In the recent past, a team I was working with was facing an architectural decision regarding what technology and deployment footprint to go with for a greenfield project.
Its been about five months now since this application has been in production.
The use-case in question was to present a suite of REST services to front a large set of “master data” dimensions for a data warehouse as well securing that data (record level ACLs). In addition to this, the security ACLs it would manage needed to be transformed and pushed downstream to various legacy systems.
With that general use-case in mind, some other items of note that were to be considered:
- The specific use case of “REST services facade for master data” was generic in nature, so the security model was to be agnostic of the specific data set being secured and have the capability of being applied across different data sets for different clients.
- Changes for a given service should be easy to fix and deploy and independent of one another with minimal interruption.
- The services need to scale easily and be deployed across several different data centers which are a mix of traditional bare-metal/ESX vm’s as well as in the cloud (azure/aws). Tight coupling to each DC should be minimized when possible.
- The services stack would potentially serve as the hub for orchestrating various other ETL related processes for the warehouse, so adding new “services” should be easy to integrate into the larger application.
- Given the sensitivity of the data, all traffic should be secured w/ TLS and REST apis locked down w/ OAuth2 client credentials based access.
Given the above requirements and much discussion we decided to go with a container based microservices architecture.
First off, this team already had significant experience w/ the traditional monolithic approach to applications and had already run into the many shortcomings of this architecture over the long term. As new features needed to be deployed, it was becoming more of a pain to add new “services” to the monolith as it required the entire stack to be redeployed which is disruptive. Given this new application would have a similar lifecycle (new services needing to be added over time) we wanted to try a different approach…. and who was the new kid on the block? “microservices”; and it was time to get one’s feet wet.
This shop was primarily focused on NodeJS, LAMP and Java stacks so after doing some research the decision was made to go with Spring Cloud as the base framework to build this new suite of services. If one does any reading on the topic of microservices, you will quickly see such architectures involve many moving parts: service discovery, configuration, calling tracing (i.e. think google dapper), load balancing etc.
Do you want to write these pattern implementations this all yourself? Probably not; I sure didn’t. So after evaluating the space at the time, Spring Cloud was the most robust solution for this and one of its biggest selling points is that it was based on many of the great frameworks that have come out of Netflix’s OSS project (Eureka, Hystrix and more..)
Lastly the decision to go w/ Docker was really a no brainer. The services would potentially need to be deployed and moved across various data centers. By using Docker DevOps would be able to have a footprint and deployment process that would be consistent regardless of what data center we would be pushing to. The only data center specific particulars our DevOps guys had to care about was, setting up the Docker infrastructure (i.e. think Docker hosts on VMs via Ansible coupling to DC specific host provisioning APIs) and the DC specific load balancers, who’s coupling to the application was just a few IP’s and ports (i.e. the IPs of the swarm nodes with exposed ports of our Zuul containers). Everything downstream from that was handled by Docker Swarm and the microservices framework itself (discovery, routing etc)
The acronym for this services backend ended up being CELL which stands for… well whatever you want it to stand for…. I guess think of it (the app) as an organism made up of various cells (services). CELL’s services are consumed by various applications that present nice user interfaces to end users.
The above diagram gives a high level breakdown of its footprint. Its broken up into several services:
Core services that all other app services utilize:
- cell-discovery: Netflix Eureka: Participating services both register on startup and use this to discover the cell-config service (to bootstrap themselves) plus discover any other peer level services they need to talk to.
- cell-config: spring-cloud-config: Git sourced application configuration (w/ encryption support). Each application connects to this on startup to configure itself.
- oauth2-provider: All services are configured w/ an OAuth2 client credentials compliant token generation endpoint to authenticate and get tokens that all peer services validate (acting as resource servers)
- tracing-service: zipkin: All services are instrumented w/ hooks that decorate all outbound http requests (and interpret them upon reception) with zipkin compliant tracing headers to collect call tracing metrics etc. Background threads send this data periodically to the tracing service.
- cell-event-bus: kafka and spring-cloud-stream: Certain services publish events that other services subscribe to to maintain local caches or react to logic events. This provides a bit looser coupling than direct service to service communication; leveraging Kafka gives us the ability to take advantage of such concepts of consumer groups for different processing requirements. (i.e. all or one)
- cell-router: Netflix zuul: Router instances provide a single point of access to all application services under a https://router/service-name/ facade (discovered via the discovery service). Upstream data center specific FQDN bound load balancers only need to know about the published ports for the Zuul routers on the swarm cluster to be able to access any application service that is available in CELL.
- cell-service-1-N: These represent domain specific application services that contain the actual business logic implementation invoked via external callers. Over time, more of these will be added to CELL and this is where the real modularity comes into play. We try to stick to the principle of one specific service per specific business logic use-case.
As noted above, one of the requirements for CELL was that participating services could have data they manage, gated by a generic security ACL system. To fulfill this requirement, one of those domain specific apps is the cell-security service.
The cell-security service leverages a common library that both cell-security servers and clients can leverage to fulfill both ends of the contract. The contract being defined via some general modeling (below) and standard server/client REST contracts that can easily be exposed in any new “service” via including the library and adding some spring @[secConfig] annotations in an app’s configuration classes.
- Securable: a securable is something that can have access to it gated by a SecurityService. Securables can be part of a chain to implement inheritance or any strategy one needs.
- Accessor: is something that can potentially access a securable
- ACL: Binds an Accessor to a Securable with a set of Permissions for a given context and optional expression to evaluate against the Securable
- SecurableLocator: given a securable‘s guid, can retrieve a Securable or a chain of Securables
- AccessorLocator: given a accessor‘s guid, can retrieve the Accessor
- AccessorLocatorRegistry: manages information about available AccessorLocators
- SecurableLocatorRegistry: manages information about available SecurableLocators
- ACLService: provides access to manage ACLs
- PrincipalService: provides access to manage Principals
- LocatorMetadataService: provides access to manage meta-data about Securable|Accessor Locators
- ACLExpressionEvaluator: evaluates ACL expressions against a Securable
- SecurityService: Checks access to a Securable for a requesting Accessor
The model above is expressed via standard REST contracts and interfaces in code, that are to be fulfilled by a combination of default implementations and those customized by individual application CELL services who wish to leverage the security framework. There are also a few re-usable cell-security persistence libraries we created to let services that leverage this to their persist security data (both authoritative and local consumer caches) across various databases (Mongo DB and or JPA etc). As well a another library to hook into streams of security events that flow through CELL’s Kakfa event bus.
Spring Cloud impressions
When I started using Spring Cloud (in the early days of the Brixton release), I developed a love – hate relationship with it. After a few initial early successes with a few simple prototypes I was extremely impressed with the discovery, configuration and abstract “service name” based way of access peer services (via feign clients bound to the discovery services)…. you could quickly see the advantageous to using these libraries to really build a true platform that could scale to N in several different ways and take care of a lot of the boilerplate “microservices” stuff for you.
That said, once we really got into the developing CELL we ended up having two development paths.
The first being one team working on creating a set of re-usable libraries for CELL applications to leverage and integrate into the CELL microservice ecosystem. This consisted of creating several abstractions that would bring together some of the required spring cloud libraries, pre-integrated via base configuration for CELL, and just make it easier to “drop-in” to a new CELL app without having to wade into the details of spring cloud too much and just let the service developer focus on their service. The amount of time on this part was about 70% of the development effort, heavily front loaded in the start of the project.
The second being the other team using the latter to actually build the business logic services, which was the whole point of this thing in the first place. This accounted for about 30% of the work in the beginning and today… about 80-90% of the work now that the base framework of CELL is established.
The hate part (well not true hate, but you know what I mean… friendly frustration) of this ended up being the amount of man hours spent in the start of the project dealing/learning spring-cloud. There is a tangible learning curve to be aware of. Working around bugs, finding issues in spring-cloud, both real ones or just working through perceived ones via misunderstandings due to the complexity of spring-cloud itself.
I’m not going to go into each specific issue here, however there were simply a lot of issues and time spent debugging spring cloud code trying to figure out why certain things failed or to learn how they behaved so we could customize and properly configure things. In the end most of the issues could be worked around or were not that hard to fix…. its just the time it took to figure out the underlying causation’s, produce a reproducible sample and then convey it to the spring-cloud developers to get help with. (The spring-cloud developers BTW are excellent and VERY responsive) kudos to them for that.
Lastly, taking each CELL artifact (jar) and getting it wrapped up in a Docker container was not an huge ordeal. In the deployed footprint, each CELL artifact is a separate Docker Swarm Service that is deployed on its own overlay network (separate one per CELL version). As stated previously, the CELL router (Zuul) is the only service necessary to be exposed on a published swarm port and then upstream datacenter load balancers can just point to that.
So would I recommend Spring-Cloud?
Yes. Spring Cloud at its heart is really an pretty impressive wrapper framework around a lot of other tools that are out there for microservices. It has a responsive and helpful community. (definitely leverage Gitter.im if you need help!) The project has matured considerably since I first used it and many of the issues I was dealing with are now fixed. Compared to writing all the necessary things to have a robust microservices ecosystem yourself….. I’ll take this framework any day.
Final note. I would NOT recommend using spring-data-rest. We used that on a few of the CELL application logic services and its main benefit of providing you a lot of CRUD REST services in a HATE-OS fashion…. its just not that easy to customize the behavior of, has a lot of bugs and just generally was a pain to work with. At the end of the day it would have just been easier to code our own suite of CRUD services instead of relying on it.
Do you use Docker?
Does your containerized app have the need to discover both its own IP and one or more mapped ports?
How can another container access my exposed ports and how can I do the same of my peers?
As it stands today, simple self discovery of your container’s accessible IP and one or more of its mapped ports is not exposed to your Docker container process as a native feature of the engine itself.
If you’ve attempted to containerize an app that attempts to discover its peers in order to form its own peer-level cluster etc, you’ve likely run into this challenge.
That said there are several tools out there with can help you with this issue. One of which is Registrator which is a special container that listens for events from a Docker host and acts as service discovery bridge that relays this info into other tooling such as Consul and etcd etc. In short, when your container is launched, the Registrator container collects all the info about the docker host it is running on and its exposed ports and registers this under a named service in one of the aforementioned backends.
This is all fine and great, however this still puts a lot of work on you, the container developer who needs to collect this info and then act upon it in order to form a higher level cluster between your containers.
I had this exact same problem for a Java based service that needed to form a Hazelcast cluster dynamically. Out of that use case I came up with a generic library that you can drop into your Java container application called docker-discovery-registrator-consul which is available at: https://github.com/bitsofinfo/docker-discovery-registrator-consul
The purpose of this library is for “self-discovery” from within your JVM based Docker application where you need to discover what your accessible docker-host bound IP and mapped port(s) are, as well as your peers within the same service. As noted above this is critical if your container has to do further peer discovery for other services it provides or clustering groups it must form.
You can read all the details of how it works and how to use it here: https://github.com/bitsofinfo/docker-discovery-registrator-consul
Hopefully it will be of use to you as well.