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Rittman Mead - Delivered IntelligencePerformance and OBIEE – part I – IntroductionMarch 18th, 2013 by Robin Moffatt
Performance matters. Performance really matters. And performance can actually be easy, but it takes some thinking
about. It can’t be brute-forced, or learnt by rote, or solved in a list of Best Practices, Silver Bullets and fairy dust.
The problem with performance is that it is too easy to guess and sometimes strike lucky, to pick at a “Best Practice
Tuning” setting that by chance matches an issue on your system. This leads people down the path of thinking that
performance is just about tweaking parameters, tuning settings, and twiddling knobs. The trouble with trusting this
magic beans approach is that down this path leads wasted time, system instability, uncertainty, and insanity. Your fix
that worked on another systemmight work this time, or a setting you find in a “Best Practice” document might work.
But would it not be better to know that it would?
I wanted to write this series of posts as a way of getting onto paper how I think analysing and improving performance
in OBIEE should be done and why. It is intended to address the very basic question of how do we improve the
performance of OBIEE. Lots of people work with OBIEE, and many of them will have lots of ideas about
performance, but not all have a clear picture of how to empirically test and improve performance.
Why does performance matter?
(image src)
Why does performance matter? Why are some people (me) so obsessed with testing and timing and tuning things?
Can’t we just put the system live and see how it goes, since it seems fast enough in Dev?…
Why performance matters to a project’s success
Slow systems upset users. No-one likes to be kept waiting. If you’re withdrawing cash from an ATM, you’re going
to be quite cross if it takes five minutes. In fact, a pause of five seconds will probably get you fidgeting.
Once users dislike a system, regaining their favour is an uphill battle. “Trust is hard to win and easily lost”. One of
the things about performance is perception of speed, and if a user has decided a system is slow you will have to
work twice as hard to get them to simply recognise a small improvement. You not only have to fix the
performance problem, you also have to win round the user again and prove that it is genuinely faster.
From a cost point of view, poorly performing systems are inefficient:
o They waste hardware resource, increasing the machine capacity required, decreasing the time between
hardware upgrades
o They cost more to support, particularly as performance bottlenecks can cause unpredictable stability issues
o They cost more to maintain, in two ways. Firstly, each quick-win used in an attempt to resolve the problem
will probably add to the complexity or maintenance overhead of the system. Secondly, a proper resolution of
the problem may involve a redesign on such a scale that it can become a rewrite of the entire system in all but
name.o They cost more to use. User waiting = user distracted = less efficient at his job. Eventually, User waiting =
disgruntled user = poor system usage and support from the business.
Why performance matters to the techie
Performance is not a fire-and-forget task, and box on a checklist. It has many facets and places in a project’s life
cycle.
Done properly, you will have confidence in the performance of your system, knowledge of the limits of its capacity, a
better understanding of the workings of it, and a repeatable process for validating any issues that arise or prospective
configuration changes.
Done badly, or not at all, you might hit lucky and not have any performance problems for a while. But when they do
happen, you’ll be starting from a position of ignorance, trying to learn at speed and under pressure how to diagnose
and resolve the problems. Silver bullets appear enticing and get fired at the problem in the vain hope that one will
work. Time will be wasted chasing red herrings. You have no real handle on how much capacity your server has for
an increasing user base. Version upgrades fill you with fear of the unknown. You don’t dare change your system for
fear of upsetting the performance goblin lest he wreak havoc.
Building a good system is not just about one which cranks out the correct numbers. A good system is one which not
only cranks out the good numbers, but performs well when it does so. Performance is a key component of any
system design.
OBIEE and Performance
Gone are the days of paper reports, when a user couldn’t judge the performance of a computer system except by
whether the paper reports were on their desk by 0800 on Monday morning. Now, users are more and more
technologically aware. They are more aware of the concept and power of data. Most will have smartphones and be
used to having their email, music and personal life at the finger-swipe of a screen. They know how fast
computers canwork.
One of the many strengths of OBIEE is that it enables “self-service” BI. The challenge that this gives us is that users
will typically expect their dashboards and analyses to run as fast as all their other interactions with technology. A slow
system risks being an unsuccessful system, as users will be impatient, frustrated, even angry with it.
Below I propose an approach, a method, which will support the testing and tuning of the performance of OBIEE
during all phases of a project. Every method must have a silly TLA or catchy name, and this one is no different….
Fancy a brew? Introducing T.E.A., the OBIEE Performance MethodIn working with performance one of the most important things is to retain a structured and logical approach to it. Here
is mine:1. Test creation
o A predefined, repeatable, workload
2. Execute and Measureo Run the test and collect data
3. Analyseo Analyse the test results, and if necessary apply a change to the system which is then validated through a
repeat of the cycle
The emphasis is on this method being applicable at any time in a system’s lifecycle, not just the “Performance Test”
phase. Here are a few examples to put it in context:1. Formal performance test stage of a project
1. Test : define and build a set of tests simulating users, including at high concurrency2. Execute and Measure: run test and collect detailed statistics about system profile3. Analyse : check for bottlenecks, diagnose, redesign or reconfigure system and retest
2. Continual Monitoring of performance1. Test could be a standard prebuilt report with known run time (i.e. a baseline)2. Execute could be just when the report gets run on demand, or a scheduled version of the report for
monitoring purposes. Measure just the response time, alongside standard OS metrics3. Analyse – collect response times to track trends, identify problems before they escalate. Provides a
baseline against which to test changes3. Troubleshooting a performance problem
1. Test could be existing reports with known performance times taken from OBIEE’s Usage Tracking data2. Execute Rerun reports and measure response times and detailed system metrics3. Analyse Diagnose root cause, fix and retest
Re-inventing the wheel
T.E.A. is nothing new in the overall context of Performance. It is almost certainly in existence elsewhere under
another name or guise. I have deliberately split it into three separate parts to make it easier to work with in the
context of OBIEE. The OBIEE stack is relatively complex and teasing apart the various parts for consideration has to
be done carefully. For example, designing how we generate the test against OBIEE should be done in isolation from
how we are going to monitor it. Both have numerous ways of doing so, and in several places can interlink. The most
important thing is that they’re initiallyconsidered separately.
The other reason for defining my own method is that I wanted to get something in writing on which I can then hang
my various OBIEE-specific performance rants without being constrained by the terminology of another method.
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Performance and OBIEE – part II – Test – DefineMarch 18th, 2013 by Robin Moffatt
Take the first tentative steps along the yellow brick road of investigating performance and OBIEE with this jump into
the nitty gritty of how we should define a performance test.
An overview of defining a performance testThe first step in assessing the performance of OBIEE is defining the test we are going to use. For troubleshooting a
performance problem in Production, this might be a single report. For performance testing a new system it might be
more complex. The process to define it needs to be the same though.
Let us consider for a moment what it is we want to achieve through a performance test. It should be a validation – or
possibly determination – of some or all of the following, which may or may not be formally documented in a project as
‘Non-Functional Requirements’ (NFRs):
Report response times
User concurrency levels
Hardware utilisation
It doesn’t sound too difficult does it? We can run a report and time how long it takes, and we can run the same report
multiple times in parallel and see how long it takes then, and around all of that we can gather system OS stats.
This seems to be the level at which some people perceive the complexity of performance testing; that is, not at all.
But, to put any weight in the above results would presume that the system under test had :
Only one report
No parameters or other interactivity on the report
Every user runs the report at the exact same time
How many OBIEE implementations do you know that match this description?
As soon as you accept this premise, then you have to accept the need to design your performance test properly if you
want any kind of confidence in the results that you get.
Of course, there is the chance – [sarcasm]vastly improbable and unheard of I know[/sarcasm] – that Performance
Testing is a tick box on a project plan (normally about a week before Go Live), from which there is the expectation
that it can deliver the confidence of an accurate test with the simplicity and brevity of the one-report test I describe
above. If that is the case then I hope this article will explain why it is a fallacy.
Walk before you runThere are three key things to aim for in the design of a performance test:
1. Repeatability. You must be able to rerun the test, so that you can (a) prove bugs/performance problems have been fixed, and (b) re-use the test as a baseline for future measurements
2. Accuracy. This can be difficult with a stack as complex as the OBIEE one, but you must do all you can to avoid conflation and misinterpretation of data.
3. Ease of Analysis. If the result of your test is unsuccessful, you will have to start analysing the issue. If your test is unnecessarily big or complex then it will be very difficult and time consuming to pare it back to find root cause.
Avoid the blunderbus approach. It’s tempting (and fun!) to get carried away
building a huge test and run it with a big number of users – but if it brings the system down then the only immediately
conclusion you can draw is that you can crash the system. To get to any kind of root cause of the limitation you will
have to run more tests, and you will have to simplify the test that you did run to pinpoint the actual issue(s).
Build up small tests with single users and check things work at this level first. You can then combine these together
into larger tests for load testing. By working this way you get the added benefit of a better understanding of the
workings of your load test and system being tested, as you will have gained this experience from the smaller tests.
Think of it as assembling a car’s engine for yourself versus buying a car out of the showroom. If it breaks (or you want
to tune it), you’ll be in a better place to start if you have been under the bonnet [hood] already.
Define a UserThe first step in defining a good test is to define extremely precisely and clearly what it is we are testing. This
section’s heading I take from Zed Shaw’s superb article “Programmers need to learn statistics…”. For me it
encapsulates in three words the challenge that we face in designing a good test. We know we want our system to
support more than one user, and we probably have a user concurrency figure (of which more below) that the system
must support. So, how do we define ‘a user’? To quote from the aforementioned article, do we just “click around a bit, you know, like a user”? Maybe we run the OBIEE-equivilent of SELECT 1 FROM DUAL a thousand times
simultaneously, and then declare our system can support a thousand concurrent users?
The challenge is that good test or not, test results and system capacity will often be boiled down to a discussion of
simple “user” numbers. Will the system support 100 users? 200 users? And so on.
Modelling the workload of a transactional (OLTP) system is relatively straightforward in that there is a finite number of
transactions, even if it is a large number. When we consider how to model an OBIEE user in a performance test, we
have additional challenges:
Navigation paths
o In an OLTP system such as an online store once a user clicks on ‘add to cart’ we can be pretty sure they will
then either (a) carry on shopping or (b) go to checkout. What about a BI user once they’ve run their first
report, which will they run then? Maybe a different report, but quite likely it will be navigation within the
existing report (eg drill down, prompt changes, etc)
Ad hoc
o Whilst some reporting will be fixed reports, much business value from BI comes in the analysis and
exploration of data in an ad hoc manner to discover trends etc to give competitive advantage. These ad hoc
queries cannot, by definition, be known in advance, so the performance test definition must find a way to
address this.
Think about how many types of users you have on your system, and how you would characterise and group them.
There will be different sets of data they access, and varying degrees of interactivity with which they use the tool.
Some will be using pre-built reports with fixed filters, other may be doing extensive ad-hoc analyses, and somewhere
in the middle will be pre-built reports but with a fair amount of interactivity through functionality such as prompts and
selection steps.
Define a ReportSo for each of our User types that we define, we need to precisely define the workload for them. This will be made up
of the execution of one or more dashboards/reports. Defining the report that our model users are going to run is not
as simple as it may appear. As well as the distinction to make between individual reports (Analyses/Answers) and
Dashboards, we also need to consider:
Filters
o How do we model a report which has six possible filters? We can’t just call that ‘report A’, because each filter
permutation could cause different SQL to be generated by the BI reporting tool and consequently vastly
different execution plan(s) on the database where the query runs.
Aggregate navigation
o One of OBIEE’s many strengths is its aggregate navigation capability, enabling the user to drill down into data
whilst in the background the tool switches between pre-defined aggregate tables on the database to pick the
fastest one to use. For performance testing we need to make sure that within a report each possible
aggregate is considered separately.
As an example, let us examine a dashboard from SampleApp.
At an initial glance, we could specify our “Report” as simply being “Dashboard 3.10”. But…the dashboard is made up
ofthree analyses (plus some static text/image objects)
“Product Line Analysis”
“Ragged Hierarchy”
“Value Based (Parent Child) Hierarchy”
We need to consider each of these reports individually. In addition, there are hierarchical columns which, like
Prompts, must be included in our test specification. Here’s one of the reports from the dashboard in two different
states of hierarchical navigation.
The first version, mostly collapsed, requires 3 queries against the database. The second version requires 21
queries! Clearly the performance of the two different versions of the report is going to differ.
Depending on the hierarchical column states, depending on dashboard prompt values, depending on aggregate drill
paths followed, the same individual report has the potential to behave quite differently.
Any report or dashboard that’s using prompts needs to have those prompt values stated in the test definition. The
execution of a query on the database can vary considerably depending on the predicates supplied, and it is in part
from the report/dashboard prompts that these predicates come.
Ad-hoc reports
Conspicuous by its absence from the above is how we deal with ad-hoc reporting with OBIEE in our performance
tests. This is the elephant in the room when it comes to performance testing OBIEE, because we cannot go through
the same methodical process of cataloging the workload – because it is ad hoc. This is discussed in detail later.
User workloadHaving precisely defined the reports which are going to be run, we can sequence them together to represent different
types of users. Clearly this is going to be a generalisation, an estimation, of how a particular group of users is going
to tend to use the reports. Unlike an OLTP transaction, where the steps are much more predefined and free from
possible deviations, users of OBIEE have the flexibility to run any number of reports, drill within existing reports, and
so on.
Taking the above SampleApp dashboard as an example, one user (let’s call them the “Product Analyst”) might have
the following test sequence:
1. View Dashboard 3.10 in default state2. Drill into hierarchical columns of Product Line Analysis3. View 1.3 Sample Sales - Product Details
Some user types may just run one report and exit; they don’t have to have a complex sequence.
You don’t need to restrict yourself to one user type per actual group of users. You could model several different
instances of each user group in order or increase the test coverage and realism of different reports running
concurrently. Be aware that the more user types and tests you have, the longer it will take to build, and the more
complex the troubleshooting could be.
Think Time
Users are not computers, which is a shame because it would make reasoning with them a lot easier. It also means
that they do not just one run report after another. They run a report, look at the numbers, get a cup of tea, change a
prompt value, have a chat with a colleague, and so on.
As part of your user workload, plan in some think times. Don’t spend too long thinking about it (geddit), because
remember this user workload is an approximation of what a certain type of user is going to be doing.
User concurrency
Once we have designed a set of individual user workloads, we can bring these together into a larger test to assess
the performance of the system as a whole under load. The aim of load testing is to assess what happens to the
response time of reports, and hardware utilisation, as the number of concurrent users on the system increases. There
are three key things to define in a load test:
the workload of each user
the different types of user
the number of concurrent users
The first two of these are discussed above. When it comes to user concurrency, we need to be very careful how we
derive our figure, because it has a big impact on the scale of the tests that we run and the discussion of the
outcomes.
First, make sure that you are clear in the definition of concurrency that you use. These include:
Concurrent user sessions logged into OBIEE, viewing dashboards, formatting reports, and so on.
Concurrently executing queries at the BI Server level, which could be a single user running one report or one
dashboard.
Concurrently executing queries on the database, which could be a single user in OBIEE running a single
report or dashboard requiring more than one SQL request to get its data.
Each of these have their own particular load on the system, but can easily differ by orders of magnitude. Typically a
user concurrency figure will be derived from “bums on seats”, i.e. number of users. From this, a reckoning is made on
how many will be using the system at one time. So unless it’s qualified otherwise, I would work with the first definition
– concurrent user sessions logged into OBIEE. But if this is your definition, remember it when we come to
simulating users later on …
For some reason, user concurrency is often a contentious number to define. With the greatest of apologies to Monty
Python, I offer this paraphasing of a familiar conversation:
“yah, my system needs to support 100 concurrent users”
“100? I’d take that as a QUIET DAY for my system, bwahaha, I need to test at least 1,000 concurrent users!”
“1,000? You were LUCKY! My system would run a gazillion concurrent users at three o’clock in the morning, work
twenty hours down t’pit and still run 5,000 concurrent users before having a handful of cold gravel for its tea”
There does seem to be BSD syndrome around user concurrency figures, with people
always pitching high numbers. This is counterproductive. If you run a performance test using a completely simulated
workload with an excessively high number of users as the target then the margin of ‘error’ (how accurately your test
represents reality) inherent in the simulation approach will be magnified as the user concurrency increases. The net
effect of this is that you cannot be confident that any problems that do happen will actually happen in real life.
Conversly, if you are taking real workload and running it for many users, you can honestly say if it doesn’t work in
testing, it won’t work in real life.
If you are worried about pitching your user estimate too low and so double it or stick a zero on the end just in case,
consider the following: If you define your test as being a fail if it does not support your high figure of concurrent users
– what then? You have to ask for money, either literally to buy bigger hardware, or in kind, to support the time to
optimise the system you do have. It’s not a great place to be in, and one which may be unnecessary. A sensible
approach is the following:
1. Load test to confirm whether the system can support the realistic number of users in the short-term, e.g. go-live and a few months beyond
2. Defer part of the load testing until after go-live, when you can run a test for medium/long-term user numbers using real user workload for your tests. The benefit of this is that your second test will be far more
accurate because your simulation will not be based on how you think you system will be used but how you know is it being used.
If this approach doesn’t suit, then your alternative should be to
1. take a realistic figure to start with for your first test2. run a second performance test to validate a future growth figure for your user base. If you don’t have a future
growth figure, then you could consider the second test being to simply determine how many users your system can support by ramping numbers up until things start to creak.
The point of doing it this way is that the first test – assuming it gives a positive result – gives clarity and focus to your
overall results. If the second test fails, then at least you have the confidence from the first with which to proceed with
a decision. If you just run one big test then it’s an “all or nothing” approach, and if the test fails you have the option of
either delaying launch until a successful test, or just crossing your fingers and hoping.
User concurrency numbers for an existing system
If you have an existing system then it is easy to determine your actual user concurrency through either Usage
Tracking (you do have it enabled, don’t you?) or the OBI DMS metrics.
If your user concurrency figure is based on users logged into OBIEE, not necessarily running reports, then the
metricOracle BI PS Sessions : Sessions Logged In should be appropriate.
For the number of queries (logical SQL) concurrently running on the BI Server, use Usage Tracking (see below),
or DMS metric t.b.c!
The number of concurrent queries on the database can either be obtained from your database’s monitoring tool,
or through the Oracle BI DB Connection Pool DMS metrics.
To find out more about viewing and capturing OBI metrics over time, see here.
To estimate user concurrency from Usage Tracking, you can use the following query. It will analyse Usage Tracking
to give an approximate number of concurrent users, but this is based on individual queries run on the BI Server, so it
not the same as users simply logged into OBIEE, nor the same as dashboard refreshes (since one dashboard could
be many queries):
-- Usage Tracking Concurrency usage estimate-- Per minute granularity
SELECT TO_CHAR(start_ts, 'YYYY-MM-DD HH24:MI') AS start_ts_min, COUNT(DISTINCT user_name) AS distinct_users FROM s_nq_acct GROUP BY TO_CHAR(start_ts, 'YYYY-MM-DD HH24:MI') ORDER BY 1 DESC
Estimating user concurrency numbers
If you don’t have empirical numbers to use then 10% of your total user base is the rule of thumb often worked to.
User concurrency is multi dimensional
Just as there is no such thing as “A User”, to ensure an accurate performance test user concurrency figures should
also be specified in terms of :
Types of concurrent user, for example:
o “10 Concurrent Finance Power Users”
o “2 Concurrent Finance Ad-Hoc Users”
o “60 Concurrent HR Report Consumers”
Periods of system usage, for example:
o Monday morning peak
o EOD peak
o ‘Average’
You may only actually test the peak user concurrency model, but it’s a good idea to have a picture of the system
profile, particularly when analysing the impact and capacity for ad-hoc usage.
Summary : User and Report DefinitionRemember:
Before you can measure something you really need to lay down a very concrete definition of what you’re
measuring. –Zed Shaw
Whatever kind of testing you are doing, aim to make it modular wherever possible. It will make both analysis and
reuse of test code easier.
Almost all test definitions should be made up of one or more of the following levels:1. Specific Report or dashboard along with all prompt values, hierarchical column settings, etc. This is the base
unit of your performance test.2. Then take these individual tests, and use them together to create an individual User Workload model for each
user.3. Finally, combine the User models to produce an overall Concurrent User workload simulation.
See below for a worked example of a sample test definition.
Points to remember in your test definition:
1. Whilst each dashboard does have its own name, a good idea is to use your own ID system to make it easy to refer to a specific dashboard state. For example, when using prompts there might be two or more versions of the dashboard to test (for different prompt values).It’s also easier to refer to something like “ID01” in conversation and notes than it is it “Finance Dashboard – Profitability EMEA blah blah blah”.
2. Don’t forget default values in your navigation. If you are testing specific dashboard prompt values, you’ll need to also test the default values first – because that is what will get executed when the user first clicks onto the dashboard.
3. As well as testing dashboard response times, factor in the usability of any prompts that the user will interact with. A fast dashboard is not quite so impressive if the user had to wait a long time for the dashboard prompts to display the available values.
4. You need to be writing down nuts and bolts detail here. Resist the temptation to skimp on the detail or assume you’ll remember – you won’t, and certainly your colleague who can reuse your testing won’t appreciate having to fill in the blanks to make use of it.
What do you mean, not all of it has been tested?
Before you run off to write down all your dashboards and types of user,
consider the following. Unless you have :
1. A very small system2. A lot of time, and/or an infinite number of monkeys
you are not going to be able to actually test all of your OBIEE system. Fact.
There are too many factors (prompt values, aggregate paths, ad hoc usage) to be able to model all plausible (let
alone possible) permutations of these.
Designing the scope of a performance test needs to take into account theacceptable risk of things going wrong
vs time/budget available vs functionalityrequired. By functionality, I mean factors such as the breadth of
dashboards delivered, or data made available.
In some systems the stability is paramount and therefore the relative risk must be minimised. If this is the case
and yourtime/budget is constrained then therefore the functionality will have to be constrained.
If you need to deliver a lot of functionality and assuming that time/budget is fixed, then you will need to accept
an increased risk to the stability of your system.
If you have limitless time/budget then you can deliver lots of functionality and do large and wide-ranging
performance tests to ensure minimal risk.
The point here is an important one because it is one that needs discussing with the project owner, or more
specifically, whoever will be accountable if things don’t go right in Production. For a standard OLTP system it is much
easier (relatively) to design a performance test with a high degree of confidence that most [common] workloads have
been simulated and tested. However, for a reporting system such as OBIEE there are two reasons why it isn’t so
easily determined, as discussed above and re-iterated here:
1. Permutations of fixed reporting – each dashboard can have multiple prompt dimensions, hierarchical column expansions. Users can follow many different drill and navigation paths from any one dashboard.
2. Ad hoc reporting – by definition, the report is not known until the user builds it, so we cannot test it beforehand
But, all is not lost. Even if we accept that to test every permutation of report that might run is impossible, it is still
perfectly realistic to perform a thorough performance assessment of a system prior to go-live. The most sensible
option is a hybrid approach of:
Traditional performance testing of fixed reports as described above
Testing predefined ad-hoc reports (if that isn’t a paradox)
Holistic and heuristic evaluation of the system design and data model
Resource management to curtail the impact of bad queries
Active monitoring to identify bad queries and deal with them (using the T.E.A. method)
Feedback loop : as system usage becomes clearer, refine original performance testing using a more accurately
informed model (using the T.E.A. method)
This may seem a bit of an excuse for a cop-out. If we can’t performance test all our system and be confident in the
results, what’s the point? Here, I defer to Cary Millsap, a great proponent of the rigorous, scientific, and rational
approach to performance testing:
How can you possibly test a new application enough to be confident that you’re not going to wreck your production
implementation with performance problems?
You can model. And you can test. However, nothing you do will be perfect. It is extremely difficult to create models
and tests in which you’ll foresee all your production problems in advance of actually encountering those problems in
production.
Some people allow the apparent futility of this observation to justify not testing at all. Don’t get trapped in that
mentality. The following points are certain:
You’ll catch a lot more problems if you try to catch them prior to production than if you don’t even try.
You’ll never catch all your problems in pre-production testing. That’s why you need a reliable and efficient method
for solving the problems that leak through your pre-production testing processes.
Somewhere in the middle between “no testing” and “complete production emulation” is the right amount of testing.
The right amount of testing for aircraft manufacturers is probably more than the right amount of testing for companies
that sell baseball caps. But don’t skip performance testing altogether. At the very least, your performance test plan
will make you a more competent diagnostician (and clearer thinker) when it comes time to fix the performance
problems that will inevitably occur during production operation.
An example OBIEE test definitionHere’s an example test definition, put together from SampleAppv207.
What we are going to model is two groups of users. One likes pretty pictures and just runs two chart-based
dashboards and then logs out. The other is interested in numbers and does some filtering on an analysis dashboard.
First, we’ll define the dashboards in use. For brevity, I’ve omitted the constituent analysis details, but in normal
practice it’s a good idea to include them (for future reference, ease of understanding, clarity, etc).
Dashboards
Dashboard : ID01
Dashboard : 3.20 Vanilla Visuals
Page : Standard Visuals
Path : /shared/3. Analysis and Dashboards/_portal/3.20 Vanilla Visuals
Dashboard Prompts: None
Analyses :
o /shared/3. Analysis and Dashboards/Report Views/Gallery/Examples of Standard Visualizations
Dashboard : ID02
Dashboard : 3.20 Vanilla Visuals
o Page : Trellis-Product Sales
o Path : /shared/3. Analysis and Dashboards/_portal/3.20 Vanilla Visuals Trellis-Product Sales
o Dashboard Prompts: None
o Drill state : Default (NB each chart can be clicked on to drill into detail)
o Analyses :
/shared/3. Analysis and Dashboards/Report Views/Trellis/Revenue Breakup
/shared/3. Analysis and Dashboards/Report Views/Trellis/Product Cost and Unit Price Trend
/shared/3. Analysis and Dashboards/Report Views/Trellis/Brand Cost and Unit Price Trend
Offices Region Hier : All collapsed, except AMERICAS expanded one level
Dashboard : ID03
Dashboard : 1.3 Sample Sales
Page : Brand Analysis
Path : /shared/1. Quick Demos/_portal/1.3 Sample Sales Brand Analysis
Dashboard Prompts:
o T05 Per Name Year : Default (2008, 2009, 2010, 2011)
o D4 Company : Default (none)
o T31 Cal Week : Default (between 0 and 53)
o “Products”.“Products Hierarchy” : Default (none)
Analyses :
o /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Product Line Analysis
o /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Trending Analysis
o /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Gaugeso /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Top Customers
Bookingso /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Daily Revenue
Timeline
Dashboard : ID04
Dashboard : 1.3 Sample Sales
Page : Brand Analysis
Path : /shared/1. Quick Demos/_portal/1.3 Sample Sales Brand Analysis
Dashboard Prompts:
o T05 Per Name Year : 2010, 2011
o D4 Company : Stockplus Inc.
o T31 Cal Week : between 1 and 27
o “Products”.“Products Hierarchy” : Default (none)
Analyses :
o /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Product Line Analysis
o /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Trending Analysis
o /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Gaugeso /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Top Customers
Bookingso /shared/1. Quick Demos/Simple Demo Dashboard/Brand Analysis/Daily Revenue
Timeline
Tip: Use Catalog Manager to generate a report of dashboards and their constituent analyses to make the creation of
the above specification easier. :
User Workload
Having defined the dashboards (ID01 – ID04), we define the user types and workloads:
User A
Description: Department X, executive
Workload model:
1. ID01
2. Think time: 60 seconds
3. ID02
4. Think time: 60 seconds
5. Logout
User B
Description: Department Y, analyst
Workload model:
1. ID03
2. Think time: 120 seconds
3. ID04
4. Think time: 90 seconds
5. Logout
Concurrency
Our concurrent user model looks like this. I am defining concurrent users as active users, that is, executing
dashboards or simulated thinking.Peak usage
User A : 5 concurrent users
User B : 2 concurrent users
Light usage
User A : 3 concurrent users
User B : 1 concurrent users
Types of testingBefore we can consider the types of test, we need to define what it is we are testing, hence the long section above.
Once you’ve identified the types of user and specific reports they will run, you should also look at the type of test you
want to run. The terminology is frequently used interchangably, so here’s my version of it:
Performance testing
o With a single user, is the response time acceptable?
Load testing
o With concurrent users, is the response time acceptable and stable?
Capacity testing
o How does the system behave when concurrent users are simulated? do any areas show signs of reaching
capacity of resource?
Soak testing
o How does the system behave if the concurrent user workload is left to run for many hours? For example, are
there any memory leaks lurking, or connections which are not closed down properly and will eventually run
out?
Stress testing
o As we increase the number of concurrent users beyond that required (and already tested successfully), how
far will the system go until something breaks or response times suffer?
o In a high-availability deployment, what happens to response times if we simulate partial stack failures? Does
the system handle this gracefully under load?
These types of test do not have to be mututally exclusive in execution, but in defining your testing make sure you
have covered them all, or explained why they are not necessary. An example would be performance testing a single
report change on an existing system would probably not warrant a full scale soak test.
In summaryThe output of this phase of the method should typically be a set of test definitions, not one single one. As already
discussed, keeping tests modular and simple is a much better approach than trying to kill all the birds with one
boulder.
Footnote: The soft squidgy side of performance testingTangent: Enterprise change control processes
Taking the premise discussed above – that it is impossible to test every possible report execution – raises an
interesting question around change control and the stability of a Production environment. If your company is one
where releases must be triple tested and signed off in the blood of your firstborn the you might want to have this
conversation sooner rather than later. Why? Well if your system has any element of ad-hoc usage then there is a
chance that a user will run a query that disrupts other users on the system. There is always the potential for the
perfect storm of it being a critical business period, high user traffic, and a rogue ad-hoc query. Without proper
understanding of the nature of ad-hoc queries, an almighty witch hunt can ensue, looking for who broke Production.
If this kind of risk is unacceptable then you will have to look into mitigating it with things like resource management
and environment partitioning.
Estimating performance test for project planning
The challenge that the complexity and multiple facets of performance testing gives is finding a way to communicate it
accurately to project managers and business sponsors.
Unlike an estimate for code delivery, which will be <x> days and after which the code will either function or not,
performance testing can pretty much take as long as you like, and the kicker is that the tests will only give varying
degrees of confidence that things should be OK in production, but with no guarantees.
Doing the initial analysis on the system is a fixed time, as is building the test rig(s) with which to test it. Once you
have done this, then defining the number of dashboards to test can be done based on the time available. The more
dashboards are tested, the greater certainty can be had that any problems will be uncovered. Other fixed tasks
should be a review of the overall design, as well as time to properly document the tests undertaken and an analysis
of the results obtained. It is a false economy to skimp on the documentation, as to do so renders almost useless the
results obtained for future work. Good documentation of testing undertaken and results found can greatly shorten the
time necessary in the future when revisiting the work either for a new phase of development or to troubleshoot
performance problems that have arisen.
What next?Next up is designing the test that we’ve just defined … read on!
o Home
Rittman Mead - Delivered IntelligencePerformance and OBIEE – part III – Test – DesignMarch 18th, 2013 by Robin Moffatt
Having defined our test, we can now design it. Wordplay? Not really. Regardless of how we run our tests, we’ve got
to define up front what it is we are testing. Once we’ve got that, we can then work out how to actually create a test
that simulates what we’ve defined.
Pretty boring huh? Can’t we just, y’know, like, run LoadRunner a bunch of times?
Sure, go ahead. But unless everything is hunky-dory and passes with flying colours, you’ll only end up retrofitting the
definition to your test in order to properly analyse the problem and create further tests to get to root-cause. Therein
lies the path to madness, and more importantly, wasted time and poor quality tests.
Don’t jump the gun and wade into the ‘fun’ part, otherwise you’ll spend too much time trying to figure out what to build
into your test on the hoof instead of carefully working it out up front.
The OBI stackThe key to designing a good performance test, and comprehending the results from it, is understanding the OBI
stack. Doing so enables us to inject tests into the OBI stack at various points to focus our testing and reduce the time
it takes to build a test. Being able to pinpoint where the work happens is also essential to focussing troubleshooting.
At a very high level, the stack looks like this:
(NB this is not an exhaustive diagram, for example, it doesn’t include any alternative security providers)
The user accesses OBI through their web browser, which connects to WebLogic. Weblogic is a web and application
server. It connects to OBI which in turn generates queries to run on the database
Examining the stack in a bit more detail, it looks like this:
When the user interacts with OBI all the web browser calls are done through WebLogic. WebLogic runs Java
deployments including one called analytics which communicates with the Presentation Services OBI component.
When the user requests a dashboard, the following happens:1. Presentation Services takes all of the constituent analyses in the dashboard and sends the Logical SQL for
each one to BI Server2. BI Server runs each Logical SQL through the RPD and generates one or more Physical SQL queries to run
on theDatabase3. Once the Database has executed all of the queries required for one of the Logical SQL statements, BI
Serverprocesses the results and does any further calculations, federation, and so on. A single resultset per Logical SQL request is passed back to Presentation Services.
4. Presentation Services receives the data from BI Server, sends any chart requests to Javahost, and does additional processing on the data for things like pivot tables. It then sends the rendered data for each analysis back to the web browser.
5. In the web browser each analysis in the dashboard can receive its data independently. As the data is received, the browser will render it.
For each Logical SQL request from Presentation Services, there will be one or more Physical SQL queries, but
just oneresultset passed back to Presentation Services. For each Dashboard there may be many Logical
SQL queries run.
£5 for the hammer, £95 for knowing where to hit itBuilding an accurate, flexible performance test rig takes time and therefore it is wise to test only what needs
testing. For example:
If our performance test is for a greenfield OBI deployment then we’d be pretty foolish not to conduct an end-to-
end test. We need to make sure that all the components are performing together at the required workload.
However, what about if we’re testing a existing mature deployment where there’s a specific problem in the
database? Wecould do an end to end test, but if all that’s changing is the database (for example, new indexes)
then we should probably just focus our test on the database.
What about if the RPD is changing in an existing deployment and nothing else? Is there really any point
performance testing the response time all the way to the user? If the data returned is the same then we can
presume that theperformance in Presentation Services and back to the user is going to be the same. Therefore
we can build a more streamlined (and thus efficient) test against just the BI Server (because the RPD has
changed) and the database (because a changed RPD could generate different Physical SQL).
The flipside of not testing what doesn’t need testing is to test what does need testing and may get overlooked.
For example, are your users connecting over 3G yet you’re conducting your performance tests on a LAN?
Tools & Designs for OBI performance testing
There are several different ways to build a performance test for OBIEE. I have described them here as well as
suggested where each one is most appropriate to use.
End-to-end (almost)
Traditional load testing tools such as JMeter, Oracle Application Testing Suite (OATS), and HP LoadRunner can be
used to simulate a user clicking on a report in a web browser and recording how long it takes for the report to run.
They can have multiple report definitions, and run many concurrently. This is usually sufficient for a comprehensive
performance test. One should be aware when using these that there is still an element that has not been tested:
1. Outbound. A user interacting with OBI uses a modern web browser with lots of asynchronous traffic (AJAX). For example, clicking on a prompt to get a list of values, clicking on the dashboard menu to see a list of available dashboards. A standard load test will typically only send the HTTP request necessary to trigger the dashboard refresh itself.
2. Inbound. A real user’s web browser will receive the data from a dashboard as it is returned from the OBI server and render it. Once it’s finished rendering, it is viewable to the user. If it is a large pivot table with hundreds of thousands of cells (“because that’s how the Excel report we’re duplicating does it”) then this might take many seconds or even minutes – or even not work at all. But a load test tool will parse the data that comes back from the server, and once all the data’s received, it will mark that as the completed response time – the time to render is not included.
This is not to dismiss this approach of test design – generally it is a very good one. These are the subtleties that a
standard load test can overlook. The AJAX dashboard menu call might not take long, but the list of values might be a hidden bottleneck if it hits an unoptimised data source (for example, SELECT DISTINCT DIM_VALUE FROM 60M_ROW_UNAGGREGATED_DIM_TABLE).
It is important that the above limitations are noted, and the test design adjusted to compensate where these
weaknesses are observed (for example, large pivot tables, list of values, etc).
Presentation Services via Web Services
Snuck in with the other goodies of SampleApp v207 is LoadTest.jar. You’ll find it in /home/oracle/scripts/loadtest, along with a readme file detailing how to use it. It takes as input a
single Dashboard path, and runs concurrent user requests against it. Since it is using a dashboard path we can
presume it is interfacing with Presentation Services, and my guess is that it utilises the OBIEE web services to
generate the load.
This utility will presumably have the same limitation as JMeter, LoadRunner et al in that page render times are not
included in the response time. LoadTest.jar also only returns timings at a total level for a batch of concurrent users –
how long does it take, in total, for x users to run a given dashboard. Individual response times from the BI Server for
the constituent queries can be seen in Usage Tracking. LoadTest.jar could also be invoked multiple times to simulate
multiple users/dashboards and the timing for each invocation be recorded to get a more granular set of response
times. Finally, LoadTest.jar can only fetch a dashboard in its default state – there is no apparent way to change
prompt values or interact with hierarchical columns, drill paths, etc.
An alternative to LoadTest.jar for this type of testing could be using JMeter to invoke the web services, which could
give more flexibility (at the expense of time to set it up and complexity to maintain).
BI Server + Database
The BI Server supports ODBC or JDBC for communication with clients (such as Presentation Services). We can use
a client to send Logical SQL requests to it and record the response time. Because Presentation Services also sends
Logical SQL to BI Server this test approach is a good one because from the BI Server + Database point of view, the
behaviour is exactly the same whether the Logical SQL comes from Presentation Services or another client (eg
injected by us for performance testing).
nqcmd is the ODBC client that is provided with OBIEE. You can wrap this in a series of shell scripts to create a
complete test, or you could build a test using JMeter with the OBIEE JDBC driver for a more flexible and scalable
test rig.
What this method doesn’t test is Presentation Services and upstream, so the additional processing of resultsets,
passing the data back to the web browser, and the rendering of it. But, if you are targeting your testing at just the
RPD and Database then this is a very good way to avoid unnecessary complexity.
Database
If nothing in your OBI configuration has changed and you just want to test the performance of the database, then you
can just use standard database performance test tools. Take the Physical SQL from the report being tested and run it
against the database using your database’s standard tools.
This is useful if you are doing tuning work at the database level (partitioning, indexing, etc), but only use this
approach if nothing is changing in your RPD. As soon as the RPD changes, different physical SQL could potentially
result.
Remember also that the response time from a physical SQL executed on the database is not the same as the
response time of a report – it still has to go back up through the stack back to the user’s web browser. For a complex
report or a large number of rows this can add significant time.
The only genuine end-to-end test – a stopwatch!
I kid you not – before spending weeks and £££ building complex LoadRunner scripts, stop and think about the testing
you are doing. If you want to test all the dashboards with a single user concurrency – find a willing monkey to sit there
for 20 minutes and run the dashboards. Maybe you need to test multiuser concurrency but can make do with fewer
dashboards tested this way than for a single user.
This is also a very easy way to determine page render times. As I discussed in the paragraph above, traditional load
test tools such as LoadRunner and JMeter only measure the time taken for an OBI dashboard to return data and not
how long it takes to render it. For the user obviously the only time they are going to be interested in is the complete
time including rendering. So using a stopwatch (or more scientifically, inbuilt browser tools such as Chrome’s
Developer Tools) you can measure the complete time as the user will experience it.
Which tool?In an OBI performance test lifecycle I would expect to use several of the different tools described above. Typically a
greenfield test will look at the performance of the stack end-to-end. If there are problems with the end-to-end, or if
the testing is in response to a problem in an existing system, I would use a response time profile (of which more
later) to understand where the bottleneck lay and focus the testing (and tuning) there. For example, if the bottleneck
appeared to be the SQL that the RPD was generating I might use nqcmd to validate changes I made to the RPD
and/or the database model
Test Design tipsWhichever method you use, bear the following in mind:
1. Generating a big load is easy; generating a big load that provides useful data is more difficult. For example, when a test shows 100 concurrent users running 10 different dashboards doesn’t work, how do you break it down into components to see where the bottleneck is? A good test design will take this into account up front and include granular response times and instrumentation.
2. K.I.S.S. : Sometimes a set of shell scripts run concurrently will do just as good a job of applying a high load to OBI as a “proper” load testing tool can. The time command will always give you the duration of an execution, and you can modularise sets of shell scripts to your heart’s content.1. If you use shell scripting, don’t forget to KISS – a complex mess of shell scripts isn’t Simple either.
3. You may see a conflict between the above points. How do you build a test that is granular and well instrumented, yet isn’t overly complex? Well, therein lies the gold ;-) Because all performance tests are different, I can only outline here good approaches to take, but you have to walk that line between complexity and flexibility in your design.
Heuristic and Holistic performance testingThere is more than one way to evaluate the performance of OBIEE. Tools like those I have described above are great
for simulating known workloads with a fixed definition. Unfortunately, all of these assume a known set of reports and
predicates, and user behaviour. What about testing a system where there is ad hoc capability and/or a great number
of possible predicate values and thus execution plans?
Here I would recommend a thorough review of the system’s implementation, with the view to determining possible
bottlenecks before they occur. It is more difficult to prescribe this area as it comes down to a combination of personal
experience of good design, careful evaluation of right practices, and the ability to isolate and test particular
configurations.
The difficulty with this type of testing is that is produces less tangible data with which to say yea or nay on a system’s
ability to perform. It means that the audience for the performance test report must be receptive to “It Depends”. Just
because a table doesn’t have a particular index doesn’t mean in itself that the performance test is a failure and the
index must be defined before go live. What it does mean is that the missing index may cause a worse execution plan
which may require additional database resources which may limit how many queries can run concurrently before a
bottleneck is reached.
For specific suggestions of what to examine in this type of evaluation, see here. I am well aware of the apparent
contradiction in advocating a heuristic approach here yet criticising “best practices”. My problem with best practices is
when they are treated as absolutes and without context; they normally originate from good intentions and are
subsequently abused. In the context of evaluating a system’s performance that is impossible to comprehensively test
(eg ad hoc report workload) then looking at how past implementations of the technology is the only sensible option.
The success of these past implementations may or may not be encoded as ‘best practices’; the point is that they are
evaluated in a conscious manner and not followed blindly.
Resource management
To reduce the risk to a Production system of running ad hoc or untested reports, resource management at the
database level could be used. On Oracle, Database Resource Manager (DBRM) enables queries to be allocated a
greater or lower priority for CPU on the database, or limit the execution time. Fixed reports which have been through
a strict performance QA process could be given greater resource since the effect has been quantified and analysed in
advance, whilst ad hoc or unproven reports could be more restricted.
To implement this, pass through the report or user name through in the connection pool, and then define a
Resource Manager rule to assign the resource management policy accordingly.
Resources can also be controlled at the BI Server level by setting restrictions on query runtimes and rows returned
on a per-application role basis.
What next?Now that we’ve defined the test we want to perform, and have designed how it will be implemented, we move onto
actuallybuilding it, here
o Home
Rittman Mead - Delivered IntelligencePerformance and OBIEE – part IV – Test – BuildMarch 18th, 2013 by Robin Moffatt
This article is the fourth in a series about performance and OBIEE. In previously articles I described how test
design should be approached, and in this article I explain in detail how to use some of the tools available to build
the tests.
JMeter
JMeter is open-source software maintained by the Apache Software Foundation. It is a flexible and powerful tool that
has a GUI design interface that can run on any java-supporting desktop (e.g. Windows/Linux/Mac). The test
execution can be run from the GUI, or “headless” from the commandline.To get started with JMeter, simply download it, and uncompress the archive (tgz / zip). Go to the bin folder and
doubleclick on jmeter.sh or jmeter.bat. This will launch JMeter.
There are plenty of JMeter tutorials on the web and I am not going to replicate them here. Any tutorial that
demonstrates how to record a web browsing session into a JMeter script should suffice for creating an initial OBIEE
load test. Below I will detail some specifics that it is useful to be aware of.
When a user clicks on a dashboard in OBIEE, the server returns shortly after the text “Loading…”. You need to
make sure that your test doesn’t incorrectly accept this as the completed report. There are a couple of ways to do
this:o Design your test so that it waits until the text isn’t “Loading…”
o Set NewCursorWaitSeconds in instanceconfig.xml so that the “Loading…” text isn’t displayed
before the query results are returned. See this blog post for more details
The JMeter Plugins are very useful for additional graphing options and better user control (eg jp@gc –
Stepping Thread Group)
Try to use variables throughput to increase code reusability and reduce maintenance. It’s worth taking time to
refactor a test that has evolved into something complex.
Use the Constant Timer object to add think time
Response Assertion steps are a very good way of ensuring that your test is getting the result it should at
each stage. For example, to check the OBIEE login page is loading, check for Enter your user id and password..
Use the Not option in a Response Assertion to check for things that definitely shouldn’t be there, such
as Odbc driver returned an error or Loading…
For a flexible test, parameterise the dashboard pages fetched. This is done in several stages:
1. Add a CSV Data Set Config step, configured to read a TSV (Tab-Separated) file from a path you specify.
NB relative paths in JMeter are relative to the folder that the JMeter script (JMX) resides
If you want to end the test once each dashboard page has been run once, set Stop thread on EOF to
False. To control the longevity of the test elsewhere, set Recycle on EOF to True
2. Load OBI’s Catalog Manager and navigate to the part of the catalog for which you want to generate a list of
dashboards. Go to Tools -> Create Report. Set the Type to Dashboard and
columns: Folder and Dashboard Page Name.
Save the resulting TSV file local to the JMeter script you have built.
3. The TSV file is written with a header, and this acts as a source for the variable names, so in this
case Folder andDashboard Page Name. Use these in the Dashboard HTTP request
4. Finally, use the following trick to ensure that each dashboard is recorded as a separately labelled
transaction. Insert a BeanShell PostProcessor as a child of the HTTP Request with the following code:
5. prev.setSampleLabel("Get Dashboard: " + vars.get("Dashboard Page Name"));
Validate your JMeter test response times by actually using the system yourself, both at single user and when the
test is ramped up. If JMeter says response times are subsecond but you’re waiting 30 seconds to run a report, it’s
possible that your JMeter test is not measuring the correct timings.
If a system under test is failing to cope, an increased response time and/or increase error rate is usually seen. To
avoid unnecessarily excessive test durations, a test can be set to abort at a certain threshold, using the jp@gc –
AutoStop Listener step.
A simple OBIEE JMeter script may have the following steps:
A sample OBIEE JMeter jmx script can be downloaded from here
The other thing to remember when you are building your JMeter tests is where and how you will run them:
Be very concious of where you run your JMeter script from. If you run it over a poor wifi connection from a 3 year
old laptop, don’t be surprised if your test apparently tells you that your 40-core OBIEE server only supports a
handful of users. Rather than observing bottlenecks on OBIEE, you’ll actually just be measuring how easy it is
to saturate your local machine’s CPU and/or network connection. Particularly around network, be aware that
each dashboard request will pull back the complete dashboard content, which can be 100s of KB. Multiply that by
the number of simultaneous users you plan to simulate, and you’ll quickly see that your JMeter script needs to run
from a machine with a nice fat network pipe
JMeter can be run distributed across servers, controlled from a central one. This is a good way to simulate many
users if you find a single machine cannot keep up with the load.
JMeter can be run from the command line if you are not able (or don’t want) to use a GUI.
BI Server + DatabaseThe BI Server uses ODBC or JDBC for communication with clients (such as Presentation Services). We can use a
client to send Logical SQL requests to it and record the response time :
nqcmd
nqcmd is the ODBC client that is provided with OBIEE. You can wrap it in a series of shell scripts to create a
complete test
This method is as simple as extracting the Logical SQL for a report, putting it in a file, and referencing this file in
invoking nqcmd.
Extracting the Logical SQL can be done from several places including:
Usage Tracking
select query_blob from s_nq_acct;
nqquery.log
You’ll find this file in the path$FMW_HOME/instances/instance1/diagnostics/logs/OracleBIServer/
coreapplication_obis1/ (it will be different for deployments using scaleout).
You can also view and download nqquery.log through Enterprise Manager
Answers editor
This is particularly useful for extracting Logical SQL in a report you’re working on or investigating.
Go to Edit Report and then the Advanced tab:
Having extracted the Logical SQL, write it to a file, for example test_report.lsql. I use the suffix lsql (Logical
SQL) to differentiate it from normal SQL (‘Physical’ SQL).
Now you can send the Logical SQL to the BI Server from an ODBC/JDBC client, of which the most commonly used
with OBIEE is nqcmd.
To use nqcmd, you first need to set up the OBI environment variables. On Linux/Unix this is done by “dot-sourcing”
a file which sets the environment for the current shell. The first character to type is dot, then space, then the path to the bi-init.shscript:
cd $FMW_HOME . instances/instance1/bifoundation/OracleBIApplication/coreapplication/setup/bi-init.sh
On Windows, you can just run bi-init.cmd and it will open a new command window for you to use, with the
environment correctly set.
Once the environment is set correctly, call nqcmd, supplying:
The DSN of the BI Server
o on Linux/Unix as defined
in$FMW_HOME/instances/instance1/bifoundation/OracleBIApplication/coreapplication/setup/odbc.ini
o In Windows, it’s defined in Administrative Tools -> Data Sources
Your OBIEE username and password
The path of the Logical SQL to run:
$ nqcmd -d AnalyticsWeb -u Prodney -p Admin123 -s test_report.lsql
This will execute the Logical SQL and return the results to your screen. With a few more tweaks we can easily get
some performance data:
Unless you want to see the data, use the -o flag to route it to a file, or -q for no output. Remember if there is lots
of data from the query there will be a time overhead in sending it all to your console (or to file with -o)
Use the undocumented but very useful -T flag to return a detailed timings breakdown.
On Linux/unix prefix the nqcmd call with time to give a report of the time taken by the whole command:
$ time nqcmd -d AnalyticsWeb -u Prodney -p Admin123 -T -s test_report.lsql -q
---------------------------------------------------------------------------
---- Oracle BI ODBC Client Copyright (c) 1997-2011 Oracle Corporation, All rights
reserved ---------------------------------------------------------------------------
---- Connection open with info: [0][State: 01000] [DataDirect][ODBC lib] Application's WCHAR type must be
UTF16, because odbc driver's unicode type is UTF16 […] Clock time: batch start: 08:08:07.000 Query from: 08:08:07.000 to:
08:08:07.000 Row count: 36 total: 0.046 prepare: 0.003 execute: 0.034 fetch: 0.009 Cumulative time(seconds): Batch elapsed: 0 Query total: 0.046 prepare:
0.003, execute: 0.034, fetch: 0.009, query count: 1, cumulative rows: 0
---------------------------------------------- Processed: 1 queries real 0m1.783s user 0m0.060s
sys 0m0.017s
Running Logical SQL through JMeter
You can build a more flexible and scalable test rig based on the same design as above using JMeter with the OBIEE
JDBC driver.Take the OBI JDBC driver file bijdbc.jar from $FMW_HOME/Oracle_BI1/bifoundation/jdbc/ and
put it in the JMeter /lib folder, or specify it in JMeter’s root test step under Add directory or jar to classpath.
In JMeter add a User Group and within it:
JDBC Connection Configuration
o Variable Name : eg, OBIo Validation Query : blank
o Database URL: jdbc:oraclebi://<host>:9703/ (see the jdbc README.txt for full syntax of the
URL including clustering)o JDBC Driver class: oracle.bi.jdbc.AnaJdbcDrivero Username + Password : Valid OBIEE logins
JDBC Request
o Variable Name : OBI (or whatever you specified in the config above)
o Query Type : Select Statement
o SQL Query: <Your Logical SQL>
Add a listener to visualise the results, such as jp@gc – Response Times Over Time (part of JMeter Plugins)
Since this is JMeter, you can easily parameterise this test, running a predefined set of Logical SQL extracted from
Usage Tracking or nqquery.log.
Download the JMeter JMX script here.
DatabaseIf nothing in your OBI configuration has changed and you just want to test the performance of the database, then you
can just use standard database performance test tools. Take the Physical SQL from either the nqquery.log or (in 11.1.1.6+) tableS_NQ_DB_ACCT and run it against the database.
For example, using sqlplus, the input file would look like this, with a set termout off to supress the rows,
and exit to quit sqlplus after execution:
$ cat test_report.sql set termout off
select sum(T233609.DEPARTURES_PERFORMED) as c1, T233732.Cal_Month as c2, T233804.ROUTE as c3from BI_AIRLINES.OBIEE_ROUTE T233804 /* 18 ROUTE */ , BI_AIRLINES.OBIEE_TIME_MTH_D T233732 /* 41 TIME MONTH */ , BI_AIRLINES.DOMESTIC_SEGMENT T233609 /* 01 DOMESTIC Segment */where ( T233609.DEST = T233804.DEST and T233609.MONTH = T233732.Cal_Month and T233609.ORIGIN = T233804.ORIGIN and T233609.QUARTER = T233732.Cal_Qtr and T233609.YEAR = T233732.Cal_Year )group by T233732.Cal_Month, T233804.ROUTE;
exit
Run it with time prefix to see how long it takes:
$ time sqlplus BISAMPLE/BISAMPLE @test_report
SQL*Plus: Release 11.2.0.3.0 Production on Wed Feb 6 22:32:41 2013
Copyright (c) 1982, 2011, Oracle. All rights reserved.
Connected to:Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - ProductionWith the Partitioning, OLAP, Data Mining and Real Application Testing options
Disconnected from Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - ProductionWith the Partitioning, OLAP, Data Mining and Real Application Testing options
real 0m0.669suser 0m0.028ssys 0m0.083s
LoadTest.jarSnuck in with the other goodies of SampleApp v207 is LoadTest.jar. You’ll find it in /home/oracle/scripts/loadtest, along with a readme file detailing how to use it. It takes as input a
single Dashboard path, and runs concurrent user requests against it
[oracle@obieesampleapp loadtest]$ ./runtest Start time: Wed Feb 6 22:42:39 GMT 2013Load Test Starting...
----------------------------------------------Creating User Sessions for Concurrency Test..Total active sessions: 54
Initiating Queries..Total queries initiated: 54
Cleaning up User Sessions created for Concurrency Test.. - Remaining Active Sessions: 54Completed User Sessions Cleanup----------------------------------------------
Load Test Completed...End time: Wed Feb 6 22:42:45 GMT 2013
It requires a list of usernames (all having the same password) and these users to have been created in the security
directory beforehand.
What next?With a test designed and built we’re now ready for launch … read on to see how best to make sure all the important
metrics are captured with your test when you execute it.
o Home
Rittman Mead - Delivered IntelligencePerformance and OBIEE – part V – Execute and MeasureMarch 18th, 2013 by Robin Moffatt
Having designed and built our tests, we now move on to looking at the real nitty-gritty – how we run them and collect
data. The data that we collect is absolutely crucial in getting comprehensible test results and as a consequence
ensuring valid test conclusions.
There are several broad elements to the data collected for a test:
Response times
System behaviour
Test details
The last one is very important, because without it you just have some numbers. If someone wants to reproduce the
test, or if you want to rerun it to check a result or test a change, you’ve got to be able to run it as it was done
originally. This is the cardinal sin that too many performance tests I’ve seen commit. A set of response times in
isolation is interesting, sure, but unless I can trace back exactly how they were obtained so that I can:
Ensure or challenge their validity
Rerun the test myself
then they’re just numbers on a piece of paper.
The other common mistake committed in performance test execution is measuring the wrong thing. It might be a
wrong metric, or the right metric but in the wrong context. For example, if I build a test that runs
through multiple dashboards, I could get a response time for “Go to Dashboard” transaction:
But what does this tell me? All it tells me really is that some of my dashboard transactions take longer than others to
run. Sure, we can start aggregating and analysing the data, talking about percentile response times – but by only
measuring the transaction generically, rather than per dashboard or dashboard type, I’m already clouding the data.
Much better to accurately identify each transaction and easily see the clear difference in performance behaviour:
How about this enticing looking metric:
We could use that to record the report response times for our test, yes? Well, honestly, I have no idea. That’s
because a “Request” in the context of the OBIEE stack could be any number of things. I’d need to check the
documentation to find out what this number was actually showing and how it was summarised. Don’t just pick a
metric because it’s the first one you find that looks about right. Make sure it actually represents what you think it does.
As Zed Shaw puts it:
It’s pretty simple: If you want to measure something, then don’t measure other shit.
Please consider the environment before running this test
Your test should be done on as ‘clean’ an environment as possible. The more contaminating factors there are, the
less confidence you can have in your test results, to the point of them becoming worthless.
Work with a fixed code version. This can be difficult to do during a project particularly, but there is little point
testing the performance of code release 1.00 if when you come to test your tuning changes 1.50 is in use. Who
knows what the developers changed between 1.00 and 1.50? It whips the rug from out under your original test
results. By fixed code, I mean:o Database, including:
DDL
Object statistics
o BI Server Repository (RPD)
o Dashboard and report definitions (Webcat)
If you can’t insist on this – and sometimes pragmatism dictates so – then at least have a very clear
understanding of the OBIEE stack. This way you can understand the potential impact of an external code
change and caveat your test results accordingly. For example, if a change was made to the RPD but in a
different Business Model from the one you are testing then it may not matter. If, however, they have
partitioned an underlying fact table, then this could drastically change your results to the extent you should be
discarding your first results and retesting.
In an ideal world, all the above code artefacts will be under source control, and you can quote the revision/commit
number in your test log.
Make sure the data in the tables from which you are reporting is both unchanging and representative of
Production. Unchanging is hopefully obvious, but representative may benefit from elaboration. If you are going
live with 10M rows of data then you’d be pretty wise to do your utmost to run your performance test against 10M
rows of data. Different types of reports might behave differently, and this is where judgement comes in. For
example, a weekly report that is based on a fact table partitioned by week might perform roughly the same
whether all or just one partition is loaded. However, the same fact table as the source for a historical query going
back months, or a query cutting across partitions, is going need more data in to be representative. Finally, don’t
neglect future growth in your testing. If it’s a brand new system with brand new data, you’ll be starting with zero
rows on day one, but if there’ll be millions of rows within a month or so you need to be testing against a million
rows or so in your performance tests.
The configuration of the software should be constant. This means obvious configuration such as BI Server
caching, but also things like version numbers and patch levels of the OBIEE stack. Consider taking a snapshot of all main configuration files (NQSConfig.INIinstanceconfig.xml, etc) to store alongside your test data.
o You should aim to turn off BI Server caching for your initial tests, and then re-enable it if required as a
properly tested optimisation step. The appropriate use and implementation of BI Server caching is discussed
in the optimisation article of this series.
MeasureBefore you execute your performance test, work out what data you want to collect and how you will collect it. The
reason that it is worth thinking about in advance is that once you’ve run your test you can’t
usually retrospectively collect additional data.
The data you should collect for your test itself includes:
Response times at the lowest grain possible/practical – see Dashboard example above. Response times should
be 2-dimensional; transaction name, plus time offset from beginning of test.
Number of test users running over time (i.e. offset from the start of your test)
Environment details – a diagram of the top-to-bottom stack, software versions, code levels, and data volumes.
Anything that is going to be relevant in assessing the validity of the test results, or rerunning the test in the future.
System metrics – if response times and user numbers are the eye-catching numbers in a test, system metrics
are the oft-missed but vital numbers that give real substance to a test and make it useful. If response times are
bad, we need to know why. If they’re good, we need to know how good. Both these things come from the system
metrics.
Query Metrics – depending on the level at which the testing is being done, collecting metrics for individual query
executions can also be vital for aiding performance analysis. Consider this more of a second round, drill down,
layer of metrics rather than one to always collect in a large test since it can be a large volume of data.
Response timesDepending on your testing method, how you capture response time will be different. Always capture the raw response
time and test duration offset – don’t just record one aggregate figure for the whole test. Working in BI you hopefully
are clear on the fact that you can always aggregate data up, but can’t break a figure down if you don’t have the base
data.
JMeter has “Sample time” or “Response Time”. Use the setSampleLabel trick to make sure you get a response
time per specific dashboard, not just per dashboard refresh call. Some useful listeners to try out include: jp@gc - Response Times Over Time jp@gc - Response Times vs Threads (although be aware that this shows an average response time,
which is not the best summary of the metric to use) View Results in Table
JMeter can also write data to csv file, which can be a very good starting point for your own analysis of the data,
If you are doing a test for specific tuning, you might well want to capture response times at other points in the stack
too; for example from the database, BI Server, and Presentation Server.
Whichever time you capture, make sure you record what that time represents – is it response time of the query at the
BI Server, response time back to the HTTP client, is it response time including rendering – and so on. Don’t forget,
standard load test tools such as JMeter don’t include page render time.
System metrics
There are two key areas of system metrics:
Environment metrics – everything outside OBI – the host OS, the database, the database OS.
OBI metrics – internal metrics that let us understand how OBI is ticking along and where any problems may lie
One of the really important things about system metrics is that they are useful come rain or shine. If the performance
is not as desired, we use them to analyse where the bottlenecks are. If performance is good, we use them to
understand system behaviour at a “known-good” point in time, as reference for if things do go bad, and also as a
long-term capacity planning device.
Some of the tools described below for capturing system metrics could be considered for putting in place as standard
monitoring for OBIEE, whilst others are a bit more detailed than you’d want to be collecting all the time.
OS metrics
OS stats should be collected on every server involved in the end-to-end serving of OBIEE dashboards. If you have a
separate web tier, a 2-node OBIEE cluster and a database, monitor them all. The workload can manifest itself in
multiple places and the more “eyes-on” you have the easier it is to spot the anomalies in behaviour and not just be
stuck with a “it was slow” response time summary.
As well as the whole stack, you should also be monitoring the server(s) generating your load test. If you’re testing
large numbers of concurrent users the overhead on the generator can be very high, so you need to be monitoring to
ensure you’re not hitting a ceiling there, rather than in what is being monitored.
On Windows, I would use the built-in Performance Monitor (perfmon) tool to collect and analyse data. You can
capture CPU, IO, Memory, Network and process-specific data to file, and analyse it to your heart’s content afterwards
within the tool or exported to CSV.
On Linux and other *nix systems there is a swathe of tools available, my tool of choice being collectl, optionally
visualised through graphite or graphiti. There are plenty of alternatives, including sar, glance, and so on
Finally, it’s worth noting that JMeter also offers collection of OS stats through the JMeter plugins project.
OBI metrics
The OBIEE performance counters are a goldmine of valuable information, and one it’s well worth the time mining for
nuggets. The counters give you both a better picture of how different workloads are executed within OBIEE but also
where any bottlenecks may arise.
The counters can be accessed in several ways:
1. Through Fusion Middleware Control, under Capacity Management -> Metrics -> View the full set of system metrics. This gives a point-in-time view of the data for the last 15 minutes, and does not store history.
2. Presentation Services includes its own Performance Monitor which can be accessed at http://<yourserver>:<analytics port>/saw.dll?perfmon. In OBIEE 10g it showed BI Server metrics too, but in 11g seems to only show Presentation Services (Oracle BI PS) metrics. It is point in time with no history.
3. Similar to Performance Monitor but with a lot more metrics available, DMS Spy is a java deployment hosted on the Admin Server by default, available at http://<yourserver>:<adminserver port>/dms/Spy
4. Through a manual call to opmn on the commandline. For example:
5.6. [oracle@obieesampleapp bin]$ ./opmnctl metric op=query
COMPONENT_TYPE=OracleBIServerComponent7. HTTP/1.1 200 OK8. Connection: close9. Content-Type: text/html10.11. <?xml version='1.0'?>12. <!DOCTYPE pdml>13. <pdml version='11.0' name='Oracle BI Server' host='obieesampleapp'
id='4399' timestamp='1359703796346'>14. <statistics>15. <noun name="Oracle BI Server" type="Oracle_BI_Applications">16. <noun name="Usage_Tracking" type="Oracle_BI_Thread_Pool">17. <metric name="Peak_Thread_Count.value">18. <value type="integer"><![CDATA[5]]></value>19. </metric>20. <metric name="Current_Thread_Count.value">21. <value type="integer"><![CDATA[2]]></value>22. </metric>23. <metric name="Lowest_Queued_Time_(milliseconds).value">24. <value type="integer"><![CDATA[0]]></value>25. </metric>26. […]
See the documentation for details27. DMS through WLST 28. BI Server diagnostics through ODBC/JDBC 29. It is worth noting that one place you cannot get the OBI metrics from any more is through JMX. In 10g this was
an option and interfaced very well with industry-standard monitoring tools. In 11g, JMX is available for metrics outside core OBI, but not the core metrics themselves.
In addition to the out-of-the-box options above, here at RittmanMead we have developed our own OBIEE monitoring
tool. DMS metrics are stored directly on disk or through a database, enabling both immediate and retrospective
analysis. Custom dashboards enable the display of both OBIEE and OS data side-by-side for ease of analysis.
Integration with third-party tools is also an option.
Query Metrics
If you are running a test for a specific performance issue then capturing query metrics is important as these will feed
into the diagnosis that you do including building a time profile.
A Logical SQL query has two elements to it for which we capture information:
BI Server (Logical SQL)
o Query runtime
o Total Rows returned from the database
o Total Bytes returned from the database
o Number of database queries
Database (x n Physical SQL queries)
o Response time
o Rows returned
o Bytes returned
All of the above information can be obtained from the BI Server’s nqquery.log, or most of it from Usage
Tracking tablesS_NQ_ACCT and S_NQ_DB_ACCT.
For really detailed analysis you may want to capture additional information from the database about how a query ran
such as its execution plan. On Oracle, Real Time SQL Monitoring is a very useful tool, along with several others.
For further details, speak to a DBA … this is an OBIEE blog ;-)
ExecuteAnd now the moment you’ve all been waiting for … it’s party time!
Here is a checklist to work through for executing your test:
Clear down the logs of any component you’re going to be analysing (eg nqquery.log, sawlog.log)
Record any configuration/environment changes from the original baseline
Record test start time
Restart the OBIEE stack (i.e. WLS, OPMN)
Start OS metric collection
Start OBIEE metric collection (if used)
Run the test!
Monitor for errors and excessively bad response times
Record test end time
Record any errors observed during test
Copy all logs, metric outputs, etc to a named/timestamped folder
Monitoring the test as it executes is important. If something goes wrong then time can be saved by abandoning the
test rather than let the test script run to completion. There’s no point letting a big test run for hours if the results are
going to be useless. Some of the things that could go wrong include:
1. Your test script is duff. For example, there’s a typo in the login script and no users are logging in let alone executing dashboards. All your test will tell you is how fast OBIEE can reject user logins.
2. Your test has hit a performance bottleneck in the stack. If you leave your test running beyond a certain point, all you’re doing is collecting data to show how bad things still are. If response times are flat on their back at 50 concurrent users, what’s the point leaving a test to run all the way up to 200? It’s best to curtail it and move swiftly on with the analysis and tuning stage
3. Your test framework has hit a bottleneck in itself. For example, the host machine cannot sustain the CPU or network traffic required. If this happens then your test data is worthless because all you’re now measuring is the capacity of the host machine, not the OBIEE stack.
Monitoring for errors is also vital for picking up messages that OBIEE might start to produce if it is hitting an internal
threshold that could constrain performance.
Don’t fiddle the books!
If your test execution doesn’t work, or you spot an improvement or fix – resist the biggest temptation which is to
‘just fix it’. Hours become days with this approach and you lose complete track of what you changed.
Take a step back, make a note of what needs fixing or changing, and document it as part of the full cycle.
There is nothing wrong whatsoever with aborting a test for the reason that “I defined it incorrectly” or “I forgot to
change a config setting”. Better to have a half dozen aborted tests lying around showing that you got your hands dirty
than a suspiciously pristine set of perfectly executed tests.
Don’t forget that pesky documentation
Always document your testing, including method, definition, and results.
You will not remember precisely how you ran the test, even a few days later
How can you identify possible confounding of results, without recording a clear picture of the environment?
If you find something unexpected, you can quickly seek a second opinion
Without things written down, people will not be able to reproduce your testing
Test results on their own are worthless; they are just a set of numbers.
If it’s not written down, it didn’t happen
What next?With a test run completed and a set of data collected, it’s time to make sense of the numbers and understand what
they can tell us by analysing the results
o Home
Rittman Mead - Delivered IntelligencePerformance and OBIEE – part VI – Analysing resultsMarch 18th, 2013 by Robin Moffatt
This part of the OBIEE performance cycle is the one which arguably matters most. Having defined what we’re going
to test, built a means by which to test it, and executed that test, we now need to sift through the tealeaves and work
out what the data we collected is telling us. Except we’re not going to use hocus-pocus like tea leaf reading, gut
feeling or best practice checklists, we’re going to use cold hard data and analysis.
Analysing the data breaks down into several stages, and is often an iterative process:
1. Analyse the net response time. Is it as fast as it needs to be, at the required level of user concurrency?2. If the response time is too slow (“too slow” being defined by you or your users, in advance of the test),
then diagnose to determine why. This is another phase of analysis, breaking down the net response time into its constituent parts, analysing system and OBI metrics for signs of a bottleneck. The output of this phase will be an hypothesis as to the cause of the performance problem
3. Based on the diagnosis of the issue, apply one change to the system to improve it, that is, resolve the performance issue. Having made one change (and one change only), the original test should be repeated and the analysis cycle repeated to determine the impact of the tuning.
AnalysisHow you analyse your data determines whether you will be accurately and fairly representing the results of the test
in your diagnoses and conclusions.
Avoid Averages
From your test execution you will have a series of response times. You need to summarise, that is, aggregate these
into a single figure to give as a headline figure in your test report. If you take only one thing away from reading this,
let it be the following point: don’t use average figures! I’ll say it again for emphasis : Averages are not a good way
to represent your test data. What I am saying here is nothing that you won’t read in every other decent article
written on performance testing and analysis.
When you average out a series of data, you mask and muddy your data by inadvertently hiding extreme values in the
series. A much better summary to use is the percentile.
Consider a performance test of a single dashboard for a single user. It is run ten times, so as to get a representative
set of data for the response time. Here are two series of data, both with an average response time of five seconds.
If we look at the90th Percentile for the same two series of data, we can see that series ‘A’ has a response time
of ten seconds, whilst series ‘B’ has a response time of six seconds.
As a user, if you run this dashboard, which behaviour would you prefer? Series ‘A’, where it might take a second to
run or it might take ten seconds, or Series ‘B’ where it is going to be five seconds, give or take one second either
side? As human beings we like consistency and certainty. Sure, it’d be nice if the dashboard ran in a second, but
most people would ratherknow that it’s definitely going to run within six seconds and not almost double that. That
uncertainty can also be seen in the standard deviation figure in the two series. The lower the standard deviation, the
more consistent the response times are.
For more detail and clear statistical explanations, read “Averages Only” in Zed Shaw’s Programmers Need To
Learn Statistics and “Percentile Specifications” in Cary Millsap’s Thinking Clearly about Performance.
Throw away your test data
Well, not literally. But, if you are testing user concurrency, make sure that when you calculate your percentile (eg 90th
percentile) response time, do it for a given number of users. Otherwise you are distorting the figure. Typically a test
will have a ‘ramp up’ period where the concurrent users are gradually introduced onto the system, until the target
number is active, at which point the system is in ‘steady state’. It is from this point, the steady state, that you should
be deriving your response time calculation. It is useful to look at how the response time might vary as the workload is
increased, but for an accurate figure of a response time at a given number of users, you should be ignoring the data
except where the full number of users was running.
Analysis summary
The output of this phase of the analysis should be very simple:
The 90th Percentile response time for dashboard <xx> is <yy> at a user concurrency of <zz>
And this should then satisfy a pass/fail criterion that was specified when you defined the test.
If the test passes, great. Record all your test parameters and data, and move on to the next test.
If the test doesn’t pass, then you need to work out why, and for that, see below.
I’m oversimplifying, since there is other data (eg standard deviation) that you might want to include in your summary,
along with some commentary around variances observed and so on.
Diagnosing poor OBIEE performanceGet to the root of the problem
So, the test results showed that the dashboard(s) run too slowly. Now what? Now, you need to work out why there is
a performance problem. I am deliberately spelling this out, because too many people jump forward to attempting
to fix a performance problem without actually understanding exactly what the problem is. They whip out their six-
shooters loaded with silver bullets and start blasting, which is a bad idea for two reasons:1. You may never know what the problem was – so you won’t be able to avoid doing it again! Everyone makes
mistakes; the mark of a good programmer is one who learns from them.If I run a dashboard on a 2 CPU 4GB server and find it’s slow, one option could be to run it on a 8 CPU 32GB server. Tada! It’s faster. But, does that mean that every report now needs to be run on the big server? Well, yes it’d be nice – but how do we know that the original performance problem wasn’t down to machine capacity but perhaps a missing filter in the report? Or a wrong join in the RPD? It could be an expensive assumption to make that the problem’s root causewas lack of hardware capacity.
2. In determining the root cause, you will learn more about OBIEE. This better understanding of OBIEE will mean you are less likely to make performance errors in the future. You will also become better at performance diagnostics, making solving live problems in Production as well as future performance tests easier and faster to resolve.
“I broke things, so now I will jiggle things randomly until they unbreak” is not acceptable – Linus Torvalds
There are always exceptions, but exceptions can be justified and supported with data. Just beware of the the silver
bullet syndrome…The unfortunate part […] is that rarely anyone goes back and does the root cause analysis. It tends
to fall into the bucket of “problem…solved”. – Greg Rahn
Performance vs Capacity
I always try to split it into #performance tuning (response time) and capacity tuning (throughput/scalability) – Alex
Gorbachev
Performance issues can be local to a report, or global to a system implementation and exacerbated by a particular
report or set of reports – or both.
If an individual dashboard doesn’t perform with a single user running it, then it certainly isn’t going to with a 100, and
there is clearly a performance problem in the design (of the dashboard, RPD, or physical data model design or
implementation).
However, if an individual dashboard runs fine with a single user but performance gets worse and worse the more
users that run it concurrently, this would indicate a capacity problem in the configuration or physical capacity of
your system.
So which is which? An easy way to shortcut it is this: before you launch into your mega-multi-user-concurrency tests,
test the dashboard with a single user. Is the response time acceptable? If not, then you have a performance
problem. You’ve eliminated user concurrency from the equation entirely. If the response time is acceptable, then you
can move onto your user concurrency tests.
If you have already run a big user concurrency test and are trying to identify whether the issue is performance or
capacity, then look at what happens to your response time compared to the number of users running. If the response
time is constant throughout then it indicates a performance problem; if it is increasing as more users are added it
shows a capacity (which can include configuration) problem. Being able to identify this difference is why I’d never run
a user concurrency test without aramp-up period, since you don’t get to observe the behaviour of the system as
users are added.
In the above graph there are two points evident:
1. Up to ten users the response time is consistent, around 30 seconds. If the response time needs to be faster than this then there is a performance problem
2. If 30 seconds is the upper limit of an acceptable response time then we can say that the system has a capacity of 10 concurrent users, and if the user concurrency needs to be greater than this then there is a capacity problem
Errors
Don’t overlook analysing the errors that may come out during your testing. Particularly as you start to hit limits within
the stock OBIEE configuration, you might start to see things like:
Too many running queries. Server is too busy to process any more queries at this time.
com.siebel.analytics.javahost.standalone.SAJobManagerImpl$JobQueueFullException
Graph server does not appear to be responding in a timely fashion. It may be under heavy load or unavailable.
The queue for the thread pool ChartThreadPool is at it's maximum capacity of 512 jobs.
If you see errors such as these then they will often explain response time variances and problems that you observe in
your test data, and should be top of your list for investigating further to resolve or explain.
Response time profiling
A good way to get started with identifying the root cause(s) of a problem is to build a time profile of the overall
response time. This is something that I learnt from reading about Method R, and is just as applicable to OBIEE as it
is to the Oracle RDBMS about which it was originally written. This link gives a good explanation of what Method R is.
You can improve a system without profiling, and maybe you can even optimize one without profiling. But you can’t
know whether a system is optimal without knowing whether its tasks are efficient, and you can’t know whether a given
task is efficient without profiling it. –Cary Millsap
Given the number of moving parts in any complex software stack there’s often more than one imperfection. The trick
is to find the most significant that will yield the best response time improvement when resolved. It also lets you
identify which will give the “biggest bang for your buck” – maybe there are several problems, but the top one requires
a complete redesign whilst the second one is an easy resolution and will improve response times sufficiently.
So in the context of OBIEE, what does a response time profile look like? If you hark back to the OBIEE stack that I
described previously, a simple example profile could look something like this:
Here we can see that whatever we might do the speed up the chart rendering (5 seconds) the focus of our
investigation should really be on the 20 second query run on the database, as well as the 10 seconds it takes BI
Server to join the results together. Can we eliminate the need for two queries, and can we do something on the
database to improve the query run time?
When building a time profile, start at the highest level, and break down the steps based on the data you have. For
example, to determine the time it takes Presentation Services to send a query to BI Server is quite a complex process
involving low-level log files. Yet, it probably isn’t a significant line entry on the profile, so by all means mark it down
but spend the time on the bigger steps – which is usually the fetching and processing of the report data.
A more complicated profile might be something like this:
Graphing a response time profile can also help us comprehend at a glance what’s happening, and also gives a
‘template’ to hold up to profiles that are created. In general you would want to see the split of a time profile heavily
weighted to the database:
If
the response time profile shows that just as much of the total response time is happening on the BI Server then I
would want to see what could be done to shift the weight of the work back to the database:
For more on this subject of where work should ideally be occurring, see the section below “Make sure that the
database is sweating”.
Here are the sources you can look for response time profile data, starting at the user interface and going down to the
database
Rendering time – Web browser profiler such as Chrome Developer Tools, YSlow, Google Page Speed
WebLogic – access.log will show the HTTP requests coming in
Presentation Services – sawlog0.log, but may require custom log levels to get at low-level information
BI Server
o nqquery.log
Time to create physical SQL(s), i.e. compile time
Time to connect to DB
Time to execute on DB
Time to process on BI server and return to PS
o Usage Tracking
S_NQ_ACCT S_NQ_DB_ACCT
Database – whilst profiling can be extended down to the DB (for example, using an 10046 trace in Oracle), it
makes more sense to do as a standalone analysis piece on an individual query where necessary. In extreme
examples the profiling could actually go beyond the database down into the SAN, for example.
Diagnosing capacity problems
If a dashboard is performing acceptably under a single user load, but performance deteriorates unacceptably as
the user currency increases, then you have a capacity issue. This capacity could be hardware, for example, you
have exhausted your CPUs or saturated your I/O pipe. Capacity can also refer to the application and how it is
configured. OBIEE is a powerful piece of software but to make it so flexible there are by definition a lot of ways in
which is can be configured – including badly! Particularly as user concurrency (as in, concurrent executing reports)
increases into three figures and above it may be the default configuration options are not sufficient. Note that this
“three figures and above” should be taken with a large pinch of salt, since it could be lower for very ‘heavy’
dashboards, or much higher for ‘light’ dashboards. By ‘heavy’ and ‘light’ I am primarily referring to the amount of work
they cause on the BI Server (e.g. federation of large datasets), Presentation Services (e.g. large pivot tables) and
Javahost (e.g. lots of chart requests such as you’d see with trellis views).
To diagnose a capacity problem, you need data. You need to analyse the response time over time against the
measures of how the system was performing over time, and then investigate any apparent correlations in detail to
ascertain if there is causation.
ObXKCD
This is where you may need to re-run your performance test if you didn’t collect this data the first time around. See
theSystem Metrics section above for details on how and what. The easy stuff to collect is OS metrics, including CPU,
Memory, Disk IO, and Network IO. You should include both the OBI and Database server(s) in this. Look at how this
behaves over time compared to the performance test response times. Using a relatively gradual user ramp-up is a
good idea to pinpoint where things might start to get unstuck, rather than just plain break.
Network bottleneck observed as load increases beyond c.9 active users
If the OS metrics are unremarkable – that is, there is plenty of capacity left in all of the areas but response times are
still suffering as user concurrency increases – then you need to start digging deeper. This could include:
OBI Metrics
Analysis of the performance of the database against which queries are running
End-to-end stack capacity, eg Network, SAN, etc.
OBI Metrics can be particularly enlightening in diagnosing configuration issues. For example, an undersized
connection pool or saturated javahost.
Don’t forget to also include the OBI logs in your analysis, since they may also point to any issues you’re seeing in the
errors or warnings that they record.
Additional diagnosis tips
Having profiled the response time you should hopefully have pinpointed an area for investigation for coming up with
your diagnosis. The additional analysis that you may need to do to determine root cause is very dependent upon the
area you have identified. Below are some pointers to help you.
Make sure that the database is sweating
As mentioned above, a healthy OBI system will wherever possible generally push all of the ‘heavy lifting’ work such
as filtering, calculations, and aggregations down to the database. You want to see as little difference between the
data volume returned from the database to the BI Server, and that returned to the user.Use nqquery.log to look at the bytes and rows that OBIEE is pulling back from the database. For example, you
don’t want to see entries such as this:
Rows 13894550, bytes 3260497648 retrieved from database query id: xxxx
(13.8 million rows / 3GB of data!)
If you return lots of of data from the database to the BI server, performance suffers because:
You’re shifting lots of data across the network, each time the query runs
As well as the database processing lots of data, the BI Server now has to process the same volume of data to
pare it down to the results that the user wants
If the data volumes are large the BI Server will start having to write .TMP files to disk, which can have its own
overhead and implications in terms of available disk space
You can read more on this topic here.
N.B. If you’re using cross-database federation then this processing of data by the BI Server can be unavoidable, and
is of course a powerful feature of OBIEE to take advantage of when needed.
A related point to this is the general principle of Filter Early. If dashboards are pulling back data for all months and all
product areas, but the user is only looking at last month and their own product area then change the dashboard to
filter it so. And if you use dashboard prompts but have left them unset by default then every time the user initially
navigates to the dashboard they’ll be pulling back all data, so set defaults or a filter in the constituent reports.
As a last point on this particular subject – what if there are 13 million rows pulled back from the database because the
user wants 13 million rows in their report? Well, other than this:
I would say: use the right tool for the right job. Would the user’s workflow be better served by an exception-based
report rather than a vast grid of data just ‘because we’ve always done it that way’? If they really need all the data,
then it’s clear that the user is not going to analyse 13 million rows of data in OBIEE, they’re probably going to dump it
into Excel, or some other tool – and if so, then write a data extract to do it more efficiently and leave OBIEE out of the
equation. If you want to make use of the metadata model you’ve built in the RPD, you could always use an ODBC or
JDBC connection directly into the BI Server to get the data out. Just don’t try and do it through Answers/Dashboards.Instrumenting connection pools
For a detailed understanding of how the database behaves under load as the result of BI queries, consider
usinginstrumentation in your connection pools as way of correlating [problematic] workload on the database with
originating OBIEE queries and users.
I have written previously about how to this, hereWhy’s it doing what it’s doing
If a report ‘ought’ to be running well, but isn’t, there are two optimisers involved to investigate to see why it is running
how it is. When the inbound Logical SQL is received by the BI Server from Presentation Services, it is parsed
(‘compiled’) by the BI Server through the RPD to generate the Physical SQL statement(s) to run against the
database.
To see how OBIEE analyses the Logical SQL and decides how to run it, use a LOGLEVEL setting of 4 or greater.
This writes the execution plan to nqquery.log, but be aware, it’s low-level stuff and typically for Oracle support
use only. To read more about log levels, see here. The execution plan is based entirely upon the contents of the
RPD, so if you want different Physical SQL generated, you need to influence it through the RPD.
The second optimiser is the database optimiser, which will take the Physical SQL OBIEE is generating and decide
how best to execute it on the database. On Oracle this is the Cost-Based Optimiser (CBO), about which there is
plenty written already and your local friendly DBA will be able to help with.
Footnote: Hypotheses
Finally, in analysing your data to come up with a diagnosis or hypothesis as to the root cause of the problem, bear
this quotation in mind:
If you take a skeptical attitude toward your analysis you’ll look just as hard for data that refutes your hypothesis as
you will for data that confirms it. A skeptic attacks the same question from many different angles and dramatically
increases their confidence in the results. John Rauser
What next?If your testing has shown a performance problem then you should by now have a hypothesis or diagnosis of the root
cause. Read all about optimisation here. If your testing has shown performance is just fine, you might want to read
it anyway …
o Home
Rittman Mead - Delivered IntelligencePerformance and OBIEE – part VII – Optimising OBIEE performance (“Tuning”)March 18th, 2013 by Robin Moffatt
Potato potato?When I first drafted this blog post, I gave it the title “Tuning”, rather than “Optimising”. Tuning is the word used so
often in the context of performance, but it can actually be a bit misleading. A system that has fundamental design
flaws doesn’t need “tuning” – it needs redesigning. Tuning can end up being a catch-all phrase meaning “fix
performance ”, regardless of the scale of the task.
Why does this matter? It matters, because if you are in the performance test phase of a project with a deadline to
keep to it’s useful to be honest with yourself and the project manager in describing the work that needs to be
undertaken to improve performance. If your analysis has shown that the design is sound but you’re hitting bottlenecks
in performance somewhere along the way, then it is “tuning” you’re doing. However, if your analysis is showing that
the metadata model you’ve built in the RPD stinks, the data model on the database sucks, and the dashboard design
is suspect – it’s not going to take a bit of “tuning” to fix, it’s going to take some serious redevelopment. From a point of
view of time estimation and expectations management, this is important.
A rough analogy would be with piano tuning. A structurally sound piano needs tuning periodically to keep it from
being out of tune. However, a piano with woodworm needs a lot more work doing to it than simple tuning – it needs
taking to the workshop.
The piano tuning analogy serves one more purpose too: asking a piano tuner to give you a step by step guide to
tuning a piano is almost as nonsensical as it is to expect a simple checklist to provide an way to comprehensively fix
(“tune”) the performance of OBIEE.
<rant>This section is deliberately at the end of a long and detailed series of articles describing how to test and analyse the
performance of OBIEE. ‘Tuning’ can be a bit of a weasel word in IT, implying light-touch, silver bullet changes that
magically improve a system’s performance, when actually it can mean randomly changing a bunch of things in the
blind and desperate hope of making bad performance better but with no way of knowing if it worked or what ‘it’ was.
</rant>Tuning should be undertaken only as part of a performance cycle. It should be approached from one of two ways:1. A performance problem has been identified, and you need to resolve it. You should have test data showing
where the problem lies, and the analysis you’ve done should have pointed to the root cause. In this case, the tuning you do ought to be nothing more than fixing the problem. It might take lots of diagnosis and investigation to establish the resolution of a diagnosed cause, but crucially all the work you do will be focussed on the root cause that you’ve identified.
2. You have a performance baseline, and you want to see if you can improve performance or capacity in general. There is no specific problem to resolve, but reducing the runtimes of queries would be nice. To be precise, we’re looking tooptimise a system.
Anti-patterns in performance optimisationThere are anti-patterns (“Bad Practices”, if you will) to good performance troubleshooting; here are some of them.
You should have your guard up if you see these, and challenge them!
Sacred Cows
Hero worship
Best practice!
Tweaking & Tinkering
Silver bullets
Golden rules
Cast iron guarantees
Never
Always
Take my word for it
Long bullet point lists
The only “best practice” you should be using all the time is “Use Your Brain”. – Tom Kyte
Generally good design principles to observe in OBIEEThese are not Best Practices! These are things that can work well, but have to be done with brain engaged!
They’re not bad practices either, they’re just good practices.
Ensure that the RPD has a correct dimensional Star model in the Logical (BMM) layer
Push the dimensional Star schema into the database; don’t build against a normalised data model if performance
is key requirement
Create aggregate tables to support user queries
o Use Usage Tracking to spot dimension levels that would benefit, or Summary Advisor on Exalytics
o Aggregate Persistence Wizard can do the initial hard work in plumbing the metadata into the RPD for you
You still need to maintain the aggregate, unless you are happy to just drop/recreate each time the data
changes
o Don’t forget to create associated aggregated dimension tables. These are particularly useful for good
performance of prompts where a distinct list of values at a level in a dimension are returned.
Make sure that aggregates get used when appropriate. Check the SQL that OBIEE is generating isn’t using a
less-efficient source table.
OBIEE is not an extraction tool, especially not in the front-end.
o If users really want a data dump, consider doing that for them outside of the tool, for example with sql*plus.
o To still make use of the metadata model in the RPD, but without causing big problems in Presentation
Services, use an ODBC or JDBC call into the BI Server directly to get the data dump out. Using this method,
you could hook in Excel directly to the BI Server.
The fastest query is one that never runs – challenge dashboard & reports designs. Don’t just copy what an
existing system does. Analyse the user’s workflow, to see if the reports you build can support and make more
efficient what the user does.
Generally you should avoid building the RPD against database views, as they can hinder OBIEE’s SQL
generation with the result of sub-optimal queries. Database views can also hide inefficient or unnecessary joins
and logic. Air your dirty washing in public, and put the underlying tables into the Physical layer of the RPD instead
and let OBIEE work with them.o This is not a hard and fast rule, and it is not a “Best Practice” (sigh). There will be some genuine cases where
a database view is a pragmatic solution to a particular data model issue.
Minimise the work being done by the BI Server. When using federation to join data across databases it is
unavoidable but generally it is to be frowned upon if within the same database. Wherever possible, all work
should be seen to bepushed down to the database.o Check how much data the BI Server pulls back from the database as a percentage of rows returned to the
user (low % is bad).
o Monitor the BI Server’s temp directory – if this is filling up with large files it means that the BI Server is having
to crunch lots of data
o How many database queries does one report trigger? (higher is generally less efficient).
o This SQL will help identify reports for investigation, using existing Usage Tracking data:
o SELECT SAW_SRC_PATH, o ROW_COUNT, o CUM_NUM_DB_ROW, o ( ROW_COUNT / CUM_NUM_DB_ROW ) * 100 AS ROWS_PCT, o TOTAL_TIME_SEC, o NUM_DB_QUERY o FROM S_NQ_ACCT o WHERE ROW_COUNT > 0 o AND CUM_NUM_DB_ROW > 0 o -- Update these predicates to tailor your results as requiredo -- AND TOTAL_TIME_SEC > 10 o -- AND CUM_NUM_DB_ROW > 10000 o ORDER BY 4 DESC
Size hardware correctly to support the BI Server and Presentation Server (based on past experience and/or
Oracle’sdocumentation)
Make sure that there a balanced hardware configuration throughout the stack (c.f. Greg Rahn and Oracle
documentation)
Optimising OBIEE furtherThe above section outlines some of the principles you should always be aiming to follow, or have a clear reason why
you’re not. There are some other techniques that can be worth investigating when you’re looking to optimise the
performance of OBIEE further, discussed below.
BI Server Caching
How have I got this far and still not mentioned caching? To horribly mix two food metaphors, caching is the icing on
the cake, it is not the bread and butter of good performance. If you are using caching as your sole means of
ensuring good performance then you are skating on thin ice.
That is not to say caching is bad. Caching is good, when its use is thought through and evaluated carefully. Caching
has an overhead in terms of management, so you cannot just chuck it in to the mix and forget about it. You need to
manage the cache to make sure you’re not serving up stale data to your end users. It might be fast, but it’ll
be wrong.
Caching can improve performance for several reasons:
It is typically faster to return the results of a query already run and stored in the cache than it is to re-execute the
necessary Physical SQL against the source database(s)
By not executing the Physical SQL on the database(s), we reduce both the load on the database, and the amount
of network traffic, freeing up these resources for non-cached queries
The cache stores the results of a Logical SQL query sent to the BI Server, not the data that the database returns.
If the BI Server is doing lots of work, for example, federating and aggregating lots of data across sources, then by
caching theresult post-processing, all of this work can be avoided by subsequent cache hits.
The BI Server cache is more advanced than a ‘dumb’ cache where only a direct match on a previous request will
result in a hit. The BI Server will parse a Logical SQL query and recognise if it is either a direct match, a subset , or an aggregate of an existing cache entry. So a cache entry for sales by day could well satisfy a subsequent
Logical SQL query for sales by year.
Pre-seeding the cache is a good idea, so that users all benefit from the cache, not just those who come along after
the first user has run a report that gets stored in the cache. There are a couple of ways to pre-seed the cache:1. A BI Delivers Agent with the Destination set to System Services : Oracle BI Server Cache. This agent could
optionally be set to run straight after your ETL batch has loaded the data.2. Directly from an ETL tool via ODBC/JDBC using the SASeedQuery statement.
If you don’t pre-seed the cache then only users running queries based on queries already run by others users will
benefit from the cache.
The flip-side of pre-seeding the cache is purging it, and there are two sensible ways to do this :1. Event Polling Table 2. ODBC/JDBC command to the BI Server, triggered by the completion of a data load (ETL)
Watch out for the Cache Persistence time in the Physical Table – this defines how long an entry remains in the
cache, rather than how frequently to purge it. If you have a daily data load, setting the cache persistence time to 24
hours will not do what you may think. If your data is loaded at 0300, the first user queries it and creates a cache entry
at 0900, that cache entry will remain until 0900 the following day, even though the cached data would have been
stale for six hours (since the subsequent data load at 0300).
Where Cache persistence time can be useful is in systems with frequent changes in the source data and you want
todeliberately introduce a lag in the data the user sees for the benefit of generally faster response times for end-
users. For example, you may have a trickle-fed ODS from which you are running OBIEE reports. If the data is being
loaded in near-real-time, and the users want to see it 100% current, then evidently you cannot use caching. However,
if the users would be happy with a lag in the data, for example ten minutes, then you could enable caching and set
the cache persistence time for the relevant physical table to 10 minutes. For the ten minutes that the data is in the
cache, the users get fast response times. This could be a pragmatic balance between freshness of data and
response times to get the data. Bear in mind that a query taking 2 minutes to run is going to be reporting on data that
is 2 minutes out of date already.
Use fast disk for cache and/or temporary files
OBIEE writes various temporary files, including cache data and work files, to disk. By default, these reside in$FMW_HOME/instances/instance1/tmp. You may find that using fast disk (e.g. SSD) or even RAM disk to
hold some or all of these temporary files instead could improve performance. Be aware that some of the work files
that OBIEE writes can be very big (as big as the data being pulled back from the database, so in the order of
gigabytes if you’re unlucky).
Web Tier for static content caching
In a standard OBIEE installation, WebLogic acts as both the application server (running java code, communicating
with Presentation Services etc) as well as the HTTP server, handling inbound connections from the web browser,
serving up static files such as CSS stylesheets.
It can sometimes be beneficial to introduce a separate HTTP server such as Oracle HTTP Server (OHS), leaving
WebLogic to just act as the application server. A dedicated HTTP server such as OHS can be configured to cache
and compress static files which can improve the response time for users especially if the network is not a fast one.
For more information, see Venkat’s article Anatomy of BI EE Page Rendering – Use of HTTP/Web Server &
Compression
Scale out / Scale up
Increasing the number of instances of the system components can help maximise the capacity of the overall BI
Domain and enable it to use the full resource of the hardware on which it is running.
Scaling out is to add additional physical servers and extend the BI Domain onto them. Scaling up is to just increase
the number of one or more of the components that are running on an existing server.
An example of where this can be useful is the Javahost component. It is configured with a default maximum number
of jobs that it can handle. Certain workload types and volumes can hit this maximum with relative ease, so increasing
the number of Javahosts can improve the performance by reducing contention for the process.
In general, if you don’t have any evidence of a capacity limit being reached or in danger of being reached, I would be
reluctant to ‘inoculate’ a system by scaling (adding additional component instances) ‘just in case’. You will only add to
the number of moving parts to keep track of (and increase complexity of configuration such as shared presentation
catalog folder), and without really a reassurance that the change you’ve made will help. It gives a false sense of
security, since you’re just presuming, guessing, that the first bottleneck your system will reach is one which is
resolved by scaling out/up.
Mark Rittman wrote a post recently in which he discussed the idea of scaling the BI Server (nqsserver) component in
order to take advantage of multiple CPU cores, and whether this was in fact necessary. You can read his post
here : Does the BI Server System Component Make Full Use of My Multi-Socket, Multi-Core Server?
Optimising the databaseThis section is most definitely not a comprehensive study; it is a set of a few pointers that I would be looking for
before speaking to my honourable DBA colleagues who have longer beards than I and know this stuff inside out.
DBAs :
o you cannot tune the SQL that OBIEE sends to the database; it is generated by OBIEE. If there is a better way
to write the SQL query then you need to get the BI Server to generate it in that way by amending the RPD.
Missing joins etc indicate problem with the RPDo you can try optimising the physical implementation of the underlying database objects to make a query that
OBIEE generates run faster
Techniques to evaluate in your physical implementation of the data model include:
Appropriate use of Parallel query, including Auto DOP (as written about by my colleague Pete Scott recently)
Partitioning
Appropriate – but not excessive – indexing
Correct data types, particularly on join columns
Materialized Views for holding pre-built aggregations of your fact data
o (including PCT to incrementally refresh)
Statistics
o Make sure that they’re representative/accurate
o Have a proactive statistics management strategy. Don’t just rely on the auto stats jobs.
o Incremental statistics can be useful
Resource Manager is useful for granular control of resources such as parallelism, optionally between different
groups of users. For example, power users could be given a greater DOP than normal users.
Oracle’s tuning documentYou can get the Oracle official OBIEE tuning document from here: Best Practices Guide for Tuning Oracle®
Business Intelligence Enterprise Edition.
When Oracle released this document in December 2012 it caused a mini-twitter-storm amongst some OBIEE
professionals. On the one hand, publishing a list of settings to evaluate is of great help. On the other, publishing a list
of settings to evaluate with no context or method with which to validate them is no help whatsoever. In my [not very
humble] opinion, a supplemental list of configuration parameters – especially from the software vendor themselves –
should only go hand-in-hand with details of how to properly evaluate them. Here is why I think that:1. Turn the dial to 42. If Oracle says so, then that’s what we’ll do. Time is wasted changing configuration
without understanding why.2. Each configuration change is a move away from default, and thus increases chances of being missed in
environment migrations and product upgrades3. If there is a suggested default, with no context or caveat, then why isn’t it an instruction in the installation guide?
(“For a stable / an even performance over time, you should at least set two Presentation Services per server and two Javahost per server.”)
4. The document suggests enabling BI Server caching, with no observation that this could lead to users getting wrong (stale) results
5. It propagates the idea that performance is only a case of setting the correct values in configuration files. In the tuning guide there is a generic mention of “Application Design”, but in a document that discusses parameter changes throughout the OBIEE stack where is the lengthy discussion of underlying data model, appropriate RPD model, query push-down? These fundamental design principles count a thousand times over against how many tcp_listeners an OS is configured for
6. No explanation of (a) why a setting should be changed and (b) in what situation. For example, changing tcp settings will make no difference on a low-concurrency system horrendously bottlenecked on poor database queries resulting from a bad data model. If a tcp setting needs changing, which corresponding OS network stat will show a bottleneck? Or response time profile to show excessive wait times at the network layer?
7. A shopping list of parameters to change should be the last step of performance optimisation, but by being the only tuning document available, gives the impression that this is all there is to performance – turn some knobs and you’re done.
8. A little knowledge is a dangerous thing. By putting these low-level configuration options out as a generally available document it increases the chances of more harm than good being done by people. If someone doesn’t understand a system then it is best to leave it alone rather than tinker with some changes that have an Oracle stamp of authority on.If my car is running badly, I won’t start trying to remap the engine. I’ll take it to the garage. When they tell me what the problem was and what they did to fix it, I won’t take that recommendation and tell my friend whose car is running slowly to do the same.
This isn’t to say the document isn’t useful. It is very useful. It’s just a shame that it is the only OBIEE tuning
document from Oracle. It’s really useful to have at the end of a performance test once test results are in and
diagnosis and resolution of the problems start. But it’s necessary to have the diagnosis and means to validate a
change first, before throwing changes at a system.
SummaryThis is the penultimate article in this series about OBIEE and performance. For an overview and conclusion of my
method for improving performance in OBIEE, see the final post in this series, here.
o Home
Rittman Mead - Delivered IntelligencePerformance and OBIEE – Summary and FAQMarch 18th, 2013 by Robin Moffatt
This article is the final one in a series about OBIEE and performance. You can find the previous posts here:
1. Introduction 2. Test – Define
3. Test – Design 4. Test – Build 5. Execute 6. Analyse 7. Optimise
Summary The key to long term, repeatable, successful performance optimisation of a system is KNOWING where problems
lie and then resolving them accordingly. This means investing time up front to develop an understanding of the
system and its instrumentations and metrics. Once you have this, you can apply the same method over and over
to consistently and successfully resolve performance problems.
GUESSING what the problem is and throwing best practice checklists at it will only get you so far. If you hit a
lucky streak it may get you far enough to convince yourself that it is enough alone. But sooner or later you will hit
a dead-end with your guesswork and have to instead start truly diagnosing problems. When this happens, you are
starting from scratch in learning and developing your method for doing this.
Getting the best performance out of OBIEE is all about good design and empirical validation.
To be able to improve performance you must first identify the cause of problem. If you start ‘tuning’ without
identifying the actual cause you risk making things much worse.
The slightly acerbic tone at times of these articles may betray my frustration with the incorrect approach that I see
people take all too often. A methodical approach is the correct one, and I am somewhat passionate about this,
because:1. It works! You gather the data to diagnose the issue, and then you fix the issue. There is no guessing and there
is no luck2. By approaching it methodically, you learn so much more about how the OBIEE stack works, which aside from
being useful in itself means that you will design better OBIEE systems and troubleshoot performance more easily. You actually progress in your understanding, rather than remaining in the dark about how OBIEE works and throwing fixes at it to hope one works.
FAQQ: How do I improve the performance of my OBIEE dashboards/reports?
A: Start here. Use the method described to help understand where your performance is slow, and why. Then you set
to resolving it as described in this series of blog articles.
Q: No seriously, I don’t have time to read that stuff… how do I fix the performance? My boss is mad and I must fix it
urgently!
A: You can either randomly try changing things, in which case Google will turn up several lists of settings to play with,
or you can diagnose the real cause of the performance problem. If you’ve run a test then see here for how to analyse
the performance and understand where the problem lies
Q: Why are you being such a bore? Can’t you just tell me the setting to change?
A: I’m the guy putting £0.50 bets on horses because I don’t want to really risk my money with big bets. In my view,
changing settings to fix performance without knowing which setting actually needs changing is a gamble. Sometimes
the gamble pays off, but in the end the house always wins.
Q: Performance is bad. When I run the report SQL against the database manually it is fast. Why is OBIEE slow?A1: You can see from nqquery.log on the BI Server how long the SQL takes on the database, so you don’t
necessarily need to run it manually. Bear in mind the network between your BI Server and the database, and also the
user ID that the query is being executed as. Finally, the database may have cached the query so could be giving a
better impression of the speed.A2: If the query really does run faster manually against the database then look at nqquery.log to see where the
rest of the query time is being spent. It could be the BI Server is having to do additional work on the data before it is
returned up to the user. For more on this, see response time profiling.
Q: This all sounds like overkill to me. In my day we just created indexes and were done.
A: I’ve tried to make my method as comprehensive as possible, and usable in both large-scale performance tests but
also isolated performance issues. If a particular report is slow for one use, then the test define, design and build is
pretty much done already – you know the report, and to start with running it manually is probably fine. Then
you analyse why the performance isn’t as good as you want and based on that, you optimise it.
Q: Should I disable query logging for performance? I have read it is a best practice.
A: Query logging is a good thing and shouldn’t be disabled, although shouldn’t be set too detailed either.
Reading & ReferencesThe bulk of my inspiration and passion for trying to understand more about how to ‘do’ performance properly has
come from three key places:
Cary Millsap ’s blog and particularly his paper Thinking Clearly about Performance
Greg Rahn ’s blog structureddata.org
Zed Shaw ’s essay Programmers Need To Learn Statistics Or I Will Kill Them All
plus the OBIEE gurus at rittmanmead including markrittman and krisvenkat, along with lots of random twitter
conversations and stalking
of neilkod, martinberx, alexgorbachev, kevinclosson, nialllitchfield, orcldoug, martindba, jamesmorle, and
more.
CommentsI’d love your feedback on this. Do you agree with this method, or is it a waste of time? What have I overlooked or
overemphasised? Am I flogging a dead horse?
I’ve enabled comments on this article in the series only, to keep the discussion in one place. You can tell me what
you think on twitter too, @rmoffinShare7
Posted in Oracle BI Suite EE, Performance | 2 Comments »
Comments1. Alexander Hansal Says:
March 20th, 2013 at 10:05 am
Hi Robin, congratulations to this great and much needed series. I see a book(let) coming on here.
It is truly embarassing to see how this topic is dealt with by so-called “professionals”. As you say it is often not more
than just a box in the checklist.
So again, thanks for this.
Also, I love the T.E.A. acronym. W.I.S.E. men choose acronyms wisely ;-)
have a nice day
@lex
2. Rafael Soares (Tuelho) Says: July 18th, 2013 at 11:25 am
Congrats for this great series!
Certainly the principles described in this posts goes far beyond the OBIEE land. It can be used and applied to every
software performance test case.
I learned a lot about performance, capacity, monitoring, measuring and tuning with these posts an references.
I really recomend to everyone that, like me, think knows how to do a performance test.
Thanks a lot!
