Red Hat Ceph Storage: Object storage performance and sizing guide

Red Hat Ceph Storage is a proven, petabyte-scale, object storage solution designed to meet the scalability, cost, performance, and reliability challenges of large-scale, media-serving, savvy organizations. Designed for web-scale object storage and cloud infrastructures, Red Hat Ceph Storage delivers the scalable performance necessary for rich media and content-distribution workloads.

While most of us are familiar with deploying block or file storage, object storage expertise is less common. Object storage is an effective way to provision flexible and massively scalable data storage without the arbitrary limitations of traditional proprietary or scale-up storage solutions. Before building object storage infrastructure at scale, organizations need to understand how to best configure and deploy software, hardware, and network components to serve a range of diverse workloads. They also need to understand the performance and scalability they can expect from given hardware, software, and network configurations.

This reference architecture/performance and sizing guide describes Red Hat Ceph Storage coupled with QCT (Quanta Cloud Technology) storage servers and networking as object storage infrastructure. Testing, tuning, and performance are described for both large-object and small-object workloads. This guide also presents the results of the tests conducted to evaluate the ability of configurations to scale to host hundreds of millions of objects.

After hundreds of hours of [Test ⇒ Tune ⇒ Repeat] exercises, this reference architecture provides empirical answers to a range of performance questions surrounding Ceph object storage, such as (but not limited to):

  • What are the architectural considerations before designing object storage?
  • What networking is most performant for Ceph object storage?
  • What does performance look like with dedicated vs. co-located Ceph RGWs?
  • How many Ceph RGW nodes do I need?
  • How do I tune object storage performance?
  • What are the recommendations for small/large object workloads?
  • What should I do? I’ve got millions of objects to store.

And the list of questions goes on. You can unlock the performance secrets of Ceph object storage for your organization with the help of the Red Hat Ceph Storage/QCT performance and sizing guide.

Storage for RHV and OCP: Two Glusters on one platform

Architecture is an interesting discipline. There are whitepapers and best practices and reference architectures to offer pristine views of what your perfect deployment should look like. And then there are budgets and timelines and business requirements to derail all of that. It’s what makes this job so interesting and challenging—hacking together the best pieces of disparate and often seemingly unrelated systems to meet goals driven by six leaders whose bonuses are met by completely different metrics.

A recent project has involved combining OpenShift Container Platform (OCP), Red Hat Virtualization (RHV), and Red Hat Gluster Storage (Gluster) into a unified system with common lifecycle operations, minimized management points, and the lowest overall footprint in terms of both capital cost and TCO. The primary storage challenge here is in creating a Gluster environment to support both RHV and its VMs as well as OCP container persistent volume requirements.

Our architectural goals include:

  • Purchase a single flexible hardware platform to serve all the storage needs
  • Segregate Gluster for RHV and Gluster for OCP into separate pools for resource allocation and to avoid possible administration snafus (such as we experienced in early testing)
  • Maintain a single-point and single-method of management—one Heketi server to rule them all
  • Containerize as much as possible to keep lifecycle maintenance atomic

Our early version of the architecture had Gluster running as container-native storage (CNS) for OCP on top of RHV while also serving storage to RHV, but this proved to introduce a chicken-and-egg problem where a single failure (such as an etcd crash) could cause a cascading outage. So our redesign involved splitting Gluster off from OCP as a stand-alone system while still being a unified storage provider and leveraging container atomicity.

The approach we wanted involved containerized Gluster running on bare-metal container hosts. Fundamentally, this is actually pretty straightforward today with pre-build Gluster containers available from the Red Hat registry. What complicated this was our desire to run two separate containerized Gluster pools on the same hardware nodes.

Disclaimer

There’s a pretty good chance that this architecture is not explicitly supported by Red Hat. While all the components we use here are definitely supported, this particular combination is untested by our engineering, QE, and performance teams. Don’t consider anything here a recommendation for how you should run your environment, only an academic study of a possible approach to solving an interesting challenge. If you have any questions, please reach out to your Red Hat sales and support teams.

The platform

We initially wanted to build this on top of Red Hat Enterprise Linux Atomic Host, but our lab environment wasn’t setup to provision this build on our systems, so we had to go forward with RHEL plus the docker packages. For a production build, we would return to using Atomic.

Networking

Gluster containers are usually configured with host networking because they need to communicate freely with each other and need to serve storage out to other systems and containers. However, with host networking, the Gluster ports are bound to all interfaces, so it is not possible to run two Gluster containers in this mode due to port conflicts. To solve this, the networks for each Gluster pool had to be segregated.

First, a VLAN sub-interface was created on each Gluster node for the storage network interface and using VLAN ID 199. There are ifcfg files to make these persistent. So each node includes a 192.168.99.0/24 IP on the primary interface and a 192.168.199.0/24 IP on a VLAN sub-interface. The Switch ports for the storage network interfaces have been configured for the tagged VLAN ID 199. The 802.1q kernel module (for VLANs) was set to load at boot time on each node with a /etc/modules-load.d/8021q.conf file.

Containerized Gluster

Networks

Each Gluster container needs to exist on its own interface and subnet. So leveraging the system-level network stuff done above, the two interfaces were each attached to a docker macvlan network on each node.

docker network create -d macvlan --subnet=192.168.99.0/24 \

-o parent=eth1 gluster-rhv-net
docker network create -d macvlan --subnet=192.168.199.0/24 \

-o parent=eth1.199 gluster-ocp-net

Containers

The containers were pulled down from the Red Hat registry.

docker pull registry.access.redhat.com/rhgs3/rhgs-server-rhel7
docker pull registry.access.redhat.com/rhgs3/rhgs-volmanager-rhel7

The Gluster containers need to be privileged in order to access the /dev/sdX block devices. They also need a number of local persistent volume stores in order to ensure they start up properly each time.

The container fstab file needs a persistent mount. So first we should touch these files, otherwise the gluster-startup command in the container will fail.

touch /var/lib/heketi-{rhv,ocp}/fstab

Then we can run the containers.

docker run -d --privileged=true --net=gluster-rhv-net \

--ip=192.168.99.28  --name=gluster-rhv-1 -v /run \

-v /home/gluster-rhv-1-root:/root:z \

-v /etc/glusterfs-rhv:/etc/glusterfs:z \

-v /var/lib/glusterd-rhv:/var/lib/glusterd:z \

-v /var/log/glusterfs-rhv:/var/log/glusterfs:z \

-v /var/lib/heketi-rhv:/var/lib/heketi:z \

-v /sys/fs/cgroup:/sys/fs/cgroup:ro \

-v /dev:/dev rhgs3/rhgs-server-rhel7
docker run -d --privileged=true --net=gluster-ocp-net \

--ip=192.168.199.28 --name=gluster-ocp-1 -v /run \

-v /home/gluster-ocp-1-root:/root:z \

-v /etc/glusterfs-ocp:/etc/glusterfs:z \

-v /var/lib/glusterd-ocp:/var/lib/glusterd:z \

-v /var/log/glusterfs-ocp:/var/log/glusterfs:z \

-v /var/lib/heketi-ocp:/var/lib/heketi:z \

-v /sys/fs/cgroup:/sys/fs/cgroup:ro \

-v /dev:/dev rhgs3/rhgs-server-rhel7

Block device assignments

Running the containers in privileged mode allows them to access all system block devices. For our particular architectural needs, we intend to use from each node only one SSD for the gluster-rhv pool and the remaining five SSDs for the gluster-ocp pool.

 Gluster Pool  Block Devices
 gluster-rhv  sdb
 gluster-ocp  sdc, sdd, sde, sdf, sdg

Heketi

Config

The persistent Heketi config is being stored in the /etc/heketi directory on one of the nodes (we’ll call it node1). First, an ssh keypair is created and placed there.

ssh-keygen -f /etc/heketi/heketi_key -t rsa -N ''

Next, the heketi.json file is created. Right now, no auth is being used — obviously don’t do this in production. Note the ssh port is 2222, which is what the Gluster containers are configured to listen on.

{
  "_port_comment": "Heketi Server Port Number",
  "port": "8080",

  "_use_auth": "Enable JWT authorization. Please enable for deployment",
  "use_auth": false,

  "_jwt": "Private keys for access",
  "jwt": {
    "_admin": "Admin has access to all APIs",
    "admin": {
      "key": "My Secret"
    },
    "_user": "User only has access to /volumes endpoint",
    "user": {
      "key": "My Secret"
    }
  },

  "_glusterfs_comment": "GlusterFS Configuration",
  "glusterfs": {
    "_executor_comment": [
      "Execute plugin. Possible choices: mock, ssh",
      "mock: This setting is used for testing and development.",
      "      It will not send commands to any node.",
      "ssh:  This setting will notify Heketi to ssh to the nodes.",
      "      It will need the values in sshexec to be configured.",
      "kubernetes: Communicate with GlusterFS containers over",
      "            Kubernetes exec api."
    ],
    "executor": "ssh",

    "_sshexec_comment": "SSH username and private key file information",
    "sshexec": {
      "keyfile": "/etc/heketi/heketi_key",
      "user": "root",
      "port": "2222"
    },

    "_db_comment": "Database file name",
    "db": "/var/lib/heketi/heketi.db",

    "_loglevel_comment": [
      "Set log level. Choices are:",
      "  none, critical, error, warning, info, debug",
      "Default is warning"
    ],
    "loglevel" : "debug"
  }
}

SSH access

The Heketi server needs passwordless SSH access to all Gluster containers on port 2222. The public key generated above needs to be added to the authorized_keys for all of the Gluster containers. Note that we have a local persistent volume (PV) for each Gluster container’s /root directory, so this authorized_key entry was simply added to each one of those.

cat /etc/heketi/heketi_key.pub >> \

/home/gluster-rhv-1-root/.ssh/authorized_keys

NOTE: This needs to be done for each of the root home directories for each Gluster container

Container

The single Heketi container will run on node1. It needs access to both of the subnets, so the best thing to do is run the container in host networking mode. It also needs a few persistent volumes.

docker run -d --net=host --name=gluster-heketi \

-v /etc/heketi:/etc/heketi:z -v /var/lib/heketi:/var/lib/heketi:z \

rhgs3/rhgs-volmanager-rhel7

Network

Since we are running heketi-cli on the same node that we are running the Heketi container, there is a security issue we have to work through. By default, the container host cannot directly access the local container via the IP assigned to its macvlan network interface. So on the container host node1 we need to create local macvlan interfaces for each of the subnets. Use this at runtime and the /etc/rc.d/rc.local file:

/usr/sbin/ip link add macvlan0 link eth1 type macvlan mode bridge
/usr/sbin/ip addr add 192.168.99.228/24 dev macvlan0
/usr/sbin/ifconfig macvlan0 up

/usr/sbin/ip link add macvlan1 link eth1.199 type macvlan mode bridge
/usr/sbin/ip addr add 192.168.199.228/24 dev macvlan1
/usr/sbin/ifconfig macvlan1 up

The rc.local file in RHEL is for legacy support, so it has to be made executable and its systemd service has to be enabled.

chmod 755 /etc/rc.d/rc.local
systemctl enable rc-local.service

Heketi CLI

The heketi-cli needs to run $somewhere. For simplicity, the RPM is installed on node1. With the container running with networking in host mode, heketi is listening on localhost port 8080. Export the environment variable in order to be able to run heketi-cli commands.

export HEKETI_CLI_SERVER=http://localhost:8080

Setting up the Heketi clusters

A JSON file is populated at /root/heketi-rhv-plus-ocp-topology.json on node1. This file defines two separate Heketi clusters with their respective Gluster nodes (containers) and block devices.

{
    "clusters": [
        {
            "nodes": [
                {
                    "node": {
                        "hostnames": {
                            "manage": [
                                "192.168.99.28"
                            ],
                            "storage": [
                                "192.168.99.28"
                            ]
                        },
                        "zone": 1
                    },
                    "devices": [
                        "/dev/sdb"
                    ]
                },
                {
                    "node": {
                        "hostnames": {
                            "manage": [
                                "192.168.99.29"
                            ],
                            "storage": [
                                "192.168.99.29"
                            ]
                        },
                        "zone": 2
                    },
                    "devices": [
                        "/dev/sdb"
                    ]
                },
                {
                    "node": {
                        "hostnames": {
                            "manage": [
                                "192.168.99.30"
                            ],
                            "storage": [
                                "192.168.99.30"
                            ]
                        },
                        "zone": 3
                    },
                    "devices": [
                        "/dev/sdb"
                    ]
                }
            ]
        },

        {
            "nodes": [
                {
                    "node": {
                        "hostnames": {
                            "manage": [
                                "192.168.199.28"
                            ],
                            "storage": [
                                "192.168.199.28"
                            ]
                        },
                        "zone": 1
                    },
                    "devices": [
                        "/dev/sdc",
                        "/dev/sdd",
                        "/dev/sde",
                        "/dev/sdf",
                        "/dev/sdg"
                    ]
                },
                {
                    "node": {
                        "hostnames": {
                            "manage": [
                                "192.168.199.29"
                            ],
                            "storage": [
                                "192.168.199.29"
                            ]
                        },
                        "zone": 2
                    },
                    "devices": [
                        "/dev/sdc",
                        "/dev/sdd",
                        "/dev/sde",
                        "/dev/sdf",
                        "/dev/sdg"
                    ]
                },
                {
                    "node": {
                        "hostnames": {
                            "manage": [
                                "192.168.199.30"
                            ],
                            "storage": [
                                "192.168.199.30"
                            ]
                        },
                        "zone": 3
                    },
                    "devices": [
                        "/dev/sdc",
                        "/dev/sdd",
                        "/dev/sde",
                        "/dev/sdf",
                        "/dev/sdg"
                    ]
                }
            ]
        }
    ]
}

This file is passed (once) to Heketi to setup the two clusters.

heketi-cli topology load --json=heketi-rhv-plus-ocp-topology.json

It’s important to note the two different clusters. It’s not (AFAIK) possible to “name” the clusters, so we have to reference them by their UUIDs. The Gluster volumes for RHV will be created on one cluster, and those orchestrated for OCP PVs will be created on a different cluster.

RHV Gluster volumes

For the purposes of RHV, two volumes were requested—one for the Hosted Engine and one for the VM storage. These were created via heketi-cli. Note the cluster ID passed to the commands.

heketi-cli volume create --size 100 --name rhv-hosted-engine \

--clusters ae2a309d02781816adfed567693221a9
heketi-cli volume create --size 1024 --name rhv-virtual-machines \

--clusters ae2a309d02781816adfed567693221a9

These can be mounted to the RHV nodes via the 192.168.99.0/24 subnet using the Gluster native client. Example fstab entries:

192.168.99.28:rhv-hosted-engine      /100g   glusterfs       backupvolfile-server=192.168.99.29:192.168.99.30 0 0
192.168.99.28:rhv-virtual-machines      /1t   glusterfs       backupvolfile-server=192.168.99.29:192.168.99.30 0 0

OCP PV Gluster volumes

Our OCP pods are attached to the 192.168.199.0/24 subnet to communicate with the storage. First on node1 the Heketi API port (8080) needs to be opened in the firewall.

firewall-cmd --add-port 8080/tcp
firewall-cmd --add-port 8080/tcp --permanent

Then the storage class for OCP is defined with the below YAML. Note that we aren’t currently doing any authentication (but obviously we should). You see here that we explicitly define the Heketi cluster ID for this class in order to ensure that all volumes for PVCs are created only on the Gluster pool we have identified for OCP use.

kind: StorageClass
apiVersion: storage.k8s.io/v1beta1
metadata:
 name: gluster-dyn
provisioner: kubernetes.io/glusterfs
parameters:
 resturl: "http://192.168.199.128:8080"
 restauthenabled: "false"
 clusterid: "74edade536c80f14486edfbabd204151"

Then the storage class is added to OCP on the master.

oc create -f glusterfs-storageclass.yaml

From this point, PVCs (persistent volume claims) made against this storage class will interface with Heketi to dynamically provision Gluster volumes to match the claim.

Miscellaneous

Auto-start containers

Docker container systemd init scripts are tricky. I’ve found that every example on the internet is either wrong, outdated, or uses an approach I don’t like.

Below is an example systemd service file for the Heketi container, which is simple and works the way we expect it to with the docker run command in the ExecStart (/etc/systemd/system/docker-container-gluster-heketi.service). NOTE: Do not daemonize (-d) the docker run command in the init script. Also, the SuccessExitStatus is important here.

[Unit]
Description=Gluster Heketi Container
Requires=docker.service
After=docker.service

[Service]
TimeoutStartSec=60
Restart=on-abnormal
SuccessExitStatus=0 137
ExecStartPre=-/usr/bin/docker stop gluster-heketi
ExecStartPre=-/usr/bin/docker rm gluster-heketi
ExecStart=/usr/bin/docker run --net=host --name=gluster-heketi -v /etc/heketi:/etc/heketi:z -v /var/lib/heketi:/var/lib/heketi:z rhgs3/rhgs-volmanager-rhel7
ExecStop=/usr/bin/docker stop gluster-heketi

[Install]
WantedBy=multi-user.target

Reload the systemd daemon:

systemctl daemon-reload

Enable and start the service

systemctl enable docker-container-gluster-heketi

systemctl start docker-container-gluster-heketi

Known issues and TODOs

  • Security needs to be taken into account. We’ll set up appropriate key-based authentication and JWT for Heketi. We’d also like to use role-based auth. Hopefully we’ll cover this in a future blog post.
  • Likely $other_things I haven’t realized yet, or better ways of approaching this. I’d love to hear your comments.

Library of Ceph and Gluster reference architectures – Simplicity on the other side of complexity

The Storage Solution Architectures team at Red Hat develops reference architectures, performance and sizing guides, and test drives for Gluster- and Ceph-based solutions. We’re a group of architects who perform lab validation, tuning, and interoperability development for composable storage services with target workloads on optimized server and network configurations. We seek simplicity on the other side of complexity.

At the end of this blog entry is a full library of our current publications and test drives.

In our modern era, a top company asset is pivotability. Pivotability based on external market changes. Pivotability after unknowns become known. Pivotability after golden ideas become dark alleys. For most enterprises, pivotability requires a composable technology infrastructure for shifting resources to meet changing needs. Composable storage services, such as those provided by Ceph and Gluster, are part of many companies’ composable infrastructures.

Composable technology infrastructures are most frequently described by the following attributes:

  • Open source v. closed development.
  • On-demand architectures v. fixed architectures.
  • Commodity hardware v. proprietary appliances.
  • Cross-industry collaboration v. isolated single-vendor silos.

As noted in the following figure, a few companies with large staffs of in-house experts can create composable infrastructures from raw technologies. Their large investments in in-house expertise allows them to convert raw technologies into solutions with limited pre-integration by technology suppliers. AWS, Google, and Azure are all examples of DIY businesses. A larger number of other companies, also needing composable infrastructures, rely on technology suppliers and the community for solution pre-integration and guidance to reduce their in-house expertise costs. We’ll label them “Assisted DIY.” Finally, the majority of global enterprises lack the in-house expertise for deploying these composable infrastructures. They rely on public cloud providers and pre-packaged solutions for their infrastructure needs. We’ll call them “Pre-packaged.”

Brent_Slide

The reference architectures, performance and sizing guides, and test drives produced by our team are primarily focused on the “Assisted DIY” segment of companies. Additionally, we strive to make Gluster and Ceph composable storage services available to the “Pre-packaged” segment of companies by using what we learn to produce pre-packaged combinations of Red Hat software with partner hardware targeting specific workload use cases.

We enjoy our roles at Red Hat because of the many of you with whom we collaborate to produce value.  We hope you find these guides useful.

Team-produced with partner collaboration:

Partner-produced with team collaboration:

Pre-packaged solutions:

Hands-on test drives:

Struggling to containerize stateful applications in the cloud? Here’s how.

The newest release of Red Hat’s Reference Architecture “OpenShift Container Platform 3.5 on Amazon Web Services” now incorporates container-native storage, a unique approach based on Red Hat Gluster Storage to avoid lock-in, enable stateful applications, and simplify those applications’ high availability.


In the beginning, everything was so simple. Instead of going through the bureaucracy and compliance-driven process of requesting compute, storage, and networking resources, I would pull out my corporate credit card and register at the cloud provider of my choice. Instead of spending weeks forecasting the resource needs and costs of my newest project, I would get started in less than 1 hour. Much lower risk, virtually no capital expenditure for my newest pet project. And seemingly endless capacity—well, as long as my credit card was covered. If my project didn’t turn out to be a thing, I didn’t end up with excess infrastructure, either.

Until I found out that basically what I was doing was building my newest piece of software against a cloud mainframe. Not directly, of course. I was still operating on top of my operating system with the libraries and tools of my choice, but essentially I spend significant effort getting to that point with regards to orchestration and application architecture. And these are not easily ported to another cloud provider.

I realize that cloud providers are vertically integrated stacks, just as mainframes were. Much more modern and scalable with an entirely different cost structure—but, still, eventually and ultimately, lock-in.

Avoid provider lock-in with OpenShift Container Platform

This is where OpenShift comes in. I take orchestration and development cycles to a whole new level when I stop worrying about operating system instances, storage capacity, network overlays, NAT gateways, firewalls—all the things I need to make my application accessible and provide value.

Instead, I deal with application instances, persistent volumes, services, replication controllers, and build configurations—things that make much more sense to me as an application developer as they are closer to what I am really interested in: deploying new functionality into production. Thus, OpenShift offers abstraction on top of classic IT infrastructure and instead provides application infrastructure. The key here is massive automation on top of the concept of immutable infrastructure, thereby greatly enhancing the capability to bring new code into production.

The benefit is clear: Once I have OpenShift in place, I don’t need to worry about any of the underlying infrastructure—I don’t need to be aware of whether I am actually running on OpenStack, VMware, Azure, Google Cloud, or Amazon Web Services (AWS). My new common denominator is the interface of OpenShift powered by Kubernetes, and I can forget about what’s underneath.

Well, not quite. While OpenShift provides a lot of drivers for various underlying infrastructure, for instance storage, they are all somewhat different. Their availability, performance, and feature set is tied to the underlying provider, for instance Elastic Block Storage (EBS) on AWS. I need to make sure that critical aspects of the infrastructure below OpenShift are reflected in OpenShift topology. A good example are AWS availability zones (AZs): They are failure domains in a region across which an application instance should be distributed to avoid downtime in the event a single AZ is lost. So OpenShift nodes need to be deployed in multiple AZs.

This is where another caveat comes in: EBS volumes are present only inside a single AZ. Therefore, my application must replicate the data across other AZs if it uses EBS to store it.

So there are still dependencies and limitations a developer or operator must be aware of, even if OpenShift has drivers on board for EBS and will take care about provisioning.

Introducing container-native storage

With container-native storage (CNS), we now have a robust, scalable, and elastic storage service out-of-the-box for OpenShift Container Platform—based on Red Hat Gluster Storage. The trick: GlusterFS runs containerized on OpenShift itself. Thus, it runs on any platform that OpenShift is supported on—which is basically everything, from bare metal, to virtual, to private and public cloud.

With CNS, OpenShift gains a consistent storage feature set across, and independent of, all supported cloud providers. It’s deployed with native OpenShift / Kubernetes resources, and GlusterFS ends up running in pods as part of a DaemonSet:

[ec2-user@ip-10-20-4-55 ~]$ oc get pods
NAME              READY     STATUS    RESTARTS   AGE
glusterfs-0bkgr   1/1       Running   9          7d
glusterfs-4fmsm   1/1       Running   9          7d
glusterfs-bg0ls   1/1       Running   9          7d
glusterfs-j58vz   1/1       Running   9          7d
glusterfs-qpdf0   1/1       Running   9          7d
glusterfs-rkhpt   1/1       Running   9          7d
heketi-1-kml8v    1/1       Running   8          7d

The pods are running in privileged mode to access the nodes’ block device directly. Furthermore, for optimal performance, the pods are using host-networking mode. This way, OpenShift nodes are running a distributed, software-defined, scale-out file storage service, just as any distributed micro-service application.

There is an additional pod deployed that runs heketi—a RESTful API front end for GlusterFS. OpenShift natively integrates via a dynamic storage provisioner plug-in with this service to request and delete storage volumes on behalf of the user. In turn, heketi controls one or more GlusterFS Trusted Storage Pools.

Container-native storage on Amazon Web Services

The EBS provisioner has been available for OpenShift for some time. To understand what changes with CNS on AWS, a closer look at how EBS is accessible to OpenShift is in order.

  1. Dynamic provisioning
    EBS volumes are dynamically created and deleted as part of storage provisioning requests (PersistentVolumeClaims) in OpenShift.
    .
  2. Local block storage
    EBS appears to the EC2 instances as a local block device. Once provisioned, it is attached to the EC2 instance, and a PCI interrupt is triggered to inform the operating system.

    NAME                                  MAJ:MIN RM  SIZE RO TYPE MOUNTPOINT
    xvda                                  202:0    0   15G  0 disk
    ├─xvda1                               202:1    0    1M  0 part
    └─xvda2                               202:2    0   15G  0 part /
    xvdb                                  202:16   0   25G  0 disk
    └─xvdb1                               202:17   0   25G  0 part
      ├─docker_vol-docker--pool_tmeta     253:0    0   28M  0 lvm
      │ └─...                             253:2    0 23.8G  0 lvm
      │   ├─...                           253:8    0    3G  0 dm
      │   └─...                           253:9    0    3G  0 dm
      └─docker_vol-docker--pool_tdata     253:1    0 23.8G  0 lvm
        └─docker_vol-docker--pool         253:2    0 23.8G  0 lvm
          ├─...                           253:8    0    3G  0 dm
          └─...                           253:9    0    3G  0 dm
    xvdc                                  202:32   0   50G  0 disk 
    xvdd                                  202:48   0  100G  0 disk

    OpenShift on AWS also uses EBS to back local docker storage. EBS storage is formatted with a local filesystem like XFS..

  3. Not shared storage
    EBS volumes cannot be attached to more than one EC2 instance. Thus, all pods mounting an EBS-based PersistentVolume in OpenShift must run on the same node. The local filesystem on top of the EBS block device does not support clustering either.
    .
  4. AZ-local storage
    EBS volumes cannot cross AZs. Thus, OpenShift cannot failover pods mounting EBS storage into different AZs. Basically, an EBS volume is a failure domain.
    .
  5. Performance characteristics
    The type of EBS storage, as well as capacity, must be selected up front. Specifically, for fast storage a certain minimum capacity must be requested to have a minimum performance level in terms of IOPS.

This is the lay of the land. While these characteristics may be acceptable for stateless applications that only need to have local storage, they become an obstacle for stateful applications.

People want to containerize databases, as well. Following a micro-service architecture where every service maintains its own state and data model, this request will become more common. The nature of these databases differs from the classic, often relational, database management system IT organizations have spent millions on: They are way smaller and store less data than their big brother from the monolithic world. Still, with the limitations of EBS, I would need to architect replication and database failover around those just to deal with a simple storage failure.

Here is what changes with CNS:

  1. Dynamic provisioning
    The user experience actually doesn’t change. CNS is represented like any storage provider in OpenShift, by a StorageClass. PersistentVolumeClaims (PVCs) are issued against it, and the dynamic provisioner for GlusterFS creates the volume and returns it as a PersistentVolume (PV). When the PVC is deleted, the GlusterFS volume is deleted, as well.
    .
  2. Distributed file storage on top of EBS
    CNS volumes are basically GlusterFS volumes, managed by heketi. The volumes are built out of local block devices of the OpenShift nodes backed by EBS. These volumes provide shared storage and are mounted on the OpenShift nodes with the GlusterFS FUSE client.

    [ec2-user@ip-10-20-5-132 ~]$ mount
    ...
    10.20.4.115:vol_0b801c15b2965eb1e5e4973231d0c831 on /var/lib/origin/openshift.local.volumes/pods/80e27364-2c60-11e7-80ec-0ad6adc2a87f/volumes/kubernetes.io~glusterfs/pvc-71472efe-2a06-11e7-bab8-02e062d20f83 type fuse.glusterfs (rw,relatime,user_id=0,group_id=0,default_permissions,allow_other,max_read=131072)
    ...
  3. Container-shared storage
    Multiple pods can mount and write to the same volume. The access mode for the corresponding node is known as “RWX”—read-write many. The containers can run on different OpenShift nodes, and the dynamic provisioner will mount the GlusterFS volume on the right nodes accordingly. Then, this local mount directory is bind-mounted to the container.
    .
  4. Cross-availability zone
    CNS is deployed across AWS AZs. The integrated, synchronous replication of GlusterFS will mirror every write 3 times. GlusterFS is deployed across OpenShift nodes in at least different AZs, and thus the storage is available in all zones. The failure of a single GlusterFS pod, an OpenShift node running the pod, or a block device accessed by the pod will have no impact. Once the failed resources come back, the storage is automatically re-replicated. CNS is actually aware of the failure zones as part of the cluster topology and will schedule new volumes, as well as recovery, so that there is no single point of failure.
    .
  5. Predictable performance
    CNS storage performance is not tied to the size of storage request by the user in OpenShift. It’s the same performance whether 1GB or 100GB PVs are requested.
    .
  6. Storage performance tiers
    CNS allows for multiple GlusterFS Trusted Storage Pools to be managed at once. Each pool consists of at least 3 OpenShift nodes running GlusterFS pods. While the OpenShift nodes belong to a single OpenShift cluster, the various GlusterFS pods form their own Trusted Storage Pools. An administrator can use this to equip the nodes with different kinds of storage and offer their pools with CNS as distinct storage tiers in OpenShift, via its own StorageClass. An administrator instance might, for example, run CNS on 3 OpenShift nodes with SSD (e.g., EBS gp2) storage and call it “fast,” whereas another set of OpenShift nodes with magnetic storage (e.g., EBS st1) runs a separate set of GlusterFS pods as an independent Trusted Storage Pool, represented with a StorageClass called “capacity.”

This is a significant step toward simplifying and abstracting provider infrastructure. For example, a MySQL database service running on top of OpenShift is now able to survive the failure of an AWS AZ, without needing to set up MySQL Master-Slave replication or change the micro-service to replicate data on its own.

Storage provided by CNS is efficiently allocated and provides performance with the first Gigabyte provisioned, thereby enabling storage consolidation. For example, consider six MySQL database instances, each in need of 25 GiB of storage capacity and up to 1500 IOPS at peak load. With EBS, I would create six EBS volumes, each with at least 500 GiB capacity out of the gp2 (General Purpose SSD) EBS tier, in order to get 1500 IOPS guaranteed. Guaranteed performance is tied to provisioned capacity with EBS.
With CNS, I can achieve the same using only 3 EBS volumes at 500 GiB capacity from the gp2 tier and run these with GlusterFS. I would create six 25 GiB volumes and provide storage to my databases with high IOPS performance, provided they don’t peak all at the same time.

Doing that, I would halve my EBS cost and still have capacity to spare for other services. My read IOPS performance is likely even higher because in CNS with 3-way replication I would read from data distributed across 3×1500 IOPS gp2 EBS volumes.

Finally, the setup for CNS is very simple and can run on any OpenShift installation based on version 3.4 or newer.

This way, no matter where I plan to run OpenShift (i.e., which cloud provider currently offers lowest prices), I can rely on the same storage features and performance. Furthermore, the Storage Service grows with the OpenShift cluster but still provides elasticity. Only a subset of OpenShift nodes must run CNS, at least 3 ideally across 3 AZs.

Deploying container-native storage on AWS

Installing OpenShift on AWS is dramatically simplified based on the OpenShift on Amazon Web Services Reference Architecture. A set of Ansible playbooks augments the existing openshift-ansible installation routine and creates all the required AWS infrastructure automatically.

A simple python script provides a convenient wrapper to the playbooks found in the openshift-ansible-contrib repository on GitHub for deploying on AWS.

All the heavy lifting of setting up Red Hat OpenShift Container Platform on AWS is automated with best practices incorporated.

The deployment finishes with an OpenShift Cluster with 3 master nodes, 3 infrastructure nodes, and 2 application nodes deployed in a highly available fashion across AWS AZs. The external and internal traffic is load balanced, and all required network, firewall, and NAT resources are stood up.

Since version 3.5, the reference architecture playbooks now ship with additional automation to make deployment of CNS as easy. Through additional AWS CloudFormation templates and Ansible playbook tasks, the additional, required infrastructure is stood up. This mainly concerns provisioning of additional OpenShift nodes with an amended firewall configuration, additional EBS volumes, and then joining them to the existing OpenShift cluster.

In addition, compared to previous releases, the CloudFormation templates now emit more information as part of the output. These are picked up by the playbooks to further reduce the information needed from the administrator. They will simply get the right information from the existing CloudFormation stack to retrieve the proper integration points.

The result is AWS infrastructure ready for the administrator to deploy CNS. Most of the manual steps of this process can therefore be avoided. Three additional app nodes are deployed with configurable instance type and EBS volume type. Availability zones of the selected AWS region are taken into account.

Subsequent calls allow for provisioning of additional CNS pools. The reference architecture makes reasonable choices for the EBS volume type and the EC2 instance with a balance between running costs and initial performance. The only thing left for the administrator to do is to run the cns-deploy utility and create a StorageClass object to make the new storage service accessible to users.

At this point, the administrator can choose between labeling the nodes as regular application nodes or provide a storage-related label that would initially exclude them from the OpenShift scheduler for regular application pods.

Container-ready storage

The reference architecture also incorporates the concept of Container-Ready Storage (CRS). In this deployment flavor, GlusterFS runs on dedicated EC2 instances with a heketi-instance deployed separately, both running without containers as ordinary system services. The difference is that these instances are not part of the OpenShift cluster. The storage service is, however, made available to, and used by, OpenShift in the same way. If the user, for performance or cost reasons, wants the GlusterFS storage layer outside of OpenShift, this is made possible with CRS. For this purpose, the reference architecture ships add-crs-storage.py to automate the deployment in the same way as for CNS.

Verdict

CNS provides further means of OpenShift Container Platform becoming an equalizer for application development. Consistent storage services, performance, and management are provided independently of the underlying provider platform. Deployment of data-driven applications is further simplified with CNS as the backend. This way, not only stateless but also stateful applications become easy to manage.

For developers, nothing changes: The details of provisioning and lifecycle of storage capacity for containerized applications is transparent to them, thanks to CNS’s integration with native OpenShift facilities.

For administrators, achieving cross-provider, hybrid-cloud deployments just became even easier with the recent release of the OpenShift Container Platform 3.5 on Amazon Web Service Reference Architecture. With just two basic commands, an elastic and fault-tolerant foundation for applications can be deployed. Once set up, growth becomes a matter of adding nodes.

It is now possible to choose the most suitable cloud provider platform without worrying about various tradeoffs between different storage feature sets or becoming too close to one provider’s implementation, thereby avoiding lock-in long term.

The reference architecture details the deployment and resulting topology. Access the document here.