How to generate columnar PDM for HANA?
Hi Folks,
I'm quite new to PowerDesigner and have been working on generating a HANA database from MS SQLServer.
Currently I've been able to import a source SQLServer system as a Physical Data Model in PowerDesigner. I was then able to generate SQL DDL scripts to generate the DB manually in HANA, and I also watched a video about how to generate the DB directly in HANA via a connection to the repository which seems straightforward.
The challenge that I'm current facing is how to alter the entire PDM to make *all* tables of type "column" rather than "row". Since SQLServer was the source system, rather than HANA, PowerDesigner appears to default to row store... (shouldn't PD's default storage type for HANA DB's be column store by default? This make substantially more sense than row store by default... but I'm digressing.)
I can see how to do this on a table-by-table basis, but haven't yet figured out how to cascade this setting to the whole PDM. Any assistance would be very much appreciated!
I also tried following this documentation, which didn't help much... it doesn't correspond with the windows/tabs/options that I'm seeing in PD 16.5 - http://infocenter.sybase.com/help/index.jsp?topic=/com.sybase.infocenter.dc38058.1653/doc/html/oba1320752560336.html
Thanks!
Hi
If what to want is globally change the Type of all the tables in a PDM, you can do it by using the List of Tables. You display the "Type" column in the grid and change the Type value from Row to Column while doing a multi selection
veronique
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Qiwei Yin wrote:
> Dear friends,
>
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>
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D A T A D E C L A R A T I O N *
TYPES: BEGIN OF TY_EXCEL,
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How to generate test data for all the tables in oracle
I am planning to use plsql to generate the test data in all the tables in schema, schema name is given as input parameters, min records in master table, min records in child table. data should be consistent in the columns which are used for constraints i.e. using same column value..
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There are two approaches you can take with this. I'll mention both and then ask which
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One approach I would call the generic bottom-up approach which is the one I think you
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This system is a generic test data generator. It isn't designed to generate data for any
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Building on damorgan's advice define the basic hierarchy: table collection, tables, data; so start at the data level.
1. Identify/document the data types that you need to support. Start small (NUMBER, VARCHAR2, DATE) and add as you go along
2. For each data type identify the functionality and attributes that you need. For instance for VARCHAR2
a. min length - the minimum length to generate
b. max length - the maximum length
c. prefix - a prefix for the generated data; e.g. for an address field you might want a 'add1' prefix
d. suffix - a suffix for the generated data; see prefix
e. whether to generate NULLs
3. For NUMBER you will probably want at least precision and scale but might want minimum and maximum values or even min/max precision,
min/max scale.
4. store the attribute combinations in Oracle tables
5. build functionality for each data type that can create the range and type of data that you need. These functions should take parameters that can be used to control the attributes and the amount of data generated.
6. At the table level you will need business rules that control how the different columns of the table relate to each other. For example, for ADDRESS information your business rule might be that ADDRESS1, CITY, STATE, ZIP are required and ADDRESS2 is optional.
7. Add table-level processes, driven by the saved metadata, that can generate data at the record level by leveraging the data type functionality you have built previously.
8. Then add the metadata, business rules and functionality to control the TABLE-TO-TABLE relationships; that is, the data model. You need the same DETPNO values in the SCOTT.EMP table that exist in the SCOTT.DEPT table.
The second approach I have used more often. I would it call the top-down approach and I use
it when test data is needed for an existing system. The main use case here is to avoid
having to copy production data to QA, TEST or DEV environments.
QA people want to test with data that they are familiar with: names, companies, code values.
I've found they aren't often fond of random character strings for names of things.
The second approach I use for mature systems where there is already plenty of data to choose from.
It involves selecting subsets of data from each of the existing tables and saving that data in a
set of test tables. This data can then be used for regression testing and for automated unit testing of
existing functionality and functionality that is being developed.
QA can use data they are already familiar with and can test the application (GUI?) interface on that
data to see if they get the expected changes.
For each table to be tested (e.g. DEPT) I create two test system tables. A BEFORE table and an EXPECTED table.
1. DEPT_TEST_BEFORE
This table has all EMP table columns and a TEST_CASE column.
It holds EMP-image rows for each test case that show the row as it should look BEFORE the
test for that test case is performed.
CREATE TABLE DEPT_TEST_BEFORE
TESTCASE NUMBER,
DEPTNO NUMBER(2),
DNAME VARCHAR2(14 BYTE),
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2. DEPT_TEST_EXPECTED
This table also has all EMP table columns and a TEST_CASE column.
It holds EMP-image rows for each test case that show the row as it should look AFTER the
test for that test case is performed.
Each of these tables are a mirror image of the actual application table with one new column
added that contains a value representing the TESTCASE_NUMBER.
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Insert these records into DEPT_TEST_BEFORE:
INSERT INTO DEPT_TEST_BEFORE
SELECT 3, D.* FROM DEPT D where DEPNO = 20
Insert records for test case #3 into DEPT_TEST_EXPECTED that show the rows as they should
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records fro the BEFORE data set and add a new one for the new record.
When you want to run TESTCASE_ONE the process is basically (ignore for this illustration that
there is a foreign key betwee DEPT and EMP):
1. delete the records from SCOTT.DEPT that correspond to test case #3 DEPT records.
DELETE FROM DEPT
WHERE DEPTNO IN (SELECT DEPTNO FROM DEPT_TEST_BEFORE WHERE TESTCASE = 3);
2. insert the test data set records for SCOTT.DEPT for test case #3.
INSERT INTO DEPT
SELECT DEPTNO, DNAME, LOC FROM DEPT_TEST_BEFORE WHERE TESTCASE = 3;
3 perform the test.
4. compare the actual results with the expected results.
This is done by a function that compares the records in DEPT with the records
in DEPT_TEST_EXPECTED for test #3.
I usually store these results in yet another table or just report them out.
5. Report out the differences.
This second approach uses data the users (QA) are already familiar with, is scaleable and
is easy to add new data that meets business requirements.
It is also easy to automatically generate the necessary tables and test setup/breakdown
using a table-driven metadata approach. Adding a new test table is as easy as calling
a stored procedure; the procedure can generate the DDL or create the actual tables needed
for the BEFORE and AFTER snapshots.
The main disadvantage is that existing data will almost never cover the corner cases.
But you can add data for these. By corner cases I mean data that defines the limits
for a data type: a VARCHAR2(30) name field should have at least one test record that
has a name that is 30 characters long.
Which of these approaches makes the most sense for you? -
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