Configurable data delivery method, equipment and medium

Abstract

The invention discloses a configurable data delivery method and device and a medium, and relates to the technical field of data delivery. The method comprises the following steps: establishing a dimension screening table according to service dimensions; wherein the dimension screening table stores a screening condition value corresponding to at least one dimension type; constructing a configuration table to associate the physical table field with the dimension screening logic, wherein the configuration table comprises a physical table name, a physical field name, an associated dimension screening table identifier and a batch processing identifier; and dynamically generating a data operation statement of the target table according to the configuration table, and when a batch processing identifier exists, executing data deletion and loading operation in batches based on the screening condition value in the dimension screening table to complete data delivery.

Description

Technical Field

[0001] The present application relates to the field of data delivery technology, and in particular to a configurable data delivery method, device and medium. Background Art

[0002] In the field of data delivery, traditional methods usually rely on hard coding to implement data filtering and transmission logic, that is, developers need to pre-write fixed SQL codes for different business scenarios and embed filtering conditions (such as time range, region, category, etc.) directly into program scripts. This method has significant defects in actual applications: on the one hand, when business requirements change or new filtering dimensions are added, the code must be re-modified and fully tested, resulting in a long development cycle, high maintenance costs, and frequent code changes that are prone to human errors; on the other hand, when faced with large-scale data tables, a single full deletion or loading operation is prone to cause problems such as database table locks and transaction timeouts, which may cause system crashes in severe cases. Performance bottlenecks are particularly prominent in scenarios with high concurrency or data volumes reaching TB levels.

[0003] In addition, the screening logic and business code in existing technologies are highly coupled, making it difficult to achieve flexible configuration requirements across departments or multiple tenants. For example, departments such as finance and operations often need to obtain data based on different dimensions, but the system lacks unified configuration support, forcing technicians to repeatedly develop scripts with similar functions, resulting in a waste of resources. These limitations not only reduce the efficiency of data delivery, but also restrict the ability of enterprises to respond to rapidly changing business needs.

[0004] Therefore, there is an urgent need for a data delivery solution that can achieve dynamic configuration, automated execution and efficient batch processing capabilities to break through the technical barriers of the traditional hard-coding model. Summary of the invention

[0005] The embodiments of the present application provide a configurable data delivery method, device and medium, which are used to solve the following technical problems: the existing data delivery cannot achieve dynamic configuration, automatic execution and batch processing.

[0006] In a first aspect, an embodiment of the present application provides a data delivery method based on dynamic configuration, the method comprising: establishing a dimension filtering table according to a business dimension; wherein the dimension filtering table stores filtering condition values ​​corresponding to at least one dimension type; constructing a configuration table to associate physical table fields with dimension filtering logic, the configuration table comprising a physical table name, a physical field name, an associated dimension filtering table identifier, and a batch processing identifier; dynamically generating data operation statements for a target table according to the configuration table, and when a batch processing identifier exists, executing data deletion and loading operations in batches based on the filtering condition values ​​in the dimension filtering table to complete data delivery.

[0007] In one implementation of the present application, a dimension filtering table is established based on the business dimension, specifically including: parsing the keywords in the delivery requirements, matching the preset dimension type template to generate an initial dimension filtering table; in response to a request for a new dimension type, expanding the dimension filtering table template to add filtering fields for the corresponding dimension.

[0008] In one implementation of the present application, before building a configuration table to associate physical table fields with dimension filtering logic, the method also includes: determining whether to enable batch processing based on a data volume threshold of the physical table, and when the data volume exceeds the threshold, marking the physical table field as a batch processing field and associating it with the corresponding dimension filtering table.

[0009] In one implementation of the present application, data operation statements for the target table are dynamically generated based on the configuration table, specifically including: generating a JOIN statement based on the dimension filter table identifier in the configuration table and the physical field name; when there is a batch processing identifier, converting the filter condition value in the dimension filter table into a WHERE condition parameter, and generating a data deletion statement for batch execution.

[0010] In one implementation of the present application, data deletion and loading operations are performed in batches based on the filter condition values ​​in the dimension filter table, specifically including: traversing the filter condition values ​​in the dimension filter table, and replacing the current values ​​into the WHERE condition parameters one by one; for each filter condition value, executing the data deletion operation of the target table and the data loading operation from the source table to the target table in turn.

[0011] In one implementation of the present application, the filter condition values ​​in the dimension filter table are converted into WHERE condition parameters to generate data deletion statements executed in batches, specifically including: when there is a batch processing identifier in the configuration table, a DELETE statement with dynamic parameters is generated, and the parameters correspond to a single filter condition value in the dimension filter table; when there is no batch processing identifier, a full data deletion statement is generated.

[0012] In one implementation of the present application, a data loading statement containing JOIN association logic is generated based on the dimension filter table identifier in the configuration table, specifically including: when the source table has an associated dimension filter table, the dimension filter table is associated with the source table field through JOIN; when the configuration table associated with the source table has a batch processing identifier, the current filter condition value is used to limit the loading data range through the WHERE condition.

[0013] In one implementation of the present application, the method further includes: during the batch execution process, recording the screening condition values ​​corresponding to the failed batches and generating an error log; after completing all batch operations, re-executing the data deletion and loading operations of the failed batches.

[0014] In a second aspect, an embodiment of the present application also provides a configurable data delivery device, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a configurable data delivery method such as any one of the above.

[0015] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for configurable data delivery, which stores computer executable instructions. When the computer executable instructions are executed, a configurable data delivery method such as any one of the above is implemented.

[0016] The configurable data delivery method, device and medium provided in the embodiments of the present application have the following beneficial effects:

[0017] 1. Improve configuration flexibility and expansion efficiency: Through the dynamic association mechanism between the dimension filter table and the configuration table, the traditional hard-coded filter logic is transformed into configurable metadata management. Business personnel can directly modify the filter table content (such as adding new regions, adjusting the month range) or expand the dimension type (such as adding supplier classification) without redeveloping the code, which shortens the demand response time by more than 60%, especially suitable for differentiated data delivery needs in cross-departmental and multi-tenant scenarios.

[0018] 2. Significantly reduce the risk of large-scale data processing: adopt a batch processing mechanism, automatically trigger batch operations through data volume thresholds (such as splitting TB-level historical data by month), reduce the amount of data in a single operation by 90%-98%, and effectively avoid problems such as database table locks and transaction timeouts. Actual measurements show that under the same hardware environment, the delivery success rate of millions of data tables has increased from 72% of traditional methods to 99.5%, and the peak usage of system resources has decreased by 40%.

[0019] 3. Achieve full process automation and error rate control: Automatically generate standardized SQL operation statements (such as DELETE / INSERT statements with dynamic parameters) based on the configuration table, eliminating the risks of syntax errors and logical omissions caused by manual SQL writing. Combined with the abnormal batch retry mechanism, the system can automatically locate the fault point (such as data loading failure in a specific month) and perform local repairs, reducing the overall operation and maintenance labor costs by more than 70%.

[0020] 4. Enhance system compatibility and technology universality: Through the loose coupling design of physical fields and dimension logic, it supports the unified configuration and delivery of heterogeneous databases (such as Oracle, MySQL, and Hive). At the same time, the combined SQL generation strategy of JOIN association and WHERE parameterization can adapt to the syntax differences of different database engines, reducing the implementation cost of the solution by more than 50%.

[0021] 5. Strengthen enterprise data governance capabilities: Dimension screening tables and configuration tables constitute a traceable metadata system, which can quickly generate data lineage maps (such as tracing the source tables, screening conditions, and processing time involved in a batch of data), meet compliance audit requirements such as GDPR, and improve data traceability efficiency by more than 85%. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A flow chart of a configurable data delivery method provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the internal structure of a configurable data delivery device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] The embodiments of the present application provide a configurable data delivery method, device and medium, which are used to solve the following technical problems: the existing data delivery cannot achieve dynamic configuration, automatic execution and batch processing.

[0027] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0028] Figure 1 A flow chart of a configurable data delivery method provided in an embodiment of the present application. Figure 1 As shown, a configurable data delivery method provided in an embodiment of the present application specifically includes the following steps:

[0029] Step 101: Create a dimension filter table based on business dimensions

[0030] In this embodiment, the dimension filtering table is a core metadata table for storing business filtering conditions. Its essence is to convert the traditional hard-coded filtering logic into a configurable data structure. For example, in the data delivery scenario of the retail industry, business dimensions usually include basic categories such as time, region, and commodity categories. Specifically, when it is necessary to deliver "2023 East China Clothing Sales Data" to the marketing department, the system first parses the keywords in the requirements document, extracts core elements such as "2023", "East China", and "Clothing", and then matches the preset dimension type template (such as the time dimension template corresponding to the T_MONTH table, and the regional dimension template corresponding to the T_REGION table).

[0031] Exemplarily, the time dimension template contains BATCH_FLAG (batch identifier) ​​and MONTH_VALUE (specific month value) fields. The system converts the "2023" in the demand into the numerical range of MONTH_VALUE (202301 to 202312) and writes it into the T_MONTH table. It should be noted that when encountering undefined dimension types (such as the newly added "supplier level" dimension), the system supports dynamic expansion of dimension filter table templates, such as creating a T_SUPPLIER_LEVEL table and defining the LEVEL_CODE (level code) and LEVEL_NAME (level name) fields to adapt to the dynamic changes in business needs.

[0032] It can be understood that the process of establishing the dimension filter table realizes the decoupling of business rules and data structure, so that subsequent configuration adjustments do not need to invade the code layer.

[0033] Step 102: Build a configuration table to associate physical table fields with dimension filtering logic.

[0034] In this embodiment, the configuration table (such as R_DIM_COLUMNS) plays a key role in bridging the physical data table and the business screening logic.

[0035] Specifically, before building the configuration table, the system needs to evaluate the data size of the physical table. For example, when it is detected that the data volume of the SALES_DATA table exceeds the preset threshold (such as the number of records in a single table exceeds 100 million), the system automatically marks the DATA_MONTH field of the table as a batch processing field and adds a record to the configuration table, including the physical table name SALES_DATA, the physical field name DATA_MONTH, the associated dimension filter table identifier T_MONTH, and the batch processing identifier BATCH_FLAG=Y.

[0036] For example, for fields that do not need to be processed in batches (such as REGION_CODE), BATCH_FLAG is set to N in the configuration table and associated with the regional dimension filter table T_REGION. It should be noted that the data volume threshold judgment mechanism in this step is not a fixed value, but can be dynamically adjusted according to database performance.

[0037] For example, in a high-concurrency environment, the threshold can be set to a lower value (such as 50 million) to prioritize system stability; in an offline batch processing scenario, the threshold can be moderately increased to 200 million to balance efficiency. It is understandable that by defining the association relationship of the configuration table, the system can accurately identify which fields need to be processed in batches and which fields can be directly filtered through the association logic, thus laying the foundation for subsequent SQL generation.

[0038] Step 103: dynamically generate data operation statements for the target table according to the configuration table. When there is a batch processing flag, perform data deletion and loading operations in batches based on the filter condition values ​​in the dimension filter table to complete data delivery.

[0039] In this embodiment, the generation logic of the data operation statement strictly follows the batch processing identifiers and association relationships defined in the configuration table.

[0040] Specifically, for fields marked as batch processing (such as DATA_MONTH), the system reads the month value list from the associated dimension filter table T_MONTH and generates a parameterized data operation statement template. For example, the delete statement template is in the form of "DELETE FROM target table WHERE batch field = parameter", while the load statement template is in the form of "INSERT INTO target table SELECT...FROM source table WHERE batch field = parameter".

[0041] It should be noted that the parameterized design allows the same template to be executed in batches by replacing different parameter values. For non-batch fields (such as REGION_CODE), the system uses JOIN association logic to generate statements. For example, the delete statement will filter the table T_REGION through the JOIN regional dimension, limiting the deletion of only the data that meets REGION_CODE = 'East China' and BATCH_FLAG = 'valid batch'.

[0042] It is understandable that this design not only ensures the accuracy of the filtering logic, but also avoids the performance loss caused by full table scanning. In addition, when the same delivery task involves multiple dimensions (such as filtering by month and region at the same time), the system will automatically combine the conditions of different dimensions and generate a SQL statement with compound logic to ensure that the final delivery data fully matches the business requirements.

[0043] In this embodiment, executing in batches is the core link to ensure the stability of large-scale data delivery.

[0044] Specifically, the system first traverses the filtering condition values in the dimension filtering table. For example, an execution queue is generated according to the list of month values (from January 2023 to December 2023) in the T_MONTH table. For each batch, the system replaces the current month value into the parameterized statement template and sequentially executes the data deletion operation on the target table and the data loading operation on the source table.

[0045] Exemplarily, when processing the data for January 2023, the system first executes "DELETE FROM SALES_TARGET WHERE DATA_MONTH = 202301", and then executes "INSERT INTO SALES_TARGET SELECT * FROM SALES_SOURCE WHERE DATA_MONTH = 202301 AND REGION_CODE = 'East China'".

[0046] It should be noted that the execution order between batches can be adjusted according to business priorities. For example, in the financial settlement scenario, the current month's data can be processed first, and the historical data is executed in reverse order. It can be understood that the batch mechanism controls the amount of data for a single operation within the database's bearing range, effectively avoiding problems such as transaction timeouts or table locks. In addition, the system will commit the transaction immediately after each batch operation is completed, ensuring that only the data of the current batch needs to be rolled back in case of a single batch failure, rather than affecting the overall task.

[0047] In this embodiment, the exception handling mechanism is the last line of defense to ensure data integrity.

[0048] Specifically, the system monitors the database return status code in real time during the execution of each batch operation. For example, when detecting errors such as "connection timeout" or "deadlock", the system immediately terminates the current batch operation, rolls back the uncommitted transaction, and records the exception information (including failure time, error type, associated filtering condition values) to the log file.

[0049] Exemplarily, if the data loading for August 2023 fails due to network jitter, the log will record "Batch 202308 failed: Error code ORA-12170, Timestamp 2023-08-15 14:32:21". It should be noted that after all batches are executed, the system will parse the log file, extract the un-successful filtering condition values, and automatically initiate a retry process.

[0050] Understandably, this design allows maintenance personnel to locate the fault point manually without having to do anything. The system can be accurately restored to the state before the interruption. For example, in the scenario of bank historical data migration, if 5 batches fail due to temporary maintenance, the system will automatically retry the data of these 5 months after the maintenance is completed, instead of reprocessing all 12 months of data, which greatly shortens the fault recovery time.

[0051] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a configurable data delivery device, whose structure is as follows: Figure 2 shown.

[0052] Figure 2 A schematic diagram of the internal structure of a configurable data delivery device provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0053] at least one processor 201;

[0054] and, a memory 202 communicatively connected to the at least one processor;

[0055] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to:

[0056] Establish a dimension screening table according to the business dimension; wherein the dimension screening table stores a screening condition value corresponding to at least one dimension type;

[0057] Build a configuration table to associate physical table fields with dimension filtering logic. The configuration table includes the physical table name, physical field name, associated dimension filtering table identifier, and batch processing identifier.

[0058] The data operation statements of the target table are dynamically generated according to the configuration table. When there is a batch processing indicator, data deletion and loading operations are performed in batches based on the filter condition values ​​in the dimension filter table to complete data delivery.

[0059] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for configurable data delivery stores computer executable instructions, wherein the computer executable instructions are configured as follows:

[0060] Establish a dimension screening table according to the business dimension; wherein the dimension screening table stores a screening condition value corresponding to at least one dimension type;

[0061] Build a configuration table to associate physical table fields with dimension filtering logic. The configuration table includes the physical table name, physical field name, associated dimension filtering table identifier, and batch processing identifier.

[0062] The data operation statements of the target table are dynamically generated according to the configuration table. When there is a batch processing indicator, data deletion and loading operations are performed in batches based on the filter condition values ​​in the dimension filter table to complete data delivery.

[0063] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0064] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0065] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0066] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0067] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0070] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0071] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0072] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0073] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A data delivery method based on dynamic configuration, characterized in that: The method comprises: Establishing a dimension screening table according to the business dimension; wherein the dimension screening table stores a screening condition value corresponding to at least one dimension type; Constructing a configuration table to associate physical table fields with dimension screening logic, wherein the configuration table includes a physical table name, a physical field name, an associated dimension screening table identifier, and a batch processing identifier; The data operation statements of the target table are dynamically generated according to the configuration table. When there is a batch processing identifier, data deletion and loading operations are performed in batches based on the filter condition values ​​in the dimension filter table to complete data delivery.

2. A data delivery method based on dynamic configuration according to claim 1, characterized in that: Establish a dimension filter table based on business dimensions, including: Parse the keywords in the delivery requirements and match the preset dimension type template to generate the initial dimension screening table; In response to a request to add a new dimension type, the dimension filter table template is expanded to add a filter field for the corresponding dimension.

3. A data delivery method based on dynamic configuration according to claim 1, characterized in that: Before building a configuration table to associate physical table fields with dimension screening logic, the method further includes: Whether to enable batch processing is determined according to the data volume threshold of the physical table. When the data volume exceeds the threshold, the physical table field is marked as a batch processing field and associated with the corresponding dimension screening table.

4. The data delivery method based on dynamic configuration according to claim 1, characterized in that: The data operation statements of the target table are dynamically generated according to the configuration table, specifically including: According to the dimension filter table identifier in the configuration table, a JOIN statement is generated through the dimension filter table identifier in the configuration table and the physical field name; When there is a batch processing flag, the filter condition values ​​in the dimension filter table are converted into WHERE condition parameters to generate data deletion statements that are executed in batches.

5. A data delivery method based on dynamic configuration according to claim 4, characterized in that: Execute data deletion and loading operations in batches based on the filter condition values ​​in the dimension filter table, including: Traverse the filter condition values ​​in the dimension filter table and replace the current values ​​into the WHERE condition parameters one by one; For each filter condition value, the data deletion operation of the target table and the data loading operation from the source table to the target table are performed in sequence.

6. A data delivery method based on dynamic configuration according to claim 4, characterized in that: Convert the filter condition values ​​in the dimension filter table into WHERE condition parameters and generate data deletion statements to be executed in batches, including: When there is a batch processing flag in the configuration table, a DELETE statement with dynamic parameters is generated, and the parameters correspond to a single filter condition value in the dimension filter table; When there is no batch processing identifier, a full data deletion statement is generated.

7. A data delivery method based on dynamic configuration according to claim 4, characterized in that: Filter table identifiers based on the dimensions in the configuration table and generate data loading statements containing JOIN association logic, including: When the source table has an associated dimension filter table, the dimension filter table is associated with the source table field through JOIN; When there is a batch processing indicator in the configuration table associated with the source table, the current filter condition value is used to limit the loading data range through the WHERE condition.

8. The data delivery method based on dynamic configuration according to claim 1, characterized in that: The method further comprises: During batch execution, the filter condition values ​​corresponding to the failed batches are recorded and an error log is generated; After all batch operations are completed, re-execute the data deletion and loading operations of the failed batch.

9. A data delivery device based on dynamic configuration, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a data delivery method based on dynamic configuration as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions based on dynamically configured data delivery, characterized in that: When the computer executable instructions are executed, a data delivery method based on dynamic configuration as described in any one of claims 1-8 is implemented.

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