Cost Data Sets

What is the best way to upload the Cost Data Sets into P4P?
If we choose to use a SQL Load directly, which tables in the database contain the cost information?

PLM for Process provides a web service that does this, in the Cost Services web service. If you are doing this through the database directly, though, the tables it populates are:
- costmessages - used to group cost items into one load
- costitems - the individual spec cost, by cost type, currency, SCRM facility, etc.
Please see the Web Services guide for details about the data expected in these tables. However you will also have to provide a Sequence Number, which should just be the next highest number

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    the VirtualizingObservableCollection does the following:
    Implements the same interfaces and methods as ObservableCollection<T> so you can use it anywhere you’d use an ObservableCollection<T> – no need to change any of your existing controls.
    Supports true multi-user read/write without resets (maximizing performance for large-scale concurrency scenarios).
    Manages memory on its own so it never runs out of memory, no matter how large the data set is (especially important for mobile devices).
    Natively works asynchronously – great for slow network connections and occasionally-connected models.
    Works great out of the box, but is flexible and extendable enough to customize for your needs.
    Has a data access performance curve so good it’s just as fast as the regular ObservableCollection – the cost of using it is negligible.
    Works in any .NET project because it’s implemented in a Portable Code Library (PCL).
    The latest package can be found on nugget. Install-Package VirtualizingObservableCollection. The source is on github. 

    Good job, thank you for sharing
    Best Regards,
    Please remember to mark the replies as answers if they help

  • 10g: parallel pipelined table func - distributing DISTINCT data sets

    Hi,
    i want to distribute data records, selected from cursor, via parallel pipelined table function to multiple worker threads for processing and returning result records.
    The tables, where i am selecting data from, are partitioned and subpartitioned.
    All tables share the same partitioning/subpartitioning schema.
    Each table has a column 'Subpartition_Key', which is hashed to a physical subpartition.
    E.g. the Subpartition_Key ranges from 000...999, but we have only 10 physical subpartitions.
    The select of records is done partition-wise - one partition after another (in bulks).
    The parallel running worker threads select more data from other tables for their processing (2nd level select)
    Now my goal is to distribute initial records to the worker threads in a way, that they operate on distinct subpartitions - to decouple the access to resources (for the 2nd level select)
    But i cannot just use 'parallel_enable(partition curStage1 by hash(subpartition_key))' for the distribution.
    hash(subpartition_key) (hashing A) does not match with the hashing B used to assign the physical subpartition for the INSERT into the tables.
    Even when i remodel the hashing B, calculate some SubPartNo(subpartition_key) and use that for 'parallel_enable(partition curStage1 by hash(SubPartNo))' it doesn't work.
    Also 'parallel_enable(partition curStage1 by range(SubPartNo))' doesn't help. The load distribution is unbalanced - some worker threads get data of one subpartition, some of multiple subpartitions, some are idle.
    How can i distribute the records to the worker threads according a given subpartition-schema?
    +[amendment:+
    Actually the hashing for the parallel_enable is counterproductive here - it would be better to have some 'parallel_enable(partition curStage1 by SubPartNo)'.]
    - many thanks!
    best regards,
    Frank
    Edited by: user8704911 on Jan 12, 2012 2:51 AM

    Hello
    A couple of things to note. 1, when you use partition by hash(or range) on 10gr2 and above, there is an additional BUFFER SORT operation vs using partition by ANY. For small datasets this is not necessarily an issue, but the temp space used by this stage can be significant for larger data sets. So be sure to check temp space usage for this process or you could run into problems later.
    | Id  | Operation                             | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |    TQ  |IN-OUT| PQ Distrib |
    |   0 | SELECT STATEMENT                      |          |  8168 |  1722K|    24   (0)| 00:00:01 |       |       |        |      |            |
    |   1 |  PX COORDINATOR                       |          |       |       |            |          |       |       |        |      |            |
    |   2 |   PX SEND QC (RANDOM)                 | :TQ10001 |  8168 |  1722K|    24   (0)| 00:00:01 |       |       |  Q1,01 | P->S | QC (RAND)  |
    |   3 |****BUFFER SORT****                    |          |  8168 |  1722K|            |          |       |       |  Q1,01 | PCWP |            |
    |   4 |     VIEW                              |          |  8168 |  1722K|    24   (0)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
    |   5 |      COLLECTION ITERATOR PICKLER FETCH| TF       |       |       |            |          |       |       |  Q1,01 | PCWP |            |
    |   6 |       PX RECEIVE                      |          |   100 |  4800 |     2   (0)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
    |   7 |        PX SEND HASH                   | :TQ10000 |   100 |  4800 |     2   (0)| 00:00:01 |       |       |  Q1,00 | P->P | HASH       |
    |   8 |         PX BLOCK ITERATOR             |          |   100 |  4800 |     2   (0)| 00:00:01 |     1 |    10 |  Q1,00 | PCWC |            |
    |   9 |          TABLE ACCESS FULL            | TEST_TAB |   100 |  4800 |     2   (0)| 00:00:01 |     1 |    20 |  Q1,00 | PCWP |            |
    -----------------------------------------------------------------------------------------------------------------------------------------------It may be in this case that you can use clustering with partition by any to achieve your goal...
    create or replace package test_pkg as
         type Test_Tab_Rec_t is record (
              Tracking_ID                 number(19),
              Partition_Key               date,
              Subpartition_Key            number(3),
              sid                    number
         type Test_Tab_Rec_Tab_t is table of Test_Tab_Rec_t;
         type Test_Tab_Rec_Hash_t is table of Test_Tab_Rec_t index by binary_integer;
         type Test_Tab_Rec_HashHash_t is table of Test_Tab_Rec_Hash_t index by binary_integer;
         type Cur_t is ref cursor return Test_Tab_Rec_t;
         procedure populate;
         procedure report;
         function tf(cur in Cur_t)
         return test_list pipelined
         parallel_enable(partition cur by hash(subpartition_key));
         function tf_any(cur in Cur_t)
         return test_list PIPELINED
        CLUSTER cur BY (Subpartition_Key)
         parallel_enable(partition cur by ANY);   
    end;
    create or replace package body test_pkg as
         procedure populate
         is
              Tracking_ID number(19) := 1;
              Partition_Key date := current_timestamp;
              Subpartition_Key number(3) := 1;
         begin
              dbms_output.put_line(chr(10) || 'populate data into Test_Tab...');
              for Subpartition_Key in 0..99
              loop
                   for ctr in 1..1
                   loop
                        insert into test_tab (tracking_id, partition_key, subpartition_key)
                        values (Tracking_ID, Partition_Key, Subpartition_Key);
                        Tracking_ID := Tracking_ID + 1;
                   end loop;
              end loop;
              dbms_output.put_line('...done (populate data into Test_Tab)');
         end;
         procedure report
         is
              recs Test_Tab_Rec_Tab_t;
         begin
              dbms_output.put_line(chr(10) || 'list data per partition/subpartition...');
              for item in (select partition_name, subpartition_name from user_tab_subpartitions where table_name='TEST_TAB' order by partition_name, subpartition_name)
              loop
                   dbms_output.put_line('partition/subpartition = '  || item.partition_name || '/' || item.subpartition_name || ':');
                   execute immediate 'select * from test_tab SUBPARTITION(' || item.subpartition_name || ')' bulk collect into recs;
                   if recs.count > 0
                   then
                        for i in recs.first..recs.last
                        loop
                             dbms_output.put_line('...' || recs(i).Tracking_ID || ', ' || recs(i).Partition_Key  || ', ' || recs(i).Subpartition_Key);
                        end loop;
                   end if;
              end loop;
              dbms_output.put_line('... done (list data per partition/subpartition)');
         end;
         function tf(cur in Cur_t)
         return test_list pipelined
         parallel_enable(partition cur by hash(subpartition_key))
         is
              sid number;
              input Test_Tab_Rec_t;
              output test_object;
         begin
              select userenv('SID') into sid from dual;
              loop
                   fetch cur into input;
                   exit when cur%notfound;
                   output := test_object(input.tracking_id, input.partition_key, input.subpartition_key,sid);
                   pipe row(output);
              end loop;
         end;
         function tf_any(cur in Cur_t)
         return test_list PIPELINED
        CLUSTER cur BY (Subpartition_Key)
         parallel_enable(partition cur by ANY)
         is
              sid number;
              input Test_Tab_Rec_t;
              output test_object;
         begin
              select userenv('SID') into sid from dual;
              loop
                   fetch cur into input;
                   exit when cur%notfound;
                   output := test_object(input.tracking_id, input.partition_key, input.subpartition_key,sid);
                   pipe row(output);
              end loop;
         end;
    end;
    XXXX> with parts as (
      2  select --+ materialize
      3      data_object_id,
      4      subobject_name
      5  FROM
      6      user_objects
      7  WHERE
      8      object_name = 'TEST_TAB'
      9  and
    10      object_type = 'TABLE SUBPARTITION'
    11  )
    12  SELECT
    13        COUNT(*),
    14        parts.subobject_name,
    15        target.sid
    16  FROM
    17        parts,
    18        test_tab tt,
    19        test_tab_part_hash target
    20  WHERE
    21        tt.tracking_id = target.tracking_id
    22  and
    23        parts.data_object_id = DBMS_MView.PMarker(tt.rowid)
    24  GROUP BY
    25        parts.subobject_name,
    26        target.sid
    27  ORDER BY
    28        target.sid,
    29        parts.subobject_name
    30  /
    XXXX> INSERT INTO test_tab_part_hash select * from table(test_pkg.tf(CURSOR(select * from test_tab)))
      2  /
    100 rows created.
    Elapsed: 00:00:00.14
    XXXX>
    XXXX> INSERT INTO test_tab_part_any_cluster select * from table(test_pkg.tf_any(CURSOR(select * from test_tab)))
      2  /
    100 rows created.
    --using partition by hash
    XXXX> with parts as (
      2  select --+ materialize
      3      data_object_id,
      4      subobject_name
      5  FROM
      6      user_objects
      7  WHERE
      8      object_name = 'TEST_TAB'
      9  and
    10      object_type = 'TABLE SUBPARTITION'
    11  )
    12  SELECT
    13        COUNT(*),
    14        parts.subobject_name,
    15        target.sid
    16  FROM
    17        parts,
    18        test_tab tt,
    19        test_tab_part_hash target
    20  WHERE
    21        tt.tracking_id = target.tracking_id
    22  and
    23        parts.data_object_id = DBMS_MView.PMarker(tt.rowid)
    24  GROUP BY
    25        parts.subobject_name,
    26        target.sid
    27  /
      COUNT(*) SUBOBJECT_NAME                        SID
             3 SYS_SUBP31                           1272
             1 SYS_SUBP32                           1272
             1 SYS_SUBP33                           1272
             3 SYS_SUBP34                           1272
             1 SYS_SUBP36                           1272
             1 SYS_SUBP37                           1272
             3 SYS_SUBP38                           1272
             1 SYS_SUBP39                           1272
             1 SYS_SUBP32                           1280
             2 SYS_SUBP33                           1280
             2 SYS_SUBP34                           1280
             1 SYS_SUBP35                           1280
             2 SYS_SUBP36                           1280
             1 SYS_SUBP37                           1280
             2 SYS_SUBP38                           1280
             1 SYS_SUBP40                           1280
             2 SYS_SUBP33                           1283
             2 SYS_SUBP34                           1283
             2 SYS_SUBP35                           1283
             2 SYS_SUBP36                           1283
             1 SYS_SUBP37                           1283
             1 SYS_SUBP38                           1283
             2 SYS_SUBP39                           1283
             1 SYS_SUBP40                           1283
             1 SYS_SUBP32                           1298
             1 SYS_SUBP34                           1298
             1 SYS_SUBP36                           1298
             2 SYS_SUBP37                           1298
             4 SYS_SUBP38                           1298
             2 SYS_SUBP40                           1298
             1 SYS_SUBP31                           1313
             1 SYS_SUBP33                           1313
             1 SYS_SUBP39                           1313
             1 SYS_SUBP40                           1313
             1 SYS_SUBP32                           1314
             1 SYS_SUBP35                           1314
             1 SYS_SUBP38                           1314
             1 SYS_SUBP40                           1314
             2 SYS_SUBP33                           1381
             1 SYS_SUBP34                           1381
             1 SYS_SUBP35                           1381
             3 SYS_SUBP36                           1381
             3 SYS_SUBP37                           1381
             1 SYS_SUBP38                           1381
             2 SYS_SUBP36                           1531
             1 SYS_SUBP37                           1531
             2 SYS_SUBP38                           1531
             1 SYS_SUBP39                           1531
             1 SYS_SUBP40                           1531
             2 SYS_SUBP33                           1566
             1 SYS_SUBP34                           1566
             1 SYS_SUBP35                           1566
             1 SYS_SUBP37                           1566
             1 SYS_SUBP38                           1566
             2 SYS_SUBP39                           1566
             3 SYS_SUBP40                           1566
             1 SYS_SUBP32                           1567
             3 SYS_SUBP33                           1567
             3 SYS_SUBP35                           1567
             3 SYS_SUBP36                           1567
             1 SYS_SUBP37                           1567
             2 SYS_SUBP38                           1567
    62 rows selected.
    --using partition by any cluster by subpartition_key
    Elapsed: 00:00:00.26
    XXXX> with parts as (
      2  select --+ materialize
      3      data_object_id,
      4      subobject_name
      5  FROM
      6      user_objects
      7  WHERE
      8      object_name = 'TEST_TAB'
      9  and
    10      object_type = 'TABLE SUBPARTITION'
    11  )
    12  SELECT
    13        COUNT(*),
    14        parts.subobject_name,
    15        target.sid
    16  FROM
    17        parts,
    18        test_tab tt,
    19        test_tab_part_any_cluster target
    20  WHERE
    21        tt.tracking_id = target.tracking_id
    22  and
    23        parts.data_object_id = DBMS_MView.PMarker(tt.rowid)
    24  GROUP BY
    25        parts.subobject_name,
    26        target.sid
    27  ORDER BY
    28        target.sid,
    29        parts.subobject_name
    30  /
      COUNT(*) SUBOBJECT_NAME                        SID
            11 SYS_SUBP37                           1253
            10 SYS_SUBP34                           1268
             4 SYS_SUBP31                           1289
            10 SYS_SUBP40                           1314
             7 SYS_SUBP39                           1367
             9 SYS_SUBP35                           1377
            14 SYS_SUBP36                           1531
             5 SYS_SUBP32                           1572
            13 SYS_SUBP33                           1577
            17 SYS_SUBP38                           1609
    10 rows selected.Bear in mind though that this does require a sort of the incomming dataset but does not require buffering of the output...
    PLAN_TABLE_OUTPUT
    Plan hash value: 2570087774
    | Id  | Operation                            | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |    TQ  |IN-OUT| PQ Distrib |
    |   0 | SELECT STATEMENT                     |          |  8168 |  1722K|    24   (0)| 00:00:01 |       |       |        |      |            |
    |   1 |  PX COORDINATOR                      |          |       |       |            |          |       |       |        |      |            |
    |   2 |   PX SEND QC (RANDOM)                | :TQ10000 |  8168 |  1722K|    24   (0)| 00:00:01 |       |       |  Q1,00 | P->S | QC (RAND)  |
    |   3 |    VIEW                              |          |  8168 |  1722K|    24   (0)| 00:00:01 |       |       |  Q1,00 | PCWP |            |
    |   4 |     COLLECTION ITERATOR PICKLER FETCH| TF_ANY   |       |       |            |          |       |       |  Q1,00 | PCWP |            |
    |   5 |      SORT ORDER BY                   |          |       |       |            |          |       |       |  Q1,00 | PCWP |            |
    |   6 |       PX BLOCK ITERATOR              |          |   100 |  4800 |     2   (0)| 00:00:01 |     1 |    10 |  Q1,00 | PCWC |            |
    |   7 |        TABLE ACCESS FULL             | TEST_TAB |   100 |  4800 |     2   (0)| 00:00:01 |     1 |    20 |  Q1,00 | PCWP |            |
    ----------------------------------------------------------------------------------------------------------------------------------------------HTH
    David

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    Currency—The currency of the entered cost.

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          no_batch         = 2
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          selection_error  = 4
          OTHERS           = 5.
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            access_denied           = 13
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    ENDFORM.                    " f1003_pre_file
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         upload the file from the application server
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      IF sy-subrc EQ 0.
        DO.
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            PERFORM f0025_record_split.
    *-- Populate internal table.
            APPEND wa_input TO i_input.
            CLEAR wa_input.
            IF sy-subrc <> 0.
              MESSAGE s000 WITH text-030.
              EXIT.
            ENDIF.
          ELSE.
            EXIT.
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        ENDDO.
      ENDIF.
    ENDFORM. " f1004_app_file
    Move the assembly layer file into the work area
    FORM f0025_record_split .
      CLEAR wa_input.
      SPLIT wa_input_rec AT c_split INTO
        wa_input-legacykey
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    ENDFORM.                    " f0025_record_split
    Reward points if this helps.
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    Hi,
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    For quick review of new features, try virtual labs: http://msdn.microsoft.com/en-us/aa570323

    Hi Asam,
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  • Multiple data sets: a common global dataset and per/report data sets

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  • SQL Update a Single Row Multiple Times Using 2 Data Sets

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  • Exception Handling for OPEN DATA SET and CLOSE DATA SET

    Hi ppl,
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    Many Thanks.

    HI,
    try this way....
      DO.
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    End of changes CHRK941728
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  • Download using open data set and close data set

    can any body please send some sample pgm using open data set and close data set .the data should get downloaded in application server
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    Hi Arun,
    See the Sample code for BDC using OPEN DATASET.
    report ZSDBDCP_PRICING no standard page heading
    line-size 255.
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    f2(4),
    f3(2),
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    *--Internal Table To hold condition records details .
    data : begin of it_details occurs 0,
    key(4),
    f4(18),
    f5(16),
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    data : v_sno(2),
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    v_fname(40).
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    CALL FUNCTION 'UPLOAD'
    EXPORTING
    FILENAME = 'C:\WINDOWS\Desktop\pricing.txt'
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    TABLES
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    EXCEPTIONS
    CONVERSION_ERROR = 1
    INVALID_TABLE_WIDTH = 2
    INVALID_TYPE = 3
    NO_BATCH = 4
    UNKNOWN_ERROR = 5
    GUI_REFUSE_FILETRANSFER = 6
    OTHERS = 7.
    WRITE : / 'Condition Records ', P_FNAME, ' on ', SY-DATUM.
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    if sy-subrc ne 0.
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    CLEAR : it_pricing[], it_pricing.
    DO.
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    IF SY-SUBRC NE 0.
    EXIT.
    ENDIF.
    write v_str.
    translate v_str using '#/'.
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    it_pricing-F1 it_pricing-F2 it_pricing-F3
    it_pricing-F4 it_pricing-F5 .
    APPEND it_pricing.
    CLEAR it_pricing.
    ENDDO.
    IF it_pricing[] IS INITIAL.
    WRITE : / 'No data found to upload'.
    STOP.
    ENDIF.
    loop at it_pricing.
    At new key.
    read table it_pricing index sy-tabix.
    move-corresponding it_pricing to it_header.
    append it_header.
    clear it_header.
    endat.
    move-corresponding it_pricing to it_details.
    append it_details.
    clear it_details.
    endloop.
    perform open_group.
    v_rows = sy-srows - 8.
    loop at it_header.
    perform bdc_dynpro using 'SAPMV13A' '0100'.
    perform bdc_field using 'BDC_CURSOR'
    'RV13A-KSCHL'.
    perform bdc_field using 'BDC_OKCODE'
    '/00'.
    perform bdc_field using 'RV13A-KSCHL'
    it_header-f1.
    perform bdc_dynpro using 'SAPMV13A' '1004'.
    perform bdc_field using 'BDC_CURSOR'
    'KONP-KBETR(01)'.
    perform bdc_field using 'BDC_OKCODE'
    '/00'.
    perform bdc_field using 'KOMG-VKORG'
    it_header-f2.
    perform bdc_field using 'KOMG-VTWEG'
    it_header-f3.
    **Table Control
    v_sno = 0.
    loop at it_details where key eq it_header-key.
    v_sno = v_sno + 1.
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    perform bdc_field using v_fname
    it_details-f4.
    clear v_fname.
    CONCATENATE 'KONP-KBETR(' V_SNO ')' INTO V_FNAME.
    perform bdc_field using v_fname
    it_details-f5.
    if v_sno eq v_rows.
    v_sno = 0.
    perform bdc_dynpro using 'SAPMV13A' '1004'.
    perform bdc_field using 'BDC_OKCODE'
    '=P+'.
    perform bdc_dynpro using 'SAPMV13A' '1004'.
    perform bdc_field using 'BDC_OKCODE'
    '/00'.
    endif.
    endloop.
    *--Save
    perform bdc_dynpro using 'SAPMV13A' '1004'.
    perform bdc_field using 'BDC_OKCODE'
    '=SICH'.
    perform bdc_transaction using 'VK11'.
    endloop.
    perform close_group.
    Hope this resolves your query.
    Reward all the helpful answers.
    Regards

  • What is open data set and close data set

    what is open data set and close data set,
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    hi,
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    Introduction
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    Syntax:
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    INPUT: Opens a file for READ and places the cursor at the beginning of the file.
    FOR APPENDING: Opens the file for writing and places the cursor at the end of file. If the file does not exist, it is generated.
    BINARY MODE: The READ or TRANSFER will be character wise. Each time ‘n’’ characters are READ or transferred. The next READ or TRANSFER will start from the next character position and not on the next line.
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    Defaults: If nothing is mentioned, then defaults are FOR INPUT and in BINARY MODE.
    PROCESS FILE:
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    READ Statement
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    Each READ will get one record from the dataset. In binary mode it reads the length of the field and in text mode it reads each line.
    CLOSE FILE:
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    Syntax:
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    DELETE FILE:
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    Syntax:
    DELETE DATASET <file name>.
    SY-SUBRC will be set to 0 or 8 depending on whether the DELETE is successful or not.
    Pseudo logic for processing the sequential files:
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    Open dataset for input in a particular mode.
    Start DO loop.
    Read dataset into a field.
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    Exit the loop.
    Endif.
    Do relevant processing for that record.
    End the do loop.
    Close the dataset.
    For writing:
    Open dataset for output / Appending in a particular mode.
    Populate the field that is to be transferred.
    TRANSFER the filed to a dataset.
    Close the dataset.
    Regards
    Anver
    if hlped pls mark points

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