We have a data streaming application which uses local caching to reduce future downloads. The full datasets are larger than what is streamed to the end user - i.e. just the bits the end user wants to see. The concept is pretty much like a browser, except the streamed data is exclusively jpg and png.
The usage patterns are sporadic and unpredictable. There are download spikes on initial usage while the cache is populated. What would be the theoretical and practical/experimental means of modelling and measuring the bandwidth usage of this application. We have size values of the source datasets, but little knowledge of the usage patterns.
There is not enough information to derive a useful theoretical model for bandwidth usage. If you know something about the rollout pattern, you could attempt to model the distribution of spikes. Is this a closed user group that will all get the app within a short period of time? Will you sell to individual customers that in turn will roll out to a number of employees? Are you selling to consumers? All of these will impact the distribution of peaks.
As for the steady-state bandwidth requirements, that depends a great deal on usage patterns (do they frequently re-use the same data or frequently seek new data?) This is a great thing to determine during a beta program. Log usage patterns locally and/or on the server for beta users, and try to get beta users that are representative of the overall user community.
Finally, to manage spikes in consumption, consider deploying your content on a service such as Amazon CloudFront. This allows you to pay for the bandwidth you actually use, but scale as needed to handle peaks in demand.
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For a desktop App (ERP like functionality) I'm and wondering what would be wiser to do.
Assuming that both machines are equal in performance and the server has to deal with max. 5-10 clients and no other obligations. Is it better to load all data initially (~20.000 objects) and do filtering, sorting etc. on the client (electron) or is it better to do the processing on the backend (golang + postgres) over Axios. The user interface should be as snappy as possible but also get the data as fast as possible.
A costly operation is filtering 15.000 Objects by a reference ID. (e.g. a client can have several orders)
So objects that belong to a "parent object" are displayed by querying all those objects by a parentID.
Is there a general answer to what would be more performant, or a better choice here? Doing some assumptions, like a latency of 5ms in the network + 20ms for the API + a couple for filling the store.
At which data size will this operation be slower on the frontend or completely unsustainable?
If it's not a performance problem, are there other reasons I would want to do this on the server?
Edit: Client and Server are on the same local network
You specifically mention an ERP-like software. For such software you have to carefully consider the value of consistency:
Will your software need to show the same data for all clients?
If the answer to this is yes, then the simplest implementation is to do data processing on the server which informs all clients of changing data.
If the answer to this is no, then you should be fine doing most processing on the client software.
There are of course ways to do most of your processing on the client yet still have consistency but they will add complexity to your overall design. One implementation is to broadcast changes on one client to all other clients. This is the architecture behind most multiplayer online games.
Another way to tackle this is implemented by git: the data on all clients are different from each other but there are ways to synchronize each client data with the server thus achieving eventual consistency.
Another consideration you have to think about is the size of your data:
Will downloading all the data from the server take more than a few seconds?
If downloading all data from the server takes too long then the UI will be essentially unresponsive when starting.
I was trying to wrap my brain around the CAP theorem. I understand that Network partitions can occur (eventually leading to the nodes in the cluster not able to sync up with the WRITE operations happening on the other nodes.)
In this case, either the Cluster could still be up and the load-balancer in front of the cluster could route the request to any of the nodes and after a WRITE operation on one of the nodes, the other nodes who can't sync with that data, still have STALE data and any subsequent READS to these nodes will serve STALE data.
[So we are Loosing CONSISTENCY as we choose AVAILABILITY (i.e., we have choose the cluster to give STALE responses back.)]
Or we could SHUTDOWN the cluster whenever a network partition occurs! (There by loosing AVAILABILITY as we don't want to hamper consistency among the nodes.)
I have 2 things I would like to know the answer for it:
In Reality, When would anyone choose to be AVAILABLE and still trade off CONSISTENCY? Who on this earth (practically) would be interested in STALE data?
Please help me understand by listing more than one scenarios.
In case, we would like to choose CONSISTENCY over AVAILABILITY,
the cluster is down. Who on earth (real-time scenarios) practically would accept to design their system to be DOWN in order to preserve CONSISTENCY.
Please list some scenarios.
Won't majority of us look for High availability no matter what? what are our options? please enlighten.
If I send you a message on FB and you send one to me, I'd rather prefer to see messages in an incorrect order(message sent at 1pm comes before message sent at 2pm) rather than not seeing them at all(example of AVAILABILITY of messages prefered over read-after-write CONSISTENCY of messages). Another example, If I gather web site metrics, I'd rather skip or drop some signal rather then force my users to wait for a page load while my consistent transaction is stuck.
Keep in mind that consistency doesn't mean STALE data, also data can be inconsistent in different ways(https://aphyr.com/posts/313-strong-consistency-models)
Financial transactions are a classic example of data that requires consistency over availability. As a bank, I'd rather decline user request for money transfer, than accept it and lose customer's money due to DB being down.
I'd like to point out that CAP theorem is a high-level concept. There are a lot of ways you can treat terms consistency, availability or even partitioning, and different businesses have different requirements. Software engineering as a whole and distributed systems engineering, in particular, is about making trade-offs.
An example where you may choose Availability over Consistency is collaborative editing (e.g. Google Docs). It may be perfectly acceptable (and in fact desirable) to allow users to make local modifications to the documents and deal with conflict resolution once network is restored.
A bank ATM is an example where you'd choose Consistency over Availability. Once ATM is disconnected from the network you would not want to allow withdrawals (thus, no Availability). Or, you could pick partial Availability, and allow deposits or read-only access to your bank statements.
I want to know how to construct servers physically in this situation.
Let's assume that my service provides in the USA.
And my business is quite successful so, I want to expand my business location in Asia.
but I don't want to localized service, so I just got some API server in Asia to provide service which is just use API that located in headquater, but my main components are still in the USA.
But the problem is that my API which is located in Asia needs to call head-quater API which is located in the USA, and the response is quite often slow because of far physical distance.
so In this situation, How can I overcome?
In my opinion, I get some CDN for static contents. but I have no idea how to improve the API response time problem which is originated from physical distance.
If it is a stupid question, please understand, I'm quite a newbie in architect.
EDIT:
Also, How can I construct database replication in this situation.
If I get a replication which is replicate from the USA in Asia, I think the replication performance is quite poor because of phisical distance.
How Amazon or any global service construct it?
Replication performance can be quite poor. It is important to understand how much of your data is changing so that you can estimate the bandwidth required and understand whether your replication can keep up.
Amazon and other global services deal with this via a combination of replication, edge-caching (CDN), and other methodologies that bring the data closer to the consumer.
As a first step, you also might want to look at just making your API more coarse-grained. The fewer calls you have to make, the higher the performance (as the problem is likely latency, not bandwidth). See if you can batch things up instead of handling them one-at-a-time.
You also can look critically at caching. Instead of making your read-only API calls all the time, introduce some cache-control headers to specify the acceptable age of your requests. A lot of data is very static, things like user data, departments, product-info etc... Some of this data can leverage caching layers to become much more performant.
If you want to use AWS and want to host main components in a specific region, then you may think of hosting it yourself in EC2(s) [as Origin Server] in the region of your choice and use Cloudfront (CDN) to serve the content globally. AWS employs their own High Speed Backbone Network to reduce latency between geographically distant locations, by reducing no of Network hops.
From a caching standpoint, as Rob rightly said, Cloudfront performs different caching mechanisms for hot objects, warm objects (edge-caching, regional-caching); Also the Origin servers can send minimum expiration time and maximum expiration time over HTTP Headers to define Caching TTL.
If however, you don't want to use the advantage of High Speed Backbone Network, you should consider application design of your endpoints and functionality keeping latency as a constraint; and use appropriate TTL for caching of objects and define appropriate caching strategy, keeping in mind the R/W ratio of your application.
I want write a code to get the available bandwidth.
Using one of the algorithm.ex.spruce / pathload.
I wanted to a code in C++ in Windows.
I have got linux code .
But i wanted a Windows based code , which can get me up and down bandwidth.
Bandwidth for what resource? If this is a network resource there isn't anything in any language or the OS that will give you any real estimation of bandwidth. You would need to call out to something at the other end of the link you need to traverse and get an estimation of bandwidth at that point in time.
Or better said... You would need to call a file on a web server to test the download speed of someone's home Internet connection. Keep in mind that the numbers obtained are only accurate for that point in time though. As the bandwidth on any resource can be higher or lower when you actually use them since external factors always affect bandwidth (other prorcesses, users, etc.)
Why do you need the bandwidth and for what resource?
If you asking, you not up to it. Converting linux to windows requires knowledge of both platform, which you clearly doesnt have.
In my experience, almost all network friendly bandwidth estimation algorithm (pathload, pathchirp etc) are unsuitable for high speed bandwidth. Those old algorithm are suitable and practical if the bandwidth is around 1mb. Also, these algorithm assume the network is 'clean'(no other traffic). Nowadays, almost all of these 'network friendly' algorithm is not practical.
Other variant bandwidth estimation tools like netperf, netcps is based on brute force method. Brute force method are not network friendly. Most of this algorithm have problem with latency(if tcp based) and reached hdd read/write speed(if write to hdd instead of memory).
IMO, the best bandwidth estimation tools is UDP based(not influenced by latency unlike tcp) brute force(not influenced by other traffic) with custom made control flow tuned for high speed networks.
Other problem you will encounter is code optimization. You must ensure that your code is highly optimized. If you use c#, GC will pose a possible problem.
We're looking at setting up a MSMQ system with ~8000 clients and one queue per client. On average the system needs to handle ~2000 messages daily from each client, where the message size will range from 1K to MSMQ Max size (4MB).
Is this at all possible with MSMQ?
I know I'm not providing a lot of details here, but I just want feedback on whether or not anyone has been able to run a similar setup.
Well, broadbrush answer is yes, it will scale out no problem, as its a mature product, on the go for over 10 years.
There are a number of very large implementations out there, banks mostly, like Barclays use it, for I think between 60-90k desktops, but only if it has been correctly designed, and each of your processing boxes has enough memory, and suitable network bandwidth.
As regards messaging throughput, 2k messages a day, is nothing really. I was working in the City a few years ago, where one derivatives f/x app was processing 1600 message/sec.
I can't offer you any advice without specifics, but I hope that helps.
Bob.
In theory you can do this and you would have a maintenance nightmare. Employ one/few customer facing queue(s) and deploy Content Routing and/or Competing Consumers patterns downstream.
Throughput is not an issue with your projected volumes but remember that there are fundamental disk files supporting your queues. If you deploy 8,000 queues you may risk getting disk I/O issues unless you have a RAID solution.