Big tables

[Jon Hawks]

Partition the table.  
Example:
We had 10,000,000 active customers with daily
activity.
We created 10 separate storage areas (table spaces) and
determined to use a partition key constraint to
separate these customers.  
Partition 1   A,B,C
Partition 2   D,E,F
... and so on.
Each partition is managed independently of the others. 
The input data was sorted to be updated into a specific
partition.  We ran parallel update streams to update
all ten at the same time.
Each storage area was independently monitored using
database tuning tools.  By activating the base for
parallel update and also moving the partitions across
disk drives, we also optimized mount points to help
disk i/o's across our disk arrays.

These concepts should get you started.  We achieved a
23:1 throughput increase on our tables.  We also had
one table with over 70 million rows.



--- l.dyer@mgn.co.uk wrote:
> 
> 
> 
> We have an Rdb database on VMS (Powerhouse 710g1)
> that contains only one
> table. This table contains some 48,000,000 records
> which we wish to update
> in a timely fashion.
> 
> We have dropped all the indexes and are proceeding
to
> update with QTP.
> 
> We obviously have the option of running an SQL
script
> if necessary.
> 
> I realise that we can use RMU to create a flat file
> which we can update and
> reload, but I am not sure that this will
> significantly reduce running time.
> 
> Does anyone have any comments on the above
> alternatives or any other
> methods that may significantky reduce update time.
> 
> Lynden
> 
> 
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