One of the benefits of Microservice architecture is one can scale heavily used parts of the application without scaling the other parts. This supposedly provides benefits around cost.
However, my question is, if a heavily used microservice is dependent on other microservice to do it's work wouldn't you have to scale the other services as well seemingly defeating the purpose. If a microservice is calling other micro service at real time to do it's job, does it mean that Micro service boundaries are not established correctly.
There's no rule of thumb for that.
Scaling usually depends on some metrics and when some thresholds are reached then new instances are created. Same goes for the case when they are not needed anymore.
Some services are doing simple, fast tasks, like taking an input and writing it to the database and others may be longer running task which can take any amount of time.
If a service that needs scale is calling a service that can easily handle heavy loads in a reliable way then there is no need to scale that service.
That idea behind scaling is to scale up when needed in order to support the loads and then scale down whenever loads get in the regular metrics ranges in order to reduce the costs.
There are two topics to discuss here.
First is that usually, it is not a good practice to communicate synchronously two microservices because you are coupling them in time, I mean, one service has to wait for the other to finish its task. So normally it is a better approach to use some message queue to decouple the producer and consumer, this way the load of one service doesn't affect the other.
However, there are situations in which it is necessary to do synchronous communication between two services, but it doesn't mean necessarily that both have to scale the same way, for example: if a service has to make several calls to other services, queries to database, or other kind of heavy computational tasks, and one of the service called only do an array sorting, probably the first service has to scale much more than the second in order to process the same number of request because the threads in the first service will be occupied longer time than the second
I was wondering about possible ways to track down performance bottlenecks in distributed systems. I am aware of tools like X-Trace and its offspring (e.g. Dapper) but I am more curious about the methodology rather than specific tools.
In other words, given a distributed system without any obvious bottlenecks, how do you study and improve its performance?
I've used a method that has a pro, and a con.
The pro is that it works - it finds problems that, when they are fixed, result in nice snappy performance.
The con is that it's a good amount of manual work.
I even wrote a book, and included the method.
The work is to collect time-stamped event logs and merge them together into a common timeline.
Then you carefully examine it, tracing the flow of related messages through the network of asynchronous agents.
What you are looking for are needless message cycles, or delays that don't necessarily have to happen.
For example, in looking at this picture, receipt of a message is being delayed due to the task "post status to DB". When that is understood, the posting could actually be done on a separate thread.
Honestly, that's a great question, and there isn't a consensus on what's the best way to do this. One of the most basic ways is logging, where you basically just dump a bunch of system events into a file and you can parse those logs to find the timing between events to figure out how long they take. Another approach is tracing (which is used by Xtrace). In tracing, you keep track of the lifetime of a request. For example, if you send a request to a service that uses a microservice architecture, you will keep track of the thread, process ID, and latency of the request as it goes through the microservices of the system.
The tricky part is figuring out what you want to keep track in the trace of a request, and that will be dependent on what your distributed system is trying to accomplish. For example, an obvious metric of interest for performance is latency, so you will measure how long the request spent on each service. Another metric that could be interesting is contention, so you can measure the contention in the CPU when the request was going through the system. One of the problems with many of these profiling tools is that they give you overall metrics for the system or a request, but when you want to find a performance issue you want to figure out if a request is an outlier or not. It is thus essential to compare the latency, contention, and memory consumption of a request to other similar request in the system to figure out whether it is abnormal.
I'm developing a multiplayer 2D-game where players and other objects move on the screen in a simple coordinate system. So player's client sends its movements to the server, and according those movements, server's game room thread periodically (maybe 50-100ms) calculates new speed, coordinates and angle for every object. Then it sends data to every player about those changes.
For example, we have ten players which can send their movements three times in a second, server has to send those movements forward to everyone. Considering other player actions than just movement, there could occur tens of little messages in a second, and that's just in one room.
So here's a bunch of questions:
1) Is Netty the framework I'm looking for? It's a real-time game, so every millisecond counts. Low latency is the most important requirement. If player's ping goes over 30, the playing is pretty poor and laggy.
2) If so, I would also like to hear your every advice for optimization and settings for this kind of server, which sends small packets, but there's a lot of them. I'm estimating that the peak of concurrent players will be about 2000-3000 users playing in 100-200 rooms, so I would be really happy if the server can handle that amount of players playing the game without lag.
3) Every game room has some kind of own thread which loops and runs the game. Is there a proper way to do this kind of threads, so it doesn't effect much to the server's normal functionality?
Thanks for your answers!
Take a look at this netty game server from github. You can get some good pointers and patterns here. Maybe you can use it directly also for your purposes
Netty can do the job. Its very well suited for high-troughput and low latency. So I you should give it a try. I think it should "just work" out of the box for you ;)
Netty comes with a NIO (non-blocking) and OIO (blocking) implementations. If you are going with NIO (which I recommend) then I will recommend to try to design your api to be event based and use as less threads as possible. Otherwise it may be hard to scale if you have thousands for "game rooms".
Is there a standard approach for deduping parallel event streams ? Before I attempt to reinvent the wheel, I want to know if this problem has some known approaches.
My client component will be communicating with two servers. Each one is providing a near real-time event stream (~1 second). The events may occasionally be out of order. Assume I can uniquely identify the events. I need to send a single stream of events to the consuming code at the same near real-time performance.
A lot has been written about this kind of problem. Here's a foundational paper, by Leslie Lamport:
http://research.microsoft.com/en-us/um/people/lamport/pubs/pubs.html#time-clocks
The Wikipedia article on Operational Transformation theory is a perfectly good starting point for further research:
http://en.wikipedia.org/wiki/Operational_transformation
As for your problem, you'll have to choose some arbitrary weight to measure the cost of delay vs the cost of dropped events. You can maintain two priority queues, time-ordered, where incoming events go. You'd do a merge-and on the heads of the two queues with some delay (to allow for out-of-order events), and throw away events that happened "before" the timestamp of whatever event you last sent. If that's no better than what you had in mind already, well, at least you get to read that cool Lamport paper!
I think that the optimization might be OS-specific. From the task as you described it I think about two threads consuming incoming data and appending it to the common stream having access based on mutexes. Both Linux and Win32 have mutex-like procedures, but they may have slow performance if you have data rate is really great. In this case I'd operate by blocks of data, that will allow to use mutexes not so often. Sure there's a main thread that consumes the data and it also access it with a mutex.
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.