Obiee and database performance tuning

© Peak Indicators Limited
OBIEE and Database Performance Tuning
Antony Heljula
Technical Architect

© Peak Indicators Limited 2
Agenda
 Aim of Presentation
 Test Scenario
 Performance Tests
 Summary & Conclusion
 And Finally….

 Aim of Presentation

OBIEE Performance Issues
 When reports take a long time to return
results, end users tend to say “OBIEE does
not perform well”
 The truth is that most (99%?) of the
processing typically takes place on the
underlying data sources
 If another BI tool was used to deliver the
same reports, the queries would probably
still run just as slowly
 It would therefore be more accurate to say
that it is the databases that are not
performing well!
WebLogic
BI Server
BI Presentation Services
“Analytics”
BI Plug-In

Aim of Presentation
 We are going to investigate how a set of OBIEE Dashboard queries can be
tuned to deliver satisfactory performance
 We’ll begin testing with a set of dashboards that perform very poorly
 We’ll then implement a number of tuning features one-by-one to see how
things improve….hopefully by the end of it we’ll have decent
performance!
 The aim will be to get all reports to return in less than 10 seconds
 The performance test results will be captured and reported on using
Usage Tracking

Aim of Presentation
 We are going to assume that the database has an optimum and balanced
configuration, so that any performance issues are “software” related and
not “hardware”

Aim of Presentation
 On an Oracle database, there are many known ways to improve query
performance. For example:
 Gather statistics
 Remove snow-flakes
 Star Transformation
 Partitioning
 Bitmap Indexes
 We will attempt to answer the following questions:
 Do they actually work?
 What performance gains do they actually deliver?
 In what situations are they most effective?
 Bitmap Join Indexes
 Compression
 Parallel Query
 Aggregation
 Denormalization

Aim of Presentation
 The aim is to deliver satisfactory performance using standard relational
database features
 The customer won’t be happy if they are told mid-way through UAT that they
need to purchase additional software licenses e.g. Oracle OLAP / Essbase
 We will however be looking at “Partitioning”, although this option does
require additional license cost it is generally purchased by most/all customers
who have large data volumes
 We are going to avoid the use of “hints”
 Hints effectively hard-code the optimization rules for database queries
 Hints are “old” technology dating back to the Rule Base Optimizer (RBO), they
do not take into account the size and complexity of the query in the way that
the Cost Based Optimizer (CBO) does
 Hints should always be the last resort (in my view)
Notes

 Test Scenario

Test Scenario
 Dell Latitude E6400:
 Windows 7 64-bit
 2.54Ghz dual-core CPU
 8GB RAM
 250GB SATA internal hard disk (7,200 RPM)
 Software:
 Oracle Database Enterprise Edition 11g R2
 Oracle BI Enterprise Edition 11.1.1.3
Hardware

Test Scenario
 To conduct the investigation, a database data-model was built entirely
from scratch:
Data-Model
Fact (Daily Summary)Time Dimension
Product Dimension
Customer Dimension
Organization Dimension

Test Scenario
 The tables were then populated with a completely fabricated set of data
 Number of records in each table is show in red
 Approximately 10GB total volume
Data Volumes
30 Million11,000
5,000
500
500,000
1,000
5,000
10,000
9,000

Test Scenario
 Examples of the fabricated data that was generated:
Fabricated Data

Test Scenario
 An RPD was developed using all the modern best-practices for a star-
schema data-model:
RPD

Test Scenario
 A “Sales Orders” dashboard was created containing 6 pages with only 1
analysis per page
 4 pages contained a “summary” analysis (month level or above)
 2 pages contained a “detail” analysis (day/week level)
Dashboards

Test Scenario
 Each performance test was conducted manually but in a strict sequence:
1. Log on
2. Go to each dashboard page one-by-one (in the same order every time)
3. Only move to the next page once the current page has returned results
4. Log off
 To ensure every performance test was fair, the following steps were taken
before each test was conducted:
 Restart database (to purge all database cache)
 Purge BI Server cache
 Purge BI Presentation Services cache
Test Rules

Test Scenario
 Before starting the tests, we had no indication as to what the results would be
– there was no certainty any firm conclusions could be made afterwards
 No attempt was made to “prepare” the data-model or the performance tests
so that the final results would look good or bad
 When each feature was tested, no effort was made to tune the particular
feature – we simply used the default settings to see if it would work straight
“out of the box”
 If we ran the exact same test twice, the timings could vary by about 5-10
seconds or 10%. We will therefore assume that timings have to be different by
10% or >10 seconds in order to conclude that any tuning feature has made a
difference
Final Notes

 Performance Tests

1. Starting Point
 To begin with, the data-model had the following features / issues:
 Plenty of snow-flaking
 No statistics generated (RBO is therefore in use)
 B-Tree indexes used throughout
 Star Transformation disabled
Overview

1. Starting Point
 446 seconds in total to run all 6 dashboard pages in sequence
 The 4 “summary” reports all perform poorly
 The 2 “detail” reports are returning in less than 10 seconds
Result

2. Gather Stats 30%
 If you have a performance issue with a database query, one of the first
questions you will get asked is “have you analysed your tables and
indexes?”
 The Oracle Database comes with a “Cost Based Optimizer” (CBO) which
determines the most appropriate way to process your query based on the
size and contents of the required tables and their indexes
 But you need to analyze your tables (or “gather statistics”) in order for the
CBO to be used, otherwise the “Rule Based Optimizer” (RBO) is used
 With large data warehouses, it is sometimes not possible to analyze all the
data and indexes as the process will take too long, so this first test will see
if gathering statistics using only a 30% sample of data is sufficient to make
the CBO work efficiently
EXEC DBMS_STATS.GATHER_TABLE_STATS (ownname => 'PEAKTEST',
tabname => 'WH_CUSTOMER',
estimate_percent => 30);
Overview

2. Gather Stats 30%
Result

2. Gather Stats 30%
 Surprisingly, performance got worse overall by 52%!
 “Summary” Reports
 2 reports had significant improvements >65%
 2 reports did not change more than 10%
 “Detail” Reports
 Both detail reports suffered worse performance, one of them actually took
125 times longer than before!
 The over-use of “hash joins” is causing the issue
Summary

3. Gather Stats 100%
 If you have performance issues after gathering statistics using a 30%
sample of data, it is quite likely that Oracle Support or a DBA will
recommend that you try gathering statistics using a greater sample of data
 So this next test will provide an indication as to whether gathering
statistics using a “full” 100% sample will make a significant difference…
EXEC DBMS_STATS.GATHER_TABLE_STATS (ownname => 'PEAKTEST',
tabname => 'WH_CUSTOMER',
estimate_percent => 100);
Overview

2. Gather Stats 30%
Result

3. Gather Stats 100%
 Performance improved overall by 9%
 Performance was essentially the same as with a 30% statistics sample
 Performance levels are still far from acceptable
 Performance did improve overall by 14%
 Performance levels are still far from acceptable
Summary

4. Star Transformation (No Bitmaps)
 The Oracle database has a special tuning feature designed for optimising
“star schemas” queries, it is enabled by setting the following parameter:
 STAR_TRANSFORMATION_ENABLED = TRUE
 It is however often overlooked that the documentation recommends that,
for this feature to work properly, all the foreign key columns on your fact
tables should have “bitmap indexes” created and not just “b-tree indexes”:
 This test will see what the effect is if you don’t have bitmap indexes on
your foreign key columns…
Overview

Result

 Performance overall got slightly worse, but not by much
 Enabling Star Transformation without bitmap indexes had no real effect
 1 report improved by 50% (11 seconds) but overall there was no significant
difference
Summary

5. Star Transformation (Bitmaps)
 This test will see what the impact is when Star Transformation is enabled
with bitmap indexes created for the foreign keys on the fact table
(as recommended by Oracle)
CREATE BITMAP INDEX WH_SALES_FACT_DAY_XFK_1 ON WH_SALES_FACT_DAY_XFK (CUSTOMER_KEY);
CREATE BITMAP INDEX WH_SALES_FACT_DAY_XFK_2 ON WH_SALES_FACT_DAY_XFK (PRODUCT_KEY);
CREATE BITMAP INDEX WH_SALES_FACT_DAY_XFK_3 ON WH_SALES_FACT_DAY_XFK (ORG_KEY);
CREATE BITMAP INDEX WH_SALES_FACT_DAY_XFK_4 ON WH_SALES_FACT_DAY_XFK (TIME_DAY_KEY);
Overview

Result

 Performance improved overall by 50%!
 Finally things are starting to get under control, but we still need to do better
 15% overall improvement
 2 of the reports had significant improvements
 A 78% improvement overall, although one of the reports increased from 8 to
60 seconds
Summary

6. Remove Snow-Flakes (Dim Cols.)
 Snow-flaking occurs when you have a chain of Dimension tables joined
together
 The Oracle Data-Warehousing states that performance can be improved
(especially with “Star Transformation”) if you data-model does not consist
of snow-flakes
 So this test will show what happens when we eliminate snow-flaking by
combining the snow-flaked tables into the main dimension table (forming a
pure star-schema):
Overview

Result

 Surprisingly, it made things slightly worse, no reports showed improvement!
 Moving snow-flaked dimension columns into the main dimension table had no
real effect, just a few seconds worse overall
 Moving snow-flaked dimension columns into the main dimension table had no
real effect, just a few seconds worse overall
Summary

7. Remove Snow-Flakes (Add FKs)
 As the previous method of eliminating snow-flakes did not work, this time
we will try a slightly different approach
 In this test we will eliminate snow-flakes by adding extra foreign keys to the
central fact table which join directly to the snow-flaked dimension tables
(with “bitmap indexes” created)
 Will this more perfect “star-schema” improve performance significantly?
Overview

Result
* Test 6 was backed out

 It made things much worse….response times more than doubled!
 Adding more FKs made the fact table much larger, making it longer to scan?
 More FKs more complexity?
 136% worse overall
 90% worse overall
Summary

8. Bitmap Join Indexes
 Oracle promote “bitmap join indexes” as a way of increasing performance by
“orders of magnitude”
 A bitmap join index is a bitmap index which stores the actual results of a join
between two or more tables. For example, here is a bitmap join index that
stores the result of the joins from the fact table to four columns in the “Day”
dimension table:
CREATE BITMAP INDEX WH_SALES_FACT_DAY_BMJ_8
ON WH_SALES_FACT_DAY (PER_NAME_YEAR, PER_NAME_QTR, PER_NAME_MONTH,PER_NAME_WEEK)
FROM WH_SALES_FACT_DAY F, WH_TIME_DAY TD
WHERE F.TIME_DAY_KEY = TD.TIME_DAY_KEY;
 For this test 8 bitmap join indexes were created to store the join results of all
the joins made between the fact and dimension tables across the 6
dashboard pages
Overview

Result
* Tests 6 and 7 were backed out

 Bitmap join indexes improved performance overall by 55 seconds (18%)
 However only 1 “detail” report actually used a bitmap join index
 It seems the Oracle database will only use a bitmap join index if it deems the query is
at a low enough granularity to make it worthwhile
 No changes in performance
 Bitmap join indexes did not get used for any of the summary reports!
 One report reduced from 60 down to only 3 seconds. A massive improvement!
 The other detail report did not change, bitmap join indexes did not get used
Summary

 Bitmap Join indexes do have some restrictions:
Overview

9. Partition by Month
 Table “partitioning” is a very common recommendation when dealing with very large
tables. On a data warehouse the most obvious thing to do is partition your large fact
tables by Day / Week / Month
 It is essential to make sure that your partition strategy will result in effect “partition
pruning”. This means that, when OBIEE runs a query, the database will know it only
needs to scan a sub-set of the table partitions, rather than scanning the whole table
 In our case, we test out partitioning tables by “Month” based on the existing
“TIME_DAY_KEY” foreign key column on the fact table, it is in YYYYMMDD format
 When OBIEE queries for a specific time period, the database will lookup the
TIME_DAY_KEY values in the “Time” dimension table and know exactly which fact
table partitions to scan:
Overview
TIME_DAY_KEY
TIME_DAY_KEY

 Here is the SQL statement used to build our partitioned fact table which has a
partition for each of the 24 months of data that exist:
Overview

 Note that when you partition a table, you need to create “LOCAL” bitmap indexes on
the foreign key columns
 “LOCAL” means that the bitmap index will also be broken down into partitions,
potentially improving performance even further as the smaller index partitions will
not take so long to process:
CREATE BITMAP INDEX WH_SALES_FACT_DAY_1 ON WH_SALES_FACT_DAY (CUSTOMER_KEY) LOCAL;
CREATE BITMAP INDEX WH_SALES_FACT_DAY_2 ON WH_SALES_FACT_DAY (PRODUCT_KEY) LOCAL;
CREATE BITMAP INDEX WH_SALES_FACT_DAY_3 ON WH_SALES_FACT_DAY (ORG_KEY) LOCAL;
CREATE BITMAP INDEX WH_SALES_FACT_DAY_4 ON WH_SALES_FACT_DAY (TIME_DAY_KEY) LOCAL;
 Remember to analyze / gather statistics afterwards!
Overview

Result

 Overall performance improved by 57 seconds (23%)
 3 out of the 4 summary reports had excellent improvement >50%
 1 summary report took 30 seconds (33%) longer. This report contains two
“Year Ago” and “Year-to-Date” calculations which meant that most/all the table
partitions had to be scanned (scanning lots of small partitions may therefore be
less efficient than scanning a smaller number of larger partitions)
 A good overall improvement of 6 seconds (35%)
Summary

10. Parallel Query (Auto)
 The Oracle database offers parallel query capability, the idea being that one
sequential task can be broken down in the multiple tasks running in parallel
 Parallel query is a good contender once you have implemented table
partitioning, is the Oracle database can easily process each partition in
parallel
 On our test environment we have 1 CPU and 1 disk – not the most appropriate
environment for testing parallel query
 For this test, we are enabling parallel query simply by enabling the “auto
tuning” parallel query feature for the Oracle Database 11g R2
 This feature ignores any “parallel” degree settings you may have manually set for
your tables:
alter system set PARALLEL_DEGREE_POLICY=AUTO;
Overview

Result

 A slight performance improvement overall to the “Summary” reports, the
“Detail” reports were unaffected
 NOTE:
 Even with no parallelism enabled, our single disk was already operating at >80% capacity
 So by enabling parallelism there was not much for improvement as our disk was already
near to bottlenecking
 It would be better to run this test on a system with >1 CPU and >1 disk
 The longest running report showed the best improvement of 30 seconds (25%)
 The detail reports had no change, the explain plans showed that the Oracle
Database did not attempt to use parallel query for the granular queries (an
excellent feature)
Summary

Explain Plans with PARALLEL_DEGREE_POLICY=AUTO
“Summary” report
has parallel query
“Detail” report has
no parallel query

11. Compression
 The Oracle database allows you to store data in a compressed (zipped) format,
with three possible benefits:
 Reduced overall storage requirements
 Performance improvement due to less disk reads (each record uses less space)
 Improved memory efficiency (data is stored in memory in compressed format)
 The potential drawback is with higher CPU activity since all data blocks have to be
uncompressed at run-time in order to be processed
 Compression may be a good option in our test environment, since the CPU usage is
small compared to the disk usage (20-40% versus 80-100%)
 Compressing our fact table reduced the data volume from 1.46GB down to
1.09GB, a reduction of about 25%
 Database was given a default 8K block size, increasing to 32/64K could increase the
compression ratio significantly
 NOTE: bitmap indexes always store data in compressed format
Overview

11. Compression
 Here is the SQL statement used to build our compressed and partitioned fact table:
Overview
NOTE: There are limitations!
e.g. insert records with the hint:
INSERT /*+ APPEND */

11. Compression
Result

11. Compression
 With a 25% compression ratio, performance improved by about 15% across
the board, although it had more effect on the “Summary” reports where
more data was being extracted from disk
 NOTE: Oracle also has an “Advanced Compression” feature which is designed to
provide even greater compression ratios
 Approximately 15% improvement
 No real change
Summary

 Many customers can find themselves in this situation: we have already implemented
several different tuning mechanisms but our “Summary” dashboards are still taking far
too long…..an average of 41 seconds each (with only 1 user!)
 Apart from adding more hardware (CPUs/memory/disks), what is the next option?
 Sometimes there is no alternative to summarising the data in order to reduce the
amount of data being processed
12. Aggregation (MVs)
Overview

 Implementing an aggregate strategy is often a balancing act:
 You want to keep the number of aggregates to an absolute minimum
 You want to summarise the data as much as possible
BUT
 You want aggregates that can serve multiple reports/dashboards (plus ad hoc)
 You want aggregates to support multiple drill-down levels across many hierarchies
 Sometimes you have no choice other than to create an aggregate for each dashboard
(in which case you may need to consider cube engines, more hardware etc):
Overview

 In our test environment, we have managed to build a single aggregate which can:
 Reduce the number of records from 31M down to 8M
 Support all 4 “Summary” dashboards
 Provide at least 2 levels of drill-down across all the hierarchies
Overview

 HINT: Use the “Number of Elements” in the RPD to help you determine how much
more efficient an aggregate table could be
Overview

 Aggregate tables on an Oracle database can be either:
 Standard physical tables (updated or rebuilt during ETL)
 Materialized Views
 In theory there is no difference between the two options as they both contain
snapshots of the summarised data
 Materialized Views are easier to maintain and quicker to develop
 If you use Materialized Views, then please note:
 Do not rely too heavily on the “Query Rewrite” feature as OBIEE can generate
queries that are too complex for the database to rewrite
 It is often better to model the MVs into the RPD as if they were normal physical
aggregate tables (so you rely on OBIEE “aggregate navigation” rather than
database “query rewrite”)
 Remember to partition your MVs and create bitmap indexes on any foreign keys
Overview

 s
Overview
MV partitioned by
month
LOCAL bitmap indexes
(don’t forget to analyze)

Result

 Finally! All queries are now taken 10 seconds or less
 All reports improved, running over 7 times faster than before! (a total of 143
seconds down to 23)
 The aggregate MVs were designed for the summary reports, so the detail
reports were unaffected and perform exactly the same as before
Summary

 Summary & Conclusion

Summary
The biggest gains
for “Detail” reports:
• Star Transformation
• Bitmap Join Indexes
• Partitioning
The biggest gains for
“Summary” reports:
• Gathering Stats 30%
• Aggregation
• Star Transformation
• Partitioning

Conclusion
 The Oracle database provides a wide variety of tuning features, but you need to
adopt a combination of tuning features
 The various tuning mechanisms suit different types of reports
 If you have performance issues with “Summary” reports then consider:
 Gathering statistics 30/100%
 Partitioning
 Parallel Query
 Aggregation
 Compression
 If you have performance issues with “Detail” reports then consider:
 Gathering statistics 100% (as opposed to 30%)
 Partitioning
 Bitmap Join Indexes

Conclusion
 Always test thoroughly! Whilst the various tuning mechanisms can lead to an
overall positive improvement, there can be surprises where specific reports
suffer worse performance (sometimes significantly worse):

And Finally….
 What happens if none of the tuning mechanisms discussed give you the
desired level of performance?
 Buy bigger/better hardware (more CPUs+Disks+Memory)
 Archive data to reduce volume
 Oracle OLAP (now properly supported with OBIEE 11.1.1.5)
 Oracle Essbase
 Oracle Exadata

 Questions?

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