Message type: E = Error
Message class: TPDA - Debugger
Message number: 491
Message text: Watchpoint on table: Potential performance loss (see long text)
Setting a watchpoint on an internal table may cause a severe
deterioration in performance regarding the runtime of the program.
Using the empirical formular:
,,f = 5 + R/10 + B/10000
where
,,f: Factor by which the program slows down
,,R: Number of rows in the internal table
,,B: Size of the internal table in bytes
you can estimate how much slower the program will run.
For example, if you have an internal table with 1000 characters and 1000
bytes per row, executing the program slows down by a factor of 205.
The system issues an error message and will not allow you to continue with this transaction until the error is resolved.
In many cases it is therefore advisable to only activate watchpoints for
internal tables for short program sections.
Tip:
In cases where you are only interested in the change in number of rows
of the internal table, you can alternatively proceed as follows:
Set a watchpoint on the header of the internal table, such as "*ITAB[]",
possibly with the condition:
Not equal to the current row number, such as: "LINES( ITAB ) <> 1000"
Error message extract from SAP system. Copyright SAP SE.
TPDA491
- Watchpoint on table: Potential performance loss (see long text) ?The SAP error message TPDA491 indicates a watchpoint on a specific table, which suggests that there may be potential performance issues related to that table. This message is typically associated with the SAP performance analysis tools and is meant to alert developers or system administrators to potential inefficiencies in database access or table design.
Cause: The error message TPDA491 is triggered when the SAP system detects that a particular table may be causing performance degradation. This can happen due to several reasons, including: Table Size: The table may have grown significantly in size, leading to slower access times. Indexing Issues: Lack of proper indexing or outdated statistics can lead to inefficient query execution plans. Data Model Design: Poorly designed data models or excessive normalization can lead to complex joins and slow performance. High Volume of Transactions: A high volume of read/write operations on the table can lead to contention and locking issues. Database Configuration: Suboptimal database configuration
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