Are there any tools to generate an automated scenario with a predefined ramp up of user requests (running same map-reduce job) and monitoring some specific metrics of Hadoop cluster under load? I am looking ideally for something like LoadRunner but free/open source tool.
The tool does not have to have a cool UI but rather an ability to record and save scenarios that include a ramp up and a rendezvous point for several users (wait until other users reach some point and do some action simultaneously).
The Hadoop distribution I am going to test is the latest MapR.
Searching internet did not bring any good free alternatives to HP LoadRunner. In case you had an experience with Hadoop (or MapR in particular) load testing, please share what tool you have used.
Every solution you will look at has both a tool quotient and a labor quotient in the total price. There are many open source tools which take the tool cost to zero but the labor charge is so high that your total cost to deliver will be higher than a purchase of a commercial tool with a lower labor charge. Also, many people look at performance testing tools as load generation alone, ignoring the automated collection of monitoring data and the analysis of the results where you can pin an increase in response times with a correlated use of resources at the same time. This is a laborious process made longer to do when you are using decoupled tools.
As you have mentioned LoadRunner, when you are provided a tool you should compare what is available in that tool to whatever you are provided. For instance,
there are Java, C, C++ & VB interfaces available in LoadRunner. You are going to find a way to exercise your map and reduce infrastructure. Compare the integrated monitoring capabilities (native/SNMP/terminal user with command line...) as well as analysis and reporting. Where capabilities do not exist you will either need to build the capability or acquire it elsewhere.
You have also brought up the concept of Rendezvous. You will want to be careful with its application in any tool. Unless you have a very large population the odds of Simultaneous collision in the same area of code/action at the same time becomes quite small. Humans are chaotic instruments, arriving and departing independently from one another. On the other hand, if you are automating an agent which is based upon a clock tick then rendezvous makes a lot more sense. Taking a look at your job submission logs by IP address can provide an objective model for how many are submitted simultaneously (rendezvous) versus how many are running concurrently. I audit a lot of tests and rendezvous is the most abused item across tools, resulting in thousands of lost engineering hours chasing engineering ghosts that would never occur in natural use.
We are re-implementing(yes from scratch) a web application which is currently in production. We have decided to start doing some performance tests on the new app, to get some early information of the capabilities.
As the old application is currently in production and has a good performance we would like to extract some performance parameters, and then use this parameters as a reference or base goal of the performance of the new application.
Which do you think are the most relevant performance parameters we should be obtaining from the current production application?
Thanks!
Determine what pages are used the most.
Measure a latency histogram for the total time it takes to answer the request. Don't just measure the mean, measure a histogram.
From the histogram you can see how many % of requests have which latency in milliseconds. You can choose to key performance indicators by takes the values for 50% and 95%. This will tell you the average latency and the worst latency (for the worst 10% of requests).
Those two numbers alone will bring you great confidence regarding the experience your users will have.
Throughput does not matter for users, but for capacity planning.
I also recommend that you track the performance values over time and review them twice a year.
Just in case you need an HTTP client, there is weighttp, a multi-threaded client written by the guys from Lighttpd.
It has the same syntax used by ApacheBench, but weighttp lets you use several client worker threads (AB is single-threaded so it cannot saturate a modern SMP Web server).
The answer of "usr" is valid, but you can as well record the minnimum, average and maximum latencies (that's useful to see in which range they play). Here is a public-domain C program to automate all this on a given concurrency range.
Disclamer: I am involved in the development of this project.
Suppose you have a web application, no specific stack (Java/.NET/LAMP/Django/Rails, all good).
How would you decide on which hardware to deploy it? What rules of thumb exist when determining how many machines you need?
How would you formulate parameters such as concurrent users, simultaneous connections, daily hits and DB read/write ratio to a decision on how much, and which, hardware you need?
Any resources on this issue would be very helpful...
Specifically - any hard numbers from real world experience and case studies would be great.
Capacity Planning is quite a detailed and extensive area. You'll need to accept an iterative model with a "Theoretical Baseline > Load Testing > Tuning & Optimizing" approach.
Theory
The first step is to decide on the Business requirements: how many users are expected for peak usage ? Remember - these numbers are usually inaccurate by some margin.
As an example, let's assume that all the peak traffic (at worst case) will be over 4 hours of the day. So if the website expects 100K hits per day, we dont divide that over 24 hours, but over 4 hours instead. So my site now needs to support a peak traffic of 25K hits per hour.
This breaks down to 417 hits per minute, or 7 hits per second. This is on the front end alone.
Add to this the number of internal transactions such as database operations, any file i/o per user, any batch jobs which might run within the system, reports etc.
Tally all these up to get the number of transactions per second, per minute etc that your system needs to support.
This gets further complicated when you have requirements such as "Avg response time must be 3 seconds etc" which means you have to figure in network latency / firewall / proxy etc
Finally - when it comes to choosing hardware, check out the published datasheets from each manufacturer such as Sun, HP, IBM, Windows etc. These detail the maximum transactions per second under test conditions. We usually accept 50% of those peaks under real conditions :)
But ultimately the choice of the hardware is usually a commercial decision.
Also you need to keep a minimum of 2 servers at each tier : web / app / even db for failover clustering.
Load testing
It's recommended to have a separate reference testing environment throughout the project lifecycle and post-launch so you can come back to run dedicated performance tests on the app. Scale this to be a smaller version of production, so if Prod has 4 servers and Ref has 1, then you test for 25% of the peak transactions etc.
Tuning & Optimizing
Too often, people throw some expensive hardware together and expect it all to work beautifully. You'll need to tune the hardware and OS for various parameters such as TCP timeouts etc - these are published by the software vendors, and these have to be done once the software are finalized. Set these tuning params on the Ref env, test and then decide which ones you need to carry over to Production.
Determine your expected load.
Setup a machine and run some tests against it with a Load testing tool.
How close are you if you only accomplished 10% of the peak load with some margin for error then you know you are going to need some load balancing. Design and implement a solution and test again. Make sure you solution is flexible enough to scale.
Trial and error is pretty much the way to go. It really depends on the individual app and usage patterns.
Test your app with a sample load and measure performance and load metrics. DB queries, disk hits, latency, whatever.
Then get an estimate of the expected load when deployed (go ask the domain expert) (you have to consider average load AND spikes).
Multiply the two and add some just to be sure. That's a really rough idea of what you need.
Then implement it, keeping in mind you usually won't scale linearly and you probably won't get the expected load ;)
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.