Performance optimization in SQL

I have a SQL that retrieves data from BSAK with 13 million records and it is taking very long time. I have the following SQL and I don't see how else I can optimize it! If anyone has any suggestions, please let me know.
select bbukrs bzuonr bgjahr bmonat bbelnr bbuzei
           blifnr bblart bbldat bxblnr b~budat
           bshkzg bwrbtr bsknto baugbl
           b~augdt
          into corresponding fields of table i_bsak
          from bsak as b
                 where b~bukrs in s_bukrs
                    and b~lifnr in s_lifnr
                    and b~umsks in s_umsks
                    and b~umskz in s_umskz
                    and b~augdt in s_augdt
                    and b~augbl in s_augbl
                    and b~zuonr in s_zuonr
                    and b~gjahr in s_gjahr
                    and b~belnr in s_belnr
                    and b~buzei in s_buzei
                    and b~blart in s_blart
                    and b~monat in s_monat
                    and b~bldat in s_bldat
                    and b~xblnr in s_xblnr
                    and b~cpudt in s_cpudt
                    and b~budat in s_budat
                    and b~wrbtr in s_wrbtr
                    and b~sknto in s_sknto.
Thanks.
Mithun

hi Mithun,
some suggestions:
"into corresponding fields of table i_bsak"
==> replace with INTO TABLE i_BSAK (and make sure that it has exactly the same structure like the fields you select)
than you have to consider if you really need all of these fields in the WHERE:
and b~gjahr in s_gjahr
and b~monat in s_monat
and b~budat in s_budat
==> either gajhr and monat OR budat only (gjahr and monat will come from budat, so there is no need to check all three)
and b~buzei in s_buzei
==> this one you definetly don't need
and so on...
hope this helps
ec

Similar Messages

What is the best way to Optimize a SQL query : call a function or do a join?

Hi, I want to know what is the best way to optimize a SQL query, call a function inside the SELECT statement or do a simple join?

Hi,
If you're even considering a join, then it will probably be faster. As Justin said, it depends on lots of factors.
A user-defined function is only necessary when you can't figure out how to do something in pure SQL, using joins and built-in functions.
You might choose to have a user-defined function even though you could get the same result with a join. That is, you realize that the function is slow, but you believe that the convenience of using a function is more important than better performance in that particular case.

Performance optimization during database selection.

hi gurus,
pls any explain about this...
Strong knowledge of performance optimization during database selection.
regards,
praveen

Hi Praveen,
Performance Notes
1.Keep the Result Set Small
You should aim to keep the result set small. This reduces both the amount of memory used in the database system and the network load when transferring data to the application server. To reduce the size of your result sets, use the WHERE and HAVING clauses.
Using the WHERE Clause
Whenever you access a database table, you should use a WHERE clause in the corresponding Open SQL statement. Even if a program containing a SELECT statement with no WHERE clause performs well in tests, it may slow down rapidly in your production system, where the data volume increases daily. You should only dispense with the WHERE clause in exceptional cases where you really need the entire contents of the database table every time the statement is executed.
When you use the WHERE clause, the database system optimizes the access and only transfers the required data. You should never transfer unwanted data to the application server and then filter it using ABAP statements.
Using the HAVING Clause
After selecting the required lines in the WHERE clause, the system then processes the GROUP BY clause, if one exists, and summarizes the database lines selected. The HAVING clause allows you to restrict the grouped lines, and in particular, the aggregate expressions, by applying further conditions.
Effect
If you use the WHERE and HAVING clauses correctly:
 There are no more physical I/Os in the database than necessary
 No unwanted data is stored in the database cache (it could otherwise displace data that is actually required)
 The CPU usage of the database host is minimize
 The network load is reduced, since only the data that is required by the application is transferred to the application server.
Minimize the Amount of Data Transferred
Data is transferred between the database system and the application server in blocks. Each block is up to 32 KB in size (the precise size depends on your network communication hardware). Administration information is transported in the blocks as well as the data.
To minimize the network load, you should transfer as few blocks as possible. Open SQL allows you to do this as follows:
Restrict the Number of Lines
If you only want to read a certain number of lines in a SELECT statement, use the UP TO <n> ROWS addition in the FROM clause. This tells the database system only to transfer <n> lines back to the application server. This is more efficient than transferring more lines than necessary back to the application server and then discarding them in your ABAP program.
If you expect your WHERE clause to return a large number of duplicate entries, you can use the DISTINCT addition in the SELECT clause.
Restrict the Number of Columns
You should only read the columns from a database table that you actually need in the program. To do this, list the columns in the SELECT clause. Note here that the INTO CORRESPONDING FIELDS addition in the INTO clause is only efficient with large volumes of data, otherwise the runtime required to compare the names is too great. For small amounts of data, use a list of variables in the INTO clause.
Do not use * to select all columns unless you really need them. However, if you list individual columns, you may have to adjust the program if the structure of the database table is changed in the ABAP Dictionary. If you specify the database table dynamically, you must always read all of its columns.
Use Aggregate Functions
If you only want to use data for calculations, it is often more efficient to use the aggregate functions of the SELECT clause than to read the individual entries from the database and perform the calculations in the ABAP program.
Aggregate functions allow you to find out the number of values and find the sum, average, minimum, and maximum values.
Following an aggregate expression, only its result is transferred from the database.
Data Transfer when Changing Table Lines
When you use the UPDATE statement to change lines in the table, you should use the WHERE clause to specify the relevant lines, and then SET statements to change only the required columns.
When you use a work area to overwrite table lines, too much data is often transferred. Furthermore, this method requires an extra SELECT statement to fill the work area. Minimize the Number of Data Transfers
In every Open SQL statement, data is transferred between the application server and the database system. Furthermore, the database system has to construct or reopen the appropriate administration data for each database access. You can therefore minimize the load on the network and the database system by minimizing the number of times you access the database.
Multiple Operations Instead of Single Operations
When you change data using INSERT, UPDATE, and DELETE, use internal tables instead of single entries. If you read data using SELECT, it is worth using multiple operations if you want to process the data more than once, other wise, a simple select loop is more efficient.
Avoid Repeated Access
As a rule you should read a given set of data once only in your program, and using a single access. Avoid accessing the same data more than once (for example, SELECT before an UPDATE).
Avoid Nested SELECT Loops
A simple SELECT loop is a single database access whose result is passed to the ABAP program line by line. Nested SELECT loops mean that the number of accesses in the inner loop is multiplied by the number of accesses in the outer loop. You should therefore only use nested SELECT loops if the selection in the outer loop contains very few lines.
However, using combinations of data from different database tables is more the rule than the exception in the relational data model. You can use the following techniques to avoid nested SELECT statements:
ABAP Dictionary Views
You can define joins between database tables statically and systemwide as views in the ABAP Dictionary. ABAP Dictionary views can be used by all ABAP programs. One of their advantages is that fields that are common to both tables (join fields) are only transferred once from the database to the application server.
Views in the ABAP Dictionary are implemented as inner joins. If the inner table contains no lines that correspond to lines in the outer table, no data is transferred. This is not always the desired result. For example, when you read data from a text table, you want to include lines in the selection even if the corresponding text does not exist in the required language. If you want to include all of the data from the outer table, you can program a left outer join in ABAP.
The links between the tables in the view are created and optimized by the database system. Like database tables, you can buffer views on the application server. The same buffering rules apply to views as to tables. In other words, it is most appropriate for views that you use mostly to read data. This reduces the network load and the amount of physical I/O in the database.
Joins in the FROM Clause
You can read data from more than one database table in a single SELECT statement by using inner or left outer joins in the FROM clause.
The disadvantage of using joins is that redundant data is read from the hierarchically-superior table if there is a 1:N relationship between the outer and inner tables. This can considerably increase the amount of data transferred from the database to the application server. Therefore, when you program a join, you should ensure that the SELECT clause contains a list of only the columns that you really need. Furthermore, joins bypass the table buffer and read directly from the database. For this reason, you should use an ABAP Dictionary view instead of a join if you only want to read the data.
The runtime of a join statement is heavily dependent on the database optimizer, especially when it contains more than two database tables. However, joins are nearly always quicker than using nested SELECT statements.
Subqueries in the WHERE and HAVING Clauses
Another way of accessing more than one database table in the same Open SQL statement is to use subqueries in the WHERE or HAVING clause. The data from a subquery is not transferred to the application server. Instead, it is used to evaluate conditions in the database system. This is a simple and effective way of programming complex database operations.
Using Internal Tables
It is also possible to avoid nested SELECT loops by placing the selection from the outer loop in an internal table and then running the inner selection once only using the FOR ALL ENTRIES addition. This technique stems from the time before joins were allowed in the FROM clause. On the other hand, it does prevent redundant data from being transferred from the database.
Using a Cursor to Read Data
A further method is to decouple the INTO clause from the SELECT statement by opening a cursor using OPEN CURSOR and reading data line by line using FETCH NEXT CURSOR. You must open a new cursor for each nested loop. In this case, you must ensure yourself that the correct lines are read from the database tables in the correct order. This usually requires a foreign key relationship between the database tables, and that they are sorted by the foreign key. Minimize the Search Overhead
You minimize the size of the result set by using the WHERE and HAVING clauses. To increase the efficiency of these clauses, you should formulate them to fit with the database table indexes.
Database Indexes
Indexes speed up data selection from the database. They consist of selected fields of a table, of which a copy is then made in sorted order. If you specify the index fields correctly in a condition in the WHERE or HAVING clause, the system only searches part of the index (index range scan).
The primary index is always created automatically in the R/3 System. It consists of the primary key fields of the database table. This means that for each combination of fields in the index, there is a maximum of one line in the table. This kind of index is also known as UNIQUE.
If you cannot use the primary index to determine the result set because, for example, none of the primary index fields occur in the WHERE or HAVING clause, the system searches through the entire table (full table scan). For this case, you can create secondary indexes, which can restrict the number of table entries searched to form the result set.
You specify the fields of secondary indexes using the ABAP Dictionary. You can also determine whether the index is unique or not. However, you should not create secondary indexes to cover all possible combinations of fields.
Only create one if you select data by fields that are not contained in another index, and the performance is very poor. Furthermore, you should only create secondary indexes for database tables from which you mainly read, since indexes have to be updated each time the database table is changed. As a rule, secondary indexes should not contain more than four fields, and you should not have more than five indexes for a single database table.
If a table has more than five indexes, you run the risk of the optimizer choosing the wrong one for a particular operation. For this reason, you should avoid indexes with overlapping contents.
Secondary indexes should contain columns that you use frequently in a selection, and that are as highly selective as possible. The fewer table entries that can be selected by a certain column, the higher that columns selectivity. Place the most selective fields at the beginning of the index. Your secondary index should be so selective that each index entry corresponds to at most five percent of the table entries. If this is not the case, it is not worth creating the index. You should also avoid creating indexes for fields that are not always filled, where their value is initial for most entries in the table.
If all of the columns in the SELECT clause are contained in the index, the system does not have to search the actual table data after reading from the index. If you have a SELECT clause with very few columns, you can improve performance dramatically by including these columns in a secondary index.
Formulating Conditions for Indexes
You should bear in mind the following when formulating conditions for the WHERE and HAVING clauses so that the system can use a database index and does not have to use a full table scan.
Check for Equality and Link Using AND
The database index search is particularly efficient if you check all index fields for equality (= or EQ) and link the expressions using AND.
Use Positive Conditions
The database system only supports queries that describe the result in positive terms, for example, EQ or LIKE. It does not support negative expressions like NE or NOT LIKE.
If possible, avoid using the NOT operator in the WHERE clause, because it is not supported by database indexes; invert the logical expression instead.
Using OR
The optimizer usually stops working when an OR expression occurs in the condition. This means that the columns checked using OR are not included in the index search. An exception to this are OR expressions at the outside of conditions. You should try to reformulate conditions that apply OR expressions to columns relevant to the index, for example, into an IN condition.
Using Part of the Index
If you construct an index from several columns, the system can still use it even if you only specify a few of the columns in a condition. However, in this case, the sequence of the columns in the index is important. A column can only be used in the index search if all of the columns before it in the index definition have also been specified in the condition.
Checking for Null Values
The IS NULL condition can cause problems with indexes. Some database systems do not store null values in the index structure. Consequently, this field cannot be used in the index.
Avoid Complex Conditions
Avoid complex conditions, since the statements have to be broken down into their individual components by the database system.
Reduce the Database Load
Unlike application servers and presentation servers, there is only one database server in your system. You should therefore aim to reduce the database load as much as possible. You can use the following methods:
Buffer Tables on the Application Server
You can considerably reduce the time required to access data by buffering it in the application server table buffer. Reading a single entry from table T001 can take between 8 and 600 milliseconds, while reading it from the table buffer takes 0.2 - 1 milliseconds.
Whether a table can be buffered or not depends its technical attributes in the ABAP Dictionary. There are three buffering types:
 Resident buffering (100%) The first time the table is accessed, its entire contents are loaded in the table buffer.
 Generic buffering In this case, you need to specify a generic key (some of the key fields) in the technical settings of the table in the ABAP Dictionary. The table contents are then divided into generic areas. When you access data with one of the generic keys, the whole generic area is loaded into the table buffer. Client-specific tables are often buffered generically by client.
 Partial buffering (single entry) Only single entries are read from the database and stored in the table buffer.
When you read from buffered tables, the following happens:
1. An ABAP program requests data from a buffered table.
2. The ABAP processor interprets the Open SQL statement. If the table is defined as a buffered table in the ABAP Dictionary, the ABAP processor checks in the local buffer on the application server to see if the table (or part of it) has already been buffered.
3. If the table has not yet been buffered, the request is passed on to the database. If the data exists in the buffer, it is sent to the program.
4. The database server passes the data to the application server, which places it in the table buffer.
5. The data is passed to the program.
When you change a buffered table, the following happens:
1. The database table is changed and the buffer on the application server is updated. The database interface logs the update statement in the table DDLOG. If the system has more than one application server, the buffer on the other servers is not updated at once.
2. All application servers periodically read the contents of table DDLOG, and delete the corresponding contents from their buffers where necessary. The granularity depends on the buffering type. The table buffers in a distributed system are generally synchronized every 60 seconds (parameter: rsdisp/bufreftime).
3. Within this period, users on non-synchronized application servers will read old data. The data is not recognized as obsolete until the next buffer synchronization. The next time it is accessed, it is re-read from the database.
You should buffer the following types of tables:
 Tables that are read very frequently
 Tables that are changed very infrequently
 Relatively small tables (few lines, few columns, or short columns)
 Tables where delayed update is acceptable.
Once you have buffered a table, take care not to use any Open SQL statements that bypass the buffer.
The SELECT statement bypasses the buffer when you use any of the following:
 The BYPASSING BUFFER addition in the FROM clause
 The DISTINCT addition in the SELECT clause
 Aggregate expressions in the SELECT clause
 Joins in the FROM clause
 The IS NULL condition in the WHERE clause
 Subqueries in the WHERE clause
 The ORDER BY clause
 The GROUP BY clause
 The FOR UPDATE addition
Furthermore, all Native SQL statements bypass the buffer.
Avoid Reading Data Repeatedly
If you avoid reading the same data repeatedly, you both reduce the number of database accesses and reduce the load on the database. Furthermore, a "dirty read" may occur with database tables other than Oracle. This means that the second time you read data from a database table, it may be different from the data read the first time. To ensure that the data in your program is consistent, you should read it once only and then store it in an internal table.
Sort Data in Your ABAP Programs
The ORDER BY clause in the SELECT statement is not necessarily optimized by the database system or executed with the correct index. This can result in increased runtime costs. You should only use ORDER BY if the database sort uses the same index with which the table is read. To find out which index the system uses, use SQL Trace in the ABAP Workbench Performance Trace. If the indexes are not the same, it is more efficient to read the data into an internal table or extract and sort it in the ABAP program using the SORT statement.
Use Logical Databases
SAP supplies logical databases for all applications. A logical database is an ABAP program that decouples Open SQL statements from application programs. They are optimized for the best possible database performance. However, it is important that you use the right logical database. The hierarchy of the data you want to read must reflect the structure of the logical database, otherwise, they can have a negative effect on performance. For example, if you want to read data from a table right at the bottom of the hierarchy of the logical database, it has to read at least the key fields of all tables above it in the hierarchy. In this case, it is more efficient to use a SELECT statement.
Work Processes
Work processes execute the individual dialog steps in R/3 applications. The next two sections describe firstly the structure of a work process, and secondly the different types of work process in the R/3 System.
Structure of a Work Process
Work processes execute the dialog steps of application programs. They are components of an application server. The following diagram shows the components of a work process:
Each work process contains two software processors and a database interface.
Screen Processor
In R/3 application programming, there is a difference between user interaction and processing logic. From a programming point of view, user interaction is controlled by screens. As well as the actual input mask, a screen also consists of flow logic. The screen flow logic controls a large part of the user interaction. The R/3 Basis system contains a special language for programming screen flow logic. The screen processor executes the screen flow logic. Via the dispatcher, it takes over the responsibility for communication between the work process and the SAPgui, calls modules in the flow logic, and ensures that the field contents are transferred from the screen to the flow logic.
ABAP Processor
The actual processing logic of an application program is written in ABAP - SAPs own programming language. The ABAP processor executes the processing logic of the application program, and communicates with the database interface. The screen processor tells the ABAP processor which module of the screen flow logic should be processed next. The following screen illustrates the interaction between the screen and the ABAP processors when an application program is running.
Database Interface
The database interface provides the following services:
 Establishing and terminating connections between the work process and the database.
 Access to database tables
 Access to R/3 Repository objects (ABAP programs, screens and so on)
 Access to catalog information (ABAP Dictionary)
 Controlling transactions (commit and rollback handling)
 Table buffer administration on the application server.
The following diagram shows the individual components of the database interface:
The diagram shows that there are two different ways of accessing databases: Open SQL and Native SQL.
Open SQL statements are a subset of Standard SQL that is fully integrated in ABAP. They allow you to access data irrespective of the database system that the R/3 installation is using. Open SQL consists of the Data Manipulation Language (DML) part of Standard SQL; in other words, it allows you to read (SELECT) and change (INSERT, UPDATE, DELETE) data. The tasks of the Data Definition Language (DDL) and Data Control Language (DCL) parts of Standard SQL are performed in the R/3 System by the ABAP Dictionary and the authorization system. These provide a unified range of functions, irrespective of database, and also contain functions beyond those offered by the various database systems.
Open SQL also goes beyond Standard SQL to provide statements that, in conjunction with other ABAP constructions, can simplify or speed up database access. It also allows you to buffer certain tables on the application server, saving excessive database access. In this case, the database interface is responsible for comparing the buffer with the database. Buffers are partly stored in the working memory of the current work process, and partly in the shared memory for all work processes on an application server. Where an R/3 System is distributed across more than one application server, the data in the various buffers is synchronized at set intervals by the buffer management. When buffering the database, you must remember that data in the buffer is not always up to date. For this reason, you should only use the buffer for data which does not often change.
Native SQL is only loosely integrated into ABAP, and allows access to all of the functions contained in the programming interface of the respective database system. Unlike Open SQL statements, Native SQL statements are not checked and converted, but instead are sent directly to the database system. Programs that use Native SQL are specific to the database system for which they were written. R/3 applications contain as little Native SQL as possible. In fact, it is only used in a few Basis components (for example, to create or change table definitions in the ABAP Dictionary).
The database-dependent layer in the diagram serves to hide the differences between database systems from the rest of the database interface. You choose the appropriate layer when you install the Basis system. Thanks to the standardization of SQL, the differences in the syntax of statements are very slight. However, the semantics and behavior of the statements have not been fully standardized, and the differences in these areas can be greater. When you use Native SQL, the function of the database-dependent layer is minimal.
Types of Work Process
Although all work processes contain the components described above, they can still be divided into different types. The type of a work process determines the kind of task for which it is responsible in the application server. It does not specify a particular set of technical attributes. The individual tasks are distributed to the work processes by the dispatcher.
Before you start your R/3 System, you determine how many work processes it will have, and what their types will be. The dispatcher starts the work processes and only assigns them tasks that correspond to their type. This means that you can distribute work process types to optimize the use of the resources on your application servers.
The following diagram shows again the structure of an application server, but this time, includes the various possible work process types:
The various work processes are described briefly below. Other parts of this documentation describe the individual components of the application server and the R/3 System in more detail.
Dialog Work Process
Dialog work processes deal with requests from an active user to execute dialog steps.
Update Work Process
Update work processes execute database update requests. Update requests are part of an SAP LUW that bundle the database operations resulting from the dialog in a database LUW for processing in the background.
Background Work Process
Background work processes process programs that can be executed without user interaction (background jobs).
Enqueue Work Process
The enqueue work process administers a lock table in the shared memory area. The lock table contains the logical database locks for the R/3 System and is an important part of the SAP LUW concept. In an R/3 System, you may only have one lock table. You may therefore also only have one application server with enqueue work processes.
Spool Work Process
The spool work process passes sequential datasets to a printer or to optical archiving. Each application server may contain several spool work process.
The services offered by an application server are determined by the types of its work processes. One application server may, of course, have more than one function. For example, it may be both a dialog server and the enqueue server, if it has several dialog work processes and an enqueue work process.
You can use the system administration functions to switch a work process between dialog and background modes while the system is still running. This allows you, for example, to switch an R/3 System between day and night operation, where you have more dialog than background work processes during the day, and the other way around during the night.
ABAP Application Server
R/3 programs run on application servers. They are an important component of the R/3 System. The following sections describe application servers in more detail.
Structure of an ABAP Application Server
The application layer of an R/3 System is made up of the application servers and the message server. Application programs in an R/3 System are run on application servers. The application servers communicate with the presentation components, the database, and also with each other, using the message server.
The following diagram shows the structure of an application server:
The individual components are:
Work Processes
An application server contains work processes, which are components that can run an application. Work processes are components that are able to execute an application (that is, one dialog step each). Each work process is linked to a memory area containing the context of the application being run. The context contains the current data for the application program. This needs to be available in each dialog step. Further information about the different types of work process is contained later on in this documentation.
Dispatcher
Each application server contains a dispatcher. The dispatcher is the link between the work processes and the users logged onto the application server. Its task is to receive requests for dialog steps from the SAP GUI and direct them to a free work process. In the same way, it directs screen output resulting from the dialog step back to the appropriate user.
Gateway
Each application server contains a gateway. This is the interface for the R/3 communication protocols (RFC, CPI/C). It can communicate with other application servers in the same R/3 System, with other R/3 Systems, with R/2 Systems, or with non-SAP systems.
The application server structure as described here aids the performance and scalability of the entire R/3 System. The fixed number of work processes and dispatching of dialog steps leads to optimal memory use, since it means that certain components and the memory areas of a work process are application-independent and reusable. The fact that the individual work processes work independently makes them suitable for a multi-processor architecture. The methods used in the dispatcher to distribute tasks to work processes are discussed more closely in the section Dispatching Dialog Steps.
Shared Memory
All of the work processes on an application server use a common main memory area called shared memory to save contexts or to buffer constant data locally.
The resources that all work processes use (such as programs and table contents) are contained in shared memory. Memory management in the R/3 System ensures that the work processes always address the correct context, that is the data relevant to the current state of the program that is running. A mapping process projects the required context for a dialog step from shared memory into the address of the relevant work process. This reduces the actual copying to a minimum.
Local buffering of data in the shared memory of the application server reduces the number of database reads required. This reduces access times for application programs considerably. For optimal use of the buffer, you can concentrate individual applications (financial accounting, logistics, human resources) into separate application server groups.
Database Connection
When you start up an R/3 System, each application server registers its work processes with the database layer, and receives a single dedicated channel for each. While the system is running, each work process is a user (client) of the database system (server). You cannot change the work process registration while the system is running. Neither can you reassign a database channel from one work process to another. For this reason, a work process can only make database changes within a single database logical unit of work (LUW). A database LUW is an inseparable sequence of database operations. This has important consequences for the programming model explained below.
Dispatching Dialog Steps
The number of users logged onto an application server is often many times greater than the number of available work processes. Furthermore, it is not restricted by the R/3 system architecture. Furthermore, each user can run several applications at once. The dispatcher has the important task of distributing all dialog steps among the work processes on the application server.
The following diagram is an example of how this might happen:
 1. The dispatcher receives the request to execute a dialog step from user 1 and directs it to work process 1, which happens to be free. The work process addresses the context of the application program (in shared memory) and executes the dialog step. It then becomes free again.
 2. The dispatcher receives the request to execute a dialog step from user 2 and directs it to work process 1, which is now free again. The work process executes the dialog step as in step 1.
 3. While work process 1 is still working, the dispatcher receives a further request from user 1 and directs it to work process 2, which is free.
 4. After work processes 1 and 2 have finished processing their dialog steps, the dispatcher receives another request from user 1 and directs it to work process 1, which is free again.
 5. While work process 1 is still working, the dispatcher receives a further request from user 2 and directs it to work process 2, which is free.
From this example, we can see that:
 A dialog step from a program is assigned to a single work process for execution.
 The individual dialog steps of a program can be executed on different work processes, and the program context must be addressed for each new work process.
 A work process can execute dialog steps of different programs from different users.
The example does not show that the dispatcher tries to distribute the requests to the work processes such that the same work process is used as often as possible for the successive dialog steps in an application. This is useful, since it saves the program context having to be addressed each time a dialog step is executed.
Dispatching and the Programming Model
The separation of application and presentation layer made it necessary to split up application programs into dialog steps. This, and the fact that dialog steps are dispatched to individual work processes, has had important consequences for the programming model.
As mentioned above, a work process can only make database changes within a single database logical unit of work (LUW). A database LUW is an inseparable sequence of database operations. The contents of the database must be consistent at its beginning and end. The beginning and end of a database LUW are defined by a commit command to the database system (database commit). During a database LUW, that is, between two database commits, the database system itself ensures consistency within the database. In other words, it takes over tasks such as locking database entries while they are being edited, or restoring the old data (rollback) if a step terminates in an error.
A typical SAP application program extends over several screens and the corresponding dialog steps. The user requests database changes on the individual screens that should lead to the database being consistent once the screens have all been processed. However, the individual dialog steps run on different work processes, and a single work process can process dialog steps from other applications. It is clear that two or more independent applications whose dialog steps happen to be processed on the same work process cannot be allowed to work with the same database LUW.
Consequently, a work process must open a separate database LUW for each dialog step. The work process sends a commit command (database commit) to the database at the end of each dialog step in which it makes database changes. These commit commands are called implicit database commits, since they are not explicitly written into the application program.
These implicit database commits mean that a database LUW can be kept open for a maximum of one dialog step. This leads to a considerable reduction in database load, serialization, and deadlocks, and enables a large number of users to use the same system.
However, the question now arises of how this method (1 dialog step = 1 database LUW) can be reconciled with the demand to make commits and rollbacks dependent on the logical flow of the application program instead of the technical distribution of dialog steps. Database update requests that depend on one another form logical units in the program that extend over more than one dialog step. The database changes associated with these logical units must be executed together and must also be able to be undone together.
The SAP programming model contains a series of bundling techniques that allow you to group database updates together in logical units. The section of an R/3 application program that bundles a set of logically-associated database operations is called an SAP LUW. Unlike a database LUW, a SAP LUW includes all of the dialog steps in a logical unit, including the database update.
Happy Reading...
shibu

Performance optimization related.

Hi.
I am doing Performance optimization on code.
Actually, I am doing performance optimization for old code where it is of JDK1.4 related. I met up with a doubt when I optimize code for JDK1.5.
Problem statement:
Collection errors = new ArrayList();
errors.add(new GenericException(ErrorCodes.EMPLOYEE_INVALID_PERMISSION));
setErrorsInRequest(request, errors);In the above code the compiler tells us to Parameterize the Collection type reference. If we don't make any parameterization for Collection type, will that be dealing with Performace issue?
Please help me out to resolve the problem statement.
Thanks and regards,
Leslie V
www.googlestepper.blogspot.com
www.scrollnroll.blogspot.com

If we don't make any parameterization for Collection type, will that be dealing with Performace issue?No. Not really. But performance isn't really the issue... it's runtime-type-safety which is at issue. There's nothing to prevent me from adding an Integer (like an error number) to your collection of exceptions.
And "GenericException"... Sheesh, come down from the trees allready. WTF am I (the user of this class/method/package) supposed to with a friggin "GenericException"... you may as well have thrown a raw RuntimeException and saved all that cumbersom interveening try/catch code.

Need clear steps for doing performance tuning on SQL Server 2008 R2 (DB Engine, Reporting Services and Integration Services)

We have to inverstigate about a reporting solution where things are getting slow (may be material, database design, network matters).
I have red a lot in MSDN and some books about performance tuning on SQL Server 2008 R2 (or other) but frankly, I feel a little lost in all that stuff
I'am looking for practical steps in order to do the tuning. Someone had like a recipe for that : a success story...
My (brain storm) Methodology should follow these steps:
Resource bottlenecks: CPU, memory, and I/O bottlenecks
tempdb bottlenecks
A slow-running user query : Missing indexes, statistics,...
Use performance counters : there are many, can one give us the list of the most important
how to do fine tuning about SQL Server configuration
SSRS, SSIS configuration ?
And do the recommandations.
Thanks
"there is no Royal Road to Mathematics, in other words, that I have only a very small head and must live with it..."
Edsger W. Dijkstra

Hello,
There is no clear defined step which can be categorized as step by step to performance tuning.Your first goal is to find out cause or drill down to factor causing slowness of SQL server it can be poorly written query ,missing indexes,outdated stats.RAM crunch
CPU crunch so on and so forth.
I generally refer to below doc for SQL server tuning
http://technet.microsoft.com/en-us/library/dd672789(v=sql.100).aspx
For SSIS tuning i refer below doc.
http://technet.microsoft.com/library/Cc966529#ECAA
http://msdn.microsoft.com/en-us/library/ms137622(v=sql.105).aspx
When I face issue i generally look at wait stats ,wait stats give you idea about on what resource query was waiting.
--By Jonathan KehayiasSELECT TOP 10
wait_type ,
max_wait_time_ms wait_time_ms ,
signal_wait_time_ms ,
wait_time_ms - signal_wait_time_ms AS resource_wait_time_ms ,
100.0 * wait_time_ms / SUM(wait_time_ms) OVER ( )
AS percent_total_waits ,
100.0 * signal_wait_time_ms / SUM(signal_wait_time_ms) OVER ( )
AS percent_total_signal_waits ,
100.0 * ( wait_time_ms - signal_wait_time_ms )
/ SUM(wait_time_ms) OVER ( ) AS percent_total_resource_waits
FROM sys.dm_os_wait_stats
WHERE wait_time_ms > 0 -- remove zero wait_time
AND wait_type NOT IN -- filter out additional irrelevant waits
( 'SLEEP_TASK', 'BROKER_TASK_STOP', 'BROKER_TO_FLUSH',
'SQLTRACE_BUFFER_FLUSH','CLR_AUTO_EVENT', 'CLR_MANUAL_EVENT',
'LAZYWRITER_SLEEP', 'SLEEP_SYSTEMTASK', 'SLEEP_BPOOL_FLUSH',
'BROKER_EVENTHANDLER', 'XE_DISPATCHER_WAIT', 'FT_IFTSHC_MUTEX',
'CHECKPOINT_QUEUE', 'FT_IFTS_SCHEDULER_IDLE_WAIT',
'BROKER_TRANSMITTER', 'FT_IFTSHC_MUTEX', 'KSOURCE_WAKEUP',
'LAZYWRITER_SLEEP', 'LOGMGR_QUEUE', 'ONDEMAND_TASK_QUEUE',
'REQUEST_FOR_DEADLOCK_SEARCH', 'XE_TIMER_EVENT', 'BAD_PAGE_PROCESS',
'DBMIRROR_EVENTS_QUEUE', 'BROKER_RECEIVE_WAITFOR',
'PREEMPTIVE_OS_GETPROCADDRESS', 'PREEMPTIVE_OS_AUTHENTICATIONOPS',
'WAITFOR', 'DISPATCHER_QUEUE_SEMAPHORE', 'XE_DISPATCHER_JOIN',
'RESOURCE_QUEUE' )
ORDER BY wait_time_ms DESC
use below link to analyze wait stats
http://www.sqlskills.com/blogs/paul/wait-statistics-or-please-tell-me-where-it-hurts/
HTH
PS: for reporting services you can post in SSRS forum
Please mark this reply as the answer or vote as helpful, as appropriate, to make it useful for other readers

Need help in improving the performance for the sql query

Thanks in advance for helping me.
I was trying to improve the performance of the below query. I tried the following methods used merge instead of update, used bulk collect / Forall update, used ordered hint, created a temp table and upadated the target table using the same. The methods which I used did not improve any performance. The data count which is updated in the target table is 2 million records and the target table has 15 million records.
Any suggestions or solutions for improving performance are appreciated
SQL query:
update targettable tt
set mnop = 'G',
where ( x,y,z ) in
select a.x, a.y,a.z
from table1 a
where (a.x, a.y,a.z) not in (
select b.x,b.y,b.z
from table2 b
where 'O' = b.defg
and mnop = 'P'
and hijkl = 'UVW';

987981 wrote:
I was trying to improve the performance of the below query. I tried the following methods used merge instead of update, used bulk collect / Forall update, used ordered hint, created a temp table and upadated the target table using the same. The methods which I used did not improve any performance. And that meant what? Surely if you spend all that time and effort to try various approaches, it should mean something? Failures are as important teachers as successes. You need to learn from failures too. :-)
The data count which is updated in the target table is 2 million records and the target table has 15 million records.Tables have rows btw, not records. Database people tend to get upset when rows are called records, as records exist in files and a database is not a mere collection of records and files.
The failure to find a single faster method with the approaches you tried, points to that you do not know what the actual performance problem is. And without knowing the problem, you still went ahead, guns blazing.
The very first step in dealing with any software engineering problem, is to identify the problem. Seeing the symptoms (slow performance) is still a long way from problem identification.
Part of identifying the performance problem, is understanding the workload. Just what does the code task the database to do?
From your comments, it needs to find 2 million rows from 15 million rows. Change these rows. And then write 2 million rows back to disk.
That is not a small workload. Simple example. Let's say that the 2 million row find is 1ms/row and the 2 million row write is also 1ms/row. This means a 66 minute workload. Due to the number of rows, an increase in time/row either way, will potentially have 2 million fold impact.
So where is the performance problem? Time spend finding the 2 million rows (where other tables need to be read, indexes used, etc)? Time spend writing the 2 million rows (where triggers and indexes need to be fired and maintained)? Both?

Regarding performance optimization and tuning...

hi all,
please provide me the performance tuning scenarios and parameters of an R/3 system with Oracle..
i heartly welcome all docs and pdf links or notes related to this issue..
please provide ur suggestions at the earliest...
expecting ur response..
Vineeth

Hello,
there are many SAP Notes regarding performance issues. Here are just a couple of them:
618868
805934
793113
805934
Please also have a look at the lists of the relating Notes at the end of each Note.
But still much more effective would be to read the book of
<a href="http://www.sap-press.de/katalog/buecher/titel/gp/titelID-1155?GalileoSession=66220888A2.lRCIISlE">T.Schneider Performance Optimization Guide</a>.
It's the best performance tuning guide. The course ADM315 (or BC315?) ist also very helpful.
Regards,
Natalia

Performance Optimization Self Service- SAP help requirement

Hi,
I want to know whether for SAP's help is required for performing the self service of Performance Optimization.
If we collect ST12 trace and use it to perform the self service then is the report which is generated from the self service sufficient to take further action or will I need some SAP expertise to implement / take corrective actions?
In short, whether I can do the Performance Optimization by myself or I need help from SAP?
Regards,
Vishal

hi,
1) Is this service available to all the customer? (by all the customers I mean "Max Attention", "Enterprise Support" etc)
i answer this above is it, from mz above reply, have you checked
enterprise support customers can get five EGI sessions as free per year. please check
http://service.sap.com/esacademy
- click browse egis
for your second question also I answered above
Does the report itself gives suggestions or we need to provide the report to SAP
here my reply above
because Guided procedure itself the proven methodlogy from SAP, the report provides the lots of suggestions against the SAP best practices.
you can use it yourself most of the time. if still you need expert guidance from SAP, book for EGI sessions. they called as expert guided implementations, remote support. duration might vary based on the session.
again, service report is the source, you have to review yourself, if you are in EGI, sap use that report for guiding. Please review
Thanks
Jansi

Performance optimization

Hi All,
i am trying to learn sap basis. i want to know that what is performance optimization in sap basis and how it will be done. kindly help me. thanks in advance
Regards,
Subhash

Hi Subhash,
an answer to that question would easily fill a book, so I'd like to suggest one:
SAP Performance Optimization Guide
Thomas Schneider
SAP Press
ISBN 1-59229-069-8
It's the performance bible and covers every aspect of performance optimization.
Kind regards
Dirk

How To Performance Optimize GRC Access Control 5.3

Hi Everyone,
GRC RIG published in BPX the following How-To-Guide:
How To Performance Optimize GRC Access Control 5.3
https://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/library/uuid/90aa3190-8386-2b10-c4ba-ced67322ea6d
We appreciate any feedback and will keep track of all suggestions to improve future versions of this guide.
Frank Rambo
Director RIG EMEA
SAP Governance, Risk and Complicance

to access CUP:
http://<hostname>:<portnumber>/AE
To access RAR:
http://<hostname>:<portnumber>/webdynpro/dispatcher/sap.com/grc~ccappcomp/ComplianceCalibrator
to access SPM
http://<hostname>:<port>/webdynpro/dispatcher/sap.com/grc~ffappcomp/Firefighter
to access ERM:
http://<hostname>:<portnumber>/RE
Launch pad link:
http://<server name>:5<instance>00/webdynpro/dispatcher/sap.com/grc~acappcomp/AC
NW start page:
http://<hostname>:<portnumber>/index.html

How to optimize this sql by writing MINUS function.

Hi all,
how to optimize the sql by writing MINUS function.
these are my tables
1. CREATE TABLE POSTPAID
RECORD VARCHAR2(2000 BYTE),
FLAG NUMBER
Record format:
Mobile no in 1:10 of that length
2. CREATE TABLE SUBSCRIBER
PHONE_NO VARCHAR2(10 BYTE)
My requirement is following sql need write using ‘minus’ as this one is very slow
select record record from POSTPAID where substr(record,9,10) NOT in (select PHONE_NO from SUBSCRIBER)
Thanks

Why are you very particular about using "MINUS". You can optimize the sql by using "NOT EXISTS" instead of "NOT IN" as below:
SELECT RECORD FROM POSTPAID A WHERE NOT EXISTS (SELECT 1 FROM SUBSCRIBER B WHERE SUBSTR(A.RECORD,9,10) = B.PHONE_NO)

ABAP Performance Optimizer

dear abapers,
i have a problem in optimize abap code, especially report program.
is there any documentation / step by step guide / tips n tricks for best practise in abap performance optimizer ?
if there any, could you please send to my mail : [email protected]
many thanks in advance
regards
eddhie

Hi,
Take a look at the links below they have useful info and tips.
http://www.sapdevelopment.co.uk/perform/performhome.htm
http://www.thespot4sap.com/Articles/SAPABAPPerformanceTuning_PerformanceAnalysisTools.asp
http://help.sap.com/saphelp_nw2004s/helpdata/en/6b/4b5ceb971afa469a02268df33f545f/content.htm
http://www.asug.com/client_files/Calendar/Upload/ACF3DBF.ppt#264,1,Slide 1
Cheers
VJ

Performance impact on SQL DB in Virtualized servers

So, your performance monitoring indicates that it's bottlenecked on CPU, correct?
What about the physical host? Does that have spare CPU cycles to give?

hi all,
one of my customer is facing the performance issues on sql db. CPU allocated currently is 20 VCPU and it utilization shows very high.
Increasing VCPU would serve the purpose or any indexing or other best practices to be followed.
Please suggest
This topic first appeared in the Spiceworks Community

I don't know how I can optimize this SQL

Dear ALL:
I don't know how I can optimize this SQL.
Is it possible to make a better SQL or PL/SQL?
Please let me know your good thought.
Thank you.
Sincerely,
===========================================================
(SELECT     A, B, C, SUM(D) as D, AVG(E) as E
FROM      T1, T2
WHERE     T1.timestamp BETWEEN TO_DATE('00:00:00','HH24:MI:SS')
AND TO_DATE('01:00:00','HH24:MI:SS')
GROUP BY A, B, C
UNION ALL
(SELECT     A, B, C, SUM(D) as D, AVG(E) as E
FROM      T1, T2
WHERE     T1.timestamp BETWEEN TO_DATE('01:00:01','HH24:MI:SS')
AND TO_DATE('02:00:00','HH24:MI:SS')
GROUP BY A, B, C
UNION ALL
(SELECT     A, B, C, SUM(D) as D, AVG(E) as E
FROM      T1, T2
WHERE     T1.timestamp BETWEEN TO_DATE('02:00:01','HH24:MI:SS')
AND TO_DATE('03:00:00','HH24:MI:SS')
GROUP BY A, B, C
UNION ALL
(SELECT     A, B, C, SUM(D) as D, AVG(E) as E
FROM      T1, T2
WHERE     T1.timestamp BETWEEN TO_DATE('03:00:01','HH24:MI:SS')
AND TO_DATE('04:00:00','HH24:MI:SS')
GROUP BY A, B, C
ORDER BY A ASC, B ASC, C ASC
===========================================================

Dear Warren:
Actually, for this query, it takes a few second to complete the query.
But, I have to create several time period and I have to optimize the SQL.
Time period is 1 hour, 30 min, 15 min, 5 min.
That is,
===========================================================
WHERE T1.timstamp BETWEEN TO_DATE('00:00:00','HH24:MI:SS')
AND TO_DATE('01:00:00','HH24:MI:SS')
UNION ALL
WHERE T1.timestamp BETWEEN TO_DATE('23:00:01','HH24:MI:SS')
AND TO_DATE('24:00:00','HH24:MI:SS')
===========================================================
WHERE T1.timstamp BETWEEN TO_DATE('00:00:00','HH24:MI:SS')
AND TO_DATE('00:30:00','HH24:MI:SS')
UNION ALL
WHERE T1.timestamp BETWEEN TO_DATE('23:30:01','HH24:MI:SS')
AND TO_DATE('24:00:00','HH24:MI:SS')
===========================================================
WHERE T1.timstamp BETWEEN TO_DATE('00:00:00','HH24:MI:SS')
AND TO_DATE('00:15:00','HH24:MI:SS')
UNION ALL
WHERE T1.timestamp BETWEEN TO_DATE('23:45:01','HH24:MI:SS')
AND TO_DATE('24:00:00','HH24:MI:SS')
===========================================================
WHERE T1.timstamp BETWEEN TO_DATE('00:00:00','HH24:MI:SS')
AND TO_DATE('00:05:00','HH24:MI:SS')
UNION ALL
WHERE T1.timestamp BETWEEN TO_DATE('23:55:01','HH24:MI:SS')
AND TO_DATE('24:00:00','HH24:MI:SS')
===========================================================
Do you know how this SQL is optimized?
Thank you,

How can i optimize this SQL

Is there anyway i could optimize the below mentioned SQL. Since our test DB is down now, i'll be posting the EXPLAIN PLAN later.
Is there anything i could do syntactically to optimize this sql?
SELECT SUBSTR(STK_INF.TASK_GENRTN_REF_NBR,1,12) AS PICK_WAVE_NBR,
 ( CASE WHEN ( SUM(STK_INF.QTY_ALLOC) > 0 ) THEN
 ROUND ( (SUM(CASE WHEN (STK_INF.NEXT_TASK_ID = 0 AND STK_INF.ALLOC_INVN_DTL_ID = 0) THEN
 STK_INF.QTY_ALLOC ELSE 0 END ) / (SUM(STK_INF.QTY_ALLOC))),2 ) * 100
 ELSE 0 END ) AS PICK_PER,
 ( CASE WHEN ( SUM(STK_INF.QTY_ALLOC) > 0 ) THEN
 ROUND( (SUM(CASE WHEN (STK_INF.NEXT_TASK_ID = 0 AND STK_INF.ALLOC_INVN_DTL_ID = 0) THEN
 STK_INF.QTY_PULLD ELSE 0 END ) / (SUM(STK_INF.QTY_ALLOC))),2 ) * 100
 ELSE 0 END ) AS TILT_PERCENT
 FROM STK_INF, TRK_DTL
 WHERE STK_INF.TASK_GENRTN_REF_CODE = '44' AND STK_INF.CARTON_NBR IS NOT NULL
 AND ((STK_INF.NEXT_TASK_ID = 0 AND STK_INF.STAT_CODE <= 90) OR (STK_INF.NEXT_TASK_ID > 0 AND STK_INF.STAT_CODE < 99))
 AND PULL_LOCN_ID = TRK_DTL.LOCN_ID(+) AND (TRK_DTL.AREA <>'C' OR TRK_DTL.AREA IS NULL)
 GROUP BY SUBSTR(STK_INF.TASK_GENRTN_REF_NBR,1,12)

This is the EXPLAIN PLAIN i got after i issued EXECUTE DBMS_STATS.GATHER_TABLE_STATS ('schema_name','table_name'); for both the tables involved.
The number of row returned is still 222 rows. But in the EXPLAIN PLAN it is shown as 3060 !!
SQL> select * from table(dbms_xplan.display);
PLAN_TABLE_OUTPUT
Plan hash value: 3267659036
| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 3060 | 194K| | 16527 (10)| 00:03:19 |
| 1 | HASH GROUP BY | | 3060 | 194K| | 16527 (10)| 00:03:19 |
|* 2 | FILTER | | | | | | |
|* 3 | HASH JOIN RIGHT OUTER| | 177K| 11M| 7344K| 16397 (10)| 00:03:17 |
| 4 | TABLE ACCESS FULL | TRK_DTL | 313K| 3669K| | 2378 (6)| 00:00:29 |
|* 5 | TABLE ACCESS FULL | STK_INF | 177K| 9205K| | 13030 (11)| 00:02:37 |
PLAN_TABLE_OUTPUT
Predicate Information (identified by operation id):
 2 - filter("TRK_DTL"."AREA"<>'C' OR "TRK_DTL"."AREA" IS NULL)
 3 - access("PULL_LOCN_ID"="TRK_DTL"."LOCN_ID"(+))
 5 - filter("STK_INF"."CARTON_NBR" IS NOT NULL AND
 "STK_INF"."TASK_GENRTN_REF_CODE"='44' AND ("STK_INF"."NEXT_TASK_ID"=0 AND
 "STK_INF"."STAT_CODE"<=90 OR "STK_INF"."NEXT_TASK_ID">0 AND
 "STK_INF"."STAT_CODE"<99))
22 rows selected.You mentioned about creating indexes in STK_INF table. STK_INF.NEXT_TASK_ID has 186385 distinct values out of a total 782087. Does NEXT_TASK_ID make a good candidate for a B-Tree index
STK_INF.STAT_CODE has only four distinct values, would this make a good candidate for a Bitmap index?

Performance optimization in SQL

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