Clustering etc

Hi,
Sorry for long post and thanks for patience (read at leas first question (-:)
I would like to ask abt. session beans with Local interface in cluster enviroment and a WEB want to access such bean. I can guess that with stateless beans the situation is easy: the bean will be created locally and used, but what with stateful beans if the previous request was realized on other machine? will it replicate? on what basis?
My question is part of a bigger one: what are the cases where I need to use remote interface: I know "the sparate JVM or EAR" but I mean in practice:
-I have separate EARs if I have two applications - that is clear.
- But when I have separete JVM?:
a) In cluster enviroment: but Local interface can be used
b) when WEB and EJB container are separated: in what situation it is desired
c) other?
Thnks for answers in advance.
BTW: I looked in google first - didnt find all the answers

but what with stateful beans if the previous request was realized on other
machine? will it replicate? on what basis?Generally, Stateful Session beans's state si replicated/saved at the end of a method request OR at the end of the transaction in which it participated comiits/rollsback. This way if there is a failure, the SFSB can be resurrected properly.
My question is part of a bigger one: what are the cases where I need to use >remote interface: I know "the sparate JVM or EAR" but I mean in practice:
I have separate EARs if I have two applications - that is clear.
But when I have separete JVM?:
a) In cluster enviroment: but Local interface can be used
b) when WEB and EJB container are separated: in what situation it is >desired
c) other?If your client and EJBs are always going to be colocated (Like a Session bean calling a EntityBean), then can use LocalInterface otherwise you can use Remote interface.
What dow you mean by: If the Web and EJB Containers are separated
In Glassfish, a cluster of instances essentially contain a group of homogenous instances (meaning each instance in the cluster has the exact set of bits and hence has exact set of applications deployed). Hence each instance has both WEB and EJB Containers.
If you are talking about a setup where the WebContainer (like Tomcat) talking to another JavaEE Server (like GlassFish), the obviously you have to deploy you .war files into Tomcat (WebContainer) and then the .jar to the JavaEE Server. The replication will be handled by the respective containers.

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    | 21 | TABLE ACCESS FULL | R_CFG_CAT_HIERARCHY_MAS | 475 | 5225 | 3 | | |
    | 22 | BUFFER SORT | | 4432 | 173K| *18E*| | |
    | 23 | TABLE ACCESS BY INDEX ROWID | R_DEVICE_INFO | 4432 | 173K| *18E*| | |
    | 24 | BITMAP CONVERSION TO ROWIDS| | | | | | |
    | 25 | BITMAP INDEX SINGLE VALUE | R_DEVICE_INFO_CPYKEY | | | | | |
    Note
    - 'PLAN_TABLE' is old version
    I can see cost in 18E which is huge and ridiculous.
    I can explain points that I noted on this table.
    This table is having more than 1 BILLIION RECORDS AND I am querying for a user which has got 5 MILLION RECORDS.
    This table is properly partitioned and is having proper indexes.
    We got some BITMAP indexes on this table also.
    This table has got proper partitioned indexes also. (But the indexes and the table are in the same tablespace).
    Can Anyone give me some sugessitions on how to reduce the overall cost.
    Appreciate your response on this one.
    Thanks,
    Madhu K.

    569725 wrote:
    I can see cost in 18E which is huge and ridiculous.For a cartesian merge join... Cartesian joins are notoriously expensive.
    This table is having more than 1 BILLIION RECORDS AND I am querying for a user which has got 5 MILLION RECORDS.Does not matter. The number of rows (note we store rows in tables, not records) does not matter. It is about how much I/O is needed to process that amount of data.
    And this is what invariably we design for.. using relational design, using indexes, using partitioning, using clustering, etc. All these are techniques to reduce I/O. From eliminating duplication of data to providing optimal I/O paths to the data.
    A real world example:
    SQL> set timing on
    SQL> select count(*) from daily_xxxxx;
      COUNT(*)
    2160339906
    Elapsed: 00:00:10.09
    SQL>
    SQL> select count(*) from all_objects;
      COUNT(*)
         51255
    Elapsed: 00:01:43.60So despite "+processing+" over 2 billion rows, the count is significantly faster than the count of the very much smaller ALL_OBJECTS view.
    Why? Because it took less I/O to count those 2 billion rows (using a single index) than the nearly 52 thousand objects in the data dictionary (using several indexes and tables).
    The rules that governs performance do not change with the number of rows in a table. The same rules and principles apply. What does change is the "+error margin+" - you are going to see a performance problem quite a lot sooner on a large table than a very small table. You may not notice that a 1ms overhead per row is a performance problem on a 100 row table. You will definitely notice it on a million row table.
    So forget about the number of rows in these tables. Make sure that the SQL is designed logically correctly. Make sure that the table/db design provides optimal I/O paths for that SQL.
    And catersian merge joins, nested loops with full table scans and so on.. that points to both these as being potential problems. SQL designed incorrectly (like forgetting join criteria). Table design lacking (requiring nested loop full table scans as oppose to index range scans or hash joins).
    Oh yeah - and as already mentioned, forget the cost that the execution plan shows. Unless you know what unit that cost is expressed in and how to interpret it?

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