Scalable On-Demand Hadoop Clusters with Docker and Mesos

1. Scalable On-Demand Hadoop Clusters with Docker and Mesos Andrew Nelson, Nutanix @vmwnelson http://virtual-hiking.blogspot.com Chris Mutchler, VMware @chrismutchler http://virtualelephant.com V

2. Agenda  New Approach for Hadoop Ops  Infrastructure Resource Considerations  Docker as the new “Unit of Work”  Future Work 2

3. Last Year’s State of the Art  Self-service and multi-tenant Hadoop  Elastic and decoupled infrastructure  Extensible blueprinting 3

4. New Goals  Operationalize multiple frameworks  Decoupled service architecture  Flexible and developer-friendly form factor 4

5. Apache Mesos Introduction  Started at Berkeley  Graduated to top level Apache project 2013  Commercial entity is Mesosphere  https://github.com/apache/mesos/ 5

6. Mesos Architecture 6 Source: http://mesos.apache.org/assets/img/documentation/architecture3.jpg

7. Mesos as a Multi-Tenant Resource Pool 7 Source: https://github.com/mesos/myriad/blob/phase1/docs/how-it-works.md

8. Tools to Build and Scale  Serengeti, Vmware  https://github.com/vmware-serengeti  BOSH, Pivotal  https://github.com/cloudfoundry/bosh  Cloudify, Gigaspaces  https://github.com/CloudifySource/cloudify  Cloudbreak, SequenceIQ  https://github.com/sequenceiq/cloudbreak 8

9. Advantages for Ops  Mesos as a Resource Pool  Multiple concurrent frameworks  Decouple frameworks from resource pools 9

10. Compute Partitions on Mesos 10 Shared Hadoop Storm Spark Kafka Hadoop Cassandra Storm Spark Marathon Cassandra Siloed

11. HDFS as a Service 11 Namenode Standby Namenode Secondary Namenode HDFS MapReduce Spark Hive Storm …

12. Networking Services  Service Discovery  Handled per framework  Port range resource managed by Mesos slave  For example, Marathon uses HAProxy for request routing  Per-container network monitoring  Egress rate-limiting 12

13. Scheduling Options  Mesos scheduling  Capacity Scheduler  Fair Scheduler  Tenant scheduling examples  Hadoop on Mesos  Myriad (YARN) on Mesos 13

14. Dev Workflow  Code Repo / Registry  Pull / Push / Commit / Run  Automated Builds  Version tagging  Marathon CI / CD  Dependencies  Rolling restarts 14

15. Registry Services  Pluggable storage  Webhooks  Image control  Security  Logging 15 Registry Repository Repository Image Image Image

16. Advantages for Developers  Interchangeable verbs for code<->containers  Choice of framework to use as their PaaS  Adopt microservices approach to app pipeline 16

17. Recommendations for Success  Start small, scale fast  Use most appropriate framework for the job  Think ahead, decouple  Plan for rolling restart capacity up front 17

18. Gap Analysis  Be prepared to “look under the hood”  Variable maturity and resiliency of the layers  Networking  Security 18

19. Where Are We Going Next  Scale and learn  Container-focused OS  Software-defined networking services  Discover key performance and availability metrics 19

20. Wrapping up  Mesos allows for choice of framework  Devs utilize Docker with familiar workflow  Portable, flexible, and scalable architecture 20

Notes de l'éditeur

I'm going to be discussing some new opportunities to change the operational model of Hadoop and how to accommodate new services as well as work on better integration and end to end testing of modern application pipelines. This has everything to do with how ops can provide devs with the most flexible building environment without stretching too far to try and support everything. Key takeaways: Hadoop+docker for lightweight self-service on your laptop, in your cloud For building modern app pipelines, need CI/CD, to iterate faster, need this self-service, customizable framework to build what the devs want to build Evaluate whether yarn fits your needs or mesos Just pick a physical form factor or pick a cloud and move on, with portability in mind, unique situation in so many software choices that will affect your ultimate product more than hardware will Test and iterate, scale and learn
Last year, Chris and I talked about how Adobe was virtualizing their Hadoop clusters in order to emulate a public cloud environment. Developers wanted to be able to be more flexible in what kind of Hadoop cluster was deployed, sizing, which templates, and which distro they wanted to work with. All of these things could be customized and were enabled for self-service. Potentially each developer could utilize their own private, dedicated cluster for experimentation and not have to worry about dedicated hardware. The automation and blueprints necessary were shared via catalog and extended to accommodate more than just Hadoop to include other distributed systems such as Storm, Kafka, Mesos, etc.
One key realization is that you can't get there with just one framework. There are a ton of different solutions out there for cluster management and for different frameworks, different building blocks that devs can use to build their app and its date pipeline. So we needed to be able to be more flexible in giving developers options for building their desired service. Should they be building realtime or batch workloads, how will they scale? What if parameters need to be changed as they scale? So many questions and new code to look at and devs need to be just as quick about evaluating what tools are helpful and worth including as what code they are adding in themselves With all of these different frameworks, and to retain the element of flexibility once they go down a road, the devs need to ensure they remain loosely coupled. Otherwise all this flexibility was kinda pointless. What's flexible about having to go back and start from scratch? You could do that before and it was in a lot simpler system right? Now we're all platform-building, even if we're using someone else's services to bootstrap basic functionality. We need to deliver reliability somewhere before we get to the top of the stack. That's what CI and CD are basically about, imo. So what we need that is telatively portable, easily resizable across these different frameworks and reasonably self-contained so that we can pick it up and move it around when we need to? Last year the currency was VMs. We could resize, repurpose, share hardware, and blueprint. I have worked with VMs in high performance and I don't think that's the issue. However, they are not developer-friendly. Dev-friendly to me is basically infrastructure as code, or even infra as text files. As an architect I want devs to feel free to customize, do it themselves, and be able to interact with the system in a form factor that is consistent with their processes. Key part of self-service is choice
Users: Twitter, Airbnb, Apple, Ebay Aurora, Marathon, Chronos
http://mesos.apache.org/documentation/latest/mesos-architecture/ http://mesos.apache.org/assets/img/documentation/architecture3.jpg So from an infara perspective, why not just work on YARN. Well, YARN is not a hierarchical scheduler frmawork. It’s a framework for writing scalable analytics jobs and it does that really well. But how to encapsulate infra for jobs that don't fit that model. Maybe next year, YARN will have a competely different set of capabilities but for now, we have devs with those diverse set of job characteristics. Allows for multiple executors Allows for multiple independent schedulers Allows for multiple frameworks / toolsets Highly available master The master enables fine-grained sharing of resources (cpu, ram, …) across applications by making them resource offers. Each resource offer contains a list of . The master decides how many resources to offer to each framework according to a given organizational policy, such as fair sharing, or strict priority. To support a diverse set of policies, the master employs a modular architecture that makes it easy to add new allocation modules via a plugin mechanism. A framework running on top of Mesos consists of two components: a scheduler that registers with the master to be offered resources, and an executor process that is launched on slave nodes to run the framework’s tasks (/documentation/latest/see theApp/Framework development guide for more details about application schedulers and executors). While the master determines how many resources are offered to each framework, the frameworks' schedulers select which of the offered resources to use. When a frameworks accepts offered resources, it passes to Mesos a description of the tasks it wants to run on them. In turn, Mesos launches the tasks on the corresponding slaves.
https://github.com/mesos/myriad/blob/phase1/docs/how-it-works.md https://github.com/mesos/myriad/raw/phase1/docs/images/how-it-works.png Each tenant has their own framework Each tenant can derive their own scheduling Each tenant can leverage services in a decoupled fashion
This list will probably keep growing before it becomes consolidated. This is about blueprinting the distributed systems. There will typically be an infrastructure layer and a configuration management layer. Vmw is a solution based on vmware vcenter and chef obviously. There is the flexibility of creating your own roles and recipes but dependent on vmw licensing based on sockets. There is only a single template ever at any given time and calls are blocking meaning only one cluster can be in any stage of cration at any given time. Bosh is its own animal, originally conceived as a way to stand up cloud foundry because it is its own distributed system that can't instantiate itself. There is a director-based version or bosh-init as a quick and less heavyweight CLI. Bosh uses yaml as its conf format of choice. It can handle any cloud platform with a known CPI or cloud platform interface. Its templates are called stemcells. It has an async queue kv store with multiple workers that can build in parallel. Networking and dns are fully declared in the manifest but have to be much more explicit. Cloudbreak is relatively new cloud agnostic framework that uses cloud specific APIs for building out components, for example aws cloudformation. For hadoop blueprints, it uses ambari and at the guest-image level, everything is docker with swarm for clustering and consul for communication and service mgmt Clouidfy uses open source tosca blueprints which are yaml files that contain srvice definitions, tiers and dependencies. Cloudify determines the infra compatibility layer and config mgmt is chef or puppet
Mesos is fundamentally a framework for accommodating different frameworks on the same hardware using cgroups, docker
http://mesos.apache.org/documentation/latest/mesos-frameworks/ Compute is determined by resource offers. Instead of trying to fit a workload on whats left of a host, the host or worker advertises some resources, its up to the framework what it can accept and provision or wait.
You have HA, checkpointing, and a common durable and resilient storage layer that can support the ecosystem of compute platforms. MapReduce (batch) Spark (In-memory) HIVE (SQL) Storm (streaming) Solr (Lucene Search) Flume Kafka (with Camus)
Imo, the most immature portion of the tenant svcs of mesos but still headed in the right direction. Frameworks don’t want to manage ports or physical networking. Allow for per container granularity monitoring and logging which is good for debugging.
These are the top-level scheduling algorithms that Mesos can use. Remember that it’s a hierarchy. When a job request comes into the YARN resource manager, YARN evaluates all the resources available, and it places the job. It’s the one making the decision where jobs should go… YARN is optimized for scheduling Hadoop jobs, which are historically (and still typically) batch jobs with long run times. This means that YARN was not designed for long-running services, nor for short-lived interactive queries…, and while it’s possible to have it schedule other kinds of workloads, this is not an ideal model. … uses a two-level scheduling mechanism where resource offers are made to frameworks (applications that run on top of Mesos). The Mesos master node decides how many resources to offer each framework, while each framework determines the resources it accepts and what application to execute on those resources. This method of resource allocation allows near-optimal data locality when sharing a cluster of nodes amongst diverse frameworks. This open source software project is both a Mesos framework and a YARN scheduler that enables Mesos to manage YARN resource requests. When a job comes into YARN, it will schedule it via the Myriad Scheduler, which will match the request to incoming Mesos resource offers. Mesos, in turn, will pass it on to the Mesos worker nodes. The Mesos nodes will then communicate the request to a Myriad executor which is running the YARN node manager. Myriad launches YARN node managers on Mesos resources, which then communicate to the YARN resource manager what resources are available to them. YARN can then consume the resources as it sees fit. Myriad provides a seamless bridge from the pool of resources available in Mesos to the YARN tasks that want those resources.
Developers can push their code and Dockerfile to Git, as they usually do From there, Jenkins can build a container from the Dockerfile and then publish to a registry
As typical, will there be template-creep? Container-creep? Image curation and testing necessary, but hopefully this fits into your CI/CD methodology.
Working with Docker for developers should feel very familiar. Docker push, pull, commit Version dependency and tag-based search verbs Can choose from Marathon, YARN 2.7.0 CI/CD with cloudbees, shippable, drone, jenkins, on and on
Logging is key, of course, best to test and iterate since stuff will break and pick a method that allows you to revert easily Decouple! Be ready to pull in network teams and security teams early and often The SDN decoupling is in progress but for now, infra should be ready to be explicit so devs don’t have to be Don’t just shift complexity, abstract Security, SDLC and infrastructure and ops and…
Often need to change as we scale Remove the guest os as much as possible, options are multiplying, coreos, lxd, msft nano, rhat atomic, vmware photon Don’t know which will work better so need to test and iterate, ultimately we want decoupled so it doesn’t or shouldn’t matter A lot of maturation in the SDN space, controllers are just reaching scalability of thousands of VMs, what happens when I throw a million containers at them? Test and iterate
YARN can be first class citizen, avoids siloeing datacenter Avoid siloing dev into specific frameworks Docker is the new currency for continuous test and deployment of code in infrastructure as text form factor for CI/CD

Scalable On-Demand Hadoop Clusters with Docker and Mesos

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Notes de l'éditeur