Method for realizing data verification and conversion based on Flink

Abstract

The invention particularly relates to a method for realizing data verification and conversion based on Flink. According to the method for realizing data verification and conversion based on the Flink, an Flink framework execution environment is created and used for managing and scheduling data processing tasks, and real-time data streams are accessed from a message queue through a DataStream API of the Flink framework; performing field existence check, format verification, numerical range check and business logic verification on the data by using a user-defined verification function; carrying out error log recording on the data which does not pass the verification, and sending an alarm or a notice; and performing conversion processing on the data passing the verification, and outputting and storing the converted data in a specified storage system. According to the method for realizing data verification and conversion based on the Flink, real-time verification and efficient conversion of the data stream are realized, the accuracy and efficiency of data processing are remarkably improved, meanwhile, higher flexibility can be given to a user, and the requirements of different service scenes are met.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing, and in particular to a method for implementing data verification and conversion based on Flink. Background Art

[0002] With the advent of the digital economy era, the rise of big data platforms and industrial Internet, the value of data is constantly being explored. Social organizations, functional management departments and enterprises are constantly accelerating the transformation and construction of digitalization. Through the data lake construction platform, unified data governance, integrated development, services, and visualization are carried out to help social organizations, functional management departments and enterprises implement big data and data middle platform strategies, realize data-driven business intelligence, and promote digital transformation and upgrading and business model innovation of social organizations, functional management departments and enterprises, which has become the mainstream of the current era.

[0003] With the rapid development of big data technology, data quality issues have become increasingly prominent. The quality of data directly affects the accuracy and effectiveness of subsequent work such as data analysis and data mining. Traditional data verification and conversion methods are usually based on batch processing mode, which has obvious delays when processing large amounts of data and cannot provide real-time data processing results.

[0004] In addition, traditional methods often lack flexibility and are difficult to customize verification and conversion operations according to different business scenarios. Therefore, how to effectively verify the quality of data and efficiently convert data has become an urgent problem to be solved in the current field of big data processing.

[0005] In order to solve the above problems, the present invention proposes a method for realizing data verification and conversion based on Flink. Summary of the invention

[0006] In order to make up for the defects of the prior art, the present invention provides a simple and efficient method for realizing data verification and conversion based on Flink.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for implementing data verification and conversion based on Flink, characterized in that it includes the following steps:

[0009] Step S1: Create a Flink framework execution environment ExecutionEnvironment to manage and schedule data processing tasks, and access real-time data streams from the message queue through the Flink framework's DataStream API;

[0010] In step S1, the data access source also includes a file data source, a Kafka data source, a JDBC data source and a custom data source;

[0011] The file data source is data stored in the file system, which is accessed using the file data source connector provided by the Flink framework. By specifying the path and format of the file, the Flink framework automatically reads the data in the file and converts it into a data stream DataStream or a data set DataSet for subsequent processing.

[0012] The Kafka data source provides real-time data streams, which are accessed using the Kafka connector of the Flink framework. By configuring the Kafka connection parameters, the Flink framework consumes data from Kafka and converts it into a data stream DataStream for real-time processing.

[0013] The JDBC data source provides data stored in a relational database and is accessed using the JDBC connector of the Flink framework. By configuring database connection information, the Flink framework executes SQL queries and converts the results into a data stream DataStream or a data set DataSet.

[0014] The custom data source is a data source that users customize according to their needs when the existing connectors cannot meet the needs. It implements the SourceFunction interface of the Flink framework or uses the RichSourceFunction interface to pull data from an external system according to a custom protocol or API and convert it into a data stream that the Flink framework can process.

[0015] Step S2: perform data verification on the accessed data stream, including using a custom verification function to perform field existence check, format verification, value range check, and business logic verification on the data; record error logs for data that fails the verification, and send an alarm or notification;

[0016] In step S2, a series of customized verification functions are designed and implemented according to specific business requirements and data formats, and detailed format checks, content verification and business logic verification are performed on each piece of data in the data stream;

[0017] Validation functions include but are not limited to regular expression matching, field existence checking, value range verification, and date format verification;

[0018] For data that does not meet the verification rules, in addition to recording error logs and sending alerts, the data must also be cleaned or directly discarded.

[0019] The verification function is divided into custom data source verification and Keystone function verification:

[0020] Step S2.1: Implement preliminary data verification through custom data source. The specific implementation steps are as follows:

[0021] Step S2.1.1, inherit the RichSourceFunction interface of the Flink framework and create a custom data source class;

[0022] Step S2.1.2: In the implementation of the data source class, add data format verification logic, including checking whether the data separator, number of fields and data type meet the expected standards;

[0023] If the data format does not meet the requirements, the data source class records the error information and throws an exception or performs other error handling operations, so as to timely discover and handle data format problems during the data reading phase, and prevent invalid or erroneous data from entering the subsequent processing flow;

[0024] Step S2.2: In order to further enhance the accuracy of the data, the Keystone verification function is introduced to further verify the format of the data during the data stream processing; the specific implementation steps are as follows:

[0025] Step S2.2.1, create a Keystone function that accepts a data stream as input and performs detailed format verification on each data element; the verification content includes the integrity, legality and consistency of the data;

[0026] Step S2.2.2, the Keystone function returns the verification result as an attribute of the data stream, and subsequent operations are processed accordingly according to this attribute;

[0027] Step S2.2, applying the custom verification function to the incoming data stream to ensure that the data is verified in real time during the flow;

[0028] Through the parallel processing capabilities of the Flink framework, multiple data stream partitions can be verified simultaneously to improve processing efficiency.

[0029] Step S3: convert the verified data, including using the DataStream API of the Flink framework to perform field mapping, data screening, and data aggregation operations;

[0030] In step S3, the specific implementation steps are as follows:

[0031] Step S3.1: Data cleaning and mapping

[0032] Use the map() and flatMap() functions to clean and transform each element in the data stream one-to-one or many-to-one, standardizing the data into a unified format for subsequent data analysis;

[0033] Step S3.2, data filtering and aggregation:

[0034] Use the filter() function to filter out data records that do not meet specific conditions, remove invalid or unnecessary data, and improve data quality; at the same time, use the reduce() aggregation function, sum() aggregation function, min() aggregation function, and max() aggregation function to summarize and analyze the data, and then implement statistics and calculations on the elements in the data stream to obtain valuable analysis results;

[0035] Step S3.3: Complex data processing

[0036] Use the window operation and state management functions of the Flink framework to implement more complex data processing requirements. The specific implementation steps are as follows:

[0037] Step S3.3.1: Window operation

[0038] Use the window function of the Flink framework to aggregate data based on time or quantity, and perform more complex data verification and conversion operations within the window, including but not limited to time series analysis and anomaly detection. Through window operations, you can better understand and analyze the changing trends and characteristics of data.

[0039] Step S3.3.2: State management

[0040] Use the state management function of the Flink framework to maintain data status across windows or time periods and process more complex business logic, including but not limited to calculating sliding averages and identifying data trends. Track and analyze data changes in multiple windows or time periods through state management to draw more accurate conclusions.

[0041] Step S3.4, error handling and logging:

[0042] During the data conversion process, a complete error handling mechanism is designed for any errors or abnormal situations that occur. The detailed error information and processing results are recorded through the log system to facilitate subsequent troubleshooting and data recovery.

[0043] Step S4: output the converted data to a new data stream and store it in a designated storage system.

[0044] In step S4, the output data is further partitioned, sorted or compressed, and stored in a designated storage system, including but not limited to a distributed file system HDFS, a database or a message queue.

[0045] The present invention implements a method for data verification and conversion based on Flink, continuously evaluates the quality of real-time data, and performs performance tuning on Flink jobs, including adjusting parallelism and optimizing state management to improve data processing efficiency; uses the checkpoint and savepoint mechanisms of the Flink framework to implement fault-tolerant processing; when a job fails, it can restore the state from the checkpoint or savepoint and continue processing to ensure the continuity and accuracy of data processing; optimizes processing performance by adjusting the parallelism and window size parameters to meet different throughput and latency requirements;

[0046] The monitoring tools provided by the Flink framework monitor the running status, throughput, latency, and performance indicators of the data quality verification process in real time. When abnormal or erroneous data is found, the alarm mechanism is triggered.

[0047] Regularly analyze data that fails verification, and adjust and optimize data verification and conversion rules as well as the configuration and logic of Flink jobs based on the analysis results.

[0048] A system for implementing data verification and conversion based on Flink, characterized by comprising a data access module, a data verification module, a data conversion module, a data output and storage module, a performance monitoring and optimization module, and a fault tolerance and recovery module, wherein:

[0049] The data access module is used to access the real-time data stream from the message queue through Flink's DataStream API;

[0050] The data verification module is used to perform data verification on the accessed data stream, including field existence check, format verification and value range check;

[0051] The data conversion module is used to convert the verified data, including field mapping, data screening and data aggregation operations;

[0052] The data output and storage module is used to output the converted data to a new data stream and store it in an external storage system;

[0053] The performance monitoring and optimization module is used to monitor indicators such as processing delay and throughput, and adjust parallelism and resource allocation to optimize performance.

[0054] The fault tolerance and recovery module is used to enable checkpoints to ensure that the processing state can be quickly restored when a failure occurs.

[0055] A device for implementing data verification and conversion based on Flink, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above method when executing the computer program.

[0056] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method when executed by a processor.

[0057] The beneficial effects of the present invention are as follows: the method for implementing data verification and conversion based on Flink realizes real-time verification and efficient conversion of data streams, significantly improves the accuracy and efficiency of data processing, and at the same time can give users greater flexibility to adapt to the needs of different business scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Attached Figure 1 The figure is a schematic diagram of a method for realizing data verification and conversion based on Flink in the present invention. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0061] In modern big data applications, the processing speed and accuracy of data streams are crucial for business decision-making. Traditional data processing methods often cannot meet the needs of real-time processing, especially in data verification and conversion.

[0062] Apache Flink (Flink for short) is a high-performance, distributed stream processing and batch processing computing framework that is widely used in big data processing scenarios.

[0063] The method for implementing data verification and conversion based on Flink includes the following steps:

[0064] Step S1: Create a Flink framework execution environment ExecutionEnvironment to manage and schedule data processing tasks. Use the Flink framework's DataStream API to access real-time data streams from message queues. The DataStream API provides the ability to perform various operations on data streams, such as mapping, filtering, and aggregation.

[0065] In step S1, the data access source also includes a file data source, a Kafka data source, a JDBC data source and a custom data source;

[0066] The file data source is data stored in a file system (such as HDFS distributed storage system, S3 object storage system, etc.), and is accessed using the file data source connector provided by the Flink framework. By specifying the file path and format (such as CSV, JSON, Parquet, etc.), the Flink framework automatically reads the data in the file and converts it into a data stream DataStream or a data set DataSet for subsequent processing.

[0067] The Kafka data source provides real-time data streams, such as log data and sensor data, which are accessed using the Kafka connector of the Flink framework. By configuring Kafka connection parameters (such as broker address, topic name, etc.), the Flink framework consumes data from Kafka and converts it into a data stream DataStream for real-time processing.

[0068] The JDBC data source provides data stored in a relational database and is accessed using the JDBC connector of the Flink framework. By configuring database connection information (such as JDBC URL, user name, password, etc.), the Flink framework executes SQL queries and converts the results into a data stream DataStream or a data set DataSet.

[0069] The custom data source is a data source that users customize according to their needs when existing connectors cannot meet the needs. It implements the SourceFunction interface of the Flink framework or uses a more advanced RichSourceFunction interface to pull data from an external system according to a custom protocol or API and convert it into a data stream that the Flink framework can process.

[0070] Step S2: perform data verification on the accessed data stream, including using a custom verification function to perform field existence check, format verification, value range check, and business logic verification on the data; record error logs for data that fails the verification, and send an alarm or notification;

[0071] In step S2, a series of customized verification functions are designed and implemented according to specific business requirements and data formats, and detailed format checks, content verification and business logic verification are performed on each piece of data in the data stream;

[0072] Validation functions include but are not limited to regular expression matching, field existence checking, value range verification, and date format verification;

[0073] For data that does not meet the verification rules, in addition to recording error logs and sending alerts, the data must also be cleaned or directly discarded.

[0074] The verification function is divided into custom data source verification and Keystone function verification:

[0075] Step S2.1: Implement preliminary data verification through custom data source. The specific implementation steps are as follows:

[0076] Step S2.1.1, inherit the RichSourceFunction interface or other related interfaces of the Flink framework and create a custom data source class;

[0077] Step S2.1.2: In the implementation of the data source class, add data format verification logic, including checking whether the data separator, number of fields and data type meet the expected standards; for example, for data in CSV format, we can check whether the number of columns in each row of data is consistent with the expected number of columns, whether the data type matches the expected one, etc.

[0078] If the data format does not meet the requirements, the data source class records the error information and selectively throws an exception or performs other error handling operations, so as to timely discover and handle data format problems during the data reading phase, and prevent invalid or erroneous data from entering the subsequent processing flow;

[0079] Step S2.2: In order to further enhance the accuracy of the data, the Keystone verification function is introduced to further verify the format of the data during the data stream processing; the specific implementation steps are as follows:

[0080] Step S2.2.1. Create a Keystone function that accepts a data stream as input and performs detailed format verification on each data element; the verification content includes the integrity, legality and consistency of the data; for example, use regular expressions to verify whether certain fields in the data conform to the expected format.

[0081] Step S2.2.2, the Keystone function returns the verification result as an attribute of the data stream, and subsequent operations are processed accordingly based on this attribute; for example, for data that fails verification, we can choose to filter it out or correct it.

[0082] Step S2.2, applying the custom verification function to the incoming data stream to ensure that the data is verified in real time during the flow;

[0083] Through the parallel processing capabilities of the Flink framework, multiple data stream partitions can be verified simultaneously to improve processing efficiency.

[0084] Step S3: convert the verified data, including using the DataStream API of the Flink framework to perform field mapping, data screening, and data aggregation operations;

[0085] In step S3, the specific implementation steps are as follows:

[0086] Step S3.1: Data cleaning and mapping

[0087] Use the map() and flatMap() functions to perform one-to-one or many-to-one cleaning and transformation on each element in the data stream. For example, we can extract specific fields in the data, convert data types, rename fields, etc. Through these operations, the data is standardized into a unified format to facilitate subsequent data analysis;

[0088] Step S3.2, data filtering and aggregation:

[0089] Use the filter() function to filter out data records that do not meet specific conditions, remove invalid or unnecessary data, and improve data quality; at the same time, use the reduce() aggregation function, sum() aggregation function, min() aggregation function, and max() aggregation function to summarize and analyze the data, and then implement statistics and calculations on the elements in the data stream to obtain valuable analysis results;

[0090] Step S3.3: Complex data processing

[0091] Use the window operation and state management functions of the Flink framework to implement more complex data processing requirements. The specific implementation steps are as follows:

[0092] Step S3.3.1: Window operation

[0093] Use the window functions of the Flink framework (such as timeWindow() or countWindow()) to aggregate data based on time or quantity, and perform more complex data verification and conversion operations within the window, including but not limited to time series analysis and anomaly detection. Through window operations, you can better understand and analyze the changing trends and characteristics of data.

[0094] Step S3.3.2: State management

[0095] Use the state management function of the Flink framework to maintain data status across windows or time periods and process more complex business logic, including but not limited to calculating sliding averages and identifying data trends. Track and analyze data changes in multiple windows or time periods through state management to draw more accurate conclusions.

[0096] Step S3.4, error handling and logging:

[0097] During the data conversion process, a complete error handling mechanism is designed for any errors or abnormal situations that occur. The detailed error information and processing results are recorded through the log system to facilitate subsequent troubleshooting and data recovery.

[0098] Step S4: output the converted data to a new data stream and store it in a designated storage system.

[0099] In step S4, the output data is further partitioned, sorted or compressed and stored in a designated storage system, including but not limited to a distributed file system HDFS, a database (such as HBase, Cassandra, etc.) or a message queue (such as Kafka).

[0100] This Flink-based data verification and conversion method continuously evaluates the quality of real-time data, including counting the number and proportion of abnormal data, calculating data quality scores and other indicators.

[0101] Perform performance tuning on Flink jobs, including adjusting parallelism and optimizing state management to improve data processing efficiency; use the checkpoint and savepoint mechanisms of the Flink framework to achieve fault-tolerant processing; when a job fails, restore the state from the checkpoint or savepoint and continue processing to ensure the continuity and accuracy of data processing; optimize processing performance by adjusting parallelism and window size parameters to meet different throughput and latency requirements;

[0102] Through the monitoring tools provided by the Flink framework, the running status, throughput, latency and performance indicators of the data quality verification process are monitored in real time; when abnormal or erroneous data is found, the alarm mechanism is triggered to promptly notify relevant personnel for processing. Monitor the effectiveness of data governance to ensure the effective implementation of governance strategies and the stable improvement of data. Use the monitoring tools provided by Flink (such as Web UI, Metrics System, etc.) to monitor the system status and performance indicators in real time to ensure the stability and reliability of data governance.

[0103] Regularly analyze data that fails verification to identify common error types and causes. Based on the analysis results, adjust and optimize data verification and conversion rules as well as the configuration and logic of Flink jobs. At the same time, maintain close communication with the business department and technical team to ensure that data verification and conversion work is consistent with actual needs.

[0104] Finally, a well-designed modular and componentized structure makes the validation and conversion logic easy to extend and maintain, and provides rich configuration options and interfaces so that users can flexibly adjust the validation rules and conversion logic.

[0105] Example:

[0106] 1) Data preparation

[0107] a. Data source: Assume that we have a real-time data stream that contains data on user shopping behavior. The data format is CSV, and the fields include user ID, product ID, purchase quantity, and purchase time.

[0108] b. Data access: Use the DataStream API of the Flink framework to access real-time data streams from message queues such as Kafka.

[0109] 2). Data verification:

[0110] a. Custom validation function: define data validation rules, such as field existence check (make sure each field in the CSV data exists and is not empty), format validation (use regular expressions to verify whether the format of the purchase time and user ID is correct), and value range check (make sure the purchase quantity is an integer greater than 0).

[0111] b. Apply verification function: Apply the custom verification function to the incoming data stream and perform real-time verification on each piece of data.

[0112] c. Error handling: For data that fails verification, the error log is recorded and sent to a specific error handling flow for subsequent analysis or cleaning.

[0113] 3). Data conversion:

[0114] a. Field mapping: Use the map operation to convert the user ID and product ID into unique identifiers for the internal system.

[0115] b. Data screening: Use the filter operation to filter out records with purchase quantities greater than a certain threshold (such as 5) for subsequent focused analysis.

[0116] c. Data aggregation: Use reduce or aggregate operations to summarize users' purchase behaviors, such as calculating the total purchase quantity or total amount for each user.

[0117] d. Analyze the overall status and existing problems of data quality based on the results of real-time data detection. Formulate corresponding data conversion strategies, including data correction, data cleaning, data replacement, etc.

[0118] e. Apply transformation rules: Use the real-time computing capabilities of the Flink framework to apply custom transformation rules to the incoming real-time data stream and perform real-time transformation on each piece of data.

[0119] 4). Output and storage:

[0120] a. Output stream: Output the transformed data to a new data stream for subsequent analysis or visualization.

[0121] b. Data storage: Persist the data in the data stream to a storage system such as a distributed file system HDFS or a database for offline analysis or backup.

[0122] 5). Performance and fault tolerance:

[0123] a. Performance optimization: According to the real-time requirements of the system, adjust the parallelism and resource allocation of Flink tasks to ensure efficient data processing capabilities.

[0124] b. Fault-tolerant processing: Enable the checkpoint function of the Flink framework and save the processing status regularly so that it can quickly recover and continue to process data when a failure occurs.

[0125] 6) Use the monitoring tools provided by Flink to monitor and alert the data verification and conversion process in real time, and monitor the running status, throughput, latency, performance and other indicators of the data quality verification process and abnormal information in real time. When abnormal or erroneous data is found, the alarm mechanism is triggered in time and the relevant personnel are notified to handle it. This monitoring and alarm mechanism helps to improve the reliability and stability of data quality verification and ensure the accuracy, security, compliance and availability of data.

[0126] 7) Regularly analyze error data, identify common error types and causes, and optimize and adjust data validation rules, conversion rules, and Flink job configuration and logic based on the analysis results. At the same time, maintain close communication with the business department and technical team to ensure that data validation and conversion work is consistent with actual needs.

[0127] 8). Expansion and maintenance:

[0128] Modular design: Encapsulate data validation, conversion, output and other logic into independent modules to facilitate subsequent expansion and maintenance.

[0129] Configuration: Provide configuration files or command line parameters so that users can flexibly adjust the validation rules and conversion logic to meet different business needs.

[0130] The system for implementing data verification and conversion based on Flink includes a data access module, a data verification module, a data conversion module, a data output and storage module, a performance monitoring and optimization module, and a fault tolerance and recovery module, among which:

[0131] The data access module is used to access the real-time data stream from the message queue through Flink's DataStream API;

[0132] The data verification module is used to perform data verification on the accessed data stream, including field existence check, format verification and value range check;

[0133] The data conversion module is used to convert the verified data, including field mapping, data screening and data aggregation operations;

[0134] The data output and storage module is used to output the converted data to a new data stream and store it in an external storage system;

[0135] The performance monitoring and optimization module is used to monitor indicators such as processing delay and throughput, and adjust parallelism and resource allocation to optimize performance.

[0136] The fault tolerance and recovery module is used to enable checkpoints to ensure that the processing state can be quickly restored when a failure occurs.

[0137] The device for implementing data verification and conversion based on Flink includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above method when executing the computer program.

[0138] The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0139] Compared with the existing technology, this method of implementing data verification and conversion based on Flink has the following characteristics:

[0140] First, it improves real-time performance: Traditional data verification and conversion methods are often based on batch processing mode, and there is a large delay in the processing process. This method uses the stream processing capabilities of the Flink framework to ensure that data can be verified and converted in real time during the flow process, thereby greatly reducing processing time and meeting the needs of modern applications for real-time data.

[0141] Second, it enhances flexibility: Different business scenarios may require the application of different data validation rules and conversion logic. This method greatly enhances the flexibility of data processing by allowing users to customize validation functions and conversion operations. Users can adjust the strictness of validation and the complexity of conversion according to actual needs, ensuring that the data processing process is closely linked to business needs.

[0142] Third, it can ensure data accuracy: In the process of big data processing, the accuracy and integrity of data are crucial. This method introduces a strict verification mechanism to instantly detect and correct errors or data that does not meet format requirements when the data flow passes, thereby ensuring that subsequent analysis and decision-making are based on accurate data sets.

[0143] Fourth, it improves system scalability: As the amount of data continues to grow, the scalability of the processing system becomes particularly important. This method uses Flink's distributed computing capabilities to easily cope with the increase in data traffic and maintain efficient and stable processing performance without large-scale changes or upgrades to the system.

[0144] Fifth, it simplifies the data processing process: Traditional data processing processes may involve multiple independent steps and tools, which increases complexity and management difficulty. This method simplifies the processing process, reduces maintenance costs, and improves overall efficiency by integrating data verification and conversion functions into a unified Flink framework.

[0145] In summary, the method of implementing data verification and conversion based on Flink implements data verification and conversion based on Flink. By integrating the stream processing capabilities of the Flink framework, customized verification functions, and flexible data conversion logic, an efficient, reliable, and easily expandable data verification and conversion system is implemented, which meets the real-time and high-efficiency requirements in the big data environment and ensures the accuracy and consistency of the data. At the same time, through flexible rule definition and dynamic adjustment mechanism, it can meet the business needs in different business scenarios. Through the monitoring and alarm mechanism, data quality problems can be discovered and handled in a timely manner, providing a strong guarantee for subsequent data analysis and mining.

[0146] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for implementing data verification and conversion based on Flink, characterized by: The following steps are involved: Step S1: Create a Flink framework execution environment ExecutionEnvironment to manage and schedule data processing tasks, and access real-time data streams from the message queue through the Flink framework's DataStream API; Step S2: perform data verification on the accessed data stream, including using a custom verification function to perform field existence check, format verification, value range check, and business logic verification on the data; Record error logs for data that fails verification and send alerts or notifications; Step S3: convert the verified data, including using the DataStream API of the Flink framework to perform field mapping, data screening, and data aggregation operations; Step S4: output the converted data to a new data stream and store it in a designated storage system.

2. According to the method for realizing data verification and conversion based on Flink in claim 1, it is characterized in that: In step S1, the data access source also includes a file data source, a Kafka data source, a JDBC data source and a custom data source; The file data source is data stored in the file system, which is accessed using the file data source connector provided by the Flink framework. By specifying the path and format of the file, the Flink framework automatically reads the data in the file and converts it into a data stream DataStream or a data set DataSet for subsequent processing. The Kafka data source provides real-time data streams, which are accessed using the Kafka connector of the Flink framework. By configuring the Kafka connection parameters, the Flink framework consumes data from Kafka and converts it into a data stream DataStream for real-time processing. The JDBC data source provides data stored in a relational database and is accessed using the JDBC connector of the Flink framework. By configuring database connection information, the Flink framework executes SQL queries and converts the results into a data stream DataStream or a data set DataSet. The custom data source is a data source that users customize according to their needs when the existing connectors cannot meet the needs. It implements the SourceFunction interface of the Flink framework or uses the RichSourceFunction interface to pull data from an external system according to a custom protocol or API and convert it into a data stream that the Flink framework can process.

3. The method for realizing data verification and conversion based on Flink according to claim 1 is characterized in that: In step S2, a series of customized verification functions are designed and implemented according to specific business requirements and data formats, and detailed format checks, content verifications, and business logic verifications are performed on each piece of data in the data stream; Validation functions include but are not limited to regular expression matching, field existence checking, value range verification, and date format verification; For data that does not meet the verification rules, in addition to recording error logs and sending alerts, the data must also be cleaned or directly discarded.

4. The method for realizing data verification and conversion based on Flink according to claim 3 is characterized in that: The verification function is divided into custom data source verification and Keystone function verification: Step S2.1: Implement preliminary data verification through custom data source. The specific implementation steps are as follows: Step S2.1.1, inherit the RichSourceFunction interface of the Flink framework and create a custom data source class; Step S2.1.2: In the implementation of the data source class, add data format verification logic, including checking whether the data separator, number of fields and data type meet the expected standards; If the data format does not meet the requirements, the data source class records the error information and throws an exception or performs other error handling operations, so as to timely discover and handle data format problems during the data reading phase, and prevent invalid or erroneous data from entering the subsequent processing flow; Step S2.2: In order to further enhance the accuracy of the data, the Keystone verification function is introduced to further verify the format of the data during the data stream processing; the specific implementation steps are as follows: Step S2.2.1, create a Keystone function that accepts a data stream as input and performs detailed format verification on each data element; the verification content includes the integrity, legality and consistency of the data; Step S2.2.2, the Keystone function returns the verification result as an attribute of the data stream, and subsequent operations are processed accordingly according to this attribute; Step S2.2, applying the custom verification function to the incoming data stream to ensure that the data is verified in real time during the flow; Through the parallel processing capabilities of the Flink framework, multiple data stream partitions can be verified simultaneously to improve processing efficiency.

5. The method for realizing data verification and conversion based on Flink according to claim 1, characterized in that: In step S3, the specific implementation steps are as follows: Step S3.1: Data cleaning and mapping Use the map() and flatMap() functions to clean and transform each element in the data stream one-to-one or many-to-one, standardizing the data into a unified format for subsequent data analysis; Step S3.2, data filtering and aggregation: Use the filter() function to filter out data records that do not meet specific conditions, remove invalid or unnecessary data, and improve data quality; at the same time, use the reduce() aggregation function, sum() aggregation function, min() aggregation function, and max() aggregation function to summarize and analyze the data, and then implement statistics and calculations on the elements in the data stream to obtain valuable analysis results; Step S3.3: Complex data processing Use the window operation and state management functions of the Flink framework to implement more complex data processing requirements. The specific implementation steps are as follows: Step S3.3.1: Window operation Use the window function of the Flink framework to aggregate data based on time or quantity, and perform more complex data verification and conversion operations within the window, including but not limited to time series analysis and anomaly detection. Through window operations, you can better understand and analyze the changing trends and characteristics of data. Step S3.3.2: State management Use the state management function of the Flink framework to maintain data status across windows or time periods and process more complex business logic, including but not limited to calculating sliding averages and identifying data trends; Track and analyze data changes over multiple windows or time periods through state management to draw more accurate conclusions; Step S3.4, error handling and logging: During the data conversion process, a complete error handling mechanism is designed for any errors or abnormal situations that occur. The detailed error information and processing results are recorded through the log system to facilitate subsequent troubleshooting and data recovery.

6. The method for realizing data verification and conversion based on Flink according to claim 5 is characterized in that: In step S4, the output data is further partitioned, sorted or compressed, and stored in a designated storage system, including but not limited to a distributed file system HDFS, a database or a message queue.

7. The method for realizing data verification and conversion based on Flink according to claim 5 is characterized in that: Continuously evaluate the quality of real-time data and perform performance tuning on Flink jobs, including adjusting parallelism and optimizing state management to improve data processing efficiency; use the checkpoint and savepoint mechanisms of the Flink framework to achieve fault-tolerant processing; when a job fails, restore the state from the checkpoint or savepoint and continue processing to ensure the continuity and accuracy of data processing; optimize processing performance by adjusting parallelism and window size parameters to meet different throughput and latency requirements; Use the monitoring tools provided by the Flink framework to monitor the running status, throughput, latency, and performance indicators of the data quality verification process in real time. When abnormal or erroneous data is found, an alarm mechanism is triggered; Regularly analyze data that fails verification, and adjust and optimize data verification and conversion rules as well as the configuration and logic of Flink jobs based on the analysis results.

8. A system for implementing data verification and conversion based on Flink, characterized by: It includes data access module, data verification module, data conversion module, data output and storage module, performance monitoring and optimization module and fault tolerance and recovery module, among which: The data access module is used to access the real-time data stream from the message queue through Flink's DataStream API; The data verification module is used to perform data verification on the accessed data stream, including field existence check, format verification and value range check; The data conversion module is used to convert the verified data, including field mapping, data screening and data aggregation operations; The data output and storage module is used to output the converted data to a new data stream and store it in an external storage system; The performance monitoring and optimization module is used to monitor indicators such as processing delay and throughput, and adjust parallelism and resource allocation to optimize performance; The fault tolerance and recovery module is used to enable checkpoints to ensure that the processing state can be quickly restored when a failure occurs.

9. A device for implementing data verification and conversion based on Flink, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 7 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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