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Better performance: ALV or Classical

Former Member
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Hello ,

I need your comments on arranging the following according to their performance:

1. ALV reports

2. Classical Reports

3. ALV reports using OO methodology

Also kindly state the reasons...

Harmeet S.

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Former Member
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Hi,

ALV output is a bit slower, because layout has to be applied. But the difference won't be noticeable, so it should not be an argument to choose one above another.

But, if you want to output a lot of data, ALV migth be less performant. For ALV (non objec-oriented), you have to gather all output in an internal table before output. This means you have to hold a lot of data in memory; With output using write-statements you can write line by line, without having to put all output in an internal table. For object-oriented ALV, I'm not sure if the output data has to be passed in once or not.

Hope this is helpfull.

Bart

Hi,

ALV output is a bit slower, because layout has to be applied. But the difference won't be noticeable, so it should not be an argument to choose one above another.

But, if you want to output a lot of data, ALV migth be less performant. For ALV (non objec-oriented), you have to gather all output in an internal table before output. This means you have to hold a lot of data in memory; With output using write-statements you can write line by line, without having to put all output in an internal table. For object-oriented ALV, I'm not sure if the output data has to be passed in once or not.

Hope this is helpfull.

Bart

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Former Member
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1,552

Hi,

ALV output is a bit slower, because layout has to be applied. But the difference won't be noticeable, so it should not be an argument to choose one above another.

But, if you want to output a lot of data, ALV migth be less performant. For ALV (non objec-oriented), you have to gather all output in an internal table before output. This means you have to hold a lot of data in memory; With output using write-statements you can write line by line, without having to put all output in an internal table. For object-oriented ALV, I'm not sure if the output data has to be passed in once or not.

Hope this is helpfull.

Bart

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Former Member
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Hi

the performance of the report allways mainly effects on the DATA BASE LOAD

if your program writen logic in better way then its works finely

see these points to improve performance

Ways of Performance Tuning

1. Selection Criteria

2. Select Statements

• Select Queries

• SQL Interface

• Aggregate Functions

• For all Entries

Select Over more than one internal table

Selection Criteria

1. Restrict the data to the selection criteria itself, rather than filtering it out using the ABAP code using CHECK statement.

2. Select with selection list.

SELECT * FROM SBOOK INTO SBOOK_WA.

CHECK: SBOOK_WA-CARRID = 'LH' AND

SBOOK_WA-CONNID = '0400'.

ENDSELECT.

The above code can be much more optimized by the code written below which avoids CHECK, selects with selection list

SELECT CARRID CONNID FLDATE BOOKID FROM SBOOK INTO TABLE T_SBOOK

WHERE SBOOK_WA-CARRID = 'LH' AND

SBOOK_WA-CONNID = '0400'.

Select Statements Select Queries

1. Avoid nested selects

SELECT * FROM EKKO INTO EKKO_WA.

SELECT * FROM EKAN INTO EKAN_WA

WHERE EBELN = EKKO_WA-EBELN.

ENDSELECT.

ENDSELECT.

The above code can be much more optimized by the code written below.

SELECT PF1 PF2 FF3 FF4 INTO TABLE ITAB

FROM EKKO AS P INNER JOIN EKAN AS F

ON PEBELN = FEBELN.

Note: 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. One should therefore use nested SELECT loops only if the selection in the outer loop contains very few lines or the outer loop is a SELECT SINGLE statement.

2. Select all the records in a single shot using into table clause of select statement rather than to use Append statements.

SELECT * FROM SBOOK INTO SBOOK_WA.

CHECK: SBOOK_WA-CARRID = 'LH' AND

SBOOK_WA-CONNID = '0400'.

ENDSELECT.

The above code can be much more optimized by the code written below which avoids CHECK, selects with selection list and puts the data in one shot using into table

SELECT CARRID CONNID FLDATE BOOKID FROM SBOOK INTO TABLE T_SBOOK

WHERE SBOOK_WA-CARRID = 'LH' AND

SBOOK_WA-CONNID = '0400'.

3. When a base table has multiple indices, the where clause should be in the order of the index, either a primary or a secondary index.

To choose an index, the optimizer checks the field names specified in the where clause and then uses an index that has the same order of the fields. In certain scenarios, it is advisable to check whether a new index can speed up the performance of a program. This will come handy in programs that access data from the finance tables.

4. For testing existence, use Select.. Up to 1 rows statement instead of a Select-Endselect-loop with an Exit.

SELECT * FROM SBOOK INTO SBOOK_WA

UP TO 1 ROWS

WHERE CARRID = 'LH'.

ENDSELECT.

The above code is more optimized as compared to the code mentioned below for testing existence of a record.

SELECT * FROM SBOOK INTO SBOOK_WA

WHERE CARRID = 'LH'.

EXIT.

ENDSELECT.

5. Use Select Single if all primary key fields are supplied in the Where condition .

If all primary key fields are supplied in the Where conditions you can even use Select Single.

Select Single requires one communication with the database system, whereas Select-Endselect needs two.

Select Statements SQL Interface

1. Use column updates instead of single-row updates

to update your database tables.

SELECT * FROM SFLIGHT INTO SFLIGHT_WA.

SFLIGHT_WA-SEATSOCC =

SFLIGHT_WA-SEATSOCC - 1.

UPDATE SFLIGHT FROM SFLIGHT_WA.

ENDSELECT.

The above mentioned code can be more optimized by using the following code

UPDATE SFLIGHT

SET SEATSOCC = SEATSOCC - 1.

2. For all frequently used Select statements, try to use an index.

SELECT * FROM SBOOK CLIENT SPECIFIED INTO SBOOK_WA

WHERE CARRID = 'LH'

AND CONNID = '0400'.

ENDSELECT.

The above mentioned code can be more optimized by using the following code

SELECT * FROM SBOOK CLIENT SPECIFIED INTO SBOOK_WA

WHERE MANDT IN ( SELECT MANDT FROM T000 )

AND CARRID = 'LH'

AND CONNID = '0400'.

ENDSELECT.

3. Using buffered tables improves the performance considerably.

Bypassing the buffer increases the network considerably

SELECT SINGLE * FROM T100 INTO T100_WA

BYPASSING BUFFER

WHERE SPRSL = 'D'

AND ARBGB = '00'

AND MSGNR = '999'.

The above mentioned code can be more optimized by using the following code

SELECT SINGLE * FROM T100 INTO T100_WA

WHERE SPRSL = 'D'

AND ARBGB = '00'

AND MSGNR = '999'.

Select Statements Aggregate Functions

• If you want to find the maximum, minimum, sum and average value or the count of a database column, use a select list with aggregate functions instead of computing the aggregates yourself.

Some of the Aggregate functions allowed in SAP are MAX, MIN, AVG, SUM, COUNT, COUNT( * )

Consider the following extract.

Maxno = 0.

Select * from zflight where airln = ‘LF’ and cntry = ‘IN’.

Check zflight-fligh > maxno.

Maxno = zflight-fligh.

Endselect.

The above mentioned code can be much more optimized by using the following code.

Select max( fligh ) from zflight into maxno where airln = ‘LF’ and cntry = ‘IN’.

Select Statements For All Entries

• The for all entries creates a where clause, where all the entries in the driver table are combined with OR. If the number of entries in the driver table is larger than rsdb/max_blocking_factor, several similar SQL statements are executed to limit the length of the WHERE clause.

The plus

• Large amount of data

• Mixing processing and reading of data

• Fast internal reprocessing of data

• Fast

The Minus

• Difficult to program/understand

• Memory could be critical (use FREE or PACKAGE size)

Points to be must considered FOR ALL ENTRIES

• Check that data is present in the driver table

• Sorting the driver table

• Removing duplicates from the driver table

Consider the following piece of extract

Loop at int_cntry.

Select single * from zfligh into int_fligh

where cntry = int_cntry-cntry.

Append int_fligh.

Endloop.

The above mentioned can be more optimized by using the following code.

Sort int_cntry by cntry.

Delete adjacent duplicates from int_cntry.

If NOT int_cntry[] is INITIAL.

Select * from zfligh appending table int_fligh

For all entries in int_cntry

Where cntry = int_cntry-cntry.

Endif.

Select Statements Select Over more than one Internal table

1. Its better to use a views instead of nested Select statements.

SELECT * FROM DD01L INTO DD01L_WA

WHERE DOMNAME LIKE 'CHAR%'

AND AS4LOCAL = 'A'.

SELECT SINGLE * FROM DD01T INTO DD01T_WA

WHERE DOMNAME = DD01L_WA-DOMNAME

AND AS4LOCAL = 'A'

AND AS4VERS = DD01L_WA-AS4VERS

AND DDLANGUAGE = SY-LANGU.

ENDSELECT.

The above code can be more optimized by extracting all the data from view DD01V_WA

SELECT * FROM DD01V INTO DD01V_WA

WHERE DOMNAME LIKE 'CHAR%'

AND DDLANGUAGE = SY-LANGU.

ENDSELECT

2. To read data from several logically connected tables use a join instead of nested Select statements. Joins are preferred only if all the primary key are available in WHERE clause for the tables that are joined. If the primary keys are not provided in join the Joining of tables itself takes time.

SELECT * FROM EKKO INTO EKKO_WA.

SELECT * FROM EKAN INTO EKAN_WA

WHERE EBELN = EKKO_WA-EBELN.

ENDSELECT.

ENDSELECT.

The above code can be much more optimized by the code written below.

SELECT PF1 PF2 FF3 FF4 INTO TABLE ITAB

FROM EKKO AS P INNER JOIN EKAN AS F

ON PEBELN = FEBELN.

3. Instead of using nested Select loops it is often better to use subqueries.

SELECT * FROM SPFLI

INTO TABLE T_SPFLI

WHERE CITYFROM = 'FRANKFURT'

AND CITYTO = 'NEW YORK'.

SELECT * FROM SFLIGHT AS F

INTO SFLIGHT_WA

FOR ALL ENTRIES IN T_SPFLI

WHERE SEATSOCC < F~SEATSMAX

AND CARRID = T_SPFLI-CARRID

AND CONNID = T_SPFLI-CONNID

AND FLDATE BETWEEN '19990101' AND '19990331'.

ENDSELECT.

The above mentioned code can be even more optimized by using subqueries instead of for all entries.

SELECT * FROM SFLIGHT AS F INTO SFLIGHT_WA

WHERE SEATSOCC < F~SEATSMAX

AND EXISTS ( SELECT * FROM SPFLI

WHERE CARRID = F~CARRID

AND CONNID = F~CONNID

AND CITYFROM = 'FRANKFURT'

AND CITYTO = 'NEW YORK' )

AND FLDATE BETWEEN '19990101' AND '19990331'.

ENDSELECT.

1. Table operations should be done using explicit work areas rather than via header lines.

READ TABLE ITAB INTO WA WITH KEY K = 'X‘ BINARY SEARCH.

IS MUCH FASTER THAN USING

READ TABLE ITAB INTO WA WITH KEY K = 'X'.

If TAB has n entries, linear search runs in O( n ) time, whereas binary search takes only O( log2( n ) ).

2. Always try to use binary search instead of linear search. But don’t forget to sort your internal table before that.

READ TABLE ITAB INTO WA WITH KEY K = 'X'. IS FASTER THAN USING

READ TABLE ITAB INTO WA WITH KEY (NAME) = 'X'.

3. A dynamic key access is slower than a static one, since the key specification must be evaluated at runtime.

4. A binary search using secondary index takes considerably less time.

5. LOOP ... WHERE is faster than LOOP/CHECK because LOOP ... WHERE evaluates the specified condition internally.

LOOP AT ITAB INTO WA WHERE K = 'X'.

" ...

ENDLOOP.

The above code is much faster than using

LOOP AT ITAB INTO WA.

CHECK WA-K = 'X'.

" ...

ENDLOOP.

6. Modifying selected components using “ MODIFY itab …TRANSPORTING f1 f2.. “ accelerates the task of updating a line of an internal table.

WA-DATE = SY-DATUM.

MODIFY ITAB FROM WA INDEX 1 TRANSPORTING DATE.

The above code is more optimized as compared to

WA-DATE = SY-DATUM.

MODIFY ITAB FROM WA INDEX 1.

7. Accessing the table entries directly in a "LOOP ... ASSIGNING ..." accelerates the task of updating a set of lines of an internal table considerably

Modifying selected components only makes the program faster as compared to Modifying all lines completely.

e.g,

LOOP AT ITAB ASSIGNING <WA>.

I = SY-TABIX MOD 2.

IF I = 0.

<WA>-FLAG = 'X'.

ENDIF.

ENDLOOP.

The above code works faster as compared to

LOOP AT ITAB INTO WA.

I = SY-TABIX MOD 2.

IF I = 0.

WA-FLAG = 'X'.

MODIFY ITAB FROM WA.

ENDIF.

ENDLOOP.

8. If collect semantics is required, it is always better to use to COLLECT rather than READ BINARY and then ADD.

LOOP AT ITAB1 INTO WA1.

READ TABLE ITAB2 INTO WA2 WITH KEY K = WA1-K BINARY SEARCH.

IF SY-SUBRC = 0.

ADD: WA1-VAL1 TO WA2-VAL1,

WA1-VAL2 TO WA2-VAL2.

MODIFY ITAB2 FROM WA2 INDEX SY-TABIX TRANSPORTING VAL1 VAL2.

ELSE.

INSERT WA1 INTO ITAB2 INDEX SY-TABIX.

ENDIF.

ENDLOOP.

The above code uses BINARY SEARCH for collect semantics. READ BINARY runs in O( log2(n) ) time. The above piece of code can be more optimized by

LOOP AT ITAB1 INTO WA.

COLLECT WA INTO ITAB2.

ENDLOOP.

SORT ITAB2 BY K.

COLLECT, however, uses a hash algorithm and is therefore independent

of the number of entries (i.e. O(1)) .

9. "APPEND LINES OF itab1 TO itab2" accelerates the task of appending a table to another table considerably as compared to “ LOOP-APPEND-ENDLOOP.”

APPEND LINES OF ITAB1 TO ITAB2.

This is more optimized as compared to

LOOP AT ITAB1 INTO WA.

APPEND WA TO ITAB2.

ENDLOOP.

10. “DELETE ADJACENT DUPLICATES“ accelerates the task of deleting duplicate entries considerably as compared to “ READ-LOOP-DELETE-ENDLOOP”.

DELETE ADJACENT DUPLICATES FROM ITAB COMPARING K.

This is much more optimized as compared to

READ TABLE ITAB INDEX 1 INTO PREV_LINE.

LOOP AT ITAB FROM 2 INTO WA.

IF WA = PREV_LINE.

DELETE ITAB.

ELSE.

PREV_LINE = WA.

ENDIF.

ENDLOOP.

11. "DELETE itab FROM ... TO ..." accelerates the task of deleting a sequence of lines considerably as compared to “ DO -DELETE-ENDDO”.

DELETE ITAB FROM 450 TO 550.

This is much more optimized as compared to

DO 101 TIMES.

DELETE ITAB INDEX 450.

ENDDO.

12. Copying internal tables by using “ITAB2[ ] = ITAB1[ ]” as compared to “LOOP-APPEND-ENDLOOP”.

ITAB2[] = ITAB1[].

This is much more optimized as compared to

REFRESH ITAB2.

LOOP AT ITAB1 INTO WA.

APPEND WA TO ITAB2.

ENDLOOP.

13. Specify the sort key as restrictively as possible to run the program faster.

“SORT ITAB BY K.” makes the program runs faster as compared to “SORT ITAB.”

Internal Tables contd…

Hashed and Sorted tables

1. For single read access hashed tables are more optimized as compared to sorted tables.

2. For partial sequential access sorted tables are more optimized as compared to hashed tables

Hashed And Sorted Tables

Point # 1

Consider the following example where HTAB is a hashed table and STAB is a sorted table

DO 250 TIMES.

N = 4 * SY-INDEX.

READ TABLE HTAB INTO WA WITH TABLE KEY K = N.

IF SY-SUBRC = 0.

" ...

ENDIF.

ENDDO.

This runs faster for single read access as compared to the following same code for sorted table

DO 250 TIMES.

N = 4 * SY-INDEX.

READ TABLE STAB INTO WA WITH TABLE KEY K = N.

IF SY-SUBRC = 0.

" ...

ENDIF.

ENDDO.

Point # 2

Similarly for Partial Sequential access the STAB runs faster as compared to HTAB

LOOP AT STAB INTO WA WHERE K = SUBKEY.

" ...

ENDLOOP.

This runs faster as compared to

LOOP AT HTAB INTO WA WHERE K = SUBKEY.

" ...

ENDLOOP.

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Hi Naresh,

Thanks for your reply.. I am concerned only about the performance with regard to ALV output or classical output.

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matt
Active Contributor
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Naresh - can't you just post the links to sap help, rather than cut and paste the text?

Harmeet: For user performance reasons, you should use ALV Grid - it gives them more functionality, which makes them more productive. You get lots of things for free like the ability to download to excel etc.

Classical is likely to be slightly more performant.

matt

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As Matt says use ALV for the functionallity.

ALV will use more procssing, but it will do it at a different time, so to the user there is not a long wait for the report to appear. What I mean is:

In a classical report all the 'conversion exits', (dates, WBS numbers, UOMs, etc..) have to be processed before the first line of the report is seen.

With ALV only the first page of data has the conversion exits applied. Each page has the conversion exits applied as it is viewed.

So to display a new page in ALV is slightly slower, (the user will not notice), than a classical report, the first page will be displayed much sooner (with ALV).

MattG.

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Maybe it's "slightly" slower with small amounts of data, but for very large reports, ALV can be quite a bit slower.

Rob

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Former Member
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Thanks to Matt(both) for their helpful answers...