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I've heard reports that Hadoop is poised to replace data warehousing. So I was wondering if there were actual case studies done with success/failure rates or if some of the developers here had worked on a project where this was done, either totally or partially?
With the advent of "Big Data" there seems to be a lot of hype with it and I'm trying to figure out fact from fiction.
We have a huge database conversion in the works and I'm thinking this may be an alternative solution.
Ok so there are a lot of success stories out there with Big Data startups, especially in AdTech, though it's not so much "replace" the old expensive proprietary ways but they are just using Hadoop first time round. This I guess is the benefit of being a startup - no legacy systems. Advertising, although somewhat boring from the outside, is very interesting from a technical and data science point of view. There is a huge amount of data and the challenge is to more efficiently segment users and bid for ad space. This usually means some machine learning is involved.
It's not just AdTech though, Hadoop is used in banks for fraud detection and various other transactional analysis.
So my two cents as to why this is happening I'll try to summarise with a comparison of my main experience, that is using HDFS with Spark and Scala, vs traditional approaches that use SAS, R & Teradata:
HDFS is a very very very effective way to store huge amounts of data in an easily accessible distributed way without the overhead of first structuring the data.
HDFS does not require custom hardware, it works on commodity hardware and is therefore cheaper per TB.
HDFS & the hadoop ecosystem go hand in glove with dynamic and flexible cloud architectures. Google Cloud and Amazon AWS have such rich and cheap features that completely eliminate the need for in house DCs. There is no need to buy 20 powerful servers and 100s TB of storage to then discover it's not enough, or it's too much, or it's only needed for 1 hour a day. Setting up a cluster with cloud services is getting easier and easier, there are even scripts out there that make doing it possible for those with only a small amount of sysadm/devops experience.
Hadoop and Spark, particularly when used with a high level statically typed language like Scala (but Java 8 is also OK-ish) means data scientists can now do things they could never do with scripting languages like R, Python and SAS. First they can wire up their modelling code with other production systems, all in one language, all in one virtual environment. Think about all the high velocity tools written in Scala; Kafka, Akka, Spray, Spark, SparkStreaming, GraphX etc, and in Java: HDFS, HBase, Cassandra - now all these tools are highly interoperable. What this means is for first time in history, data analysts can reliably automate analytics and build stable products. They have the high-level functionality they need, but with the predictability and reliability of static typing, FP and unit testing. Try building a large complicated concurrent system in Python. Try writting unit tests in R or SAS. Try compiling your code, watching the tests pass, and conclude "hey it works! lets ship it" in a dynamically typed language.
These four points combined means that A: storing data is now a lot lot cheaper, B: processing data is now a lot lot cheaper and C: human resource costs are much much cheaper as now you don't need several teams siloed off into analysts, modellers, engineers, developers, you can mash these skills together to make hybrids ultimately needing to employ less people.
Things won't change over night, currently the labour market is majorly lacking two groups; good Big Data DevOps and Scala engineers/developers, and their rates clearly reflect that. Unfortunately the supply is quite low even though the demand is very high. Although I still conjecture Hadoop for warehousing is much cheaper, finding talent can be a big costs that is restricting the pace of transition.
I want to do load testing for 10 million users for my site. The site is a Java based web-app. My approach is to create a Jmeter test plan for all the links and then take a report for the 10 million users. Then use jvisualVM to do profiling and check if there are any bottlenecks.
Is there any better way to do this? Is there any existing demo for doing this? I am doing this for the first time, so any assistance will be very helpful.
You are on the correct path, but your load limit is of with a high factor.
Why I'm saying this is cause your site probably will need more machine to handle 10Milj Concurrent users. A process alone would probably struggle to handle concurrent 32K TCP-streams. Also do some math of the bandwidth it would take to actually handle 10Milj users.
Now I do not know what kind of service you thinking of providing on your site, but when thinking of that JVisualVM slows down processing by a factor 10 (or more for method tracing), you would not actually measure the "real world" if you got JMeter and JVisualVM to work at the same time.
JVisualVM is more useful when you run on lower loads.
To create a good measurement first make sure your have a good baseline.
Make a test with 10 concurrent users, connect up JVisuamVM and let it run for a while, not down all interesting values.
After you have your baseline, then you can start adding more load.
Add 10times the load (ea: 100 users), look at the changes in JVisualVM. Continue this until it becomes obvious that JVisualVM slows you down, for every time to add extra load, make sure you have written down the numbers your are interested in. Plot down the numbers in a graph.
Now... Interpolate the graph (by hand) for the number of users you want. This works for memory usage, disc access etc, but not for used CPU time, cause JVisualVM will eat CPU and give you invalid numbers on that (especially if you have method tracing turned on).
If you really want to go as high as 10Milj users, I would not trust JMeter either, I would write a little test program of my own that performs the test you want. This would be okey, since the the setting up the site to handle 10Milj will also take time, so spending a little extra time of the test tools are not a waste.
Just because you have 10 million users in the database, doesn't mean that you need to load test using that many users. Think about it - is your site really going to have 10 million simultaneous users? For web applications, a ratio of 1:100 registered users is common i.e. you are unlikely to have more than 100K users at any moment.
Can JMeter handle that kind of load? I doubt it. Please try faban instead. It is very light-weight and can support thousands of users on a single VM. You also have much better flexibility in creating your workload and can also automate monitoring of your entire test infrastructure.
Now to the analysis part. You didn't say what server you were using. Any Java appserver will provide sufficient monitoring support. Commercial servers provide nice GUI tools while Tomcat provides extensive monitoring via JMX. You may want to start here before getting down to the JVM level.
For the JVM, you really don't want to use VisualVM while running such a large performance test. Besides to support such a load, I assume you are using multiple appserver/JVM instances. The major performance issue is usually GC, so use the JVM options to collect and log GC information. You will have to post-process the data.
This is a non-trivial exercise - good luck!
There are two types of load testing - bottleneck identification and throughput. The question leads me to believe this is about bottlenecks, so number of users is a something of a red herring, instead the goal being for a given configuration finding areas that can be improved to increase concurrency.
Application bottlenecks usually fall into three categories: database, memory leak, or slow algorithm. Finding them involves putting the application in question under stress (i.e. load) for an extended period of time - at least an hour, perhaps up to several days. Jmeter is a good tool for this purpose. One of the things to consider is running the same test with cookie handling enabled (i.e. Jmeter retains cookies and sends with each subsequent request) and disabled - sometimes you get very different results and this is important because the latter is effectively a simulation of what some crawlers do to your site. Details for bottleneck detection follow:
Database
Tables without indices or SQL statements involving multiple joins are frequent app bottlenecks. Every database server I've dealt with, MySQL, SQL Server, and Oracle has some way of logging or identifying slow running SQL statements. MySQL has the slow query log, whereas SQL Server has dynamic management views that track the slowest running SQL. Once you've got your hands on the slow statements use explain plan to see what the database engine is trying to do, use any features that suggest indices, and consider other strategies - such as denormalization - if those two options do not solve the bottleneck.
Memory Leak
Turn on verbose garbage collection logging and a JMX monitoring port. Then use jConsole, which provides much better graphs, to observe trends. In particular leaks usually show up as filling the Old Gen or Perm Gen spaces. Leaks are a bottleneck with the JVM spends increasing amounts of time attempting garbage collection unsuccessfully until an OOM Error is thrown.
Perm Gen implies the need to increase the space as a command line parameter to the JVM. While Old Gen implies a leak where you should stop the load test, generate a heap dump, and then use Eclipse Memory Analysis Tool to identify the leak.
Slow Algorithm
This is more difficult to track down. The most frequent offenders are synchronization, inter process communication (e.g. RMI, web services), and disk I/O. Another common issue is code using nested loops (look mom O(n^2) performance!).
Best way I've found to find these issues absent some deeper knowledge is generating stack traces. These will tell what all threads are doing at a given point in time. What you're looking for are BLOCKED threads or several threads all accessing the same code. This usually points at some slowness within the codebase.
I blogged, the way I proceeded with the performance test:
Make sure that the server (hardware can be as per the staging/production requirements) has no other installations that can affect the performance.
For setting up the users in DB, a procedure can be used and can be called as a part of jmeter test plan.
Install jmeter on a separate machine, so that jmeter won't affect the performance.
Create a test plan in jmeter (as shown in the figure 1) for all the uri's, with response checking and timer based requests.
Take the initial benchmark, using jmeter.
Check for the low performance uri's. These are the points to expect for bottlenecks.
Try different options for performance improvement, but focus on only one bottleneck at a time.
Try any one fix from step 6 and then take an benchmark. If there is any improvement commit the changes and repeat from step 5. Otherwise revert and try for any other options from step 6.
The next step would be to use load balancing, hardware scaling, clustering, etc. This may include some physical setup and hardware/software cost. Give the results with the scalability options.
For detailed explanation: http://www.daemonthread.com/2011/06/site-performance-tuning-using-jmeter.html
I started using JMeter plugins.
This allows me to gather application metrics available over JMX to use in my Load Test.
I have some basic questions around understanding fundamentals of Performance testing. I know that under various circumstances we might want to do
- Stress Testing
- Endurance Testing etc.
But my main objective here is to ensure that response time is decent from application under a set of load which is towards a higher end or in least above average load.
My questions are as follows:
When you start to plan your expected response time of application; what do you consider. If thats the first step at all. I mean, I have a web application now. Do I just pull out a figure from air and say "I would expect application to take 3 seconds to respond to each request". and then go about figuring out what my application is lacking to get that response time?
OR is it the other way round, and you start performance test with a given set of hardware and say, lets see what response time I get now, and then look at results and say, well it's 8 seconds right now, I'd like it to be 3 seconds at max, so lets see how we can optimize it to be 3 seconds? But again is 3 seconds out of air? I am sure, scaling up machines only will not get response time up. It'll get response time up only when single machine/server is under load and you start clustering?
Now for one single user I have response time as 3 seconds but as the load increases it goes down exponentially; so where do I draw the line between "I need to optimize code further" (which has it's upper limit) and "I need to scale up my servers" (Which has a limit too)
What are the best free tools to do performance and load testing? I have used Jmeter a bit. But is there anything else, that is good and open source?
If I have to optimize code, I start profiling the specific flows which took lot of time responding to requests?
Basically I'd like to see how one goes about from end to end doing performance testing for their application. Any links or articles would be very helpful.
Thanks.
The Performance Testing Council is your gateway to freely exchange experiences, knowledge, and practice of performance testing.
Also read Microsoft Patterns & Practises for Performance testing. This guide shows you an end-to-end approach for implementing performance testing.
phoenix mentioned the Open Source tools.
First of all you can read
Best Practices for Speeding Up Your Web Site
For tools
Open source performance testing tools
performance: tools
This link and this show an example and method of performance tuning an application when the application does not have any obvious "bottlenecks". It works most intuitively on individual threads. I have no experience using it on web applications, although other people do. I agree that profiling is not easy, but I've always relied on this technique, and I think it is pretty easy / effective.
First of all, design your application properly.
Use a profiler, see where the bottlenecks in your application are, and take them away if possible. MEASURE performance before improving it.
I will try to provide basic step by step guide, which can be used for implementing Performance testing in you project.
1 - Before you start testing you should know amount of physical memory and amount of memory allocated for JVM, or whatever. DB size collect as much metrics as possible for your current environment. Know you environment
2 - Next step would be to identify common DB production size and expected yearly growth. You will want to test how your application will behave after year, two, five etc.,
3 - Automate environment setup, this is will help you a lot in future for regression testing and defect fix validation. So you need to have DB dumps for your tests. With current (baseline), one year, five year volume.
4 - Once you're done if gathering basic information - Think about monitoring your servers under load, maybe you already have some monitoring solution like http://newrelic.com/ this will help you to identify cause of performance degradation (CPU/Mem/Amount of threads etc.,) Some performance testing tools do have built in monitoring systems.
At this you are ready to move with tooling and load selection, there is already provided materials on how to do that so I will skip part with workload selection.
5 - Select tool I think that JMeter + http://blazemeter.com/ is what you need at this point, both do have a lot nice articles and education materials, for your script recording I would recommend to use blazemeters Chrom Extension instead of inbuilt JMeters solution. If you still think that you do lack knowledge on how things are done in JMeter I recommend to get this book - Performance Testing With JMeter 2.9 by Bayo Erinle
6 - Analyze results, review test plan and take corresponding actions.
I want to create a system that delivers user interface response within 100ms, but which requires minutes of computation. Fortunately, I can divide it up into very small pieces, so that I could distribute this to a lot of servers, let's say 1500 servers. The query would be delivered to one of them, which then redistributes to 10-100 other servers, which then redistribute etc., and after doing the math, results propagate back again and are returned by a single server. In other words, something similar to Google Search.
The problem is, what technology should I use? Cloud computing sounds obvious, but the 1500 servers need to be prepared for their task by having task-specific data available. Can this be done using any of the existing cloud computing platforms? Or should I create 1500 different cloud computing applications and upload them all?
Edit: Dedicated physical servers does not make sense, because the average load will be very, very small. Therefore, it also does not make sense, that we run the servers ourselves - it needs to be some kind of shared servers at an external provider.
Edit2: I basically want to buy 30 CPU minutes in total, and I'm willing to spend up to $3000 on it, equivalent to $144,000 per CPU-day. The only criteria is, that those 30 CPU minutes are spread across 1500 responsive servers.
Edit3: I expect the solution to be something like "Use Google Apps, create 1500 apps and deploy them" or "Contact XYZ and write an asp.net script which their service can deploy, and you pay them based on the amount of CPU time you use" or something like that.
Edit4: A low-end webservice provider, offering asp.net at $1/month would actually solve the problem (!) - I could create 1500 accounts, and the latency is ok (I checked), and everything would be ok - except that I need the 1500 accounts to be on different servers, and I don't know any provider that has enough servers that is able to distribute my accounts on different servers. I am fully aware that the latency will differ from server to server, and that some may be unreliable - but that can be solved in software by retrying on different servers.
Edit5: I just tried it and benchmarked a low-end webservice provider at $1/month. They can do the node calculations and deliver results to my laptop in 15ms, if preloaded. Preloading can be done by making a request shortly before the actual performance is needed. If a node does not respond within 15ms, that node's part of the task can be distributed to a number of other servers, of which one will most likely respond within 15ms. Unfortunately, they don't have 1500 servers, and that's why I'm asking here.
[in advance, apologies to the group for using part of the response space for meta-like matters]
From the OP, Lars D:
I do not consider [this] answer to be an answer to the question, because it does not bring me closer to a solution. I know what cloud computing is, and I know that the algorithm can be perfectly split into more than 300,000 servers if needed, although the extra costs wouldn't give much extra performance because of network latency.
Lars,
I sincerely apologize for reading and responding to your question at a naive and generic level. I hope you can see how both the lack of specifity in the question itself, particularly in its original form, and also the somewhat unusual nature of the problem (1) would prompt me respond to the question in like fashion. This, and the fact that such questions on SO typically emanate from hypotheticals by folks who have put but little thought and research into the process, are my excuses for believing that I, a non-practionner [of massively distributed systems], could help your quest. The many similar responses (some of which had the benefits of the extra insight you provided) and also the many remarks and additional questions addressed to you show that I was not alone with this mindset.
(1) Unsual problem: An [apparently] mostly computational process (no mention of distributed/replicated storage structures), very highly paralellizable (1,500 servers), into fifty-millisecondish-sized tasks which collectively provide a sub-second response (? for human consumption?). And yet, a process that would only be required a few times [daily..?].
Enough looking back!
In practical terms, you may consider some of the following to help improve this SO question (or move it to other/alternate questions), and hence foster the help from experts in the domain.
re-posting as a distinct (more specific) question. In fact, probably several questions: eg. on the [likely] poor latency and/or overhead of mapreduce processes, on the current prices (for specific TOS and volume details), on the rack-awareness of distributed processes at various vendors etc.
Change the title
Add details about the process you have at hand (see many questions in the notes of both the question and of many of the responses)
in some of the questions, add tags specific to a give vendor or technique (EC2, Azure...) as this my bring in the possibly not quite unbuyist but helpful all the same, commentary from agents at these companies
Show that you understand that your quest is somewhat of a tall order
Explicitly state that you wish responses from effective practionners of the underlying technologies (maybe also include folks that are "getting their feet wet" with these technologies as well, since with the exception of the physics/high-energy folks and such, who BTW traditionnaly worked with clusters rather than clouds, many of the technologies and practices are relatively new)
Also, I'll be pleased to take the hint from you (with the implicit non-veto from other folks on this page), to delete my response, if you find that doing so will help foster better responses.
-- original response--
Warning: Not all processes or mathematical calculations can readily be split in individual pieces that can then be run in parallel...
Maybe you can check Wikipedia's entry from Cloud Computing, understanding that cloud computing is however not the only architecture which allows parallel computing.
If your process/calculation can efficitively be chunked in parallelizable pieces, maybe you can look into Hadoop, or other implementations of MapReduce, for an general understanding about these parallel processes. Also, (and I believe utilizing the same or similar algorithms), there also exist commercially available frameworks such as EC2 from amazon.
Beware however that the above systems are not particularly well suited for very quick response time. They fare better with hour long (and then some) data/number crunching and similar jobs, rather than minute long calculations such as the one you wish to parallelize so it provides results in 1/10 second.
The above frameworks are generic, in a sense that they could run processes of most any nature (again, the ones that can at least in part be chunked), but there also exist various offerings for specific applications such as searching or DNA matching etc. The search applications in particular can have very short response times (cf Google for example) and BTW this is in part tied to fact that such jobs can very easily and quickly be chunked for parallel processing.
Sorry, but you are expecting too much.
The problem is that you are expecting to pay for processing power only. Yet your primary constraint is latency, and you expect that to come for free. That doesn't work out. You need to figure out what your latency budgets are.
The mere aggregating of data from multiple compute servers will take several milliseconds per level. There will be a gaussian distribution here, so with 1500 servers the slowest server will respond after 3σ. Since there's going to be a need for a hierarchy, the second level with 40 servers , where again you'll be waiting for the slowest server.
Internet roundtrips also add up quickly; that too should take 20 to 30 ms of your latency budget.
Another consideration is that these hypothethical servers will spend much of their time idle. That means they're powered on, drawing electricity yet not generating revenue. Any party with that many idle servers would turn them off, or at the very least in sleep mode just to conserve electricity.
MapReduce is not the solution! Map Reduce is used in Google, Yahoo and Microsoft for creating the indexes out of the huge data (the whole Web!) they have on their disk. This task is enormous and Map Reduce was built to make it happens in hours instead of years, but starting a Master controller of Map Reduce is already 2 seconds, so for your 100ms this is not an option.
Now, from Hadoop you may get advantages out of the distributed file system. It may allow you to distribute the tasks close to where the data is physically, but that's it. BTW: Setting up and managing an Hadoop Distributed File System means controlling your 1500 servers!
Frankly in your budget I don't see any "cloud" service that will allow you to rent 1500 servers. The only viable solution, is renting time on a Grid Computing solution like Sun and IBM are offering, but they want you to commit to hours of CPU from what I know.
BTW: On Amazon EC2 you have a new server up in a couple of minutes that you need to keep for an hour minimum!
Hope you'll find a solution!
I don't get why you would want to do that, only because "Our user interfaces generally aim to do all actions in less than 100ms, and that criteria should also apply to this".
First, 'aim to' != 'have to', its a guideline, why would u introduce these massive process just because of that. Consider 1500 ms x 100 = 150 secs = 2.5 mins. Reducing the 2.5 mins to a few seconds its a much more healthy goal. There is a place for 'we are processing your request' along with an animation.
So my answer to this is - post a modified version of the question with reasonable goals: a few secs, 30-50 servers. I don't have the answer for that one, but the question as posted here feels wrong. Could even be 6-8 multi-processor servers.
Google does it by having a gigantic farm of small Linux servers, networked together. They use a flavor of Linux that they have custom modified for their search algorithms. Costs are software development and cheap PC's.
It would seem that you are indeed expecting at least 1000-fold speedup from distributing your job to a number of computers. That may be ok. Your latency requirement seems tricky, though.
Have you considered the latencies inherent in distributing the job? Essentially the computers would have to be fairly close together in order to not run into speed of light issues. Also, the data center in which the machines would be would again have to be fairly close to your client so that you can get your request to them and back in less than 100 ms. On the same continent, at least.
Also note that any extra latency requires you to have many more nodes in the system. Losing 50% of available computing time to latency or anything else that doesn't parallelize requires you to double the computing capacity of the parallel portions just to keep up.
I doubt a cloud computing system would be the best fit for a problem like this. My impression at least is that the proponents of cloud computing would prefer to not even tell you where your machines are. Certainly I haven't seen any latency terms in the SLAs that are available.
You have conflicting requirements. You're requirement for 100ms latency is directly at odds with your desire to only run your program sporadically.
One of the characteristics of the Google-search type approach you mentioned in your question is that the latency of the cluster is dependent on the slowest node. So you could have 1499 machines respond in under 100ms, but if one machine took longer, say 1s - whether due to a retry, or because it needed to page you application in, or bad connectivity - your whole cluster would take 1s to produce an answer. It's inescapable with this approach.
The only way to achieve the kinds of latencies you're seeking would be to have all of the machines in your cluster keep your program loaded in RAM - along with all the data it needs - all of the time. Having to load your program from disk, or even having to page it in from disk, is going to take well over 100ms. As soon as one of your servers has to hit the disk, it is game over for your 100ms latency requirement.
In a shared server environment, which is what we're talking about here given your cost constraints, it is a near certainty that at least one of your 1500 servers is going to need to hit the disk in order to activate your app.
So you are either going to have to pay enough to convince someone to keep you program active and in memory at all times, or you're going to have to loosen your latency requirements.
Two trains of thought:
a) if those restraints are really, absolutely, truly founded in common sense, and doable in the way you propose in the nth edit, it seems the presupplied data is not huge. So how about trading storage for precomputation to time. How big would the table(s) be? Terabytes are cheap!
b) This sounds a lot like a employer / customer request that is not well founded in common sense. (from my experience)
Let´s assume the 15 minutes of computation time on one core. I guess thats what you say.
For a reasonable amount of money, you can buy a system with 16 proper, 32 hyperthreading cores and 48 GB RAM.
This should bring us in the 30 second range.
Add a dozen Terabytes of storage, and some precomputation.
Maybe a 10x increase is reachable there.
3 secs.
Are 3 secs too slow? If yes, why?
Sounds like you need to utilise an algorithm like MapReduce: Simplified Data Processing on Large Clusters
Wiki.
Check out Parallel computing and related articles in this WikiPedia-article - "Concurrent programming languages, libraries, APIs, and parallel programming models have been created for programming parallel computers." ... http://en.wikipedia.org/wiki/Parallel_computing
Although Cloud Computing is the cool new kid in town, your scenario sounds more like you need a cluster, i.e. how can I use parallelism to solve a problem in a shorter time.
My solution would be:
Understand that if you got a problem that can be solved in n time steps on one cpu, does not guarantee that it can be solved in n/m on m cpus. Actually n/m is the theoretical lower limit. Parallelism is usually forcing you to communicate more and therefore you'll hardly ever achieve this limit.
Parallelize your sequential algorithm, make sure it is still correct and you don't get any race conditions
Find a provider, see what he can offer you in terms of programming languages / APIs (no experience with that)
What you're asking for doesn't exist, for the simple reason that doing this would require having 1500 instances of your application (likely with substantial in-memory data) idle on 1500 machines - consuming resources on all of them. None of the existing cloud computing offerings bill on such a basis. Platforms like App Engine and Azure don't give you direct control over how your application is distributed, while platforms like Amazon's EC2 charge by the instance-hour, at a rate that would cost you over $2000 a day.
I'm about to start testing an intranet web application. Specifically, I've to determine the application's performance.
Please could someone suggest formal/informal standards for how I can judge the application's performance.
Use some tool for stress and load testing. If you're using Java take a look at JMeter. It provides different methods to test you application performance. You should focus on:
Response time: How fast your application is running for normal requests. Test some read/write use case
Load test: How your application behaves in high traffic times. The tool will submit several requests (you can configure that properly) during a period of time.
Stress test: Do your application can operate during a long period of time? This test will push your application to the limits
Start with this, if you're interested, there are other kinds of tests.
"Specifically, I have to determine the application's performance...."
This comes full circle to the issue of requirements, the captured expectations of your user community for what is considered reasonable and effective. Requirements have a number of components
General Response time, " Under a load of .... The Site shall have a general response time of less than x, y% of the time..."
Specific Response times, " Under a load of .... Credit Card processing shall take less than z seconds, a% of the time..."
System Capacity items, " Under a load of .... CPU|Network|RAM|DISK shall not exceed n% of capacity.... "
The load profile, which is the mix of the number of users and transactions which will take place under which the specific, objective, measures are collected to determine system performance.
You will notice the the response times and other measures are no absolutes. Taking a page from six sigma manufacturing principals, the cost to move from 1 exception in a million to 1 exception in a billion is extraordinary and the cost to move to zero exceptions is usually a cost not bearable by the average organization. What is considered acceptable response time for a unique application for your organization will likely be entirely different from a highly commoditized offering which is a public internet facing application. For highly competitive solutions response time expectations on the internet are trending towards the 2-3 second range where user abandonment picks up severely. This has dropped over the past decade from 8 seconds, to 4 seconds and now into the 2-3 second range. Some applications, like Facebook, shoot for almost imperceptible response times in the sub one second range for competitive reasons. If you are looking for a hard standard, they just don't exist.
Something that will help your understanding is to read through a couple of industry benchmarks for style, form, function.
TPC-C Database Benchmark Document
SpecWeb2009 Benchmark Design Document
Setting up a solid set of performance tests which represents your needs is a non-trivial matter. You may want to bring in a specialist to handle this phase of your QA efforts.
On your tool selection, make sure you get one that can
Exercise your interface
Report against your requirements
You or your team has the skills to use
You can get training on and will attend with management's blessing
Misfire on any of the four elements above and you as well have purchased the most expensive tool on the market and hired the most expensive firm to deploy it.
Good luck!
To test the front-end then YSlow is great for getting statistics for how long your pages take to load from a user perspective. It breaks down into stats for each specfic HTTP request, the time it took, etc. Get it at http://developer.yahoo.com/yslow/
Firebug, of course, also is essential. You can profile your JS explicitly or in real time by hitting the profile button. Making optimisations where necessary and seeing how long all your functions take to run. This changed the way I measure the performance of my JS code. http://getfirebug.com/js.html
Really the big thing I would think is response time, but other indicators I would look at are processor and memory usage vs. the number of concurrent users/processes. I would also check to see that everything is performing as expected under normal and then peak load. You might encounter scenarios where higher load causes application errors due to various requests stepping on each other.
If you really want to get detailed information you'll want to run different types of load/stress tests. You'll probably want to look at a step load test (a gradual increase of users on system over time) and a spike test (a significant number of users all accessing at the same time where almost no one was accessing it before). I would also run tests against the server right after it's been rebooted to see how that affects the system.
You'll also probably want to look at a concept called HEAT (Hostile Environment Application Testing). Really this shows what happens when some part of the system goes offline. Does the system degrade successfully? This should be a key standard.
My one really big piece of suggestion is to establish what the system is supposed to do before doing the testing. The main reason is accountability. Get people to admit that the system is supposed to do something and then test to see if it holds true. This is key because because people will immediately see the results and that will be the base benchmark for what is acceptable.