Updating ztable from the table maintenance

hi,
       I want to knw how and where to check , whether the records which user has entered in the table control of new entries screen exists in the ztable table or not . if the same record exists then that record should not go for saving it shd be deleted when it is detected that it exists in the ztable . can any one tell me how . where and what code to be written . so that the duplicate entries are deleted and others entries get appended in the database.
thank you.

Hi
if you have declared some Primary key fields then by default when you enter the records in the Table maintenance using SM30 it will not allow duplicate records with the same key fields
You can check the Table entries in SE16 Tcode
go to Se16 enter table name execute and see the table records
Reward points for useful Answers
Regards
Anji

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  • Update ztable from database table directly

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  • Need to update Ztable from final internal table

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    Moderator message: please (re)search yourself before asking.
    Edited by: Thomas Zloch on Jul 13, 2010 12:00 PM

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    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
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    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:
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    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 column’s 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
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    The actual processing logic of an application program is written in ABAP - SAP’s 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
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    • 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:
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    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.
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    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:
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    The following diagram shows the structure of an application server:
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    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.
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    • A work process can execute dialog steps of different programs from different users.
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