Oracle performance with increased data

I have a table1 that is being accessed by process 1 (a store proc). This process runs for 1 to 2 hours and that is normal. Now I am going to add anew table2 and create a new process 2 (again a store proc). Will this slowdown the process 1 in anyway? I will not be running the two processes at same time (if i did it would obviously mean process1 will slowdown). I am just looking at the increased data volume in my database. Will addition of more data slow down oracle even though I am adding in a different table?
The data I am talking here is huge. Both table1 and table2 each occupy almost 500GB. Each table has 200+ partitions. BTW, I am using 10g - 10.2.0.3.0.
Edited by: user6794035 on Aug 12, 2009 4:26 AM
Edited by: user6794035 on Aug 12, 2009 4:27 AM

user6794035 wrote:
why should CPU and resources come into play here? As I said nothing has changed except the data volume and two processes will not run at same time.
Consider there is no process2. I just have table 1 and process1. I just added another table of the size I mentioned above (500GB). Just the fact, that I added more data in another table will slow down Oracle's processing speed in anyway?Not until and while you are actually performing processing against that table. Your question seems to indicate that you fear that the mere presence of a table in the database causes a performance impact.

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URGENT Please
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5. The rendering engine.
Whatever rendering engine one uses make sure you try and understand fully what it can and cannot do. AutoDesk's MapGuide is an excellent product but I have seen it simply cache table/column data and never dynamically access it. Also, I have been at one site which was running Deegree and MapViewer and MapViewer was so fast in comparison to Deegree that I was called in to find out why. I discovered that Deegree was using SDO_RELATE(... ANYINTERACT ...) for all MBR queries while MapViewer was using SDO_FILTER. Just this difference was causing some queries to perform at < 10% of the speed of MapViewer!!!!
6. Consider "denormalising" data
There is an old adage in databases that is "normalise for edit, denormalise for performance". When we load spatial data we often get it from suppliers in a fairly flat or normalised form. In consort with spatial sorting, consider denormalising the data via aggregations based on a rendering attribute and some sort of spatial unit. For example, if you have 1 million points stored as single points in SDO_GEOMETRY.SDO_POINT which you want to render by a single attribute containing 20 values, consider aggregating the data using this attribute AND some sort of spatial BUCKET or BIN. So, consider using SDO_AGGR_UNION coupled with Spatial Analysis and Mining package functions to GROUP the data BY <<column_name>> and a set of spatial extents.
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If your rendering engine (app server) and database are on separate machines you need to investigate what sort of fetch sizes are being used when returning data from queries to the middle-tier. Fetch sizes for attribute only data rows and rows containing spatial data can be, and normally are, radically different. Accepting the default settings for these sizes could be killing you (as could the sort_area_size of the Oracle session the application server has created on the database). For example I have been informed that MapInfo Pro uses a fixed value of 25 records per fetch when communicating with Oracle. I have done some testing to show that this value can be too small for certain types of spatial data. SQL Developer's GeoRaptor uses 100 which is generally better (but this one can modify this). Most programmers accept defaults for network properties when programming in ADO/ODBC/OLEDB/JDBC: just be careful as to what is being set here. (This is one of the great strengths of ArcSDE: its TCP/IP network transport is well written, tuneable and very efficient.)
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Just some things to think about.
regards
Simon

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How should I show you an example, when it's simply
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p_string
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'chg_date')
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C.

Performance with dates in the where clause

Performance with dates in the where clause
CREATE TABLE TEST_DATA
FNUMBER NUMBER,
FSTRING VARCHAR2(4000 BYTE),
FDATE DATE
create index t_indx on test_data(fdata);
query 1: select count(*) from TEST_DATA where trunc(fdate) = trunc(sysdate);
query 2: select count(*) from TEST_DATA where fdate between trunc(sysdate) and trunc(SYSDATE) + .99999;
query 3: select count(*) from TEST_DATA where fdate between to_date('21-APR-10', 'dd-MON-yy') and to_date('21-APR-10 23:59:59', 'DD-MON-YY hh24:mi:ss');
My questions:
1) Why isn't the index t_indx used in Execution plan 1?
2) From the execution plan, I see that query 2 & 3 is better than query 1. I do not see any difference between execution plan 2 & 3. Which one is better?
3) I read somewhere - "Always check the Access Predicates and Filter Predicates of Explain Plan carefully to determine which columns are contributing to a Range Scan and which columns are merely filtering the returned rows. Be sceptical if the same clause is shown in both."
Is that true for Execution plan 2 & 3?
3) Could some one explain what the filter & access predicate mean here?
Thanks in advance.
Execution Plan 1:
SQL> select count(*) from TEST_DATA where trunc(fdate) = trunc(sysdate);
COUNT(*)
283
Execution Plan
Plan hash value: 1486387033
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 1 | 9 | 517 (20)| 00:00:07 |
| 1 | SORT AGGREGATE | | 1 | 9 | | |
|* 2 | TABLE ACCESS FULL| TEST_DATA | 341 | 3069 | 517 (20)| 00:00:07 |
Predicate Information (identified by operation id):
2 - filter(TRUNC(INTERNAL_FUNCTION("FDATE"))=TRUNC(SYSDATE@!))
Note
- dynamic sampling used for this statement
Statistics
4 recursive calls
0 db block gets
1610 consistent gets
0 physical reads
0 redo size
412 bytes sent via SQL*Net to client
380 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
Execution Plan 2:
SQL> select count(*) from TEST_DATA where fdate between trunc(sysdate) and trunc(SYSDATE) + .99999;
COUNT(*)
283
Execution Plan
Plan hash value: 1687886199
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 1 | 9 | 3 (0)| 00:00:01 |
| 1 | SORT AGGREGATE | | 1 | 9 | | |
|* 2 | FILTER | | | | | |
|* 3 | INDEX RANGE SCAN| T_INDX | 283 | 2547 | 3 (0)| 00:00:01 |
Predicate Information (identified by operation id):
2 - filter(TRUNC(SYSDATE@!)<=TRUNC(SYSDATE@!)+.9999884259259259259259
259259259259259259)
3 - access("FDATE">=TRUNC(SYSDATE@!) AND
"FDATE"<=TRUNC(SYSDATE@!)+.999988425925925925925925925925925925925
9)
Note
- dynamic sampling used for this statement
Statistics
7 recursive calls
0 db block gets
76 consistent gets
0 physical reads
0 redo size
412 bytes sent via SQL*Net to client
380 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows
Execution Plan 3:
SQL> select count(*) from TEST_DATA where fdate between to_date('21-APR-10', 'dd-MON-yy') and to_dat
e('21-APR-10 23:59:59', 'DD-MON-YY hh24:mi:ss');
COUNT(*)
283
Execution Plan
Plan hash value: 1687886199
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
| 0 | SELECT STATEMENT | | 1 | 9 | 3 (0)| 00:00:01 |
| 1 | SORT AGGREGATE | | 1 | 9 | | |
|* 2 | FILTER | | | | | |
|* 3 | INDEX RANGE SCAN| T_INDX | 283 | 2547 | 3 (0)| 00:00:01 |
Predicate Information (identified by operation id):
2 - filter(TO_DATE('21-APR-10','dd-MON-yy')<=TO_DATE('21-APR-10
23:59:59','DD-MON-YY hh24:mi:ss'))
3 - access("FDATE">=TO_DATE('21-APR-10','dd-MON-yy') AND
"FDATE"<=TO_DATE('21-APR-10 23:59:59','DD-MON-YY hh24:mi:ss'))
Note
- dynamic sampling used for this statement
Statistics
7 recursive calls
0 db block gets
76 consistent gets
0 physical reads
0 redo size
412 bytes sent via SQL*Net to client
380 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed

Hi,
user10541890 wrote:
Performance with dates in the where clause
CREATE TABLE TEST_DATA
FNUMBER NUMBER,
FSTRING VARCHAR2(4000 BYTE),
FDATE DATE
create index t_indx on test_data(fdata);Did you mean fdat<b>e</b> (ending in e)?
Be careful; post the code you're actually running.
query 1: select count(*) from TEST_DATA where trunc(fdate) = trunc(sysdate);
query 2: select count(*) from TEST_DATA where fdate between trunc(sysdate) and trunc(SYSDATE) + .99999;
query 3: select count(*) from TEST_DATA where fdate between to_date('21-APR-10', 'dd-MON-yy') and to_date('21-APR-10 23:59:59', 'DD-MON-YY hh24:mi:ss');
My questions:
1) Why isn't the index t_indx used in Execution plan 1?To use an index, the indexed column must stand alone as one of the operands. If you had a function-based index on TRUNC (fdate), then it might be used in Query 1, because the left operand of = is TRUNC (fdate).
2) From the execution plan, I see that query 2 & 3 is better than query 1. I do not see any difference between execution plan 2 & 3. Which one is better?That depends on what you mean by "better".
If "better" means faster, you've already shown that one is about as good as the other.
Queries 2 and 3 are doing different things. Assuming the table stays the same, Query 2 may give different results every day, but the results of Query 3 will never change.
For clarity, I prefer:
WHERE fdate >= TRUNC (SYSDATE)
AND fdate < TRUNC (SYSDATE) + 1(or replace SYSDATE with a TO_DATE expression, depending on the requirements).
3) I read somewhere - "Always check the Access Predicates and Filter Predicates of Explain Plan carefully to determine which columns are contributing to a Range Scan and which columns are merely filtering the returned rows. Be sceptical if the same clause is shown in both."
Is that true for Execution plan 2 & 3?
3) Could some one explain what the filter & access predicate mean here?Sorry, I can't.

Oracle performance with increased data

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