Did anyone compare performance(Latency, Throughput, TPS) between orderer with Kafka and RAFT Orderer?
I could see here a considerable difference in terms of latency, throughput, and TPS.
I tried with the same setup with the same resource configuration on two different VM(the Only difference is the orderer system).
Note: Used Single orderer in both networks.Fabric Version: 1.4.4
Orderer with Kafka is more efficient than RAFT. I am using the default configuration for RAFT and Kafka.
I tried with a load generator at a rate of 100 TPS. WIth Kafka all parameters are fine(latency- 0.3 to 2 sec) whereas using RAFT, latency is gradually increasing 2 to 15+ seconds, the tx failure rate is also high.
What could be the reason for this considerable difference in terms of TPS, throughput, and latency?
Please correct If I am doing something wrong.
For starters I would not run performance tests using a single orderer. These fault tolerance systems are there to handle distribution and consensus of a distributed system, so by running a single orderer you are fundamentally removing the reason they exist. It's as if you are comparing two sports cars on a dirt road and wonder which is the fastest.
Then there are other things that come into play, such as if you connect the services over TLS, the general network latency as well as how many brokers/nodes you are running.
Chris Ferris performed an initial performance analysis of the two systems prior to the release of Raft, and it seemed it was both faster and could handle almost twice as many transactions per second. You can read his blog post here: Does Hyperledger Fabric perform at scale?
You should also be aware of the double-spending problem and key collisions that can occur if you run a distributed system under high load. You should take necessary steps to avoid this, which can cause a bottle-neck. See this Medium post about collisions, and Hyperledger Fabric's own documentation on setting up a high throughput network.
I am using a Apache nifi for one of my clickstream projects to do some ETL.
I am getting traffic around 300 messages per second currently with the following infra:
RAM - 16 GB
Swap - 6 GB
CPU - 16 cores
Disk - 100GB (Persistance not required)
Cluster - 6 nodes
The entire cluster UI has become extremely slow with the following issues
Processors giving back pressure when some failure happens, which consumes lot of threads
Provenance writing becomes very slow
Heartbeat across nodes becomes slow
Cluster Heart beat
I have the following questions on the setup
Is RPG use recommended, as it is a HTTP call, which i using to spread
across all the nodes, as there is an existing issue with EMQTT
process for consumer group.
What is the recommended value of thread count that should be allotted
per core?
What are the guidelines for infrastructure sizing
What are the tuning parameters for a large cluster with high incoming requests and lot of heavy JSON parsing for transformation
A couple of suggestions
Yes RPG usage is recommended, at least from what I've experienced, RPG seems to offer better distribution. Take a look at [3] below
Some processors are CPU intensive then others so there's no clear cut answer for what value can be set for Concurrent Tasks. This is more of trial and error or testing and fine tuning approach that you'd have to master. One suggestion is, if you set too many Concurrent Tasks for a CPU intensive processor, it will have serious impact on the nodes.
Hortonworks have made a detailed guide regarding this. I've provided the link below. [1]
Some best practices and handy guides:
https://community.hortonworks.com/articles/7882/hdfnifi-best-practices-for-setting-up-a-high-perfo.html
http://ijokarumawak.github.io/nifi/2016/11/22/nifi-jolt/
https://pierrevillard.com/2017/02/23/listfetch-pattern-and-remote-process-group-in-apache-nifi/
I've wanted to test StatsD perfromance with some stress test which I've made.
Eventually I found out that when there are something like 80,000 packets per sec the Node.js is getting to 100% cpu utilization on my environment.
I know that 80,000 event per sec is quite huge amount of events , but I wonder if anyone knows what are the limits of StatsD in regarding to Node.js. What is a normal events rate?
Also, is there something I can do to imporve Node.js performance so it won't get to 100% cpu utilization?
According to a StatsD contributer, the metrics rate which was tested and measured was between 15,000 - 20,000 metrics per sec.
Thats quite good enough and it is what I was looking for.
You can see more details about it in the issue I've opened at StatsD's github project:
https://github.com/etsy/statsd/issues/249
We're using a Geoserver, and we've a performance problems in production with a large number of users.
We've made some load test with : 250, 150, and 20 threads. We've noticed that Geoserver works better with 20 threads than with 150 threads, and when thread number increase (150 or 250), performance decrease.
Is it normal ? How Geoserver manage the users request ? Does Geoserver use asynchronous strategy to manage users request ?
Thanks in advance.
bsh
Sounds pretty normal. Threads (and cpu context switches) aren't free, and at some point you are going to spend more time thrashing around switch threads than actually doing anything useful. Often better to have a much smaller number of threads (number of cores * 2 is often reasonable) combined with some sort of front end queue that will accept a connection and hold it until a worker is free.
Here are some real-world use case statistics for you; in production, for mobile/web apps serving 'google-maps' style users for the outdoor market, my company has tested various configurations (several of these discussed by theonlysandman, a contributor to this question), and which also support the observation by Tyler Evans, also a contributor to this question).
We need loads of greater than 5000 requests / second ('qps), and as our Geoserver instances ubiquitously topped at nearly 100 qps each, we'd need to horizontally and vertically scale to over 50 Geoserver instances.
Parameters: mostly vector sources, local PostGIS databases all less than 2tb each and no table > 1M records (or if greater than 1M, simplified geometry at nodes > 1m apart), 60%-40%-10% WMS/WMTS/WFS requests, google cloud hosted servers, each 32 core, ssd drive cluster to 4Tb.
The bottleneck of qps appears to be Geoserver itself. (Styling, reprojection, all the niceties that come with it). I'm not advocating it is poorly written, but the heavier a car gets the slower it might drive.
If we replicate the wfs requests using GO or python +/- gdal to directly access postgis data we get faster throughput than geoserver (up to 1000 qps each instance or more, where PostGIS becomes the bottleneck).
The same goes for our homemade Java microservice based on PostGIS that creates pbf/mvt tiles from postgis- it, too, was very quick- at about 1000 qps.
Nginx for us performed slightly better than php (~110 qps vs ~89 qps), but this could be a result of a configuration of apache.
Where do we go from here? In all of our production use cases, for our users, serving miniature sharded sqlite/mbtile databases (vector or raster)... and maintaining them with custom code... was far more performant and scalable.
We may write a Java plugin for geoserver that pushes GeoWebCache TMS tiles into a Google Storage Bucket designed for slippy z/x/y calls... this way we could more easily maintain a tile pyramid with updates etc., using Geoserver tools.
The more threads, the harder the load on the server. See WikiPedia article on thrasing.
Geoserver performance is affect by many things. My advice is to look at each one and see where the bottle neck be occurring.
Here are a list of question to set you on the correct path:
What are the specs of your machine? It should have an SSD.
Are you generating your tiles on the way? Or are they pre-seeded?
If they you are pre-seeding, is that running?
NOTE: pre-seeding helps but hammers the system so best done out of production.
What is the source for your data, if postgis, are you using spatial indexes?
Is PostgreSQL/postgis on the same machine?
How many types of tiles are you generating?
NOTE: you could be generating extra tiles which you don't need/use.
Do you use GeoWebCache?
With some more details, I can help you out.
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.