I'm new to drools and I'm trying to understand when multiple kieSessions should be used in a drools project.
I did not manage to find much on this topic in the documentation other than:
"You could decide to create multiple sessions ... if you need multiple
sessions for scalability reasons."
I'm not quite sure what scalability refers to here. Is it about the number of facts inserted in the kie session? or is it about the number of rules? Or is it simply about running the same project but for different clients by assigning to each client 1 kie Session?
trying to understand when multiple kieSessions should be used in a drools project
Stateful sessions require separate session per client for repeated requests (stateful means the session keeps the data); stateless sessions do not.
Related
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.
Here is the background:
We have a cluster (of 3) different services deployed on various containers (like Tomcat, TomEE, JBoss) etc. Each of the services does one thing. Like one service manages a common DB and provides REST services to CRUD the db. One service puts some data into a JMS Queue, Another service reads from the Queue and updates the DB. There is a client app that makes a REST service call to one of the service that sets off creating a row in the db, pushing that row into a queue etc.
Question: We need to implement the client app so that we know at any given point in time where the processing is. How do I implement this in RcJava 2/Java 9?
First, you need to determine what functionality in RxJava 2 will benefit you.
Coordination between asynchronous sources. Since you have a) event-driven requests from one side, and b) network queries on the other sides, this is a good fit so far.
Managing a stream of data, transforming and combining from one or more sources. You have given no indication that this is required.
Second, you need to determine what RxJava 2 does not provide:
Network connections. This is provided by your existing libraries.
Database management. Again, this is provided in your existing solutions.
Now, you have to decide whether the firstlies add up to something you can benefit from, given the up-front costs of learning a new library.
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.
I'm newbie to Drools. For powerful drools fusion or timer based rule, most of them are stateful. So, an obvious issue is coming: if the server of the stateful session is down, is that possible to recover the session by Kie execution server?
For example, I start a timer(int:30s) rule, but the server that hosts the ksession is down after 15s. How to recover it?
I've read some of the blogs like:
http://mswiderski.blogspot.com/2016/04/kie-server-clustering-and-scalability.html
http://planet.jboss.org/post/unified_kie_execution_server_part_1
I've also read a little bit about the VFS Clustering in official doc. But I'm still confused about is there an easy way to achieve my case?
Thanks,
So I am doing some research into using Service Fabric for a very large application. One thing I need to have is a service that is partitioned by name, which seems fairly trivial at the application manifest level.
However, I really would like to be able to add and remove named partitions on the fly without having to republish the application.
Each partition represents our equivalent of a tenant, and we want to have a backend management app to add new tenants.
Each partition will be a long-running application that fires up a TCP server that uses a custom protocol, and I'll need to be able to query for the address by name from the cluster.
Is this possible with Service Fabric, and if so is there any documentation on this, or something I should be looking for?
Each partition represents our equivalent of a tenant, and we want to have a backend management app to add new tenants.
You need to rethink your model. Partitioning is for distributing data so it accessible fast, for read and write. But within the same logical container.
If you want to do some multitenant in Service Fabric you can deploy an Application multiple times to the cluster.
From Visual Studio it seems you can only have one instance of an Application. This is because in the ApplicationManifest.xml there are DefaultServices defined. This is okay for developing on the local Service Fabric cluster. For production you might want to consider deploying the application with powershell, this will open up the possibility to deploy the same application multiple times with settings for each instance(like: tenant name, security, ... )
And not only Applications can be deployed multiple times, stateful/stateless services as well. So you could have one application and for each tenant you deploy a service of a certain type. Services are findable via the naming service inside Service Fabric, see the FabricClient class for more info on that.
It is not possible to change the partition count for an existing application.
From https://azure.microsoft.com/en-us/documentation/articles/service-fabric-concepts-partitioning/#plan-for-partitioning (emphasis mine):
In rare cases, you may end up needing more partitions than you have initially chosen. As you cannot change the partition count after the fact, you would need to apply some advanced partition approaches, such as creating a new service instance of the same service type. You would also need to implement some client-side logic that routes the requests to the correct service instance, based on client-side knowledge that your client code must maintain.
You are encouraged to do up-front capacity planning to determine the maximum number of partitions you will need - and if you end up needing more, you'll need to implement some special client side handling to cope.
We had the same problem and ended up creating an instance of the service for each tenant. This is pretty easy to do and will scale to any number of tenants.