urgent need to solve the above problems by building a data middle platform based on Debezium, Flink, and Hudi to create a real-time data inflow link to the lake. Summary of the Invention

[0005] To address the problems in related technologies, this invention provides a method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi, thereby overcoming the aforementioned technical problems in existing related technologies.

[0006] To solve the aforementioned technical problem, the present invention is achieved through the following technical solution: This invention provides a method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi, specifically including: Deploy the data acquisition component and execute a lock-free incremental snapshot to capture source data, and output the decomposed logical deletion and addition events; A message queue is introduced to perform data buffering and automatic pattern registration. It receives the logical deletion event and the addition event, converts them into binary message bodies, and outputs them to the physical partition of a dedicated business topic. Based on the execution state processing and dynamic primary key adaptation of the streaming computing engine, the binary message body is pulled from the physical partition to generate a unified primary key identifier, and the streaming data is emitted to the subsequent mapping operator; Configure the lake entry matching rules and perform storage management of the read-time merge table. Based on the unified primary key identifier, execute the version arbitration mechanism to write the target data entity to disk into the read-time merge table. Establish a full-link monitoring module and an anomaly self-healing mechanism, measure data flow telemetry indicators and perform anomaly classification, triggering retry and link status rollback operations.

[0007] As a preferred embodiment of the method for building a real-time data link for data middleware based on Debezium, Flink, and Hudi according to the present invention, the deployment of the acquisition component includes: building a data acquisition application using Debezium Embedded mode; The process of executing lock-free incremental snapshots to capture source data includes: logically dividing the full stock data into multiple consecutive primary key interval blocks; alternately polling the latest generated incremental change log records during the interval between primary key interval block query actions; and introducing a segmented watermark mechanism to quantify the processing progress during data extraction. The output of the decomposed logical deletion and addition events includes: intercepting the primary key change atomic transactions recorded in the underlying database logs; mounting a custom single message conversion module interface in the data acquisition connector configuration; when the operation type is identified as an update operation, comparing the primary key identifier of the previous mirror and the primary key identifier of the next mirror; determining that two independent record objects are instantiated after a primary key change event occurs; constructing a logical deletion event for the first record object and an addition event for the second record object; extracting the primary key identifier of the previous mirror and setting it as the common routing key for the decomposed dual-path events and injecting it into the event metadata header; and performing hash routing calculation based on the common routing key to determine the target physical partition number.

[0008] As a preferred embodiment of the method for building a real-time data link for a data platform based on Debezium, Flink, and Hudi as described in this invention, the introduction of a segmented water level mechanism to quantify processing progress includes: Based on the system time, the number of record rows contained in the snapshot block, and the smooth scan rate obtained by using the exponentially weighted moving average algorithm, a logical timestamp is generated for the snapshot block. The preset dynamic minimum rate threshold is determined based on the absolute physical time taken to generate the number of incremental log records from the source database. Extract the smooth scan rate and compare it with the dynamic minimum rate threshold. When the smooth scan rate is lower than the dynamic minimum rate threshold, update the continuous overload counter. When the continuous overload counter is greater than or equal to the preset hysteresis judgment threshold, assign a specific logical timestamp -1 to the snapshot block to indicate invalidity, terminate data merging and trigger the timeout retry mechanism. When the continuous overload counter is less than the hysteresis judgment threshold, the logical timestamp is extracted and compared with the event timestamp of the incremental change data to determine the priority order of data processing.

[0009] As a preferred embodiment of the method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi as described in this invention, the step of introducing a message queue to perform data buffering and automatic schema registration includes: initiating a schema registration request to the Schema Registry component to obtain a schema version identification code; deploying a Kafka message queue cluster using a distributed architecture and creating a dedicated business topic responsible for receiving serialized event streams; Receiving the logical deletion event and the addition event and converting them into a binary message body includes: structuring and reorganizing the underlying relational data change event into a standardized data dictionary; calling the Avro serializer to convert the standardized data dictionary into an Avro binary data stream; and using a standard line transmission format to perform continuous byte-level concatenation of the single-byte magic number prefix, mode version identifier, and change payload data to generate a binary message body. The output to the physical partition of the dedicated business topic includes: sequentially appending the binary message body to the persistent storage log of the physical partition in combination with the target physical partition sequence number.

[0010] As a preferred embodiment of the method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi as described in this invention, the dedicated business topic is allocated a total number of partitions, and the steps for determining the total number of partitions include: The underlying operating system probe is invoked to collect the physical indicators of the file descriptors of the agent nodes in real time. Based on the system's hard resource limit, the total number of occupied file descriptors, the number of deterministic partition replica factors, and the basic file handle constant, the maximum allowed partition threshold is calculated. Set a minimum safe consumption baseline and a minimum hard partition lower limit; Based on the data ratio calculated from the peak data generation rate and the real-time limit stable consumption rate, when the real-time limit stable consumption rate is lower than the minimum safe consumption baseline, the effective consumption rate will be converged to the minimum safe consumption baseline. The minimum value is obtained by combining the data ratio result with the maximum allowed partition threshold, and the maximum value is obtained by combining the minimum value result with the minimum hard partition lower limit. The total number of partitions is then forcibly constrained between the minimum hard partition lower limit and the maximum allowed partition threshold.

[0011] As a preferred embodiment of the method for building a real-time data link for a data middle platform based on Debezium, Flink, and Hudi as described in this invention, the step of performing state processing and dynamic primary key adaptation based on the streaming computing engine includes: instantiating a Kafka source operator within the Flink streaming computing engine and constructing a water level allocator based on a bounded out-of-order tolerance mechanism to dynamically generate logical timestamps. The step of retrieving binary message bodies from physical partitions to generate unified primary key identifiers includes: retrieving binary message bodies from physical partitions and constructing a dynamic primary key adapter, retaining the original information using string concatenation, and concatenating the source database name string, the source table name string, and the original primary key value to generate unified primary key identifiers; The process of transmitting the data to the subsequent mapping operator includes: extracting the event timestamp of the data and comparing it with the historical maximum event timestamp recorded in the value status handle; and then, based on the comparison result, transmitting the data to the subsequent mapping operator or routing it to a dedicated side output stream.

[0012] As a preferred embodiment of the method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi as described in this invention, after generating a unified primary key identifier, the method further includes performing primary key dimension deduplication and storage optimization in the state backend: Physical partitioning is performed based on a unified primary key identifier, and value state objects carrying lifecycle management attributes are instantiated in the state backend memory. By combining the maximum out-of-order tolerance time, physical refresh interval, and dynamic security compensation time to deal with downstream storage latency, the forced expiration time of median state objects in the backend is calculated and a positive correlation is established between the dynamic security compensation time and the checkpoint timeout time of the stream computing engine. Inject a forced expiration time into the expiration cleanup component in the state backend, and call the underlying garbage collection thread to clear outdated state items in incremental scanning mode; A checkpoint snapshot mechanism is introduced to synchronize persistent value state objects. When a failure occurs, the most recently successfully built checkpoint metadata is extracted to reset the state backend, and the restored in-memory state object is aligned with the downstream persistent data version.

[0013] As a preferred embodiment of the method for building a real-time data inbound link based on Debezium, Flink, and Hudi in the data platform described in this invention, the step of configuring the inbound matching rules and performing storage management of the read-time merging table includes: constructing a write configuration descriptor, declaring the unified primary key identifier as the record primary key field, and declaring the source business event timestamp field in the flowing data entity as the pre-merging field; specifying a hash bucket index mechanism to perform physical distribution positioning of the data, executing an operator parameter collaborative configuration protocol based on end-to-end topology, and statically binding the parallelism of the Hudi data write operator to the total number of partitions; The version arbitration mechanism is executed based on the unified primary key identifier, including: triggering the version arbitration mechanism in the data payload processing class to compare the source event timestamp of the newly arrived data entity with the source event timestamp of the historical reference data entity; The step of writing the target data entity to disk into the read-time merging table includes: writing the target data entity that has been approved for disk writing into the read-time merging table based on the numerical comparison results.

[0014] As a preferred embodiment of the method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi as described in this invention, the physical distribution and positioning of the execution data includes: declaring that the total number of physical storage buckets of the read-time merge table is an integer multiple of the parallelism of the write operator, establishing an end-to-end collaborative binding relationship from message queue partitions, write operators to physical storage buckets; constructing a local physical storage bucket directional routing model within the write operator, and locating and generating the Hudi target physical storage bucket sequence number based on the operator subtask sequence number, physical storage bucket expansion ratio, and hash value corresponding to the unified primary key identifier, guiding a single computation operator instance to complete hash positioning in local memory, and cutting off cross-node network interaction and data redistribution process; The storage management of the read-time merging table also includes: configuring a persistent asynchronous compression scheduler in the background of the data lake platform, generating a trigger status word for the asynchronous compression task by comparing the total number of incremental submission batches with the cumulative threshold of submission batches, and comparing the absolute elapsed time with the maximum waiting time limit, and determining whether to activate the compression process based on the trigger status word.

[0015] As a preferred embodiment of the method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi as described in this invention, the construction of the full-link monitoring module and the anomaly self-healing mechanism includes: deploying lightweight indicator probes in the data acquisition layer, the stream computing layer, and the data lake storage layer respectively. The measurement of data flow telemetry indicators and the execution of anomaly classification include: measuring the physical duration of end-to-end data acquisition delay, back pressure ratio, physical consumption time for a single batch of data to complete lake entry and merging and landing, and the data anomaly flow ratio, serializing and persisting them to a time series database; and deploying anomaly interceptors to perform anomaly classification. The triggering of retry and link state rollback operations includes: for non-fatal errors, the retry controller takes over, triggers an exponential backoff algorithm that introduces a concurrency jitter factor to prevent jitter, calculates the physical duration of the suspension wait, and re-initiates the operation request; when the cumulative number of retry attempts exceeds the maximum tolerable retry threshold, a runtime exception is thrown to the streaming computing engine, triggering a physical rollback contingency plan at the storage layer, restarting the job according to the fault tolerance configuration, and completing the end-to-end link state rollback.

[0016] The present invention has the following beneficial effects: 1. This invention intercepts atomic transactions involving primary key changes using the Debezium component, calls a single message conversion module to decompose them into logical deletion events for the old primary key and addition events for the new primary key, forces the old primary key to be set as a common routing key and performs hash routing calculation, ensuring that the decomposed dual-path events strictly flow into the same physical partition of the downstream message queue. It guides the single-parallelism task operators within the streaming computing engine to sequentially perform state clearing and state reconstruction operations according to the event timestamp order. Simultaneously, a dynamic primary key adapter is constructed, concatenating the source database name string, the source table name string, and the original primary key value to generate a unified primary key identifier. This unified primary key identifier is explicitly declared as the record primary key field in the Hudi data lake write configuration descriptor. This solves the data flow disorder problem caused by primary key changes in the source business database in existing technologies, avoids data duplication and mixed arrangement of old and new data, maintains the atomicity of transaction changes in the data flow chain, and ensures that the data within the lake accurately maps to the real business state.

[0017] 2. This invention employs a distributed architecture to deploy a message queue cluster for data buffering. Based on a capacity planning model, it calculates the total number of partitions and performs asynchronous peak shaving and valley filling on the link. At the storage layer, it constructs an end-to-end topology-based operator parameter collaborative configuration protocol and a directed routing model for physical storage buckets. The parallelism of writing data lake data to operators is statically bound one-to-one with the number of upstream message queue partitions. Within the operator, a unified primary key identifier is routed to the bound physical storage bucket through hash mapping. By establishing an end-to-end collaborative binding relationship from message queue partitions, computation operators, to physical storage buckets, it cuts off cross-node network interactions and data redistribution processes, eliminates the performance overhead caused by data skew and global index comparison in high-concurrency scenarios, and decouples the rigid coupling between the data acquisition layer's generation rate and the computation layer's processing rate, thereby improving the end-to-end throughput efficiency and system stability of the data platform in multi-source, heterogeneous, and dynamic scenarios.

[0018] 3. This invention establishes a full-link real-time monitoring module based on a time-series database. Lightweight indicator probes are deployed at the data acquisition layer, stream computing layer, and data lake storage layer to measure the physical duration of data acquisition latency, operator backpressure ratio, physical consumption time for a single batch of data to complete lake merging and disk write, and data anomaly flow ratio. Anomaly interceptors are deployed at the data flow computing nodes and storage write endpoints to perform anomaly classification. For non-fatal errors, an exponential backoff algorithm is triggered to calculate the physical duration of suspension waiting. When the number of retries exceeds the maximum tolerable retry threshold, a runtime anomaly is thrown to the stream computing engine, triggering the physical rollback contingency plan of the Hudi data lake transaction manager. The metadata of the most recently successfully constructed checkpoint is extracted to reset the operator's state backend and consumption offset. An automatic backoff retry and degradation mechanism is provided to handle anomaly jitter and component failures, completing end-to-end link state rollback, thereby ensuring strong data consistency and system recoverability of the real-time lake link under abnormal conditions.

[0019] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, the drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 The present invention provides a flowchart of a method for building a real-time data link for data entry into a data platform based on Debezium, Flink, and Hudi.

[0022] Figure 2 This is a schematic diagram of the S4 process provided by the present invention.

[0023] Figure 3 This is a schematic diagram of the S5 process provided by the present invention.

[0024] Figure 4 This invention provides a method architecture diagram for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1

[0027] Existing real-time data ingestion methods often overlook the data flow disorder caused by primary key changes in the source business database. In actual production operations, when the business side updates the primary key, traditional change data capture tools are prone to data duplication and mixed arrangement of old and new data. Furthermore, when dealing with multi-source heterogeneous high-concurrency scenarios, they lack effective backpressure control and multi-component collaboration mechanisms, resulting in the data in the data lake failing to accurately map the real business status.

[0028] To solve the above technical problems, such as Figure 1 and Figure 4 As shown, Embodiment 1 of the present invention provides a method for building a real-time data lake link for a data middle platform based on Debezium, Flink, and Hudi. Specifically, Embodiment 1 takes a high-concurrency order business data lake ingestion scenario of a certain type of enterprise-level data middle platform as an example: This business environment involves heterogeneous databases of Oracle and MySQL, with a large daily scale of change events and high requirements for data consistency. The system deployment architecture includes a Debezium acquisition cluster, a Kafka distributed message queue, a Flink streaming computing engine, and a Hudi data lake platform based on HDFS.

[0029] In the specific implementation of Embodiment 1 above, firstly, a data acquisition component is deployed to connect with the business database. A segmented watermark mechanism is used for lock-free incremental snapshots, establishing a seamless connection between the full and incremental phases of the data link. A distributed message queue is introduced to buffer and register serialized change events, achieving system-level decoupling in a heterogeneous environment. A streaming computing engine is configured to access real-time data streams, performing dynamic primary key adaptation and state-layer memory filtering to unify the mapping of primary keys in heterogeneous databases. The system configures dedicated inbound rules before writing to the data lake, setting a strong binding and collaborative mechanism for message partitioning, computational parallelism, and hash bucket indexes to complete read-time merged storage of data. A full-link monitoring module is built to track processing points and backpressure status in real time, automatically triggering exponential backoff retry and degradation mechanisms when an anomaly is detected. This method, through inter-component parameter collaboration and deterministic primary key processing logic, avoids data deviation within the data lake caused by primary key updates in the production environment, ensuring end-to-end data consistency.

[0030] To illustrate the technical solution of Embodiment 1 of the present invention in detail, the specific implementation steps include the following: S1. Deploy the acquisition component and execute lock-free incremental snapshots to capture source data. Specifically, this involves non-intrusive extraction of data change events from heterogeneous databases and establishing an orderly connection between full snapshots and incremental logs, including the following sub-steps: S11. Enable log extraction from heterogeneous business databases at the data source end. For MySQL databases, enable a row-based binary log mechanism; for Oracle databases, enable archived logs and the LogMiner log parsing component. Build the data acquisition application using Debezium Embedded mode, embedding the Debezium engine as a native dependency library into real-time computing nodes or independent host processes to reduce external dependency chains. Write unified connection credentials for multiple source databases into the engine configuration parameters and configure regular expression parameters for filtering the collection set. Specifically, set the database containment list and table containment list as a regular expression matching mapping, enabling a single connector process to concurrently subscribe to multiple heterogeneous database instances and hundreds of underlying business tables according to the set dynamic matching rules. Enable a lock-free incremental snapshot mechanism during the data acquisition phase. Pre-build a dedicated trigger signal table within the source business database. Activate the incremental snapshot reading process by asynchronously writing snapshot trigger data rows containing the target existing table identifier to a specific signal table. The acquisition engine obtains the primary key extreme value range of the target existing table and logically divides the full existing data into multiple continuous primary key range blocks according to the system's preset block size threshold. During data extraction, the acquisition engine sequentially performs short queries on each interval block based on primary key boundary conditions, and alternately polls the latest incremental change log records between consecutive interval block query actions. This execution mechanism avoids the global read locks or table-level locks imposed on the source data table by traditional full snapshots, achieving lock-free and smooth extraction of existing datasets without blocking the regular write transaction flow and data definition language operations of the business system.

[0031] S12. Introduce a segmented water level mechanism to quantify the processing progress, assuming the entire table has... records and divided into The snapshot block, for the first snapshot block Each snapshot block defines a logical timestamp for the waterline, and its expression is: In the formula Indicates the first The logical timestamp attached when block data is written to the message queue. This indicates the system time at which the snapshot block was first scanned. This indicates the number of record rows contained in the snapshot block. This represents the smoothed scan rate obtained by using the exponentially weighted moving average algorithm.

[0032] To eliminate measurement jitter caused by transient peaks, the scan rate is smoothed. The recursive calculation model is as follows: In the formula, Indicates the first The instantaneous scan rate of a snapshot block is calculated by the ratio of the number of records in the current block to the actual scan time. This represents the smoothing weighting factor, with a value range of [value range missing]. ; This indicates the smooth scan rate of the previous snapshot block.

[0033] Based on this, an overload protection mechanism based on hysteresis judgment is constructed, and a continuous overload counter is preset. With hysteresis threshold During the execution of a snapshot block read task, the arrival rate of incremental data within the current time slice is determined by extracting the transaction log offset from the source database. The first... The dynamic minimum rate threshold corresponding to each snapshot block : In the formula, This indicates that the system has completed the first... The absolute physical time consumed by each snapshot block extraction task. Indicates in The number of new change event records actually added in the underlying transaction log of the source database within the time period.

[0034] The system extracts the smooth scan rate of the current snapshot block. With dynamic minimum rate threshold Perform numerical comparison, when When the system updates the continuous overload counter, it increments by 1. The system will reset the continuous overload counter to 0. The system will then determine when the continuous overload counter value is reached. When the system detects a persistent source-end overload, a protection mechanism is triggered, assigning a specific logical timestamp of -1 to the current snapshot block to indicate invalidity. Upon recognizing this specific logical timestamp -1, the downstream consumer terminates the data merging of the current time window and triggers a timeout retry mechanism. If the system's computational load is determined to be within the normal operating range or a tolerable range of instantaneous fluctuations, the system generates a valid logical timestamp. This information is then attached to the current snapshot block metadata. Downstream consumers extract this logical timestamp and compare it with the timestamps of subsequent incremental log stream events to determine the data processing priority and complete the timing alignment. This processing mechanism can avoid overload misjudgments caused by a sudden drop in the instantaneous rate during sudden surges in source-end business traffic, maintaining the efficiency of full synchronization execution and the stability of the link.

[0035] S13. For primary key update operations occurring in the business production database, the Debezium component intercepts the primary key change atomic transactions recorded in the underlying database logs and mounts a custom single-message conversion module interface in the data acquisition connector configuration. The core conversion function of the single-message conversion module is rewritten to extract single records from the micro-batch event stream and determine the operation type identifier. When the operation type identifier is an update operation, the before-image data record entity and the after-image data record entity are extracted from the data payload, and the before-image primary key identifier is read. With the mirrored primary key identifier The underlying byte comparison function is called to evaluate the consistency between the two. A primary key change event is detected. The data source record construction method is called to instantiate two independent record objects. For the first record object, a logical delete event is constructed, resetting the operation type identifier to a delete command and extracting the previous mirrored data record entity as the data payload. For the second record object, an add event is constructed, resetting the operation type identifier to a create command and extracting the subsequent mirrored data record entity as the data payload. The key-value containers of the two record objects are physically overwritten, and the previous mirrored primary key identifier is extracted. The common routing key is set as the shared routing key for the split dual-path events, and this shared routing key is injected into the metadata header of the logical delete event and the add event. The system performs hash routing calculation based on the shared routing key to determine the target physical partition number. The calculation formula is as follows: In the formula This represents the calculated target physical partition number. This represents the primary key identifier of the previous mirror image. This indicates that the unencrypted hash calculation function is used to map a variable-length feature string to a fixed-length hash value, outputting a dimensionless integer. This indicates the total number of partitions allocated to the target message queue. This represents the modulo operation function. When the total number of partitions... When the data is within a compliant positive integer range, the physical partition number determined by the above calculation output is executed. This guides the logical deletion event and the new event to strictly flow into the same physical partition of the downstream message queue based on the common routing key. This drives the single-parallel task operator within the streaming computing engine to execute state clearing and state reconstruction operations sequentially according to the event timestamps, maintaining the atomicity of transaction changes.

[0036] In step S1 of this embodiment, the system intercepts the atomic transaction of primary key change in the underlying database log, decomposes it into logical delete events and add events, and extracts the previous mirror primary key identifier as a common routing key to perform hash routing calculation. Specifically, for example: setting the total number of partitions allocated to the target message queue. Suppose that at a certain moment, an order primary key change operation occurs in the source database, and the probe intercepts the previous mirror primary key identifier of that record. , post-mirror primary key identifier System-level comparison and judgment Then, the logic for deleting and adding events is broken down and instantiated. Extraction The hash value is obtained by using an unencrypted hash function and set as the common routing key for the decomposed dual-path events. Substitute into the formula to calculate the target physical partition number: Based on the calculated sequence number 6, the system strictly routes and outputs the logical deletion event and the new event to the same physical partition of the downstream message queue. This step, by introducing a pre-mirror primary key identifier and deterministic modular arithmetic, guides the single-parallelism task operators within the streaming computing engine to perform state clearing and state reconstruction operations sequentially according to the event timestamps, maintaining the atomicity of transaction changes and avoiding data duplication and the mixed arrangement of old and new data.

[0037] S2. Introduce message queues to perform data buffering and automatic pattern registration. This involves building a high-throughput intermediate caching layer to connect upstream data collection nodes and downstream computing nodes. Specifically, this includes the following sub-steps: S21. The underlying relational data change events captured by the data acquisition engine are structurally reorganized into a standardized data dictionary containing pre-mirror fields, post-mirror fields, operation type identifiers, and timestamps. The Avro serializer is called to convert the standardized data dictionary into an Avro binary data stream. During serialization, the system parses the physical column structure of the source database table, maps the local data types of the source database to Avro standard data types according to preset type mapping rules, and dynamically generates a data schema file corresponding to the current table structure. A schema registration request is initiated to the independently deployed Schema Registry component, configuring the component's schema evolution strategy to backward compatibility mode, allowing the source business database to perform data definition language operations such as adding or deleting columns without interrupting the data link. The Schema Registry component receives the data schema file and calculates a global hash fingerprint for internal comparison. For new schemas that do not hit the cache, a globally unique schema version identifier is assigned. The serializer extracts the schema version identifier and encapsulates the data using the standard Confluent line transmission format. The structure expression of its binary message body is as follows: In the formula, This represents the final binary message body generated; Indicates a single-byte magic number prefix; This indicates a four-byte mode version identifier; This represents the changed load data after compression processing using the Avro algorithm; This represents a continuous concatenation operation of byte sequences. The system directly performs serialization output according to the Confluent standard specification. This allows the downstream streaming engine to directly extract the schema version identifier from the message header after the message body with schema state is sent to the distributed message queue. It then pulls the exact version of the data schema file from the Schema Registry component to perform deserialization and parsing, thus building an adaptive compatibility capability for dynamic changes in the source table structure in heterogeneous data flow links.

[0038] S22. Deploy a Kafka message queue cluster using a distributed architecture and create a dedicated business topic responsible for receiving serialized event streams. Set the total number of partitions for the business topic. The calculation formula is: ; In the formula, The total number of partitions assigned to the target business topic is a dimensionless positive integer. This indicates the peak data generation rate of the source business database within the historical observation period; and These represent the functions that take the maximum and minimum values, respectively. This represents the real-time maximum stable consumption rate of a single task operator in the downstream streaming computing engine. This indicates the preset minimum safe consumption baseline; This represents the maximum allowed partition threshold calculated objectively based on the cluster's physical storage resources; This represents the minimum hard partition limit set to guarantee the system's basic throughput under extreme conditions; This represents the floor function. During system operation, the operator's consumption status is monitored in real time, and the extracted real-time limit stable consumption rate is... Falling to the minimum safe consumption baseline When the following occurs, it is determined that the computing node has experienced performance degradation, and the capacity planning model forces the effective consumption rate to converge to [a certain value]. The theoretical number of partitions is calculated, and the final total number of partitions is strictly constrained within a closed interval by using nested extreme value functions. Internally, it prevents metadata overflow from the underlying distributed coordination components caused by indefinite operator pauses, while ensuring basic concurrent throughput under worst-case conditions. Simultaneously, it triggers the underlying resource scheduling monitoring probe to throw an overload alarm payload of consumption capacity, sending elastic scaling or fault isolation intervention instructions to the outer operation and maintenance management platform. The data sending client extracts the serialized binary message body and, combined with the target physical partition sequence number calculated by routing, sequentially appends the binary message body to the persistent storage log of the corresponding physical partition. Downstream streaming computing engines, through their parallel operators, pull binary message bodies from the corresponding physical partitions according to specific mapping relationships and memory load status, decoupling the rigid coupling between the data acquisition layer's generation rate and the computing layer's processing rate, and performing asynchronous peak shaving and valley filling.

[0039] In this embodiment, the maximum allowed partition threshold The determination method is as follows: Before creating a business topic, the system calls the underlying operating system probe to collect the physical indicators of file descriptors of each agent node in real time, and calculates the maximum allowed partition threshold based on the current objective remaining resource status of the cluster: ; In the formula, This represents the maximum allowed partition threshold calculated objectively based on the cluster's physical storage resources; This represents the total number of active agent nodes in the message queue cluster, and is a dimensionless integer. Indicates the first The maximum physical limit of file descriptors configured in the operating system kernel of each agent node, i.e. the upper limit of system hard resources, is a dimensionless integer; This indicates that at the time of the evaluation, the system probe starts from the first... The total number of occupied file descriptors collected in real time by each agent node is a dimensionless integer; This represents the number of deterministic partition replica factors required to satisfy the high availability and disaster recovery protocol for the business, and is a dimensionless positive integer. The constant representing the base file handle that must be allocated when a single partition replica is instantiated is determined by the distributed message engine structure. It is fixed to cover the log segment file, offset index file, and timestamp index file, and is a dimensionless positive integer. This represents the floor function.

[0040] It is important to emphasize that, given the underlying physical characteristic of distributed message queues—that the number of partitions can only increase unidirectionally and cannot be dynamically reduced—the formula for calculating the total number of partitions is particularly suitable for scenarios involving the initial creation of a topic or subsequent expansion to add partitions. During long-term system operation, if the actual consumption rate significantly improves due to downstream streaming job optimization or computing resource expansion, it will not trigger the partition reduction mechanism. This capacity planning method... The operational system core-level physical upper limit constraints, and the worst-case scenario based on the initial planning phase. The system is estimated to operate within a safety margin. Even if the actual number of partitions exceeds the theoretical optimal value in subsequent optimizations, the system can still effectively control the occupancy rate of underlying file handles, avoiding unnecessary small file overhead. This is treated as a safety-first static capacity planning strategy rather than a real-time control law that requires adaptive reduction.

[0041] In step S2 of this embodiment, the system, based on a capacity planning model and considering the physical limitations of the underlying cluster's hardware resources and the peak generation rate of source business data, dynamically calculates the total number of partitions for the dedicated business topic of the message queue, ensuring data buffering capacity and file system security under extreme load conditions. Specifically, for example, deploying a message queue cluster consisting of 3 proxy nodes... The system calls the underlying probe to collect the total number of available file descriptors for each agent node, i.e. Set the deterministic partition replication factor. Base file handle constant Substituting into the formula for calculating the maximum allowed partition threshold: Based on business characteristic analysis, let the peak data generation rate of the source business be... Transaction / second, the real-time maximum stable consumption rate of a single operator in the downstream streaming computing engine. Items / second, preset minimum safe consumption baseline Items / second, minimum hard partition lower limit The system, through comparative calculations, converges the effective consumption rate to... Items / second, the calculated data ratio is as follows: Based on the constraints, the final total number of partitions is calculated as follows: The system allocates 14 physical partitions to this specific business topic. This step, through strict physical resource boundary constraints and dynamic consumption capacity calculations, prevents the risk of underlying file handle exhaustion caused by the performance degradation of downstream operators, while providing a corresponding physical ingestion channel for high-concurrency data streams of 60,000 data entries per second. It decouples the rigid relationship between the data acquisition layer's generation rate and the computing layer's processing rate, effectively performing asynchronous peak shaving and valley filling.

[0042] S3. Perform state processing and dynamic primary key adaptation based on the streaming computing engine. Specifically, this involves handling heterogeneous data formats and eliminating primary key naming conflicts to provide a standardized data flow for data entering the lake. This includes the following sub-steps: S31. Instantiate the Kafka source operator within the Flink streaming engine and configure the deserialization mode to parse the Avro-formatted binary data stream pulled from the distributed message queue. Extract the source business timestamp field from the parsed data payload as the event time, and build a waterline allocator based on a bounded out-of-order tolerance mechanism to dynamically generate logical timestamps. The waterline timestamp calculation formula is: In the formula, This represents the timestamp of the water level line emitted by the current operator; This represents the timestamp of the largest valid event observed in the current data stream partition; This indicates the system's preset maximum out-of-order tolerance time. In extreme abnormal situations, if a specific upstream message partition experiences a prolonged period without data inflow due to source data interruption or network failure, resulting in... Stop updating, water level calculated according to the formula This can lead to stagnation, causing memory backlog in downstream state operators or timers that rely on the water level. The system configures an idle detection timeout threshold in the water level allocator. When the physical time without data inflow to a specific partition exceeds this threshold, it is forcibly marked as idle. This drives downstream operators to automatically remove the weight of the stagnant partition when aggregating the global water level, ensuring the timeliness of the overall real-time computing link and the security of the state backend. After data flows through the water level marker node, it performs key-value partitioning based on the composite key combining the data table identifier and the original primary key field, entering the underlying state-processing operator. A value state handle is declared in the operator context to persistently cache the historical maximum event timestamp corresponding to the current composite key. The underlying change operation code in the data payload is extracted from the incoming single-transfer data, parsed and converted, and then bound to a standardized change type identifier for each piece of transfer data, including add, modify, and delete operations.

[0043] The system extracts the event timestamp of the current flowing data and compares it with the historical maximum event timestamp recorded in the value status handle. If the current event timestamp is not less than the historical maximum event timestamp, the system refreshes the value status record and sends the flowing data with the change type identifier and event time to the subsequent mapping operator. If the current event timestamp is strictly less than the historical maximum event timestamp, the system determines that the data is invalid, out-of-order, and reversed data caused by the underlying network latency. It directly blocks the downlink delivery path of the data and isolates its route to a dedicated side output stream, filtering out the interference caused by the abnormal transaction order reversal at the source end to the downstream storage nodes from the memory computing level.

[0044] S32. Construct a dynamic primary key adapter. Preserve the original information as a deterministic and unique primary key using string concatenation, and define a mapping function from heterogeneous primary keys to a unified primary key: ; In the formula, This indicates that the Hudi table generated after mapping has a unified primary key, and the data type is a string. Represents the source database name string; Represents the source table name string; This represents the original primary key value of the record in the source database. If the original table uses a composite primary key, the value is concatenated and separated by specific characters before being used as the input item. This represents a string concatenation function. In extreme exceptional cases where the string is empty, the concatenation function automatically generates a system-preset exception placeholder.

[0045] S33. Deploy conditional filtering operators in the Flink streaming engine topology to continuously parse the unified primary key identifier carried by the streaming data. When the filtering operator subsequently identifies a unified primary key identifier containing a system-preset abnormal placeholder, it determines that the data record has an anomaly of missing core business primary key. The system calls the streaming engine's bypass output mechanism to forcibly route the abnormal data blocking the main data flow to a dedicated dead-letter queue output stream and persistently transfer it to an independent relational data quality monitoring table. The monitoring and alarm component reads the data quality monitoring table and sends a repair work order. The system provides a standardized data correction application interface to support manual intervention or automated scripts to perform backfill repair on abnormal attributes. After repair, compliant data is re-injected into the main computing link via an independent compensation data source node to participate in downstream consumption. Simultaneously, to address the memory bloat caused by high-frequency primary key updates, primary key dimension deduplication and storage optimization are performed on the Flink state backend. Configure key-value processing functions to process incoming data according to the unified primary key identifier. Perform physical partitioning and instantiate valued state objects carrying lifecycle management attributes in the state backend memory. When the same... When dealing with multiple records, the state operator extracts the event timestamp of the newly arrived data and compares it with the latest historical record event timestamp cached in the value state object. A deduplication filtering operation is then performed, and the state backend only updates and overwrites the record body with the latest event timestamp, completely discarding expired redundant change details. To ensure the cyclical reclamation of memory resources, the forced expiration time calculation formula for value state objects in the state backend is as follows: ; In the formula, This indicates the mandatory expiration time of the state object in the state backend. This represents the maximum out-of-order tolerance time set in the system's pre-operator; This indicates the physical refresh interval for downstream data lakes to perform batch merging and disk write operations; This represents a function that takes the maximum value. This represents the preset dynamic safety compensation duration to address downstream storage latency and abnormal jitter. The technical principle behind this calculation, which forces the expiration time to be bound to the data lake batch refresh interval, is that the value state object at the state backend serves as a cache to eliminate data out-of-order and duplicate data within micro-batches. It must ensure that state data resides in memory until the current batch of data is completely submitted to the downstream data lake. To prevent premature state expiration due to write failures or network latency, which could lead to incorrect processing of subsequent out-of-order data, the dynamic safety compensation duration... A positive correlation is established with the checkpoint timeout duration of the stream computing engine, forcing the state cache lifecycle to cover the entire retry and fault tolerance window. This forced expiration lifecycle is injected into the expiration cleanup component of the state backend, and the underlying garbage collection thread uses incremental scanning mode to clear outdated state items and relieve memory backlog pressure. At the fault tolerance management level, a checkpoint snapshot mechanism is introduced to ensure state consistency during fault recovery. The value state object of the state backend is persisted to the distributed file system synchronously with the global checkpoint fence. In the event of operator crash or node failure, the system extracts the metadata of the most recently successfully constructed checkpoint to reset the state backend. Relying on the atomic binding of the two-phase commit protocol between the stream computing engine and the data lake, the recovered memory state object is strictly aligned with the persistent data version of the downstream data lake, maintaining end-to-end exact one-time deduplication semantics and data integrity.

[0046] The system will calculate the forced expiration time. The state-injected backend expiration cleanup component uses an underlying garbage collection thread to periodically remove obsolete data that has not been accessed for a specific period of time using an incremental scanning mode. The status item, while ensuring accurate one-time deduplication semantics from end to end, unloads the system memory backlog caused by frequent updates to business tables.

[0047] In step S3 of this embodiment, the system constructs a dynamic primary key adapter within the stream computing engine to generate a unified primary key identifier. It then calculates the forced expiration time of backend value objects based on out-of-order tolerance and storage latency parameters, thereby performing deduplication filtering and memory cleanup optimization at the primary key level. Specifically, for example: suppose the intercepted source database name is Oracle, the source table name is Order_Table, and the extracted original primary key value is 1024. The system calls the adapter to concatenate the string to generate a unified primary key identifier. To manage the lifecycle of this primary key's state, a maximum out-of-order tolerance period is set. Physical refresh interval for downstream data lake batch disk writes Dynamic safety compensation duration for responding to abnormal downstream vibrations Substituting the above values ​​into the formula, we can calculate the forced expiration time of the state object: The system will calculate the... An expiration cleanup component is injected into the state backend. This step ensures that the state cache lifecycle can strictly cover the fault tolerance and retry window of the entire micro-batch through extreme value filtering and compensation calculation of multi-dimensional time parameters. While the underlying garbage collection thread unloads old state items on time, it maintains accurate one-time deduplication semantics and avoids abnormal system memory backlog caused by high-frequency primary key updates.

[0048] S4 configures the matching rules for data entering the lake and performs storage management for read-time merged tables. By establishing a mapping relationship between the unified primary key processed in S3 and the underlying storage of the data lake, efficient data overwriting is achieved. For example... Figure 2 As shown, the specific steps include the following: S41. Construct the Hudi data lake's write configuration descriptor at the data output endpoint of the streaming computing engine. Extract the unified primary key identifier generated in the preceding step S3. This is explicitly declared as a record primary key field in the data lake table configuration item, establishing its physical status as a globally unique associated primary key. To ensure data version correctness in concurrent out-of-order scenarios, the source business event timestamp field in the flowing data entity is extracted and declared as a pre-merged field in the data lake table configuration item. The data operation type is set to update-insert mode in the write configuration descriptor, and a data payload processing class based on the pre-merged field is attached to the table. When the Hudi storage layer identifies that the same unified primary key identifier already exists in the current micro-batch or the underlying file system during the processing phase... When multiple data records are involved, the system automatically triggers the version arbitration mechanism in the data payload processing class to determine the target data entity that is ultimately approved for disk writing after version arbitration. Its expression is: ; In the formula, This refers to the target data entity that was finally approved for disk write after version arbitration. This represents a single newly arrived data entity currently being output by the streaming engine; This indicates that the primary key identifier already exists in the Hudi data lake or is currently in the micro-batch cache and has the same primary key identifier. Historical reference data entities; Indicates the newly arrived data entity Extracted source event timestamps; Represents historical reference data entities The extracted source event timestamps. In the extreme case where timestamps are exactly equal, such as when source transactions are committed in parallel, the version arbitration mechanism prioritizes the later-arriving event in the computation chain based on the greater than or equal to criterion. To ensure idempotency of overwriting, the system performs numerical comparisons based on the aforementioned version arbitration mechanism, automatically blocking and discarding expired data entities with smaller timestamps, and forcibly performing data overwrite operations based on the latest timestamp entity. For data entities where no matching unified primary key identifier is found in the storage system... For newly arriving data entities, the system directly skips the version arbitration mechanism, allocates the internal physical offset of the underlying data file to it, and performs the new write operation to ensure that the data state in the data lake remains eventually consistent with the source business system.

[0049] S42. Instantiate Hudi's Read-Time Merge Table (MOR) at the data lake storage layer as the core data residing medium, and specify the hash bucket index mechanism to perform physical distribution location of data. To eliminate data skew in high-concurrency write scenarios and avoid performance loss caused by global index comparison, the system enforces an end-to-end topology-based operator parameter collaborative configuration protocol during the job submission phase. Extract the total number of partitions allocated by the upstream distributed message queue. This increases the parallelism of the Hudi data writing operator within the Flink streaming engine. Static binding is (Right now ), and declare the total number of physical buckets for the Hudi table. Parallelism of the write operator Multiples of the specified integer value are used to establish end-to-end point-to-point data transmission channels. To cut off cross-node network interaction and data redistribution processes, the system constructs a local physical bucket-based directed routing model within the Flink write operator: ; In the formula, This represents the calculated Hudi target physical bucket sequence number, a dimensionless non-negative integer, with a value range of [value range missing]. ; This represents the Flink write operator subtask sequence number for the currently processed data, and is a dimensionless non-negative integer with a value range of [value range missing]. ; This indicates the system's preset physical bucket expansion ratio, which is the total number of buckets. Parallelism of operators The ratio ( ), which is a dimensionless positive integer; This represents the unified primary key identifier carried by a single piece of data in the flow, and its data type is a character sequence; This represents an unencrypted hash calculation function used to map a variable-length character sequence into a large integer hash value; This represents the modulo operation function. In extremely specific cases, when the system prioritizes single-point sequential write performance, the scaling factor is increased. When configured as 1, the directed routing model degenerates into... At this point, the data flowing within a specific subtask will be completely isolated and forced into a single physical storage bucket with the same sequence number. This collaborative mechanism allows data streams extracted based on a specific old primary key to be retrieved from a single message partition, hashed in local memory by a single, fully corresponding computation operator instance, and directly appended to the incremental log file of the corresponding physical storage bucket, thus avoiding network redistribution operations within the computing cluster. Simultaneously, a regular asynchronous compression scheduler is configured in the background to periodically merge incremental log files with the basic columnar storage file to control read amplification effects.

[0050] As incremental log files continue to be appended, to control the read amplification effect, the data lake platform maintains a resident asynchronous compression scheduler in the background, constructs a trigger evaluation mechanism for compression tasks, and determines the trigger status word of the asynchronous compression tasks: ; In the formula, This represents the trigger status word for the asynchronous compression task. A value of 1 indicates that compression has been triggered, while a value of 0 indicates that the task will continue to be suspended and wait. It is a dimensionless Boolean integer. This represents the total number of incremental commit batches accumulated in the current physical bucket since the last successful compression operation; This indicates the system's preset cumulative threshold for triggering compression in submission batches; This represents the absolute elapsed time from the end of the last compression operation to the current evaluation moment; This represents the maximum waiting time limit preset to avoid long-term fragmentation of cold data. In extreme and abnormal situations, if the upstream business system experiences a prolonged standstill, resulting in a decrease in the total number of incremental submission batches... Remaining at zero, as absolute time passes Natural growth and eventually exceeding the maximum waiting time limit. The system will still output the trigger status word 1 to force the activation of the compression process to merge and rewrite the existing multi-version Avro incremental log files with the Parquet basic columnar storage files, ensuring that the number of small files in the underlying distributed file system is within a healthy baseline range.

[0051] In step S4 of this embodiment, the system constructs and writes the configuration descriptor and performs version arbitration. Simultaneously, it determines the target physical storage bucket through operator parameter collaborative configuration and a directed routing model, and evaluates the triggering status of the compression task using an asynchronous compression scheduler. Specifically, for example, it sets the total number of physical storage buckets for merging tables during data lake reads. The parallelism of the write operator obtained from the preceding steps The physical storage bucket expansion ratio was calculated. Assuming that during the data flow process, the operator subtask sequence number... Received unified primary key identifier New arriving data entities Extract the source event timestamp Meanwhile, the underlying storage system contains historical reference data entities with the same primary key. Its source event timestamp Substituting into the version arbitration mechanism formula, because... The system determines the target data entity that is ultimately authorized to be written to disk. Expired versions with smaller timestamps are discarded directly. Then, the system calculates the hash value corresponding to the primary key. Substitute the values ​​into the directional routing model formula to calculate the Hudi target physical storage bucket sequence number: The calculation result guides a single computation operator instance to directly append data to the incremental log file of the physical storage bucket with sequence number 19 in local memory. During the background compression scheduling phase, the probe obtains the total number of incremental commit batches accumulated in the current physical storage bucket. The system's preset cumulative threshold for submission batches Absolute elapsed time Maximum waiting time limit Substituting into the formula for triggering the evaluation mechanism, although... The threshold was not reached, but because When the logical OR condition is met, the system generates a trigger status word for the asynchronous compression task. This step, through multi-parameter collaboration and hash location calculation, cuts off cross-node network interaction and data redistribution processes, and ensures the normal activation of the file system's merge and rewrite mechanism under extreme shutdown conditions.

[0052] S5. Establish a full-link monitoring module and an anomaly self-healing mechanism to ensure the continuous availability of the above data links when facing network jitter or component failures. For example... Figure 3 As shown, the specific steps include the following: S51. Build a full-link real-time monitoring module based on a time-series database to perform end-to-end data flow telemetry and quantitative evaluation. Deploy lightweight indicator probes at the data acquisition layer, stream computing layer, and data lake storage layer, respectively, activating the native indicator reporters of each component to collect and aggregate heterogeneous performance indicator data in real time. The specific quantitative monitoring mechanism includes the following four dimensions: For the acquisition layer, the end-to-end acquisition latency between Debezium and the message queue was measured. The system probe extracted the event timestamp of the latest consumed record from the message queue in real time, and calculated the physical duration of the end-to-end data acquisition latency. In the formula, This indicates the physical duration of end-to-end data acquisition delay; This indicates the absolute physical system time of the message queue agent node at the moment the monitoring probe performs the measurement; This indicates the original physical time at which the change event was generated from the source business database, as parsed from the current consumption flow data load. In extreme anomalies, such as network fragmentation preventing the probe from obtaining the data... The system will force Assigning a value to a preset system extreme value will directly trigger a disconnection alarm.

[0053] For the stream processing layer, the backpressure status of Flink operators is measured. The probe periodically retrieves memory buffer pool usage metrics for each operator subtask via the stream processing engine's internal communication interface, and calculates the backpressure ratio. In the formula, This represents the back pressure ratio of the operator within a specific sampling period. It is a dimensionless floating-point number with a value range of [value range missing]. ; This represents the absolute duration during which the operator is physically blocked due to the downstream output buffer being full within the sampling window; This indicates the total duration of the system's preset monitoring and sampling time window. Under extreme load conditions, if a downstream operator deadlocks or completely stops consuming, It will approach infinitely ,lead to The system converges to the highest bit 1.0, and based on this, it determines that a severe back pressure avalanche has occurred in the link.

[0054] For the data lake layer, the batch write time of Hudi was measured. Monitoring probes periodically scan the metadata file directory in the Hudi timeline, parsing the commit logs of active and completed states, and calculating the physical time consumed for a single batch of data to be completely merged and written to disk. In the formula, This indicates the physical time consumed for a single batch of data to be entered into the lake, merged, and placed into the storage container. This indicates the starting physical timestamp at which the write action in this batch is registered as active in the data lake timeline; This indicates that the physical timestamp for the batch of data has been completed and the final checksum has been generated.

[0055] For the data quality layer, the ratio of primary key update anomalies to data duplication is measured. The probe is connected to the dead-letter queue bypass output endpoint set in step S33 of the stream processing engine to calculate the data anomaly flow ratio within the current sampling period. In the formula, This represents the rate of abnormal data flow within the current sampling period, and is a dimensionless floating-point number. This indicates the number of abnormal records identified as having missing primary keys, conflicts, or corrupted formats and routed to the dead-letter queue; This indicates the total number of records processed by the operator within the same sampling period; This represents the minimum normal value preset by the system, such as... , This is used to handle situations where no data flows in during the sampling period, i.e. In extreme cases, to prevent the underlying operations from triggering the division-by-zero crash logic paradox, and to ensure that the system outputs a stable zero anomaly rate under no-load conditions.

[0056] The system serializes the measurement results from the above four dimensions into standardized time-series data points containing key-value tags (such as node IP, table name, and task ID), and persists them to the time-series database via pull or push protocols. The time-series database provides data query support for the upper-layer rule engine and provides objective and accurate quantitative decision-making input for triggering abnormal retry and degradation mechanisms in subsequent steps.

[0057] S52. Deploy exception interceptors at data flow computing nodes and storage write endpoints to capture exception stack contexts thrown by the system in real time. Extract exception signatures and match them against a preset error type dictionary to perform exception classification. If an exception that severely disrupts the computing topology, such as a data pattern parsing crash or a state backend physical memory overflow, is captured, the system classifies it as a fatal error and directly blocks the current task process. If a recoverable exception, such as a network transient timeout, external node connection reset, or Hudi storage layer optimistic locking concurrency conflict, is captured, the system classifies it as a non-fatal error and hands it over to the retry controller.

[0058] For non-fatal errors, an exponential backoff algorithm is triggered, which introduces a factor to prevent concurrent stamping and shaking, and the calculation of the first... The physical duration of the pause before each retry: ; In the formula, Represents the calculated first... The physical duration of the pause before each retry attempt; This indicates the cumulative number of retry attempts that have been performed so far. This indicates the system's preset base backoff time step; This represents the maximum random jitter time range set to break up concurrent retry requests; This function generates uniformly distributed random numbers within a specific interval, with the output dimension being time. This represents the absolute maximum waiting time limit to prevent excessively long suspension times from causing timeouts at flow computation checkpoints. This represents a function that takes the minimum value. In extreme or exceptional cases, the number of retries increases. As the exponent continues to increase, the calculation result of the internal exponent term will grow explosively. At this point, the nested minimum function will force a longer waiting time. Truncation and convergence to the maximum waiting time limit This avoids global checkpoint fence timeout crashes caused by operators being blocked indefinitely.

[0059] The retry controller is based on the derived physical duration of the suspension wait. The execution thread sleeps and re-initiates the operation request. The system continuously increments and compares the current retries. The maximum tolerable retry threshold configured in the system If the number of retries exceeds this maximum threshold, i.e. Upon determining that the retry mechanism has completely failed, the system immediately throws a runtime exception to the streaming engine, forcibly interrupting the data flow of the current micro-batch and causing the current computing slot to fail. The streaming engine's job manager intercepts this failure signal and marks the current period's checkpoint as failed. Simultaneously, Hudi Data Lake's transaction manager periodically scans the timeline, identifies that the commit instance corresponding to this micro-batch is in an active state that has not been completed for a long time and there are no heartbeat updates upstream, automatically triggering a physical rollback plan at the storage layer, precisely deleting the residual Parquet data files and Avro log fragments generated by this failed batch in the underlying distributed file system.

[0060] After the storage layer is cleaned up, the streaming computing engine restarts the job according to the fault-tolerant configuration, extracts the metadata of the most recently successfully built global checkpoint, and precisely resets the consumption offsets of the state backends of each operator and the Kafka data source to this historical consistency point, completing the end-to-end link state rollback. Simultaneously with triggering the rollback contingency plan, the system encapsulates the job identifier, failed operator node information, final crash timestamp, and truncated exception stack trace into a standardized JSON alarm payload, which is sent to the external operations and maintenance platform via Remote Procedure Call (RPC) or message push interface. This drives the pre-defined automated work order flow or prompts operations and maintenance personnel to intervene and perform physical repair operations such as expanding memory, ensuring strong data consistency and system recoverability of the real-time inbound link under extreme failures.

[0061] In step S5 of this embodiment, the system measures multi-dimensional telemetry indicators in real time and persists them to a time-series database. An anomaly interceptor categorizes captured errors and uses an exponential backoff algorithm with a jitter factor to calculate retry waiting time, triggering a physical rollback mechanism when limits are exceeded. Specifically, for example, the total duration of the monitoring sampling time window is set. The absolute duration of operator blockage due to downstream full load during the period Substituting into the formula, the back pressure ratio is calculated. The system detected a Hudi storage layer optimistic locking concurrency conflict exception during operation, classified it as a non-fatal error, and handed it over to the retry controller. The base backoff time step was set. Maximum waiting time limit Maximum jitter time range Maximum retry threshold When executing the first... During the second retry, the random number generation function outputs... Substituting the formula into the backoff algorithm, we can calculate the physical duration of the suspension wait: Retry controller execution thread sleep The request was then resubmitted. Because... The retry mechanism remains in effect. If a prolonged network outage causes the number of attempts to reach the limit, the retry mechanism will continue. The system determines The system immediately throws a runtime exception, triggers a storage layer rollback contingency plan to clean up residual files, and resets the operator state and consumption offset from the most recent successful checkpoint. This step, by quantifying the backpressure state and dynamically adjusting the backoff step size, disperses instantaneous concurrent requests, provides an automatic backoff and degradation mechanism, and ensures strong data consistency and system recoverability under extreme failures.

[0062] In Embodiment 1 above, during the overall system operation and effect verification phase, the log extraction function of the source business database is enabled and the Debezium connection service is activated. Non-intrusive capture of business data insertion, update, and deletion operations is achieved by directly reading and parsing the database log files. The Flink streaming computing engine service is started, and the running status of the job manager and task manager is monitored in real time. A streaming structured query language program is deployed to extract real-time data streams from the Kafka distributed message queue and execute computational logic to drop them onto the Hudi data lake. During data initialization, the Debezium acquisition component, based on a combination of full snapshots and incremental logs, pushes millions of existing business data points along with incremental data to the downstream flow link. The system verifies that the initial data scale and incremental status of the data lake and the source business table remain synchronized. For dynamic transaction verification, new records carrying specific primary key identifiers are injected into the source business table, and attribute modification and record deletion operations are performed on them. Simultaneously, update commands for specific primary key value changes are manually triggered at the source. Subsequently, the real-time add, delete, and modify status of the corresponding primary key data in the Hudi data lake table, as well as the overwrite update results of the primary key identifier, are monitored to complete the closed-loop verification of end-to-end synchronization consistency in the data flow. Finally, multidimensional datasets are extracted by connecting to the front-end visualization platform and business intelligence reporting engine of the data lake. Cross-checking of various business indicators and underlying change records confirms the accuracy and completeness of the data in the real-time data inflow to the data lake under the condition of dynamic primary key updates at the source.

[0063] The preferred embodiments of the invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi, characterized in that, include: Deploy the data acquisition component and execute a lock-free incremental snapshot to capture source data, and output the decomposed logical deletion and addition events; A message queue is introduced to perform data buffering and automatic pattern registration. It receives the logical deletion event and the addition event, converts them into binary message bodies, and outputs them to the physical partition of a dedicated business topic. Based on the execution state processing and dynamic primary key adaptation of the streaming computing engine, the binary message body is pulled from the physical partition to generate a unified primary key identifier, and the streaming data is emitted to the subsequent mapping operator; Configure the lake entry matching rules and perform storage management of the read-time merge table. Based on the unified primary key identifier, execute the version arbitration mechanism to write the target data entity to disk into the read-time merge table. Establish a full-link monitoring module and an anomaly self-healing mechanism, measure data flow telemetry indicators and perform anomaly classification, triggering retry and link status rollback operations.

2. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 1, characterized in that: The deployment and acquisition components include: a data acquisition application built using the Debezium Embedded mode; The process of executing lock-free incremental snapshots to capture source data includes: logically dividing the full stock data into multiple consecutive primary key interval blocks; alternately polling the latest generated incremental change log records during the interval between primary key interval block query actions; and introducing a segmented watermark mechanism to quantify the processing progress during data extraction. The output of the decomposed logical deletion and addition events includes: intercepting the primary key change atomic transactions recorded in the underlying database logs; mounting a custom single message conversion module interface in the data acquisition connector configuration; when the operation type is identified as an update operation, comparing the primary key identifier of the previous mirror and the primary key identifier of the next mirror; determining that two independent record objects are instantiated after a primary key change event occurs; constructing a logical deletion event for the first record object and an addition event for the second record object; extracting the primary key identifier of the previous mirror and setting it as the common routing key for the decomposed dual-path events and injecting it into the event metadata header; and performing hash routing calculation based on the common routing key to determine the target physical partition number.

3. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 2, is characterized in that... The introduction of a segmented water level line mechanism to quantify processing progress includes: Based on the system time, the number of record rows contained in the snapshot block, and the smooth scan rate obtained by using the exponentially weighted moving average algorithm, a logical timestamp is generated for the snapshot block. The preset dynamic minimum rate threshold is determined based on the absolute physical time taken to generate the number of incremental log records from the source database. Extract the smooth scan rate and compare it with the dynamic minimum rate threshold. When the smooth scan rate is lower than the dynamic minimum rate threshold, update the continuous overload counter. When the continuous overload counter is greater than or equal to the preset hysteresis judgment threshold, assign a specific logical timestamp -1 to the snapshot block to indicate invalidity, terminate data merging and trigger the timeout retry mechanism. When the continuous overload counter is less than the hysteresis judgment threshold, the logical timestamp is extracted and compared with the event timestamp of the incremental change data to determine the priority order of data processing.

4. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 1, characterized in that: The introduction of message queues for data buffering and automatic schema registration includes: initiating a schema registration request to the Schema Registry component to obtain the schema version identification code; deploying a Kafka message queue cluster using a distributed architecture and creating a dedicated business topic responsible for receiving serialized event streams; Receiving the logical deletion event and the addition event and converting them into a binary message body includes: structuring and reorganizing the underlying relational data change event into a standardized data dictionary; calling the Avro serializer to convert the standardized data dictionary into an Avro binary data stream; and using a standard line transmission format to perform continuous byte-level concatenation of the single-byte magic number prefix, mode version identifier, and change payload data to generate a binary message body. The output to the physical partition of the dedicated business topic includes: sequentially appending the binary message body to the persistent storage log of the physical partition in combination with the target physical partition sequence number.

5. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 4, is characterized in that... The specialized business topic is assigned a total number of partitions, and the steps for determining the total number of partitions include: The underlying operating system probe is invoked to collect the physical indicators of the file descriptors of the agent nodes in real time. Based on the system's hard resource limit, the total number of occupied file descriptors, the number of deterministic partition replica factors, and the basic file handle constant, the maximum allowed partition threshold is calculated. Set a minimum safe consumption baseline and a minimum hard partition lower limit; Based on the data ratio calculated from the peak data generation rate and the real-time limit stable consumption rate, when the real-time limit stable consumption rate is lower than the minimum safe consumption baseline, the effective consumption rate will be converged to the minimum safe consumption baseline. The minimum value is obtained by combining the data ratio result with the maximum allowed partition threshold, and the maximum value is obtained by combining the minimum value result with the minimum hard partition lower limit. The total number of partitions is then forcibly constrained between the minimum hard partition lower limit and the maximum allowed partition threshold.

6. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 1, characterized in that: The execution state processing and dynamic primary key adaptation based on the streaming computing engine include: instantiating Kafka source operators within the Flink streaming computing engine and constructing a waterline allocator based on a bounded out-of-order tolerance mechanism to dynamically generate logical timestamps. The step of retrieving binary message bodies from physical partitions to generate unified primary key identifiers includes: retrieving binary message bodies from physical partitions and constructing a dynamic primary key adapter, retaining the original information using string concatenation, and concatenating the source database name string, the source table name string, and the original primary key value to generate unified primary key identifiers; The process of transmitting the data to the subsequent mapping operator includes: extracting the event timestamp of the data and comparing it with the historical maximum event timestamp recorded in the value status handle; and then, based on the comparison result, transmitting the data to the subsequent mapping operator or routing it to a dedicated side output stream.

7. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 6, is characterized in that... After generating a unified primary key identifier, the process also includes performing primary key dimension deduplication and storage optimization in the state backend: Physical partitioning is performed based on a unified primary key identifier, and value state objects carrying lifecycle management attributes are instantiated in the state backend memory. By combining the maximum out-of-order tolerance time, physical refresh interval, and dynamic security compensation time to deal with downstream storage latency, the forced expiration time of median state objects in the backend is calculated and a positive correlation is established between the dynamic security compensation time and the checkpoint timeout time of the stream computing engine. Inject a forced expiration time into the expiration cleanup component in the state backend, and call the underlying garbage collection thread to clear outdated state items in incremental scanning mode; A checkpoint snapshot mechanism is introduced to synchronize persistent value state objects. When a failure occurs, the most recently successfully built checkpoint metadata is extracted to reset the state backend, and the restored in-memory state object is aligned with the downstream persistent data version.

8. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 1, characterized in that: The configuration of lake entry matching rules and the storage management of the read-time merging table include: constructing a write configuration descriptor, declaring the unified primary key identifier as the record primary key field, and declaring the source business event timestamp field in the flowing data entity as the pre-merging field; specifying the hash bucket index mechanism to perform physical distribution positioning of the data, executing the operator parameter collaborative configuration protocol based on end-to-end topology, and statically binding the parallelism of the Hudi data write operator to the total number of partitions; The version arbitration mechanism is executed based on the unified primary key identifier, including: triggering the version arbitration mechanism in the data payload processing class to compare the source event timestamp of the newly arrived data entity with the source event timestamp of the historical reference data entity; The step of writing the target data entity to disk into the read-time merging table includes: writing the target data entity that has been approved for disk writing into the read-time merging table based on the numerical comparison results.

9. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 8, characterized in that: The physical distribution and location of the execution data includes: declaring that the total number of physical storage buckets of the read-time merge table is an integer multiple of the parallelism of the write operator, establishing an end-to-end collaborative binding relationship from message queue partitions, write operators to physical storage buckets; constructing a local physical storage bucket directional routing model within the write operator, and locating and generating the Hudi target physical storage bucket sequence number based on the operator subtask sequence number, physical storage bucket expansion ratio, and hash value corresponding to the unified primary key identifier, guiding a single computing operator instance to complete hash location in local memory, and cutting off cross-node network interaction and data redistribution process; The storage management of the read-time merging table also includes: configuring a persistent asynchronous compression scheduler in the background of the data lake platform, generating a trigger status word for the asynchronous compression task by comparing the total number of incremental submission batches with the cumulative threshold of submission batches, and comparing the absolute elapsed time with the maximum waiting time limit, and determining whether to activate the compression process based on the trigger status word.

10. The method for building a real-time data inflow link for a data platform based on Debezium, Flink, and Hudi according to claim 1, characterized in that: The establishment of the end-to-end monitoring module and anomaly self-healing mechanism includes: deploying lightweight indicator probes in the data acquisition layer, stream computing layer and data lake storage layer respectively; The measurement of data flow telemetry indicators and the execution of anomaly classification include: measuring the physical duration of end-to-end data acquisition delay, back pressure ratio, physical consumption time for a single batch of data to complete lake entry and merging and landing, and the data anomaly flow ratio, serializing and persisting them to a time series database; and deploying anomaly interceptors to perform anomaly classification. The triggering of retry and link state rollback operations includes: for non-fatal errors, the retry controller takes over, triggers an exponential backoff algorithm that introduces a concurrency jitter factor to prevent jitter, calculates the physical duration of the suspension wait, and re-initiates the operation request; when the cumulative number of retry attempts exceeds the maximum tolerable retry threshold, a runtime exception is thrown to the streaming computing engine, triggering a physical rollback contingency plan at the storage layer, restarting the job according to the fault tolerance configuration, and completing the end-to-end link state rollback.

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