I am building a JavaFX client application communicating with Spring MVC Restful server(Spring boot 1.4.1) application which works as expected.
Some features require fast interaction with the server to validate limits and availability before proceeding to next input example check if member number insert is valid and if has exceeded limit to insert, during accumulation of records(each confirmed record temporarily stored in a tableview before sent to server for storage) before the records are actually saved.
Within JavaFX and Spring framework(in both frontend and backend) scope, how can such kind of features made look more interactive(or live) than normal "let-me-wait-for-response" approach
If question is not clear, just ask, otherwise i think it is
It appears that the only interaction you have between client (JavaFX) and server (SpringBoot) is through a REST API. This will make short bursts of data (such a validation) take longer.
Switching to another communication mechanism (for example gRPC or Netty with Msgpack) could help. Note that once you open the door for non-REST calls it'll make you re-think the use of REST in the first place.
Non-REST communication may not be an option depending on your requirements (firewalls, etc) or may need additional setup in order to surmount other obstacles, in other words, there's no free lunch.
Related
Currently we are implementing REST API's using the spring-boot. Since our API's are growing in number we are thinking of a solution to implement the REST API's using a different approach.
The approach is as below :
Expose a single service to receive all the HTTP requests.
We will have the URI's configured in a data base table to call the
next set of services. These service are configured to listen to
particular JMS messages.
The next set of services will receive the JMS messages and process
the data.
Below are my questions :
Will the above approach still represent the REST architecture ?
What are the downsides of above approach(we are aware of network
latency) any thing other then network latency ?
What are the REST architecture benefits will we be missing.
Or can we just say that our approach is the REST architecture done differently ?
You're making 2 major choices, each can be decided separately:
1) Having a single HTTP service
2) Using JMS as the communication between this service and the underlying microservices
Regarding #1, if you do this, you can no longer call your services REST since the whole point of REST is to use HTTP verbs together with your domain objects for a predicable set of endpoints. GET on /objects/ means the object is being fetched, POST on /objects means a new object is being created, etc... Now, this is OK, you can do it this way and it can work, though it will be "non-standard".
In fact, you might want to check out GraphQL https://www.howtographql.com/basics/1-graphql-is-the-better-rest/ as its pretty close to what you're trying to do.
These days really either REST or GraphQL seems to be the two popular approaches.
Another way to do REST, if you're looking to simply expose REST services on your domain objects without having to write a lot of code, is Spring Data REST: https://spring.io/projects/spring-data-rest and if you're comfortable with Spring already, this should be pretty easy to understand.
For #2, your choice of communication between your single gateway service and the underlying services. Do most of your calls require synchronous answers, such as a UI asking for data to display in a browser or phone? If so, JMS is not a good approach. JMS would be an ok approach if the majority of your services were asyncronous - for example someone submitting a stock trade request. The UI would just need to know the request was submitted, but it will actually be processed some time later and the result will be fetched asyncronously.
Without knowing much about your application, I would recommend sticking with HTTP between your services for simplicity sake unless there is a good reason to switch to JMS.
We're currently about to migrate from monolithic design to the microservice architecture, trying to choose the best way to replace JAX-WS with RESTful and considering to use Spring WebFlux.
We currently have an JAX-WS endpoint deployed at Tomcat EE serving requests from third-party clients. Webservice endpoint makes a long running blocking call to the database and then sends a SOAP-response to the client with a data retrieved from DB (Oracle).
Oracle DB will be replaced with one of NoSQL databases soon (possibly it will be MongoDB). Since MongoDB supports asynchronous calls we're considering to substitute current implementation with a microservice exposing REST endpoint based on WebFlux.
We have about 2500 req/sec at peaks, so current endpoint often gets down with a OutOfMemoryError. It was a root cause that pushed us towards migration.
My thoughts are to create a non-blocking endpoint which will call MongoDB in asynchronous manner and send a REST-response to the client. So I have a few questions considering basic features that WebFlux provides:
As far as I concerned there is a built-in backpressure control at
the business-level (not TCP flow control) in WebFlux and it works
generally via Reactive Streams. Since our clients are not
reactive, does it means that such way of a backpressure control is
not implementable here?
Suppose that calls to a new database remains long-running in a new
architecture. Since Netty uses EventLoop to serve incoming
requests, is there possible a situation when the microservice has
accepted all incoming HTTP connections, invoke an async call to the
db and subscribed a resulted Mono to the scheduler, but, since
the request quantity keeps growing explosively, application keep
creating new workers at scheduler pools that leads to a
crashing? Is this a realistic scenario?
Suppose that calls to the database remained synchronous. Is there a
way to handle them using WebFlux in a such way that microservice
will remain reachable under load?
Which bottlenecks can be found in such design? Does this solution
looks adequate?
Does Netty (or Reactor-Netty, or whatever) has a tool to limit a
quantity of requests processing simultaneously? Say I would to limit
the endpoint to serve not more than 100 parallel requests and skip
all requests above that point, is it possible?
Suppose I will create a huge amount of threads serving async (or
maybe sync) calls to the DB. Where is a breaking point when the
application will crash or stop responding to the incoming
HTTP-requests? What will happened there - we will ran out of memory
or..?
Finally, there were no any major issues concerning perfomance during our pilot project. But unfortunately we didn't take in account some specific Linux (and also OpenShift) TCP tuning props.
They may significanly affect the overall perfomance, in our case we've gained about 10 times more requests after tuning.
So pay attention to the net.core.somaxconn and other related parameters.
I've summarized our expertise in the article.
In a distributed systems environment, we have a RESTful service that needs to provide high read throughput at low-latency. Due to limitations in the database technology and given its a read-heavy system, we decided to use MemCached. Now, in a SOA, there are atleast 2 choices for the location of the cache, basically client looks up in Cache before calling server vs client always calls server which looks up in cache. In both cases, caching itself is done in a distributed MemCached server.
Option 1: Client -> RESTful Service -> MemCached -> Database
OR
Option 2: Client -> MemCached -> RESTful Service -> Database
I have an opinion but i'd love to hear arguments for and against either option from SOA experts in the community. Please assume either option is feasible, its a architecture question. Appreciate sharing your experience.
I have seen the
Option 1: Client -> RESTful Service -> Cache Server -> Database
working very well. Pros IMHO are that you are able to operate wtih and use this layer in a way allowing you to "free" part of the load on the DB. Assuming that your end-users can have a lot of similar requests and after all the Client can decide what storage to spare for caching. Also how often to clear it.
I prefer Option 1 and I am currently using it. In this way it is easier to control the load on the DB (just as #ekostatinov mentioned). I have lots of data that are required for every user in the system, but the data is never changed (such as some system rules, types of items, etc). It really reduces the DB load. In this way you can also control the behavior of the cache (such as when to clear the items).
Option 1 is the prefered option as it makes memcache an implementation detail of the service. the other option means that if the business changes and things can't be kept in the cache (or other can etc.) the clients would have to change. Option 1 hides all that behind the service interface.
Additionally option 1 lets you evolve the service as you wish. e.g. maybe later you think you need a new technology, maybe you'd solve the performance problem with the DB etc. Again, option 1 lets you make all these changes without dragging the clients into the mess
Is the REST ful API exposed to external consumers. In that case it is up to the consumer to decide if they want to use a cache and how much stale data can they use.
As for as the REST ful service goes, the service is the container of business logic and it is the authority of data, so it decides how much to cache, cache expiry, when to flush etc. A client consuming the REST service always assumes that the service is providing it with the latest data. And hence option 1 is preferred.
Who is the client in this case?
Is it a wrapper for your REST API. Are you providing both client and the service.
I can share my experience with Enduro/X middleware implementation. For local XATMI service calls any client process connects to shared memory (LMDB) and checks the result there. If there is response saved it returns data directly from shm. If data is not there, client process goes the longest path and performs the IPC. In case of REST access, network clients still performs the HTTP invocation, but HTTP server as XATMI client returns the data from shared mem. From real life, this technique was greatly boosting the web frontend web application which used middleware via REST calls.
I have a RESTful web-service application that I developed using the Netbeans IDE. The application uses MySQL server as its back end server. What I am wondering now is how often a client application that uses my RESTful application would refresh to reflect the data change in the server.
Are there any default pull intervals that clients get from the RESTful application? Does the framework(JAX-RS) do something about it Or is that my business to take care of.
Thanks in advance
#Abraham
There are no such rules. Only thing you can use for properly implementing this is HTTP's caching capabilities. Service must include control information how long representation of a particular resource can be cached, revalidated, never cached etc...
On client application side of things each client may decide it's own path how it will keep itself in sync with a service. It can be done by locally storing data and serve end user from local cache etc... Service can not(and shouldn't know) how clients are implemented, only thing service can do is to include caching information in response messages as i already mentioned above.
It is your responsibility to schedule the service to execute again and again. We can set time out interval but there is no pull interval.
I am consuming a rate-limited web service, which only allows me to make 5 calls per second. I am using my server to proxy these calls to a web client:
Mashery > My Web Server > Client's Browser
I have optimized the usage of this web service, but there are still occasional times when I go over the rate limit. What I would like to do instead is hold the client's request for one second (or longer if warranted) before making the web service call to Mashery.
There are some ways I can solve this by building a simple queue system with a database back-end, but I'd rather avoid that if something already exists. Does something already exist to rate-limit the consuming side of this?
To reliably throttle requests to only 5 per second, I would employ a queue/worker system. But before that I would just request a higher rate limit by contacting API support for that platform. Of course, I would also consider using caching if it's a read-only method and you're pulling down a lot of the same data and it's compliant with the API TOS.