Large Survey & Table

Hi,
I am trying to create a survey/form based on a table that contains 100 columns.
I would like to achieve the following:
Page 1 = displays first 10 columns
Page 2 = displays next 15 columns
etc.
Once the user goes through all 100 columns, then the insert should occur.
I tried to use the WIZARD template but I didn't get too far.
I think I remember seeing something on OTN about this, but I don't remember where.
Can anybody help me with this?
Thanks in advance.
VC

Hi Vikas,
When I try to create the sample application using the link, the Wizard will allow for 9 pages with 9 items in each page. I suppose I can add the other items manually, but would like to know if there is any limitation to the number of items you can put on one page?
Thanks
VC

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PARALLEL 8 NOLOGGING ONLINE LOCAL;
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thank you

Hi,
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Back up and recover each partition independently
Control the mapping of partitions to disk drives (important for balancing I/O load)
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http://download-east.oracle.com/docs/cd/B19306_01/server.102/b14231/partiti.htm
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"it fails" and "there no errors and just doesnt complete and have to stop" are not the same thing.
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OR
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Hi,
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Presuming Oracle backend database.....(you didnt say)
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Hi Susana,
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Hi all,
I have define a Survey with a list of values. The user can select one of these values.
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I also found the the list of values but I can't find the selected values.
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Hello Ralf,
Hopefully I did understand your question
The user selects a value from a value list /for example: drop down/ and you would like to retrieve the value the user selected.
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Hope this helps.
Regards,
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Hello
I've got a few large tables (6-10GB+) that will have around 500k new rows added on a daily basis as part of an overnight batch job. No rows are ever updated, only inserted or deleted and then re-inserted. I want to stop the process that adds the new rows from being an overnight batch to being a near real time process i.e. a queue will be populated with requests to rebuild the content of these tables for specific parent ids, and a process will consume those requests throughout the day rather than going through the whole list in one go.
I need to provide views of the data asof a point in time i.e. what was the content of the tables at close of business yesterday, and for this I am considering using workspaces.
I need to keep at least 10 days worth of data and I was planning to partition the table and drop one partition every day. If I use workspaces, I can see that oracle creates a view in place of the original table and creates a versioned table with the LT suffix - this is the table name returned by DBMSMW.GetPhysicalTableName. Would it be considered bad practice to drop partitions from this physical table as I would do with a non version enabled table? If so, what would be the best method for dropping off old data?
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Hello Ben
Thank you for your reply.
The table structure we have is like so:
CREATE TABLE hdr
(   pk_id               NUMBER PRIMARY KEY,
    customer_id         NUMBER FOREIGN KEY REFERENCES customer,
    entry_type          NUMBER NOT NULL
CREATE TABLE dtl_daily
(   pk_id               NUMBER PRIMARY KEY,
    hdr_id              NUMBER FOREIGN KEY REFERENCES hdr
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    col2                NUMBER
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(   PARTITION ptn_200709
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        TABLESPACE x COMPRESS
CREATE TABLE dtl_hourly
(   pk_id               NUMBER PRIMARY KEY,
    hdr_id              NUMBER FOREIGN KEY REFERENCES hdr
    active_date         DATE NOT NULL,
    active_hour         NUMBER NOT NULL,
    col1                NUMBER
    col2                NUMBER
PARTITION BY RANGE(active_date)
(   PARTITION ptn_20070901
        VALUES LESS THAN (TO_DATE('20070902','YYYYMMDD'))
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    PARTITION ptn_20070902
        VALUES LESS THAN (TO_DATE('20070903','YYYYMMDD'))
        TABLESPACE x COMPRESS
    PARTITION ptn_20070903
        VALUES LESS THAN (TO_DATE('20070904','YYYYMMDD'))
        TABLESPACE x COMPRESS
    ...For every day for 20 years
/The hdr table holds one or more rows for each customer and has it's own synthetic key generated for every entry as there can be multiple rows having the same entry_type for a customer. There are two detail tables, daily and hourly, which hold detail data at those two granularities. Some customers require hourly detail, in which case the hourly table is populated and the daily table is populated by aggregating the hourly data. Other customers require only daily data in which case the hourly table is not populated.
At the moment, changes to customer data require that the content of these tables are rebuilt for that customer. This rebuild is done every night for the changed customers and I want to change this to be a near real time rebuild. The rebuild involves deleteing all existing entries from the three tables for the customer and then re-inserting the new set using new synthetic keys. If we do make this near real time, we need to be able to provide a snapshot of the data asof close of business every day, and we need to be able to report as of a point of time up to 10 days in the past.
For any one customer, they may have rows in the hourly table that goes out 20 years at a hourly granularity, but once the active date has passed(by 10 days), we no longer need to keep it. This is why we were considering partitioning as it gives us a simple way of dropping off old data, and as a nice side effect, helps to improve performance of queries that are looking for active data between a range of dates (which is most of them).
I did have a look at the idea of save points but I wasn't sure it would be efficient. So in this case, would the idea be that we don't partition the table but instead at close of business every day, we create a savepoint like "savepoint_20070921" and instead of using dbms_wm.gotodate. we would use dbms_wm.gotosavepoint. Then every day we would do
DBMS_WM.DeleteSavepoint(
   workspace                   => 'LIVE',
   savepoint_name              => 'savepoint_20070910', --10 days ago
   compress_view_wo_overwrite => TRUE,
DBMS_WM.CompressWorkspace(
   workspace                   => 'LIVE,
   compress_view_wo_overwrite => TRUE,
   firstSP                     => 'savepoint_20070911', --the new oldest save point
   );Is my understanding correct?
David
Message was edited by:
fixed some formatting
David Tyler

Large Fact Tables

I have some transactional tables that are quite large. Anywhere from 1 to 3 million records. I have been doing my best to find information on the best way to deal with these and I am having some trouble. I am a little tight for time and could use a little help from others who have dealt with large datas sets before. What are some of the best ways to deal with this.
I have tried creating database level partitions by date which didn't seem to affect the OBI performance much. If I actually create different physical tables through the ETL process for each year it helps but I would like to avoid needing to create and manage that many different mappings.
Is the best way to do this to use the OBI caching and seed the cache after loading the data? I have even tried a few indexes and not had much luck.
Any help is greatly appreciated and I thank everyone in advance for their time. :)

Presuming Oracle backend database.....(you didnt say)
Partitioning would seem a quick (and expensive $$$$) win, if you partition on date, be sure to use the same date key to perform 'partition pruning' , which is to say give the optimizer a chance to throw away all unrequired partitions before it starts going to disk. When you say 'didnt improve performance much' you need to give a partition wise join for the partitioning to become effective at query time - did you do this ? for example a dashboard prompt with a filter on week / month would help (assuming your week / month time dim column is joined on partition key)
Is your data model a star schema ? Do you have dimension metadata in your database ? I'd be looking at Materialized views and looking for some sort of aggregate rollup query re-write whereby you queries are re-written at run time to the Mview and does not hit your fact table.
As for caching, OBIEE caching is the last resort after you exhaust every other alternative, its not there to speed up queries and you would be hiding the problem, what when a user drills down or needs to run an ad-hoc report that is not cached ?
I would start with understanding Oracle execution plans, review your data model (with the view on speedy data extraction) and look at what Oracle DB gives for data warehousing appliances :
parallelism is your friend!
star transformation,
Mviews (table or Cube based)
OLAP

Large Survey & Table

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