BD53 Doesn’t Play Well With Others
I recently posted a blog about how to implement field-level IDOC reduction for the HRMD_A message type. In short, the standard SAP transaction to reduce IDOC segments and fields (transaction BD53) can’t be used because the field-level reduction is ignored by SAP. My solution leveraged TVARV as a repository for the fields to clear. Read the whole solution here. A colleague of mine was very quick to point out that instead of using TVARV as the method for controlling which fields are cleared, I should have continued to leverage transaction BD53 for the IDOC reduction maintenance and changed my code to look up field level reductions in table TBD24.
What a great idea! Too bad I hate this suggestion… and it’s all SAP’s fault!!
How To Implement Field-Level HRMD_A Reduction
I love ALE. It is super-powerful and, once you get the hang of it, is a snap to configure. Recently, I was setting up an HRMD_A interface for my client. Everything was going smoothly until I ran into a requirement to filter out the social security number (PERID), birthdate (GBDAT), and gender (GESCH) for privacy reasons. All of these fields are in segment E1P0002. Initially, I thought that this requirement was easy enough to accomplish. I just created a IDOC reduction in transaction BD53 and filtered out the three fields. I soon found out that while entire segments were getting reduced from the IDOC as configured, the individually reduced fields were still showing up in the IDOC. What’s going on?!? Read more
Changing SAP IDOCs Status In Mass / Mass Deletion Of IDOCs
Mass Change of SAP IDOC Status
From time to time it becomes necessary to change the status of SAP IDOCs in SAP. The most common scenario is the requirement to mark SAP IDOCs for deletion. There is no good way to mass mark IDOCs for deletion via the standard IDOC processing transaction BD87. However there is a program that will let you change status.
RC1_IDOC_SET_STATUS
CAUTION: This program should be used with great care and consideration. Improper use of this program can result in data consistency issues. Make sure you know what you are deleting, why you are deleting it, and what is required to correctly update you system after deleting.
Are You Managing Your Change Pointers Properly Part 5 – Going Forward
In my last post, I discussed a collaborative effort with functional business owners to devise and execute a proper cleanup plan. I also discussed a functional review of the configuration to make sure that change pointers are being created only when needed.
So now that you have achieved control over your change pointer data, how do you make sure that it does not go out of control again?
Are You Properly Managing Your SAP Change Pointers Part 4 – The Cleanup
In my last post on this topic, I discussed several change pointer data forensic methods that I use to point me in the right cleanup direction. The change pointer data forensics will determine how to design the cleanup process. Armed with meaningful statistics, I usually approach the functional business owners to collaborate on a solution. Typical of the many questions that I ask are “Why are change pointers configured for this message type if we are never distributing the IDOCs?”
Discussion points and procedures for the cleanup process.
1. Unless there is custom ABAP code depending on the continued existence of processed change pointers, they serve no further purpose and should be deleted. ABAP Program RBDCPCLR should be scheduled on a regular basis so that recent “processed” change pointers are purged.
If there is a large quantity of very old “processed” change pointer records, then consider special executions of RBDCPCLR where the selected date range is old enough to collect and purge these records.
To cleanup processed change pointers, use the Processed Change Pointers section on the select option screen of program RBDCPCLR. Make sure that the “Processed Change Pointers” check box is checked, and enter the proper date range. You can further limit the data selection by entering a message type in the message type box.
Note that on the selection screen there is also a “Test Run” check box that will list which records would be selected for purging, without actually purging them. I find this feature very useful, and I strongly recommend using it prior to actual purging.
2. “Unprocessed” change pointer records that are very old are most likely not useable. If the IDOCs that they represent were triggered now, the data distributed might no longer be valid. The important discussion point here is the definition of “very old”. Is it one year, six months, two weeks? This decision definitely needs input from the functional business owners.
Once the definition of “very old” has been established, schedule executions of RBDCPCLR with the “Obsolete Change Pointers” box checked, and the agreed-to “very old” date entered in the date box. This execution will remove both “processed” and “unprocessed” change pointer records up to the specified date.
Note that on the selection screen there is also a “Test Run” check box that will list which records would be selected for purging, without actually purging them. I find this feature very useful, and I strongly recommend using it prior to actual purging.
3. “Unprocessed” change pointers that are deemed recent must be analyzed to determine why these records are configured for creation if they are never actually processed. Perhaps, they were needed at one time, and are no longer needed now. What I need from the functional business owners here is an agreement to turn off, in configuration, the creation of these change pointers. Not turning them off will only continue to add unnecessary records to the change pointer tables.
After the creation of these change pointers is turned off in configuration, then schedule an execution of RBDCPCLR using the obsolete section on the selection screen. Enter the message type in the message type box and the current date. This will purge all change pointers of the specified message type, regardless of their status, up to the specified date. Special care must be taken here to make sure that the selection options are entered correctly.
Note that on the selection screen there is also a “Test Run” check box that will list which records would be selected for purging, without actually purging them. I find this feature very useful, and I strongly recommend using it prior to actual purging.
4. Compare the list of message types that are configured to create change pointers (use SAP transaction BD50), to the list of message types in RBDMIDOC executions and the list of message types RBDCPCLR executions. Make sure that all three lists agree. Review these lists with the functional business owners. Make sure that what is configured is meeting the needs of the current business processes.
I also ask the functional business owners to review the selected table-field values that, when changed or created new, will trigger the creation of change pointer records (use SAP transaction BD52). The goal, again, is to make sure that what is configured is meeting the needs of the current business processes.
Now that we have cleaned up, how do we keep the size of the change pointer tables in check? In my next post, I will discuss items that I recommend monitoring.
Are You Properly Managing Your SAP Change Pointers Part 3 – Using Change Pointer Data Forensics
Your batch job analysis shows that you are currently running the change pointer processing program RBDMIDOC, and the change pointer cleanup program RBDCPCLR; but you notice that the size of your change pointer tables continues to increase.
Now, it is time to analyze the change pointer data directly. Here are some targets that I pursue:
1. Count. How many change pointer records are in your tables – thousands, hundreds of thousands, millions, billions? This simple statistic alone reveals an interesting story.
While I typically try to use transaction SE11 to count the number of records in table BDCP, I have found, on occasion, that this method would time out due to an excessively large number of records in this table. In this case, you can write a simple ABAP which can be scheduled to run in background.
2. Age and status. Using the view BDCPV, determine the age and the status of your change pointer records. The age of a change pointer record is indicated by the field BDCPV-CRETIME, and the status of a change pointer record is indicated by the field BDCPV-PROCESS.
In general, are the change pointer records days, weeks, months, or years old? Are different ancient records found to be in both the ‘processed’ and ‘unprocessed’ status? Are different recent records found to be in both the ‘processed’ and ‘unprocessed’ status?
3. Message type and status. Using the BDCPV view, determine the message type and status of your change pointer records. The message type of a change pointer record is indicated by the field BDCPV-MESTYPE.
Are there different change pointer records for some message types that are never found in the ‘processed’ state? Are there different change pointer records for a particular message type that are in both the ‘processed’ and the ‘unprocessed’ status?
Custom Reporting For a more sohpisticated data analysis, you might consider writing an ABAP program to categorize your change pointer records into ageing buckets, message types, and by status. The cross-tab report shown here is one that I use to analyze change pointer records. It contains three sections - Unprocessed Change Pointers by Message Type Within Ageing Category, Processed Change Pointers by Message Type Within Ageing Category, and a Grand Totals Summary.
Example Scenario Leading to Runaway Change Pointer Records Of course, there are many reasons why you might observe some of the scenarios described above. Here is one example:
Many years ago, your company’s business processes required the daily distribution of material master data additions and changes to a remote plant. Your functional analysts correctly configured the change pointers and the ALE process for message type MATMAS to send this data to your remote plant. RBDMIDOC and RBDCPCLR were both running daily for the material master data message type MATMAS. Change pointer records for material master data were created whenever users added or changed certain data fields, IDOCs were created and sent, the change pointer records were marked as ‘processed’, and were then purged – all on a daily basis.
Last year, business process changed, and your remote plant no longer required the distribution of material master data changes. The batch jobs running RBDMIDOC and RBDCPCLR for the material master data message type MATMAS were cancelled, so that IDOCs would no longer be sent to the plant, and because there was no need to clean up after these IDOCs for this message type.
But…
No one remembered to turn off the creation of change pointers in configuration for the material master data message type MATMAS. So, every time a user added or changed a material, new change pointer records were still being created, causing the change pointer tables to needlessly increase in size. And the status of these change pointer records will forever remain ‘unprocessed’.
Do you have custom code that is creating change pointer records? There is another source of runaway change pointer table size that I would like to discuss here. In some SAP systems that I have worked on, I have found that change pointer records were being created via custom ABAP programs. This was done to support a custom process, having nothing to do with the SAP intended use of the change pointer records – the support of ALE distribution of master data. Unfortunately, the system designers also forgot to write a custom program to either clean up their “custom-purposed” change pointer records, or to allow these records to be recognized by the SAP cleanup program.
Next Steps So, now that we have discovered unnecessary change pointer records cluttering up our SAP database, what do we do next? What is the recovery cleanup process? How do we stop the creation of change pointer records that are no longer needed?
This will be discussed in my next post.
ALE 101 – Part 1: Partner Profiles
Even though this blog will primarily be about SAP XI/PI, I would be remiss if I didn’t mention other SAP integration topics. So, my first technical blog will be about my single biggest gripe regarding the broader field of SAP integration. Namely, the perils of IDOC immediate processing!
I discovered both SAP ALE (which stands for Application Link Enabling and pronounced by its individual letters– “A, L, E”) and the golden beverage of the same name in 1998. I was in college at the time. One of my collegiate peers was dabbling in ALE (the drink, not the SAP technology) and had a knowledge on the topic vastly superior to mine. One night, he brought home a variety of ales from a local brewpub. Over time, I got to know ale very well (maybe a little too well?) and have been hooked ever since.
My introduction to SAP ALE was similar. I was in college and a colleague of mine introduced me to ALE. He was on the master data distribution team on a very large SAP implementation. His job was to ensure that the SAP ALE settings were correct in the entire SAP landscape. He taught me the basics of ALE in a few hours. Over time, as I learned more about SAP ALE, I developed a fondness for it that rivaled my fondness for it alcoholic counterpart.
ALE and ale are a lot like. Neither take a large up front investment (a few transactions for ALE, a few bucks for ale), but both require a fair amount of learning appreciate fully. To truly understand ALE, you need to understand all its parts: water, malt, hops, and yeast, and how they interact. Like SAP ALE, too much ale will leave one’s head swimming.
And just like you wouldn’t want to drink mass quantities of ale and immediately take a $150,000 Ferrari out for a spin, you don’t to immediately process mass quantities of IDOCs on your productive SAP instance. The number one problem I see at clients using ALE/IDOCs is setting the partner profile to process the IDOC immediately.
The trouble with immediate IDOC processing comes when a large number IDOCs are received at the same time. What constitutes a “large number of IDOCs?” Well, that depends on your SAP system. Problems usually occur when more IDOCs come in than you have foreground work processes. When more IDOCs arrive than your system has resources not only can you affect the system’s online users (hey, there are only so man foreground work processes to go around), you also run the risk of having some of the inbound IDOCs fall into the dreaded “double 64″ status. IDOCs that cannot be processed due to limited resources are given an additional 64 status record and are left unprocessed by the system. The only way to handle these IDOCs is to process them manually (via BD87 or BD20) or by a background job (RBDAPP01).
A much better way to handle IDOC processing IDOCs is to collect the IDOCs and then process via a background job (RSEOUT00 for outbound IDOCs, RBDAPP01 for inbound IDOCs). This greatly reduces the load on the system and won’t cause your system to bite the dust in high-load situations. Moreover, there are performance tuning options available on RSEOUT00 and RBDAPP01 that will allow your IDOC processing to really sing! I will probably cover advanced settings for those programs in later blogs. I know–you can hardly wait!
In short ALE is great technology, but it is an asynchronous (i.e. batch) technology. There is very little reason why immediate IDOC processing is ever acceptable. In most cases an RBDAPP01 job that is scheduled every 5 of 10 minutes is more than fast enough for the business. If a 5 minute maximum (remember only the IDOCs that came in right after the prior job wait 5 minutes, the average wait time is 2:30) lag is not acceptable, a synchronous interface, based on BAPIs, RFCs, or web services should be considered.
SAP Interface Design
By: Timothy Yates
Description:
While there are many different ways to approach SAP system integration design, there is no right or wrong way necessarily, but success usually lies in the attention to details. The following document outlines a possible approach to an SAP integration project. Previous integration knowledge in a particular functional area is beneficial however overall integration experience is more critical. The more hours spent in general on SAP integration the better. Integration technology is continuously changing and adapting therefore continuous learning and training is required to be effective at integration design.
The 10 Golden Rules of ALE Optimization
By: Timothy Yates and Thomas Nittmann
- Interference With Other Interfaces \ System Load
- Archiving (IDOC, Workflow, Change Points, Message Control)
- Inefficient User Exit Code
- Workflow Error Handling
- Automatic tRFC Batch Retry Switched ON
- Using Immediate Processing
- Using Single Processing Instead of Parallel Processing
- Improper Sized Packets
- Hardware and Basis Configuration
- Standardization Without Interface Specific Considerations
