We are looking to use Docker container to run our batch jobs in a cluster enviroment.
We are evaluating to use AWS ECS Container Service/Chronos/Mesos.
As far as I know, Apache Mesos has some overlapping features/purpose that EC2 has, like cluster management. Chronos is a distributed scheduler.
I am having dificult to correlate all this technologies to create a architecture!
EC2 service replace Mesos? What about the scheduler?
We are a small team will little experience in cluster development. Which stack is better for our batch processing?
EDIT
I make a huge edit, and i think now i understand the architecture:
This is a sample picture with two cluster been managed by Mesos.
Reading the ECS Container Service documentation(http://docs.aws.amazon.com/AmazonECS/latest/developerguide/scheduling_tasks.html), AWS is on the way of integrete ECS with the Mesos Apache Framework. So I imagine that using in the future, we can use the mesos framework to manage the resources in the ECS Cluster. So it is going to be possible to use Chronos (for batch scheduling) and Marathon (for long running app.)
EDIT
At this moment, we dont have distributed jobs running, like hadoop jobs or sparks jobs. Our job are much simpler, running on single instances of EC2. We are planning to use Docker to run our batch running jobs.
I'd argue it depends on the type of batch jobs, but the Apache Mesos ecosystem is certainly more flexible than ECS to accomodate your needs. The flexibility comes from the fact that Mesos uses a so called two-level scheduling model, which is a fancy name for it outsources the scheduling decision into frameworks (rather than trying to implement each and every existing and future workload scheduling strategy in its core, itself).
You mentioned one such a framework already, Chronos, which is a good working horse, just maybe don't use the dependencies for jobs, ok? Then there is another great batch framework called Cook. Depending on your needs (for example SQL-based batch report generation) you could use Apache Spark. And so on and so forth.
BTW, did I mention already that with Mesos you don't risk a vendor lock-in, while being able to deploy it, depending on your needs, fully in one cloud (such as AWS), hybrid cloud (say AWS and GCP/Azure) or on-premises?
UPDATE: to clarify, of course Mesos has first-class Docker support.
Related
I am trying to migrate our organization's hadoop jobs to GCP...I am confused between GCP Data Flow and Data Proc...
I want to re-use Hadoop jobs we already have created and minimize the management of the cluster as much as possible. We also want to be able to persist data beyond the life of the cluster...
Can anyone suggest
I would just start with DataProc as it is very close to what you have.
Check out DataProc initialization actions, https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions, create a simple cluster and get a feel for it.
DataFlow is completely managed and you don't operate any cluster resources, but at the same time you cannot migrate an onsite cluster to DataFlow as is, you need to migrate (some times rewrite) your Hive/Pig/Oozie etc.
Cost for DataFlow is also calculated differently, though there is no upfront cost vs DataProc, everytime you run a job you incur some cost associated with it on DataFlow.
A lot depends on the nature of your Hadoop jobs and the activities you are performing in regards to the selection of Cloud Dataproc (managed big data platform - orientation of Hadoop/Spark) and/or Cloud Dataflow (managed big data platform - orientation of Apache Beam for streaming use cases).
In regards to ensuring persistence of data beyond the operation, you may want to consider storing your data on GCS or on PD's if that's an option basis the need of your use case.
What is the difference between Apache Mesos and Nomad?
Nomad seems to claim that it can do resource management so I wonder how that is different from Apache Mesos?
Nomad also claims the following on their website
Nomad is architecturally much simpler. Nomad is a single binary, both for clients and servers, and requires no external services for coordination or storage. Nomad combines features of both resource managers and schedulers into a single system. This makes Nomad operationally simpler and enables more sophisticated optimizations.
Well for anyone to bring up a cluster resource management alone is not enough. so for that Nomad obviously recommends to buy into rest of the HashiCorp products so I am not sure how it is architecturally simpler when one have to integrate with pretty much all of their products that are needed for a fully functional cluster?
Mesos does not support federation or multiple failure isolation regions. Nomad supports multi-datacenter and multi-region configurations for failure isolation and scalability.
Not sure if this is still true for Apache Mesos?
Nomad is currently advertised as an orchestrator for orchestrators.
Nomad only aims to provide cluster management and scheduling and is designed with the Unix philosophy of having a small scope while composing with tools like Consul for service discovery and Vault for secret management.
On the other hand, Mesos is more a framework for building distributed systems than just the container orchestrator. Of course, you can use it that way but it's only a minority of its features and not take full use of its two-level scheduling design.
Nomad is architecturally much simpler. Nomad is a single binary, both for clients and servers, and requires no external services for coordination or storage. Nomad combines a lightweight resource manager and a sophisticated scheduler into a single system. By default, Nomad is distributed, highly available, and operationally simple.
Mesos architecture is not that simple. It's multiple binaries project. Definietly not easy to set up and run. Multiple moving parts are always more complicated to setup than a monolith but enables customization.
Mesos does not support federation or multiple failure isolation regions. Nomad supports multi-datacenter and multi-region configurations for failure isolation and scalability.
That's true. Ther are some works to bring federations to Mesos but it's not done yet. https://youtu.be/kqyVQzwwD5E
Mesos and Nomad are created for sligthly different purpose. Although both of them are n-th level orchestrator and could be run one on another and probably could deliver similar features. Nomad is designed just to run simple stateless applications while Mesos alow pluging custom schedulers and make fine grained control of what/when/where is deployed.
I'm trying to figure out what would be the reasons for using Mesos. Can you come up with other ones?
Running all of your services in the same cluster instead of dedicated clusters (your end-applications + DevOps such as Jenkins)
Running different maturity applications in same cluster (dev, test, production), or is this viable? Kubernetes has a similar approach with Labels
Mesos simplifies the use of traditional distributed applications such as Hadoop by easing deployment, unified API, bin-packing of resources
Full-disclosure: I currently work at Twitter and I'm involved in both Apache Mesos and Aurora.
Mesos uses cases can vary based upon a few dimensions: scale (10 servers vs 10s of thousands), available hardware (dedicated/static or in the public cloud/scalable), and workloads (primarily services, batch, or both).
Your list is a great start. Here are a few additional use cases / features to add.
Container Orchestration
As container runtimes like Docker have become popular, lots of potential users are looking at Mesos + a scheduler to manage orchestration once container images are created. Mesos is already quite mature and has been proven at scale, which I think has given it a leg up over some emergent solutions.
Increased Resource Utilization
For companies running >50 servers, a common motivation for adopting Mesos is to increase resource utilization to reduce CapEx. There are a number of examples of this in both the public and private cloud. In the case of Ebay they have been running Jenkins on Mesos and were able to reduce their VM footprint. Mesosphere has also published a case study of HubSpot (runnning on AWS), and how they've been able to replace hundreds of smaller servers with dozens of larger ones by more-efficiently using their available hardware.
Preemption
At Twitter we're running Mesos via one scheduler: Apache Aurora. One of the ways we can improve utilization relates to your use case: running different maturity applications in the same cluster. Aurora has a concept of environments, so you can run applications that are production, development, or test. Additionally, Aurora has a built-in preemption feature which allows it to prioritize production over non-production tasks, killing non-production tasks when those resources are needed to run production ones as well as a priority system within each environment.
Long-term, functionality related to preemption will also be located in the Mesos core itself -- it's a killer feature related to both increased resource utilization and running different maturity applications (dev, test, prod). There are a few Mesos tickets to follow if you're interested in keeping up to date, including MESOS-155 for preemption, and MESOS-1474 for inverse offers.
Colocating Batch and Services
Running batch and services in a shared Mesos cluster will be key to driving up utilization even further as js84 points out. Check out Project Myriad, an effort to colocate Mesos and YARN workloads in the same cluster. At this time I'm not aware of any large deployments running both batch and services, but it's certainly the direction the community is moving in as it becomes easier for multiple frameworks to run in a shared cluster.
At least one additional use case comes to mind: Development SDK for developing distributed applications. If you have a look at Mesos Frameworks you will find a number of frameworks which have been developed on top of Mesos. Also interesting Apple's Siri framework powering Siri.
Regarding your 1): One additional angle you should keep in mind here is scaling your applications in the same cluster. I.e. at peak load of your website, shift resources easily towards the webservers while scaling down the Hadoop analytical processing.
I am relatively new to all these, but I'm having troubles getting a clear picture among the listed technologies.
Though, all of these try to solve different problems, but do have things in common too. I would like to understand what are the things that are common and what is different. It is likely that the combination of few would be great fit, if so what are they?
I am listing a few of them along with questions, but it would be great if someone lists all of them in detail and answers the questions.
Kubernetes vs Mesos:
This link
What's the difference between Apache's Mesos and Google's Kubernetes
provides a good insight into the differences, but I'm unable to understand as to why Kubernetes should run on top of Mesos. Is it more to do with coming together of two opensource solutions?
Kubernetes vs Core-OS Fleet:
If I use kubernetes, is fleet required?
How does Docker-Swarm fit into all the above?
Disclosure: I'm a lead engineer on Kubernetes
I think that Mesos and Kubernetes are largely aimed at solving similar problems of running clustered applications, they have different histories and different approaches to solving the problem.
Mesos focuses its energy on very generic scheduling, and plugging in multiple different schedulers. This means that it enables systems like Hadoop and Marathon to co-exist in the same scheduling environment. Mesos is less focused on running containers. Mesos existed prior to widespread interest in containers and has been re-factored in parts to support containers.
In contrast, Kubernetes was designed from the ground up to be an environment for building distributed applications from containers. It includes primitives for replication and service discovery as core primitives, where-as such things are added via frameworks in Mesos. The primary goal of Kubernetes is a system for building, running and managing distributed systems.
Fleet is a lower-level task distributor. It is useful for bootstrapping a cluster system, for example CoreOS uses it to distribute the kubernetes agents and binaries out to the machines in a cluster in order to turn-up a kubernetes cluster. It is not really intended to solve the same distributed application development problems, think of it more like systemd/init.d/upstart for your cluster. It's not required if you run kubernetes, you can use other tools (e.g. Salt, Puppet, Ansible, Chef, ...) to accomplish the same binary distribution.
Swarm is an effort by Docker to extend the existing Docker API to make a cluster of machines look like a single Docker API. Fundamentally, our experience at Google and elsewhere indicates that the node API is insufficient for a cluster API. You can see a bunch of discussion on this here: https://github.com/docker/docker/pull/8859 and here: https://github.com/docker/docker/issues/8781
Join us on IRC # #google-containers if you want to talk more.
I think the simplest answer is that there is no simple answer. The swift rise to power of containers, and Docker in particular has left a power vacuum for "container scheduling and orchestration", whatever that might mean. In reality, that means you have a number of technologies that can work in harmony on some levels, but with certain aspects in competition. For example, Kubernetes can be used as a one stop shop for deploying and managing containers on a compute cluster (as Google originally designed it), but could also sit atop Fleet, making use of the resilience tier that Fleet provides on CoreOS.
As this Google vid states Kubernetes is not a complete out the box container scaling solution, but is a good statement to start from. In the same way, you would at some stage expect Apache Mesos to be able to work with Kubernetes, but not with Marathon, in as much as Marathon appears to fulfil the same role as Kubernetes. Somewhere I think I've read these could become part of the same effort, but I could be wrong about that - it's really about the strategic direction of Mesosphere and the corresponding adoption of Kubernetes principles.
In the DockerCon keynote, Solomon Hykes suggested Swarm would be a tier that could provide a common interface onto the many orchestration and scheduling frameworks. From what I can see, Swarm is designed to provide a smooth Docker deployment workflow, working with some existing container workflow frameworks such as Deis, but flexible enough to yield to "heavyweight" deployment and resource management such as Mesos.
Hope this helps - this could be an enormous post. I think the key is that these are young, evolving services that will likely merge and become interoperable, but we need to ride out the next 12 months to see how it plays out. There's some very clever people on the problem, so the future looks very bright.
As far as I understand it:
Mesos, Kubernetes and Fleet are all trying to solve a very similar problem. The idea is that you abstract away all your hardware from developers and the 'cluster management tool' sorts it all out for you. Then all you need to do is give a container to the cluster, give it some info (keep it running permanently, scale up if X happens etc) and the cluster manager will make it happen.
With Mesos, it does all the cluster management for you, but it doesn't include the scheduler. The scheduler is the bit that says, ok this process needs 2 procs and 512MB RAM, and I have a machine over there with that free, so I'll run it on that machine. There are some plugin schedulers available for Mesos: Marathon and Chronos and you can write your own. This gives you a lot of power of resource distribution and cluster scaling etc.
Fleet and Kubernetes seem to abstract away those sorts of details (so you don't have to write your own scheduler basically). This means you have to define your tasks and submit them in the format/manner defined by Fleet or Kubernetes and then they take over and schedule the tasks (containers) for you.
So I guess: Using Mesos may mean a bit more work in writing your own scheduler, but potentially provides more flexibility if required.
I think the idea of running Kubernetes on top of Mesos is that Kubernetes acts as the scheduler for Mesos. Personally I'm not sure what benefits this brings over running one or the other on its own though (hopefully someone will jump in and explain!)
As MikeB said.. it's early days, and it's all up for grabs (keep an eye on Amazon's ECS as well) so there are many competing standards and a lot of overlap!
-edit- I didn't mention Docker swarm as I don't really have much experience with it.
For anyone coming to this after 2017 fleet is deprecated. Do not use it anymore.
Fleet docs say "fleet is no longer actively developed or maintained by CoreOS" and link to Container orchestration: Moving from fleet to Kubernetes. Fleet was removed from Container Linux (formerly known as CoreOS Linux) and replaced with Kubernetes kubelet (agent). This coincided with a corporate pivot to offer Tectonic (a Kubernetes distro) as their primary product.
We are considering building a service oriented architecture on top of YARN. We have different application types - some would work in Storm like streaming mode (where we connect to the running service), some in batch processing mode (when the app is started on every request).
Moreover applications might need to communicate to each other often which would require a lot of internal traffic between different applications within YARN. We want to use as well the caching of different applications, so whenever the request with the same data goes to the same app we can return cached responses.
Is YARN a good or bad solution as a basis for SOA framework? Is Yarn just a autoscaling/deployment-like tool or would it be a good fit for SOA? Would it be fast enough to do this with YARN?
The way I see it YARN is pushing Hadoop form being a distributed file system to a distributed OS. There are a lot of SOA-ish infrastructures that are being built or migrating to YARN (Storm, Samza) that are compelling servicehosts. You can also at weave from continuuity, that will help you host additional types of services.
to specifically address you q. - YARN is a good basis for SOA framework, it is more than a autoscaling it is a resource management and hosting framework and it is fast enough (esp. if you use one of the already developed infrastructures that are built on top of it)