Answer Posted / prams

1. Staged the data coming from ODBC/OCI/DB2UDB stages
or any database on the server using Hash/Sequential files
for optimum performance also for data recovery in case job
aborts.
2. Tuned the OCI stage for 'Array Size' and 'Rows per
Transaction' numerical values for faster inserts, updates
and selects.
3. Tuned the 'Project Tunables' in Administrator for
better performance.
4. Used sorted data for Aggregator.
5. Sorted the data as much as possible in DB and
reduced the use of DS-Sort for better performance of jobs
6. Removed the data not used from the source as early
as possible in the job.
7. Worked with DB-admin to create appropriate Indexes
on tables for better performance of DS queries
8. Converted some of the complex joins/business in DS
to Stored Procedures on DS for faster execution of the
jobs.
9. If an input file has an excessive number of rows
and can be split-up then use standard logic to run jobs in
parallel.
10. Before writing a routine or a transform, make sure
that there is not the functionality required in one of the
standard routines supplied in the sdk or ds utilities
categories.
Constraints are generally CPU intensive and take a
significant amount of time to process. This may be the case
if the constraint calls routines or external macros but if
it is inline code then the overhead will be minimal.
11. Try to have the constraints in the 'Selection'
criteria of the jobs itself. This will eliminate the
unnecessary records even getting in before joins are made.
12. Tuning should occur on a job-by-job basis.
13. Use the power of DBMS.
14. Try not to use a sort stage when you can use an
ORDER BY clause in the database.
15. Using a constraint to filter a record set is much
slower than performing a SELECT … WHERE….
16. Make every attempt to use the bulk loader for your
particular database. Bulk loaders are generally faster than
using ODBC or OLE.

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