The BLASTP_ALIGN query performance decreases as increases the size table Ref?

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• Question about the construction of cube / query performance (11.2.0.3)

  Hi, I have a stupid question on the performance of cube generation. By choosing the precalc %, is linear (or nearly linear) construction time to that? for example if you select 10% going to be 3 times faster than the selection of 30%? Also, is it fair to assume that if only 10% of the values are precalculated, an average end-user queries have to hit 3 times more data and therefore be about 3 times slower?
  
  Sorry, this on a virtual computer on your laptop, so test different configs build takes forever (I still have a load of cube really complete). Guess I should not be trying a cube Sun 15 on a virtual computer on your laptop, but trying to sell a DBA on the fact that she could improve the performance of our mini - DW.
  
  Thank you
  Scott

  Aggregation based on costs (aka "Pre-computes percent") was introduced in 11.1 as a simpler alternative based on the level of aggregation. Product management dream was a linear parameter, but the complexity is quickly apparent. Which could measure the linearity against? The generation time? Time of the query? Total size of the disk? The result the balances of all of these factors, but is linear against any of them. Fortunately, behavior level percentage precompute was fairly consistent between cubes and patterns in our experience, so I can give you a rough characterization. But keep in mind that this is a guide only - you have to experience on your own scheme and a system to see what works for you. In particular, you must balance your own requirements on construction time, time of the request and the disk size.

  * 0% *-this means no precomputation at all, so all data access will be dynamic. This is the recommended setting for the top of the page partition of a cube. If, for any reason, you want to use for the scores of leaves as well, then I advise you pass a cube uncompressed.

  * 1% *-he pre-computes the smallest part of the cube that is allowed by the algorithm and would take certainly greater than 1% of the time taken by an accumulation of 100%. For partitions of leaves, it is usually best to increase the amount because you'll get much better query response time for not much more profitable in terms of disc size and the generation time. It may be a good level for the top of the page partition of a cube, but should be used with caution because the top of the page scores are often too big to pre-computes.

  * 2%-19% *-these levels does not seem to be a lot of benefits since the amount of time and the total size of the disk is almost identical to a construction of 20%, but queries are slower.

  * 20%-50% *-this range is probably the best compromise in terms of construction compared to the time of the query. The default value of the AWM is 35%, which is a good starting point. Lower it to 20% if you want a faster version and get up to 50% if you want to replace the faster queries. The setting is close to linear in this interval than outside it.

  * 51%-99% *-you should probably avoid these levels, although I've seen 60% used in practice. The reason is that while the size of the cube and the length of the construction increase rapidly, the queries do not receive proportionally faster. Indeed, you may find that queries are slower because the code spends more time swapping in the pages of the disc.

  * 100% *-this will be pre-computes all (non-NULL) cells in the cube. It may be a surprise after my advice about 51%-99%, but 100% is a reasonable level to choose. This is because the code is much simpler when you know that everything is precalculated and then stored in the disk pages.

• Partitioning strategy for the OBIEE query performance

  I use partitioning for the first time and I'll have trouble determining if I partition my fact table in a way that will allow the partition size to work with queries generating OBIEE.  I've set up a simple example using query I wrote to illustrate my problem.  In this example, I have a star with a fact table schema and I join in two dimensions.  My fact table is partitioned on JOB_ID and TIME_ID RANGE LIST and those are the keys that link the two dimensions that I use in this application.
  

  
  

  Select sum (boxbase)
  

  TEST_RESPONSE_COE_JOB_QTR a
  

  Join DIM_STUDY C on A.job_id = C.job_id
  

  Join DIM_TIME B on A.response_time_id = B.time_id
  

  where C.job_name = "FY14 CSAT"
  

  and B.fiscal_quarter_name = ' quarter 1';
  

  
  

  What can I say, because the query is in fact done filtering on columns in the dimensions instead of they, the columns in the table, the size isn't actually is happening.  I see actually slightly better performance of a non-partitioned table, even though I wrote this application specifically for partitioning strategy which is now in place.

  
  

  If I run the next statement, it runs a lot faster and a plan to explain is very simple and it seems to me that it's size down to a partition of sub as I hoped.  This isn't any query generated by OBIEE how will seem so.

  
  

  Select sum (boxbase)
  

  of TEST_RESPONSE_COE_JOB_QTR
  

  where job_id = 101123480
  

  and response_time_id < 20000000;

  
  

  Any suggestions?  I get some benefits from the exchange of partition by using this configuration, but if I'm going to sacrifice performance reports then that maybe isn't useful, or at the very least, I would need to get rid of my partitions void if they are not providing any benefit.

  
  

  Here are the plans to explain that I got for two queries in my original post:

  Operation

  Name of the object

  Lines

  Bytes

  Cost

  Object node

  In/Out

  PStart

  PStop

  INSTRUCTION SELECT optimizer Mode = ALL_ROWS		1		20960
  AGGREGATION OF TRI		1	13

  
  

  
  

  
  

  
  

  VIEW

  SYS. VW_ST_5BC3A99F

  101 K

  1 M

  20960

  
  

  
  

  
  

  
  

  NESTED LOOPS

  
  

  101 K

  3 M

  20950

  
  

  
  

  
  

  
  

  PARTITION LIST SUBQUERY

  
  

  101 K

  2 M

  1281

  
  

  
  

  KEY (SUBQUERY)

  KEY (SUBQUERY)

  RANGE OF PARTITION SUBQUERY

  
  

  101 K

  2 M

  1281

  
  

  
  

  KEY (SUBQUERY)

  KEY (SUBQUERY)

  CONVERSION OF BITMAP IN ROWID

  
  

  101 K

  2 M

  1281

  
  

  
  

  
  

  
  

  BITMAP AND

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  MERGE TO BITMAP IMAGE

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  KEY ITERATION BITMAP

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  BUFFER

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  INDEX SKIP SCAN

  CISCO_SYSTEMS. DIM_STUDY_UK

  1

  17

  1

  
  

  
  

  
  

  
  

  BITMAP INDEX RANGE SCAN

  CISCO_SYSTEMS. FACT_RESPONSE_JOB_ID_BMID_12

  
  

  
  

  
  

  
  

  
  

  KEY

  KEY

  MERGE TO BITMAP IMAGE

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  KEY ITERATION BITMAP

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  BUFFER

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  VIEW

  CISCO_SYSTEMS.index$ _join$ _052

  546

  8 K

  9

  
  

  
  

  
  

  
  

  HASH JOIN

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  INDEX RANGE SCAN

  CISCO_SYSTEMS. DIM_TIME_QUARTER_IDX

  546

  8 K

  2

  
  

  
  

  
  

  
  

  INDEX FULL SCAN

  CISCO_SYSTEMS. TIME_ID_PK

  546

  8 K

  8

  
  

  
  

  
  

  
  

  BITMAP INDEX RANGE SCAN

  CISCO_SYSTEMS. FACT_RESPONSE_TIME_ID_BMIDX_11

  
  

  
  

  
  

  
  

  
  

  KEY

  KEY

  TABLE ACCESS BY ROWID USER

  CISCO_SYSTEMS. TEST_RESPONSE_COE_JOB_QTR

  1

  15

  19679

  
  

  
  

  ROWID

  L LINE

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  
  

  Operation

  Name of the object

  Lines

  Bytes

  Cost

  Object node

  In/Out

  PStart

  PStop

  INSTRUCTION SELECT optimizer Mode = ALL_ROWS

  1

  
  

  1641

  
  

  
  

  
  

  
  

  AGGREGATION OF TRI

  
  

  1

  13

  
  

  
  

  
  

  
  

  
  

  SIMPLE LIST OF PARTITION

  
  

  198 K

  2 M

  1641

  
  

  
  

  KEY

  KEY

  RANGE OF SINGLE PARTITION

  
  

  198 K

  2 M

  1641

  
  

  
  

  1

  1

  TABLE ACCESS FULL

  CISCO_SYSTEMS. TEST_RESPONSE_COE_JOB_QTR

  198 K

  2 M

  1641

  
  

  
  

  36

  36

  
  

  It seems unreasonable to think that relying on our index in a table partitioned (or partitioned in a way only focused on the help of ETL) can actually work better than partitioning in a way that we could get some size dynamic, but never static pruning?

  Yes - standard tables with indexes can often outperform partitioned tables. It all depends on types of queries and predicates to query which are typically used and the number of rows returned in general.

  Size of the partition eliminates the partitions TOGETHER - regardless of the number of rows in the partition or table. An index, on the other hand, is prohibited if the query predicate needs a significant number of lines since Oracle can determine that the cost is lower simply use close readings and make a full scan.

  A table with 1 million rows and a query predicate who wants 100 K of them probably will not use an index at all. But the same table with two partitions could easily have one of the partitions pruned by the "number of effective lines" only 500 k or less.

  If you are partitioning for performance, you should test your critical queries to make sure partitioning/pruning is effective for them.

  Select sum (boxbase)

  TEST_RESPONSE_COE_JOB_QTR a

  Join DIM_STUDY C on A.job_id = C.job_id

  Join DIM_TIME B on A.response_time_id = B.time_id

  where C.job_name = "FY14 CSAT"

  and B.fiscal_quarter_name = ' quarter 1';

  So, what is a typical value for 'A.response_time_id '? That represents a 'B.time_id '?

  Because a way of providing explicit partition keys may be to use a range of 'response_time_id' of the FACT table rather than a value of 'fiscal_quarter_name' of the DIMENSION table.

  If "1 quarter" could correspond to a range of dates from 01/01/YYYY ' at ' 03/31/yyyy '. "."

  Also, you said that on the partitioning: JOB_ID and TIME_ID

  But if your questions relate mainly to the DATES / TIMES, you might be better use the TIME_ID for PARTITIONS and JOB_ID, if necessary, for the subpartitioning.

  Date range partitioning is one of the most common around.and serves both performance and ease of maintenance (delete/archive old data).

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  A USER_SDO_GEOM_METADATA can. DIMINFO well defined to improve the query performance?
  
  
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  We want to take full advantage of our 54 GB of free memory on our server Exalytics X 4.

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