Introduction
Extracting long text from SAP systems may seem straightforward, but it often involves complexities that go beyond simple extraction methods. When traditional approaches fall short, leveraging Remote Function Calls (RFCs) can provide an effective solution for retrieving and integrating long text into SAP Business Objects Data Services (BODS) workflows.
In this blog, we’ll explore how to use the RFC_READ_TEXT function module to efficiently extract long text from SAP systems and seamlessly incorporate it into BODS for further processing and analysis. Whether you’re dealing with technical challenges or looking for a practical guide to streamline your data integration, this bog will help you leverage RFCs effectively to ensure a smoother and efficient workflow.
Key Functionalities of RFC_READ_TEXT
– Retrieval of long text data stored in SAP tables.
– Filtering of text data based on object type and other parameters.
– Returning retrieved text for further processing or display.
Understanding the Output Structure
The output of RFC_READ_TEXT is structured in a tabular format with the following key fields:
TDLINE: Contains the actual text lines retrieved from SAP.
TDOBJECT: Indicates the object type (e.g., ‘MAKT’ for material descriptions, ‘VBAK’ for sales order headers, etc.)
TDID: Identifies the specific object within the given object type.
TDNAME: Specifies the type of long text (e.g., ‘LTXT’ for general long text, ‘ATXT’ for header text).
TDSPRAS: Specifies the language of the retrieved text.
Steps to Implement RFC_READ_TEXT in SAP BODS
– Create the structure based on the number of unique IDs available.
– Group the dataset under a unique value to optimize processing.
Fig 1: Creating the extraction structure and splitting data based on multiple IDs.
Fig_2: Standard query inputs that need to be maintained.
Fig_3: Standard schema structure.
Fig_4: Query read text input and RFC calling in BODS program.
Fig_5: Input parameters for calling RFC_READ_TEXT.
Fig_6: Output parameters of RFC_READ_TEXT.
Fig_7: Unnesting the query.
OUTPUT
The RFC_READ_TEXT function returns a table containing long text along with key metadata fields. Here’s a breakdown of the main output fields:
TDLINE: Holds the actual text lines, with each entry representing a separate line.TDOBJECT: Specifies the object type associated with retrieved text (e.g., MAKT for materials, VBAK for sales orders).TDID: Uniquely identifies the text object (e.g., material number, sales order number).TDNAME: Defines the type of long text (LTXT for general long text, ATXT for header text).TDSPRAS: Indicates the language of the retrieved text.
In essence, the RFC_READ_TEXT output provides the requested long text data along with essential metadata like the object type, object ID, text type, and language. This output structure is crucial for correctly interpreting and utilizing the retrieved long text data within your application.
Fig_8: Output Data
Concatenating Rows in SAP BODS: A Custom Function Approach
When working with SAP Business Objects Data Services (BODS), a common challenge is combining multiple rows into a single row. While BODS provides a built-in reverse pivot transform for this purpose, it requires a predefined number of rows to concatenate. This constraint becomes a hurdle when the row count is unknown or variable.
Let’s explore a more flexible and efficient solution to this problem by leveraging custom functions in BODS. We’ll also discuss the limitations of traditional methods, the performance implications of database-level XML functions, and how a custom function can provide a more robust and scalable solution.
The Problem: Concatenating Rows with Unknown Counts
In many data integration scenarios, multiple rows need to be concatenated into a single row based on a unique identifier (UNIQ_ID). The built-in reverse pivot transform in BODS works only if the number of rows is fixed, making it unsuitable for dynamic datasets.
Traditional Workarounds and Their Limitations
Some common alternatives include database-level SQL functions, such as XMLAGG and XMLELEMENT. These functions can dynamically aggregate multiple rows into a single XML string, which can then be parsed and processed. While this method works, it has significant drawbacks:
Performance Overhead: XML manipulation at database level can be resource-intensive, especially when dealing with large datasets, slowing down job execution and increasing memory consumption.Scalability Issues: Large datasets (millions of records) can lead to database bottlenecks, making XML functions impractical for high-volume data processing.
A more efficient approach is to create a custom function within BODS to handle row concatenation dynamically. This offers several benefits:
Flexibility: No need to predefine the number of rows to be concatenated.Improved Performance: Potentially faster execution than database-level XML functions, especially for large datasets.Reduced Memory Usage: Optimized memory management for better performance.
Solution:
A BODS Custom Function allows concatenation of multiple rows into a single row, even when the number of rows is undefined.
Advantages of Using a Custom Function:
No need to predefine the number of rows.Enhanced job performance.Reduced memory usage for cache.Simple and straightforward implementation.
Input Data:
The source table consists of four columns: UNIQ_ID, EBELN, TDID, and GEN_ROW_NO. The source data is structured as follows:
Pre-Requisites:
Define parameters, local and global variables.Generate a sequence number for each UNIQ_ID.Group the dataset based on a unique value.
Output Data:
Data Loading into S4/HANA
Once the extracted and transformed data is generated, it is stored in a structured file format at the designated file path. Since DMC is used as the loading mechanism, the file must strictly adhere to the DMC format. The generated content should be copied and pasted into the corresponding views within the DMC template.
Once the file is created, it can be loaded into the system using the Data Migration Cockpit, after which the data can be reviewed in the S4HANA using transaction code ME23N.
Our references: Credits : Sami Mohammed
Calling RFC from BODS – SAP Community SAP BODS – Concatenate Custom Function – SAP Community
IntroductionExtracting long text from SAP systems may seem straightforward, but it often involves complexities that go beyond simple extraction methods. When traditional approaches fall short, leveraging Remote Function Calls (RFCs) can provide an effective solution for retrieving and integrating long text into SAP Business Objects Data Services (BODS) workflows. In this blog, we’ll explore how to use the RFC_READ_TEXT function module to efficiently extract long text from SAP systems and seamlessly incorporate it into BODS for further processing and analysis. Whether you’re dealing with technical challenges or looking for a practical guide to streamline your data integration, this bog will help you leverage RFCs effectively to ensure a smoother and efficient workflow.Key Functionalities of RFC_READ_TEXT- Retrieval of long text data stored in SAP tables.- Filtering of text data based on object type and other parameters.- Returning retrieved text for further processing or display.Understanding the Output StructureThe output of RFC_READ_TEXT is structured in a tabular format with the following key fields:TDLINE: Contains the actual text lines retrieved from SAP.TDOBJECT: Indicates the object type (e.g., ‘MAKT’ for material descriptions, ‘VBAK’ for sales order headers, etc.) TDID: Identifies the specific object within the given object type. TDNAME: Specifies the type of long text (e.g., ‘LTXT’ for general long text, ‘ATXT’ for header text). TDSPRAS: Specifies the language of the retrieved text. Steps to Implement RFC_READ_TEXT in SAP BODS – Create the structure based on the number of unique IDs available. – Group the dataset under a unique value to optimize processing.Fig 1: Creating the extraction structure and splitting data based on multiple IDs.Fig_2: Standard query inputs that need to be maintained.Fig_3: Standard schema structure.Fig_4: Query read text input and RFC calling in BODS program.Fig_5: Input parameters for calling RFC_READ_TEXT.Fig_6: Output parameters of RFC_READ_TEXT.Fig_7: Unnesting the query.OUTPUTThe RFC_READ_TEXT function returns a table containing long text along with key metadata fields. Here’s a breakdown of the main output fields:TDLINE: Holds the actual text lines, with each entry representing a separate line.TDOBJECT: Specifies the object type associated with retrieved text (e.g., MAKT for materials, VBAK for sales orders).TDID: Uniquely identifies the text object (e.g., material number, sales order number).TDNAME: Defines the type of long text (LTXT for general long text, ATXT for header text).TDSPRAS: Indicates the language of the retrieved text. In essence, the RFC_READ_TEXT output provides the requested long text data along with essential metadata like the object type, object ID, text type, and language. This output structure is crucial for correctly interpreting and utilizing the retrieved long text data within your application.Fig_8: Output DataConcatenating Rows in SAP BODS: A Custom Function ApproachWhen working with SAP Business Objects Data Services (BODS), a common challenge is combining multiple rows into a single row. While BODS provides a built-in reverse pivot transform for this purpose, it requires a predefined number of rows to concatenate. This constraint becomes a hurdle when the row count is unknown or variable.Let’s explore a more flexible and efficient solution to this problem by leveraging custom functions in BODS. We’ll also discuss the limitations of traditional methods, the performance implications of database-level XML functions, and how a custom function can provide a more robust and scalable solution.The Problem: Concatenating Rows with Unknown CountsIn many data integration scenarios, multiple rows need to be concatenated into a single row based on a unique identifier (UNIQ_ID). The built-in reverse pivot transform in BODS works only if the number of rows is fixed, making it unsuitable for dynamic datasets.Traditional Workarounds and Their LimitationsSome common alternatives include database-level SQL functions, such as XMLAGG and XMLELEMENT. These functions can dynamically aggregate multiple rows into a single XML string, which can then be parsed and processed. While this method works, it has significant drawbacks:Performance Overhead: XML manipulation at database level can be resource-intensive, especially when dealing with large datasets, slowing down job execution and increasing memory consumption.Scalability Issues: Large datasets (millions of records) can lead to database bottlenecks, making XML functions impractical for high-volume data processing.A more efficient approach is to create a custom function within BODS to handle row concatenation dynamically. This offers several benefits:Flexibility: No need to predefine the number of rows to be concatenated.Improved Performance: Potentially faster execution than database-level XML functions, especially for large datasets.Reduced Memory Usage: Optimized memory management for better performance.Solution:A BODS Custom Function allows concatenation of multiple rows into a single row, even when the number of rows is undefined.Advantages of Using a Custom Function:No need to predefine the number of rows.Enhanced job performance.Reduced memory usage for cache.Simple and straightforward implementation.Input Data:The source table consists of four columns: UNIQ_ID, EBELN, TDID, and GEN_ROW_NO. The source data is structured as follows:Pre-Requisites:Define parameters, local and global variables.Generate a sequence number for each UNIQ_ID.Group the dataset based on a unique value.Output Data:Data Loading into S4/HANAOnce the extracted and transformed data is generated, it is stored in a structured file format at the designated file path. Since DMC is used as the loading mechanism, the file must strictly adhere to the DMC format. The generated content should be copied and pasted into the corresponding views within the DMC template.Once the file is created, it can be loaded into the system using the Data Migration Cockpit, after which the data can be reviewed in the S4HANA using transaction code ME23N.Our references: Credits : Sami Mohammed Calling RFC from BODS – SAP Community SAP BODS – Concatenate Custom Function – SAP Community Read More Technology Blogs by Members articles
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