Inspired by my article The lightweightness of microservices – Comparing Spring Boot, WildFly Swarm, and Haskell Snap, a colleague of mine implemented the same Web service using the Go programming language. You can find his code here: Bitbucket-repo. To compare his implementations with the other ones, I integrated it into the main project (GitHub-repo) and measured it. Here are the results. 🙂
A microservice is an autonomous sub application for a strictly defined and preferably small domain. An application built from microservices is scalable, resilient, and flexible. At least, if the services and their infrastructure are well designed. One requirement on the used frameworks to achieve scalability and resilience is that they are lightweight. Lightweightness comes in different flavors. Microservices should be stopped and started fastly, and should consume few resources. The development and maintenance of microservices should be easy.
For this reason, in the Java world, Spring Boot is currently recommended as best choice regarding these requirements. Traditional Java EE application servers are too heavyweight, because they are not developed as basis for single services but as platform for running different applications simultaneously. Thus, they must be bloated.
Being a curious person I used some of my spare time in the last Christmas holidays to actually measure the lightweightness. First I chose Spring Boot and WildFly as “competitors”. I added WildFly Swarm which provides similar features as Spring Boot but is based on WildFly. Then looking at the requirements I decided to include a framework with a real small startup time in comparison to Java-based frameworks and chose Snap based an Haskell. For every framework I built a minimal micro service, wrapped it into a Docker container, and measured its weight.
If you deploy a JSF application in WildFly 8, you can omit to define the JSF serlvet mapping. In this case three default mappings will be active out of the box.
This behavior is not mentioned in the JSF 2.1 spec. But it explicitly allows implementations to use proprietary means to invoke the JSF lifecycle.
In addition to FacesServlet, JSF implementations may support other ways to invoke the JavaServer Faces request processing lifecycle, but applications that rely on these mechanisms will not be portable.
This default mapping can be problematic as it provides several path to access resources within your web application. Especially if you use security constraints to protect parts of your application. For instance if you restrict access to <context-root>/secure/* using a security constraint in your web.xml, web resources can still be accessed via <context-root>/faces/.
The Java EE platform provides a component-based architecture, which supports modular concepts to develop applications and reuse components in different applications and environments. Dependencies to the application server specific environment can be defined in deployment descriptors, such as ejb-jar.xml for EJB components.
The good old JBoss Seam framework introduced the usage of stateful session beans (SFSB) as backing beans for JSF applications. The trick was to bind the lifecycle of a SFSB to a web context, such as the session or the request context. Meanwhile this concept was integrated into the Java EE by the Context and Dependency Injection (CDI) specification. We really like to use SFSB in JSF because it provides a comfortable way to access the logic and persistence layer with an automatic and painless transaction management.
We also like to modularize our applications by separating its different layers into different Maven modules. Thus, usually the web and application logic are bundled as EJB archives, whereas the web pages are stored in a WAR archive. All modules are combined to an application as an EAR archive. In our opinion this approach is more maintainable than to mix everything into one big WAR archive.
Sometimes the web logic has to access JSF classes, i.e. to query the locale used in the current request. To do this with the JBoss EAP 6, a particularity must be taken into account. By default in the EAP6 only WAR archives containing a JSF descriptor have access to the JSF classes, EJB jars do not.
This is due to rules for implicit class loading dependencies which are added automatically by the application server at deployment time. To access JSF classes from an EJB archive, the EJB jar has to state an explicit dependency to the faces module. This is pretty simple, if you know how to do it.
As announced this is the last post of our series about clustering of the Redhat EAP 6 and JBoss AS 7. The other posts of this series were
- Clustering in JBoss AS7/EAP 6
- Managing cluster nodes in domain mode of JBoss AS 7 / EAP 6
- Scalable HA Clustering with JBoss AS 7 / EAP 6
- Load-balancing and failover of remote EJB clients in EAP6 and JBoss AS7
- Clustering of the messaging subsystem HornetQ in JBoss AS7 and EAP 6
This post will dig deeper into the clustering mechanisms of the EAP 6 and JBoss AS 7. We will show different solutions to multicast problems you will get in most cloud networks as well as some other networks. Infinispan uses JGroups to do its cluster communication. Cluster communication here means multiple things: finding other cluster nodes, providing a reliable transfer, implementing multicast communication even if there is no IP multicast available, identifying dead cluster nodes and a little bit more. In fact JGroups is able to do a lot more but Infinispan does not need all of the opportunities JGroups offers. The upcoming HornetQ version 2.3 which will be included in the EAP 6.1 will use JGroups for server discovery too. This post will explain the basic principles of JGroups and how to configure it in different network setups, especially most cloud networks.
In a recent blog-post Clustering in JBoss AS7/EAP 6 we showed how basic clustering in the new EAP 6 and JBoss AS 7 can be used. The EAP 6 is basically an AS 7 with official RedHat-support. Our cluster we described in that post was small and simple. This post will cover much more complex cluster structures, how to build them and how we can utilize the new domain-mode for our clusters. There are multiple ways to build and manage bigger JBoss cluster environments. We will describe two ways to do so: One using separating techniques also applicable to older JBoss versions and the other way using an Infinispan feature called distribution.
Scalability vs. Availability
The main challenge when building a cluster is to make it both highly available and scalable.
Availability for a cluster means: If one node fails, all the sessions on that node will be seamlessly served by another node. This can be achieved through session-replication. Session-replication is preconfigured and enabled in the
ha profile in the
domain.xml. Flat replication means that all sessions are copied to all other nodes: If you have got four nodes with 1GB memory for each of them, your cluster can only use 1GB of memory because basically all nodes store copies from each other. I. e. your cluster will not have 4*1GB=4GB memory. If you would add more nodes to this cluster you would not get more memory, you will even lose some memory due to overhead for replication. But you will get more availability and more important more network traffic due to replication overhead (all changes need to be redistributed to all other nodes). Let us call this cluster topology full-replication.
This blog post describes how to use JBoss Forge to easily generate interface classes for your Java application and use these classes with the Hibersap framework to connect to a SAP system. At the end of the post you will find a screen-cast showing you how easy and straight-forward it is to call SAP functions using the Hibersap-Forge-Plugin.
What is JBoss Forge?
Let’s take the description from the Forge homepage to answer this question:
A core framework and next-generation shell for tooling and automation at a command line level; with APIs for integration in IDEs, extending built in functionality with plugins, and scripting for automating repetitive tasks, Forge is a tool every open-source developer should be looking at.
A core framework for rapid-application development in a standards-based environment. Plugins / incremental project enhancement for Java EE, and more.
The ability to combine different servers to a cluster that hides its internal servers from the clients and offers a virtual platform for an application is important for enterprise applications. It can be used to provide
- high scalability by adding cheap computational resources to the cluster on demand or
- high availability by using a transparent failover that hides faults within single servers.
Usually high scalability limits high availability and vice versa, but it is also possible to get both. The JBoss application server can be configured to support both features.
This post is the first one of a series about clustering with the JBoss AS 7. Here, we focus on the basic concepts behind JBoss AS 7 clustering and show you how to setup a basic clustered environment with a simple Java EE application.
In the series, we concentrate on the JBoss AS 7 respectively the EAP 6, which is the Red Hat-supported version of the JBoss application server. Future posts will be about particular subsystems of the JBoss AS, such as HornetQ or Infinispan.