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Installation and Usage

Overview

This section summarizes the steps that may be needed during the entire lifecycle of PMEM in a cluster, starting with the initial preparations and ending with decommissioning the hardware. The other sections explain each step in more detail.

When setting up a cluster, the administrator must install the PMEM hardware on nodes and configure some or all of those instances of PMEM for usage by PMEM-CSI (prerequisites). Nodes where PMEM-CSI is supposed to run must have a certain label in Kubernetes.

The administrator must install PMEM-CSI, using the PMEM-CSI operator (recommended) or with scripts and YAML files in the source code. The default install settings should work for most clusters. Some clusters don't use /var/lib/kubelet as the data directory for kubelet and then the corresponding PMEM-CSI setting must be changed accordingly because otherwise kubelet does not find PMEM-CSI. The operator has an option for that in its API (kubeletDir in the DeploymentSpec), the YAML files can be edited or modified with kustomize.

A PMEM-CSI installation can only use direct device mode or LVM device mode. It is possible to install PMEM-CSI twice on the same cluster with different modes, with these restrictions:

  • The driver names must be different.
  • The installations must run on different nodes by using different node labels, or the "usage" parameter of the LVM mode driver installation one a node must be so that it leaves spaces available for the direct mode driver installation on that same node.

The administrator must decide which storage classes shall be available to users of the cluster. A storage class references a driver installation by name, which indirectly determines the device mode. A storage class also chooses which filesystem is used (xfs or ext4) and enables Kata Containers support.

Optionally, the administrator can enable monitoring of resource usage via the metrics support.

It is recommended to use volumeBindingMode: WaitForFirstConsumer as in the pmem-storageclass-late-binding.yaml example. This ensures that pods get scheduled onto nodes that have sufficient RAM, CPU and PMEM.

Optionally, the log output format can be changed from the default "text" format (= the traditional glog format) to "json" (= output via zap) for easier processing.

When using the operator, existing PMEM-CSI installations can be upgraded seamlessly by installing a newer version of the operator. Downgrading by installing an older version is also supported, but may need manual work which will be documented in the release notes.

When using YAML files, the only reliable way of up- or downgrading is to remove the installation and install anew.

Users can then create PMEM volumes via persistent volume claims that reference the storage classes or via ephemeral inline volumes.

A node should only be removed from a cluster after ensuring that there is no pod running on it which uses PMEM and that there is no persistent volume (PV) on it. This can be checked via kubectl get -o yaml pv and looking for a nodeAffinity entry that references the node or via metrics data for the node. When removing a node or even the entire PMEM-CSI driver installation too soon, attempts to remove pods or volumes via the Kubernetes API will fail. Administrators can recover from that by force-deleting PVs for which the underlying hardware has already been removed.

By default, PMEM-CSI wipes volumes after usage (eraseAfter), so shredding PMEM hardware after decomissioning it is optional.

Prerequisites

Software required

The recommended mimimum Linux kernel version for running the PMEM-CSI driver is 4.15. See Persistent Memory Programming for more details about supported kernel versions.

Hardware required

Persistent memory device(s) are required for operation. However, some development and testing can be done using QEMU-emulated persistent memory devices. See the "QEMU and Kubernetes" section for the commands that create such a virtual test cluster.

Persistent memory pre-provisioning

The PMEM-CSI driver needs pre-provisioned regions on the NVDIMM device(s). The PMEM-CSI driver itself intentionally leaves that to the administrator who then can decide how much and how PMEM is to be used for PMEM-CSI.

Beware that the PMEM-CSI driver will run without errors on a node where PMEM was not prepared for it. It will then report zero local storage for that node, something that currently is only visible in the log files.

When running the Kubernetes cluster and PMEM-CSI on bare metal, the ipmctl utility can be used to create regions. App Direct Mode has two configuration options - interleaved or non-interleaved. One region per each NVDIMM is created in non-interleaved configuration. In such a configuration, a PMEM-CSI volume cannot be larger than one NVDIMM.

Example of creating regions without interleaving, using all NVDIMMs:

$ ipmctl create -goal PersistentMemoryType=AppDirectNotInterleaved

Alternatively, multiple NVDIMMs can be combined to form an interleaved set. This causes the data to be striped over multiple NVDIMM devices for improved read/write performance and allowing one region (also, PMEM-CSI volume) to be larger than single NVDIMM.

Example of creating regions in interleaved mode, using all NVDIMMs:

$ ipmctl create -goal PersistentMemoryType=AppDirect

If the operating system on the nodes does not provide ipmctl, then it can also be run inside a container, using the PMEM-CSI image. The same invocation works with podman instead of docker.

$ sudo docker run --privileged --rm -u 0:0 docker.io/intel/pmem-csi-driver:canary ipmctl help
Intel(R) Optane(TM) Persistent Memory Command Line Interface

    Usage: ipmctl <verb>[<options>][<targets>][<properties>]
...

When running inside virtual machines, each virtual machine typically already gets access to one region and ipmctl is not needed inside the virtual machine. Instead, that region must be made available for use with PMEM-CSI because when the virtual machine comes up for the first time, the entire region is already allocated for use as a single block device:

$ ndctl list -RN
{
  "regions":[
    {
      "dev":"region0",
      "size":34357641216,
      "available_size":0,
      "max_available_extent":0,
      "type":"pmem",
      "persistence_domain":"unknown",
      "namespaces":[
        {
          "dev":"namespace0.0",
          "mode":"raw",
          "size":34357641216,
          "sector_size":512,
          "blockdev":"pmem0"
        }
      ]
    }
  ]
}
$ ls -l /dev/pmem*
brw-rw---- 1 root disk 259, 0 Jun  4 16:41 /dev/pmem0

Labels must be initialized in such a region, which must be performed once after the first boot:

$ ndctl disable-region region0
disabled 1 region
$ ndctl init-labels nmem0
initialized 1 nmem
$ ndctl enable-region region0
enabled 1 region
$ ndctl list -RN
[
  {
    "dev":"region0",
    "size":34357641216,
    "available_size":34357641216,
    "max_available_extent":34357641216,
    "type":"pmem",
    "iset_id":10248187106440278,
    "persistence_domain":"unknown"
  }
]
$ ls -l /dev/pmem*
ls: cannot access '/dev/pmem*': No such file or directory

On some virtual machines, for example VMware® vSphere, the persistent memory does not support setting labels and the ndctl init-labels nmem0 command above would fail. What can be done in that case is to convert the existing namespace from "raw" to "fsdax" mode and then run PMEM-CSI in LVM mode. Direct mode is not possible because it depends on creating additional namespaces which in turn depends on support for labels. The command for conversion is:

$ ndctl create-namespace --force --reconfig=namespace0.0 --mode=fsdax --name=pmem-csi
{
  "dev":"namespace0.0",
  "mode":"fsdax",
  "map":"dev",
  "size":67643637760,
  "uuid":"9fa6976c-ab57-491b-a00c-e52d092a4fa8",
  "sector_size":512,
  "align":2097152,
  "blockdev":"pmem0",
  "name":"pmem-csi"
}

Note the pmem-csi name for the namespace: this is how PMEM-CSI in LVM mode knows that it is allowed to use this namespace. When the VM provides only "legacy PMEM", ndctl silently drops that name. In that case, the volume group as to be created manually:

$ vgcreate --force bus0region0fsdax /dev/pmem0

See automatic node setup below for instructions on how to automate this conversion.

Installation and setup

This section assumes that a Kubernetes cluster is already available with at least one node that has persistent memory device(s). For development or testing, it is also possible to use a cluster that runs on QEMU virtual machines, see the "QEMU and Kubernetes".

  • Make sure that the alpha feature gates CSINodeInfo and CSIDriverRegistry are enabled

The method to configure alpha feature gates may vary, depending on the Kubernetes deployment. It may not be necessary anymore when the feature has reached beta state, which depends on the Kubernetes version.

  • Label the cluster nodes that provide persistent memory device(s)

PMEM-CSI manages PMEM on those nodes that have a certain label. For historic reasons, the default in the YAML files and the operator settings is to use a label storage with the value pmem.

Such a label can be set for each node manually with:

$ kubectl label node <your node> storage=pmem

Alternatively, the Node Feature Discovery (NFD) add-on can be used to label nodes automatically. In that case, the default PMEM-CSI node selector has to be changed to "feature.node.kubernetes.io/memory-nv.dax": "true". The operator has the nodeSelector field for that. For the YAML files a kustomize patch can be used.

Install PMEM-CSI driver

PMEM-CSI driver can be deployed to a Kubernetes cluster either using the PMEM-CSI operator or by using reference yaml files provided in source code.

Install using the operator

The PMEM-CSI operator facilitates deploying and managing the PMEM-CSI driver on a Kubernetes cluster.

Installing the operator from Operator Hub

If your cluster supports managing the operators using the Operator Lifecycle Manager, then it is recommended to install the PMEM-CSI operator from the OperatorHub. Follow the instructions shown by the "Install" button. When using this approach, the operator itself always runs with default parameters, in particular log output in "text" format.

Installing the operator on an OpenShift cluster using the Red Hat Catalog

NOTE: PMEM-CSI operator will go into sustaining mode starting Red Hat OpenShift platform 4.11. The last validation cycle for Red Hat OpenShift CSI Operator Certification and Badging is on 4.10. Intel is winding down future development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

The current release of the PMEM-CSI driver and operator will continue to be maintained and supported until EOL of Intel Optane PMem. New releases, new features and bug fixes will be restricted only to business-critical cases. Reference Intel’s Optane Business Unit Closure announcement.

If you run an OpenShift cluster, then it is recommended to install the PMEM-CSI operator by following the instructions shown by "Deploy & use" on Red Hat Catalog. The recommended approach is "Installing from OperatorHub using the web console".

Installing the operator from YAML

Alternatively, the you can install the operator manually from YAML files. First install the PmemCSIDeployment CRD:

$ kubectl create -f https://github.com/intel/pmem-csi/raw/devel/deploy/crd/pmem-csi.intel.com_pmemcsideployments.yaml

Then install the PMEM-CSI operator itself:

$ kubectl create -f https://github.com/intel/pmem-csi/raw/devel/deploy/operator/pmem-csi-operator.yaml

The operator gets deployed in a namespace called 'pmem-csi' which gets created by that YAML file.

WARNING: This YAML file cannot be used to stop just the operator while keeping the PMEM-CSI deployments running. That's because something like kubectl delete -f pmem-csi-operator.yaml will delete the pmem-csi namespace which then also causes all PMEM-CSI deployments that might have been created in that namespace to be deleted.

Create a driver deployment

Once the operator is installed and running, it is ready to handle PmemCSIDeployment objects in the pmem-csi.intel.com API group. Refer to the PmemCSIDeployment CRD API for a complete list of supported properties.

Here is a minimal example driver deployment created with a custom resource:

NOTE: nodeSelector must match the node label that was set in the installation and setup section.

$ kubectl create -f - <<EOF
apiVersion: pmem-csi.intel.com/v1beta1
kind: PmemCSIDeployment
metadata:
  name: pmem-csi.intel.com
spec:
  deviceMode: lvm
  nodeSelector:
    feature.node.kubernetes.io/memory-nv.dax: "true"
EOF

This uses the same pmem-csi.intel.com driver name as the YAML files in deploy and the node label created by NFD (see the hardware installation and setup section).

Once the above deployment installation is successful, we can see all the driver pods in Running state:

$ kubectl get pmemcsideployments
NAME                 DEVICEMODE   NODESELECTOR   IMAGE   STATUS   AGE
pmem-deployment      lvm                                 Running  50s

$ kubectl describe pmemcsideployment/pmem-csi.intel.com
Name:         pmem-csi.intel.com
Namespace:
Labels:       <none>
Annotations:  <none>
API Version:  pmem-csi.intel.com/v1beta1
Kind:         PmemCSIDeployment
Metadata:
  Creation Timestamp:  2020-10-07T07:31:58Z
  Generation:          1
  Managed Fields:
    ...
  Resource Version:  1235740
  Self Link:         /apis/pmem-csi.intel.com/v1beta1/pmemcsideployments/pmem-csi.intel.com
  UID:               d8635490-53fa-4eec-970d-cd4c76f53b23
Spec:
  Device Mode:  lvm
  Node Selector:
    Storage:  pmem
Status:
  Conditions:
    Last Update Time:  2020-10-07T07:32:00Z
    Reason:            Driver certificates are available.
    Status:            True
    Type:              CertsReady
    Last Update Time:  2020-10-07T07:32:02Z
    Reason:            Driver deployed successfully.
    Status:            True
    Type:              DriverDeployed
  Driver Components:
    Component:     Controller
    Last Updated:  2020-10-08T07:45:13Z
    Reason:        1 instance(s) of controller driver is running successfully
    Status:        Ready
    Component:     Node
    Last Updated:  2020-10-08T07:45:11Z
    Reason:        All 3 node driver pod(s) running successfully
    Status:        Ready
  Last Updated:    2020-10-07T07:32:21Z
  Phase:           Running
  Reason:          All driver components are deployed successfully
Events:
  Type    Reason         Age   From               Message
  ----    ------         ----  ----               -------
  Normal  NewDeployment  58s   pmem-csi-operator  Processing new driver deployment
  Normal  Running        39s   pmem-csi-operator  Driver deployment successful

$ kubectl get pod -n pmem-csi
NAME                                             READY   STATUS    RESTARTS   AGE
pmem-csi-intel-com-controller-79cd9f799d-rt54d   2/2     Running   0          51s
pmem-csi-intel-com-node-4x7cv                    2/2     Running   0          50s
pmem-csi-intel-com-node-6grt6                    2/2     Running   0          50s
pmem-csi-intel-com-node-msgds                    2/2     Running   0          51s
pmem-csi-operator-749c7c7c69-k5k8n               1/1     Running   0          3m

Install via YAML files

  • Get source code

PMEM-CSI uses Go modules and thus can be checked out and (if that should be desired) built anywhere in the filesystem. Pre-built container images are available and thus users don't need to build from source, but they may need some additional files for the following sections. To get the source code, use:

$ git clone https://github.com/intel/pmem-csi
  • Choose a namespace

By default, a pmem-csi namespace is expected to exist by the YAML deploy files. It must be created manually with

$ kubectl create ns pmem-csi

A different namespace be used by modifying the deployment with kustomize as explained below.

  • Deploy the driver to Kubernetes

The deploy/kubernetes-<kubernetes version> directory contains pmem-csi*.yaml files which can be used to deploy the driver on that Kubernetes version. The files in the directory with the highest Kubernetes version might also work for more recent Kubernetes releases. All of these deployments use images published by Intel on Docker Hub.

For each Kubernetes version, four different deployment variants are provided:

  • direct or lvm: one uses direct device mode, the other LVM device mode.
  • testing: the variants with testing in the name enable debugging features and shouldn't be used in production.

For example, to deploy for production with LVM device mode onto Kubernetes 1.18, use:

$ kubectl create -f deploy/kubernetes-1.18/pmem-csi-lvm.yaml

These variants were generated with kustomize. kubectl >= 1.14 includes some support for that. The sub-directories of deploy/kustomize-<kubernetes version> can be used as bases for kubectl kustomize. For example:

  • Change namespace:

    $ mkdir -p my-pmem-csi-deployment
$ cat >my-pmem-csi-deployment/kustomization.yaml <<EOF
namespace: pmem-driver
bases:
  - ../deploy/kubernetes-1.18/lvm
EOF
$ kubectl create --kustomize my-pmem-csi-deployment

  • Configure how much PMEM is used by PMEM-CSI for LVM (see Namespace modes in LVM device mode):

    $ mkdir -p my-pmem-csi-deployment
$ cat >my-pmem-csi-deployment/kustomization.yaml <<EOF
bases:
  - ../deploy/kubernetes-1.18/lvm
patchesJson6902:
  - target:
      group: apps
      version: v1
      kind: DaemonSet
      name: pmem-csi-node
      namespace: pmem-csi
    path: lvm-parameters-patch.yaml
EOF
$ cat >my-pmem-csi-deployment/lvm-parameters-patch.yaml <<EOF
# pmem-driver is in the container #0. Append arguments at the end.
- op: add
  path: /spec/template/spec/containers/0/args/-
  value: "-pmemPercentage=90"
EOF
$ kubectl create --kustomize my-pmem-csi-deployment

  • Wait until all pods reach 'Running' status

$ kubectl get pods -n pmem-csi
NAME                                             READY   STATUS    RESTARTS   AGE
pmem-csi-intel-com-controller-79cd9f799d-rt54d   2/2     Running   0          3m15s
pmem-csi-intel-com-node-8kmxf                    2/2     Running   0          3m15s
pmem-csi-intel-com-node-bvx7m                    2/2     Running   0          3m15s
pmem-csi-intel-com-node-fbmpg                    2/2     Running   0          3m15s

Volume parameters

A Kubernetes cluster administrators must define some volume parameters like the filesystem type in storage classes. Users then reference those storage classes when requesting generic ephemeral inline or persistent volumes. The size of volumes can be chosen by users.

xfs and ext4 are supported filesystem types. In addition to the normal parameters defined by Kubernetes, PMEM-CSI supports the following custom parameters in a storage class:

key meaning optional values
eraseAfter Clear all data by overwriting with zeroes after use and before deleting the volume Yes true (default), false
kataContainers Prepare volume for use with DAX in Kata Containers. Yes false/0/f/FALSE (default), true/1/t/TRUE
usage Determine how a volume is going to be used. Yes AppDirect (default), FileIO

By default, volumes are created for AppDirect enabled applications:

  • The namespace mode is fsdax.
  • Mount parameters include -o dax (= -o dax=always on newer kernels) which ensures that all files are automatically opened in DAX mode, i.e. reads and writes directly access the underlying PMEM.

This might not be ideal for traditional file IO because the page cache is bypassed, which may affect performance, and because applications have to be prepared to deal with partially written data sectors in case of crashes. When the goal is to run traditional applications, then usage=FileIO may be better:

  • In direct mode, the namespace mode is sector.
  • In LVM mode, the namespace mode is fsdax because currently PMEM-CSI doesn't support LVM on top of other namespaces.
  • Mount parameters do not include -o dax.

kataContainers and usage=FileIO are mutually exclusive because the former is about making AppDirect available in Kata Containers. The normal volume passthrough can be used for usage=FileIO.

Creating volumes

This section uses files from the common example directory. It is not necessary to check out the repository to use them.

Create a storage class with late binding, the recommended mode:

$ kubectl apply -f https://github.com/intel/pmem-csi/raw/devel/deploy/common/pmem-storageclass-late-binding.yaml
storageclass.storage.k8s.io/pmem-csi-sc-late-binding created

Then request a volume which uses that class:

$ kubectl apply -f https://github.com/intel/pmem-csi/raw/devel/deploy/common/pmem-pvc-late-binding.yaml
persistentvolumeclaim/pmem-csi-pvc-late-binding created

At this point, the volume is not yet getting created because of the late binding mode:

$ kubectl describe pvc/pmem-csi-pvc-late-binding
Name:          pmem-csi-pvc-late-binding
Namespace:     default
StorageClass:  pmem-csi-sc-late-binding
Status:        Pending
Volume:        
Labels:        <none>
Annotations:   <none>
Finalizers:    [kubernetes.io/pvc-protection]
Capacity:      
Access Modes:  
VolumeMode:    Filesystem
Used By:       <none>
Events:
  Type    Reason                Age               From                         Message
  ----    ------                ----              ----                         -------
  Normal  WaitForFirstConsumer  0s (x2 over 14s)  persistentvolume-controller  waiting for first consumer to be created before binding

The volume gets created once the first Pod starts to use it, on a node that is suitable for that Pod:

$ kubectl apply -f https://github.com/intel/pmem-csi/raw/devel/deploy/common/pmem-app-late-binding.yaml
pod/my-csi-app created

After a short while, the volume is created and the pod can run:

$ kubectl get pvc,pods -o wide
NAME                                              STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS               AGE
persistentvolumeclaim/pmem-csi-pvc-late-binding   Bound    pvc-ade8dc48-a4c0-4f30-b479-84460a3e0591   4Gi        RWO            pmem-csi-sc-late-binding   55s

NAME             READY   STATUS    RESTARTS   AGE
pod/my-csi-app   1/1     Running   0          47s

The volume was mounted with dax=always, therefore all file operations and memory regions mapped from that volume into the address space of an application directly access the underlying PMEM:

$ kubectl exec my-csi-app -- df /data
Filesystem                                                                              1K-blocks  Used Available Use% Mounted on
/dev/ndbus0region0fsdax/pvc-7d-83241976933418f96748a1c18d500c6cba91c1dfaa87145b7893569c   4062912 16376   3820440   1% /data

$ kubectl exec my-csi-app -- mount |grep /data
/dev/ndbus0region0fsdax/pvc-7d-83241976933418f96748a1c18d500c6cba91c1dfaa87145b7893569c on /data type ext4 (rw,relatime,seclabel,dax=always)

Troubleshooting

A few things can go wrong when trying out the previous example.

Driver or operator fails

This shows up in kubectl get pods --all-namespaces as failed Pods and can be investigated with kubectl describe --namespace <driver namespace> pods/<pod name> and kubectl logs --namespace <driver namespace> <pod name> pmem-driver or one of the other containers in that Pod.

When using deployment files from the devel branch, the corresponding container canary image might not have been published yet. Better use the latest stable release.

No driver Pod created for a node

This can be checked with kubectl get pods --all-namespaces -o wide. Have nodes been labeled as expected by the driver deployment? Check with kubectl get nodes -o yaml.

Less PMEM available than expected

This is usually the result of not preparing the node(s) as describe in persistent memory pre-provisioning.

One way of checking this is to look at the logs of the PMEM-CSI driver on a node. In this case, region0 was completely unused and the driver was configured to use 50% of that for an LVM volume group:

$ kubectl get pods --all-namespaces -l app.kubernetes.io/name=pmem-csi-node -o wide
NAMESPACE   NAME                            READY   STATUS    RESTARTS   AGE   IP                NODE                         NOMINATED NODE   READINESS GATES
pmem-csi    pmem-csi-intel-com-node-d2mfh   3/3     Running   0          75s   192.168.200.66    pmem-csi-pmem-govm-worker3   <none>           <none>
pmem-csi    pmem-csi-intel-com-node-jkbgz   3/3     Running   0          75s   192.168.133.134   pmem-csi-pmem-govm-worker1   <none>           <none>
pmem-csi    pmem-csi-intel-com-node-th56d   3/3     Running   0          75s   192.168.220.67    pmem-csi-pmem-govm-worker2   <none>           <none>

$ kubectl logs -n pmem-csi pmem-csi-intel-com-node-jkbgz pmem-driver
I0623 07:15:18.710690       1 main.go:73] "PMEM-CSI started." version="v0.9.0-188-gd451ec6f3-dirty"
I0623 07:15:18.711645       1 pmd-lvm.go:328] "LVM-New/setupNS: Checking region for fsdax namespaces" region="region0" percentage=50 size="64Gi" available="64Gi" max-available-extent="64Gi" may-use="32Gi"
I0623 07:15:18.712251       1 pmd-lvm.go:361] "LVM-New/setupNS: Create fsdax namespace" size="32Gi"
I0623 07:15:19.041186       1 region.go:282] "LVM-New/setupNS/CreateNamespace: Namespace created" region="region0" namespace="namespace0.1" usable-size="32254Mi" raw-size="32Gi" uuid="c3e6fe52-d3f2-11eb-b33e-c2b1549139a7"
I0623 07:15:19.079791       1 pmd-lvm.go:422] "LVM-New/setupVG/setupVGForNamespace: Creating new volume group" vg="ndbus0region0fsdax"
I0623 07:15:19.130041       1 mount_linux.go:163] Detected OS without systemd
I0623 07:15:19.130661       1 server.go:54] "GRPC Server: Listening for connections" endpoint="unix:///csi/csi.sock"
I0623 07:15:19.180760       1 pmem-csi-driver.go:305] "PMEM-CSI ready." capacity="32252Mi maximum volume size, 32252Mi available, 32252Mi managed, 64Gi total"

In a production environment, the metrics support could be used to monitor available PMEM per node.

Automatic node setup

The expectation is that the scripts which bring up nodes can be adapted to prepare the PMEM for usage by PMEM-CSI as explained earlier. But this might not always be easy.

For the case of converting an existing "raw" namespace to "fsdax" mode there is a possibility to do the conversion through a deployed PMEM-CSI driver:

  1. Install PMEM-CSI in LVM mode without preparing nodes. At this point only the central controller pod will run.
  2. For each node that has one or more raw namespaces that all need to be converted, set the <driver name>/convert-raw-namespaces label (usually pmem-csi.intel.com/convert-raw-namespaces) to force.
  3. This will cause the pods of the pmem-csi-intel-com-node-setup DaemonSet to run on those nodes. Those pods then will convert the namespaces, create the LVM volume group, remove the convert-raw-namespaces label (i.e. the pods will only run once) and add the normal label that enables the PMEM-CSI node driver pods to run.
  4. The normal node driver pods start up and then are ready to provision volumes.

WARNING: the raw namespaces will be converted even when they are active. If data was stored on them, it will be lost after the conversion.

The output of a successful conversion will look like this:

I0623 07:32:52.773207       1 main.go:73] "PMEM-CSI started." version="v0.9.0-188-gd451ec6f3-dirty"
I0623 07:32:52.774386       1 convert.go:79] "ForceConvertRawNamespaces/convert: checking for namespaces"
I0623 07:32:52.774871       1 convert.go:81] "ForceConvertRawNamespaces/convert: checking" bus="{\"dev\":\"ndbus0\",\"dimms\":[{}],\"provider\":\"ACPI.NFIT\",\"regions\":[{}]}"
I0623 07:32:52.775316       1 convert.go:83] "ForceConvertRawNamespaces/convert: checking" region="{\"available_size\":0,\"dev\":\"region0\",\"mappings\":[{}],\"max_available_extent\":0,\"namespaces\":[{}],\"size\":68719476736,\"type\":\"pmem\"}"
I0623 07:32:52.775444       1 convert.go:90] "ForceConvertRawNamespaces/convert: checking" namespace="{\"blockdev\":\"pmem0\",\"dev\":\"namespace0.0\",\"enabled\":true,\"id\":0,\"mode\":\"raw\",\"name\":\"\",\"size\":68719476736,\"uuid\":\"1711a2a0-358d-4b14-a43c-8efa1a9f7154\"}"
I0623 07:32:52.775550       1 convert.go:99] "ForceConvertRawNamespaces/convert: converting raw namespace" namespace="{\"blockdev\":\"pmem0\",\"dev\":\"namespace0.0\",\"enabled\":true,\"id\":0,\"mode\":\"raw\",\"name\":\"\",\"size\":68719476736,\"uuid\":\"1711a2a0-358d-4b14-a43c-8efa1a9f7154\"}"
I0623 07:32:53.397897       1 convert.go:127] "ForceConvertRawNamespaces/convert: setting up volume group" namespace="{\"blockdev\":\"pmem0\",\"dev\":\"namespace0.0\",\"enabled\":false,\"id\":0,\"mode\":\"fsdax\",\"name\":\"\",\"size\":18446744073709551615,\"uuid\":\"00000000-0000-0000-0000-000000000000\"}" vg="ndbus0region0fsdax"
I0623 07:32:53.434094       1 pmd-lvm.go:422] "ForceConvertRawNamespaces/convert/setupVGForNamespace: Creating new volume group" vg="ndbus0region0fsdax"
I0623 07:32:53.457108       1 convert.go:133] "ForceConvertRawNamespaces/convert: converted to fsdax namespace" namespace="{\"blockdev\":\"pmem0\",\"dev\":\"namespace0.0\",\"enabled\":false,\"id\":0,\"mode\":\"fsdax\",\"name\":\"\",\"size\":18446744073709551615,\"uuid\":\"00000000-0000-0000-0000-000000000000\"}" vg="ndbus0region0fsdax"
I0623 07:32:53.457148       1 convert.go:75] "ForceConvertRawNamespaces/convert: successful" converted=1
I0623 07:32:53.479512       1 convert.go:172] "ForceConvertRawNamespaces/havePMEM: Volume group will be used by PMEM-CSI in LVM mode" vg="ndbus0region0fsdax"
I0623 07:32:53.523412       1 convert.go:200] "ForceConvertRawNamespaces/relabel: Change node labels" node="pmem-csi-pmem-govm-master" patch="{\"metadata\":{\"labels\":{\"pmem-csi.intel.com/convert-raw-namespaces\": null, \"feature.node.kubernetes.io/memory-nv.dax\": \"true\"}}}"
I0623 07:32:53.523605       1 pmem-csi-driver.go:326] "Raw namespace conversion is done, waiting for termination signal."
I0623 07:33:03.954098       1 pmem-csi-driver.go:344] "Caught signal, terminating." signal="terminated"
I0623 07:33:05.016426       1 main.go:93] "PMEM-CSI stopped."

It terminates once Kubernetes notices that the pod is no longer needed. This usually happens quickly, so a log monitoring solution may be needed to see this output because kubectl logs does not work for pods that were already deleted.

The DaemonSet contains some information which is available longer:

$ kubectl describe daemonsets/pmem-csi-intel-com-node-setup
Name:           pmem-csi-intel-com-node-setup
Selector:       app.kubernetes.io/instance=pmem-csi.intel.com,app.kubernetes.io/name=pmem-csi-node-setup,pmem-csi.intel.com/deployment=lvm-production
Node-Selector:  pmem-csi.intel.com/convert-raw-namespaces=force
Labels:         app.kubernetes.io/component=node-setup
                app.kubernetes.io/instance=pmem-csi.intel.com
                app.kubernetes.io/name=pmem-csi-node-setup
                app.kubernetes.io/part-of=pmem-csi
                pmem-csi.intel.com/deployment=lvm-production
Annotations:    deprecated.daemonset.template.generation: 1
Desired Number of Nodes Scheduled: 0
Current Number of Nodes Scheduled: 0
Number of Nodes Scheduled with Up-to-date Pods: 0
Number of Nodes Scheduled with Available Pods: 0
Number of Nodes Misscheduled: 0
Pods Status:  0 Running / 0 Waiting / 0 Succeeded / 0 Failed
Pod Template:
  Labels:           app.kubernetes.io/component=node-setup
                    app.kubernetes.io/instance=pmem-csi.intel.com
                    app.kubernetes.io/name=pmem-csi-node-setup
                    app.kubernetes.io/part-of=pmem-csi
                    pmem-csi.intel.com/deployment=lvm-production
  Service Account:  pmem-csi-intel-com-node-setup
  Containers:
   pmem-driver:
    Image:      172.17.42.1:5001/pmem-csi-driver:canary
    Port:       <none>
    Host Port:  <none>
    Command:
      /usr/local/bin/pmem-csi-driver
      -v=3
      -logging-format=text
      -mode=force-convert-raw-namespaces
      -nodeSelector={"storage":"pmem"}
      -nodeid=$(KUBE_NODE_NAME)
...
Events:
  Type    Reason            Age    From                  Message
  ----    ------            ----   ----                  -------
  Normal  SuccessfulCreate  5m47s  daemonset-controller  Created pod: pmem-csi-intel-com-node-setup-fr9b8
  Normal  SuccessfulDelete  5m45s  daemonset-controller  Deleted pod: pmem-csi-intel-com-node-setup-fr9b8

If conversion fails, the pod will exit with an error and then get restarted automatically by Kubernetes to retry the conversion until it succeeds.

It is considered a user error if conversion is requested for a node which has nothing to convert. To make that obvious, the pod will print an error and then exist with an error. That way, the pod continues to exist and the log can be inspected to identify the problem.

Kata Containers support

Kata Containers support gets enabled via the kataContainers storage class parameter. PMEM-CSI then creates a filesystem inside a partition inside a file. When such a volume is used inside Kata Containers, the Kata Containers runtime makes sure that the filesystem is mounted on an emulated NVDIMM device with full DAX support.

On the host, PMEM-CSI will try to mount through a loop device with -o dax but proceed without -o dax when the kernel does not support that. Currently Linux up to and including 5.4 do not support it and it is unclear when that support will be added In other words, on the host such volumes are usable, but only without DAX.

When disabled, volumes support DAX on the host and are usable without DAX inside Kata Containers.

Raw block volumes are only supported with kataContainers: false. Attempts to create them with kataContainers: true are rejected.

At the moment (= Kata Containers 1.11.0-rc0), only Kata Containers with QEMU enable the special support for such volumes. Without QEMU or with older releases of Kata Containers, the volume is still usable through the normal remote filesystem support (9p or virtio-fs). Support for Cloud Hypervisor is in progress.

With Kata Containers for QEMU, the VM must be configured appropriately to allow adding the PMEM volumes to their address space. This can be done globally by setting the memory_offset in the configuration-qemu.toml file or per-pod by setting the io.katacontainers.config.hypervisor.memory_offset annotation in the pod meta data. In both cases, the value has to be large enough for all PMEM volumes used by the pod, otherwise pod creation will fail with an error similar to this:

Error: container create failed: QMP command failed: not enough space, currently 0x8000000 in use of total space for memory devices 0x3c100000

Note:

  • The offset is currently (= Kata Containers 2.1.0) limited to 32 bit, which implies that volumes cannot be larger than 4GiB. An enhancement request for Kata Containers is pending.
  • A newer version is also needed for a fix of issue #2018.
  • kata-deploy, at least in Kata Containers 2.1.0, does not enable the memory_offset annotation, leading to failed to create containerd task: annotation io.katacontainers.config.hypervisor.memory_offset is not enabled errors.

The examples for usage of Kata Containers with ephemeral and persistent volumes use the pod label. They assume that the kata-qemu runtime class is installed.

For the QEMU test cluster, setup-kata-containers.sh can be used to install Kata Containers. However, this currently only works on Clear Linux because on Fedora, the Docker container runtime is used and Kata Containers does not support that one.

Ephemeral inline volumes

Kubernetes CSI specific

This is the original implementation of ephemeral inline volumes for CSI drivers in Kubernetes. It is currently available as a beta feature in Kubernetes.

Volume requests embedded in the pod spec with the csi field are provisioned as ephemeral volumes. The volume request could use below fields as volumeAttributes:

key meaning optional values
size Size of the requested ephemeral volume as Kubernetes memory string ("1Mi" = 1024*1024 bytes, "1e3K = 1000000 bytes) No
eraseAfter Clear all data by overwriting with zeroes after use and before deleting the volume Yes true (default), false
kataContainers Prepare volume for use in Kata Containers. Yes false/0/f/FALSE (default), true/1/t/TRUE

Try out ephemeral volume usage with the provided example application.

Generic

This approach was introduced in Kubernetes 1.19 with the goal of using them for PMEM-CSI instead of the older approach. In contrast CSI ephemeral inline volumes, no changes are needed in CSI drivers, so PMEM-CSI already fully supports this if the cluster has the feature enabled. See pmem-app-generic-ephemeral.yaml for an example.

Raw block volumes

Applications can use volumes provisioned by PMEM-CSI as raw block devices. Such volumes use the same "fsdax" namespace mode as filesystem volumes and therefore are block devices. That mode only supports dax (= mmap(MAP_SYNC)) through a filesystem. Pages mapped on the raw block device go through the Linux page cache. Applications have to format and mount the raw block volume themselves if they want dax. The advantage then is that they have full control over that part.

For provisioning a PMEM volume as raw block device, one has to create a PersistentVolumeClaim with volumeMode: Block. See example PVC and application for usage reference.

That example demonstrates how to handle some details:

  • mkfs.ext4 needs -b 4096 to produce volumes that support dax; without it, the automatic block size detection may end up choosing an unsuitable value depending on the volume size.
  • Kubernetes bug #85624 must be worked around to format and mount the raw block device.

Storage capacity tracking

Kubernetes 1.19 introduces support for publishing and using storage capacity information for pod scheduling. It became beta in 1.21. PMEM-CSI must be deployed differently to use this feature:

  • external-provisioner must be told to publish storage capacity information via command line arguments.
  • A flag in the CSI driver information must be set for the Kubernetes scheduler, otherwise it ignores that information when considering pods with unbound volume.

The deployments for Kubernetes >= 1.21 do this automatically. The alpha API in 1.19 and 1.20 is no longer supported.

Metrics support

Metrics support is controlled by command line options of the PMEM-CSI driver binary and of the CSI sidecars. Annotations and named container ports make it possible to discover these data scraping endpoints. The metrics kustomize base adds all of that to the pre-generated deployment files. The operator also enables the metrics support.

Access to metrics data is not restricted (no TLS, no client authorization) because the metrics data is not considered confidential and access control would just make client configuration unnecessarily complex.

Metrics data

PMEM-CSI exposes metrics data about the Go runtime, Prometheus, CSI method calls, and PMEM-CSI:

Name Type Explanation
build_info gauge A metric with a constant '1' value labeled by version.
scheduler_request_duration_seconds histogram Latencies for PMEM-CSI scheduler HTTP requests by operation ("mutate", "filter", "status") and method ("post").
scheduler_in_flight_requests gauge Currently pending PMEM-CSI scheduler HTTP requests.
scheduler_requests_total counter Number of HTTP requests to the PMEM-CSI scheduler, regardless of operation and method.
scheduler_response_size_bytes histogram Histogram of response sizes for PMEM-CSI scheduler requests, regardless of operation and method.
csi_[sidecar|plugin]_operations_seconds histogram gRPC call duration and error code, for sidecar to driver (aka plugin) communication.
go_* Go runtime information
pmem_amount_available gauge Remaining amount of PMEM on the host that can be used for new volumes.
pmem_amount_managed gauge Amount of PMEM on the host that is managed by PMEM-CSI.
pmem_amount_max_volume_size gauge The size of the largest PMEM volume that can be created.
pmem_amount_total gauge Total amount of PMEM on the host.
process_* Process information
promhttp_metric_handler_requests_in_flight gauge Current number of scrapes being served.
promhttp_metric_handler_requests_total counter Total number of scrapes by HTTP status code.

This list is tentative and may still change as long as metrics support is alpha. To see all available data, query a container. Different containers provide different data. For example, the controller provides:

$ kubectl port-forward -n pmem-csi $(kubectl get pods -n pmem-csi -o name -l app.kubernetes.io/name=pmem-csi-controller) 10010
Forwarding from 127.0.0.1:10010 -> 10010
Forwarding from [::1]:10010 -> 10010

And in another shell:

$ curl --silent http://localhost:10010/metrics | grep '# '
# HELP build_info A metric with a constant '1' value labeled by version.
# TYPE build_info gauge
...

Prometheus example

An extension of the scrape config is necessary for Prometheus. When deploying Prometheus via Helm, that file can be added to the default configuration with the -f parameter. The following example works for the QEMU-based cluster and Helm v3.1.2. In a real production deployment, some kind of persistent storage should be provided. The URL can be used instead of the file name, too.

$ helm install prometheus stable/prometheus \
     --set alertmanager.persistentVolume.enabled=false,server.persistentVolume.enabled=false \
     -f deploy/prometheus.yaml
NAME: prometheus
LAST DEPLOYED: Tue Aug 18 18:04:27 2020
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
The Prometheus server can be accessed via port 80 on the following DNS name from within your cluster:
prometheus-server.default.svc.cluster.local


Get the Prometheus server URL by running these commands in the same shell:
  export POD_NAME=$(kubectl get pods --namespace default -l "app=prometheus,component=server" -o jsonpath="{.items[0].metadata.name}")
  kubectl --namespace default port-forward $POD_NAME 9090
#################################################################################
######   WARNING: Persistence is disabled!!! You will lose your data when   #####
######            the Server pod is terminated.                             #####
#################################################################################

...

After running this kubectl port-forward command, it is possible to access the Prometheus web interface and run some queries there. Here are some examples for the QEMU test cluster with two volumes created on node pmem-csi-pmem-govm-worker2.

Available PMEM as percentage:

pmem_amount_available / pmem_amount_managed
Result variable Value Tags
none 0.7332986065893997 instance = 10.42.0.1:10010
job = pmem-csi-containers
kubernetes_namespace = default
kubernetes_pod_container_name = pmem-driver
kubernetes_pod_name = pmem-csi-node-dfkrw
kubernetes_pod_node_name = pmem-csi-pmem-govm-worker2
node = pmem-csi-pmem-govm-worker2
1 instance = 10.36.0.1:10010
job = pmem-csi-containers
kubernetes_namespace = default
kubernetes_pod_container_name = pmem-driver
kubernetes_pod_name = pmem-csi-node-z5vnp
kubernetes_pod_node_name pmem-csi-pmem-govm-worker3
node = pmem-csi-pmem-govm-worker3
1 instance = 10.44.0.1:10010
job = pmem-csi-containers
kubernetes_namespace = default
kubernetes_pod_container_name = pmem-driver
kubernetes_pod_name = pmem-csi-node-zzmsd
kubernetes_pod_node_name = pmem-csi-pmem-govm-worker1
node = pmem-csi-pmem-govm-worker1

Number of CreateVolume calls in nodes:

pmem_csi_node_operations_seconds_count{method_name="/csi.v1.Controller/CreateVolume"}
Result variable Value Tags
pmem_csi_node_operations_seconds_count 2 driver_name = pmem-csi.intel.com
grpc_status_code = OK
instance = 10.42.0.1:10010
job = pmem-csi-containers
kubernetes_namespace = default
kubernetes_pod_container_name = pmem-driver
kubernetes_pod_name = pmem-csi-node-dfkrw
kubernetes_pod_node_name = pmem-csi-pmem-govm-worker2
method_name = /csi.v1.Controller/CreateVolume
node = pmem-csi-pmem-govm-worker2

PMEM-CSI Deployment CRD

PmemCSIDeployment is a cluster-scoped Kubernetes resource in the pmem-csi.intel.com API group. It describes how a PMEM-CSI driver instance is to be created.

The operator will create objects in the namespace in which the operator itself runs if the object type is namespaced.

The name of the deployment object is also used as CSI driver name. This ensures that the name is unique and immutable. However, name clashes with other CSI drivers are still possible, so the name should meet the CSI requirements:

  • domain name notation format, including a unique top-level domain
  • 63 characters or less, beginning and ending with an alphanumeric character ([a-z0-9A-Z]) with dashes (-), dots (.), and alphanumerics between.

The name is also used as prefix for the names of all objects created for the deployment and for the local /var/lib/<name> state directory on each node.

Although the operator allows running multiple PMEM-CSI driver deployments, one has to take extreme care of such deployments by ensuring that not more than one driver ends up running on the same node(s). Nodes on which a PMEM-CSI driver could run can be configured by using the nodeSelector property of the DeploymentSpec.

NOTE: Starting from release v0.9.0 reconciling of the Deployment CRD in pmem-csi.intel.com/v1alpha1 API group is not supported by the PMEM-CSI operator anymore. Such resources in the cluster must be migrated manually to new the PmemCSIDeployment API.

The current API for PmemCSIDeployment resources is:

Field Type Description
apiVersion string pmem-csi.intel.com/v1beta1
kind string PmemCSIDeployment
metadata ObjectMeta Object metadata, name used for CSI driver and as prefix for sub-objects
spec DeploymentSpec Specification of the desired behavior of the deployment

DeploymentSpec

Below specification fields are valid in all API versions unless noted otherwise in the description.

The default values are used by the operator when no value is set for a field explicitly. Those defaults can change over time and are not part of the API specification.

Field Type Description Default Value
image string PMEM-CSI docker image name used for the deployment the same image as the operator1
provisionerImage string CSI provisioner docker image name latest external provisioner stable release image2
nodeRegistrarImage string CSI node driver registrar docker image name latest node driver registrar stable release image2
pullPolicy string Docker image pull policy. either one of Always, Never, IfNotPresent IfNotPresent
logLevel integer PMEM-CSI driver logging level 3
logFormat text log output format "text" or "json" 3
deviceMode string Device management mode to use. Supports one of lvm or direct lvm
controllerReplicas int Number of concurrently running controller pods. 1
controllerResources ResourceRequirements Describes the compute resource requirements for controller pod.
4Deprecated and only available in v1alpha1.
nodeResources ResourceRequirements Describes the compute resource requirements for the pods running on node(s).
4Deprecated and only available in v1alpha1.
controllerDriverResources ResourceRequirements Describes the compute resource requirements for controller driver container running on master node. Available since v1beta1.
nodeDriverResources ResourceRequirements Describes the compute resource requirements for the driver container running on worker node(s).
Available since v1beta1.
provisionerResources ResourceRequirements Describes the compute resource requirements for the external provisioner sidecar container. Available since v1beta1.
nodeRegistrarResources ResourceRequirements Describes the compute resource requirements for the driver registrar sidecar container running on worker node(s).
Available since v1beta1.
registryCert string Encoded tls certificate signed by a certificate authority used for driver's controller registry server generated by operator self-signed CA
nodeControllerCert string Encoded tls certificate signed by a certificate authority used for driver's node controllers generated by operator self-signed CA
registryKey string Encoded RSA private key used for signing by registryCert generated by the operator
nodeControllerKey string Encoded RSA private key used for signing by nodeControllerCert generated by the operator
caCert string Certificate of the CA by which the registryCert and controllerCert are signed self-signed certificate generated by the operator
nodeSelector string map Labels to use for selecting Nodes on which PMEM-CSI driver should run. { "storage": "pmem" }
pmemPercentage integer Percentage of PMEM space to be used by the driver on each node. This is only valid for a driver deployed in lvm mode. This field can be modified, but by that time the old value may have been used already. Reducing the percentage is not supported. 100
labels string map Additional labels for all objects created by the operator. Can be modified after the initial creation, but removed labels will not be removed from existing objects because the operator cannot know which labels it needs to remove and which it has to leave in place.
kubeletDir string Kubelet's root directory path /var/lib/kubelet
maxUnavailable int or string maximum number of node drivers that are allowed to be down during a rolling update, given as absolute number or percentage of the total number of nodes with the driver 1

1 To use the same container image as default driver image the operator pod must set with below environment variables with appropriate values:

  • POD_NAME: Name of the operator pod. Namespace of the pod could be figured out by the operator.
  • OPERATOR_NAME: Name of the operator container. If not set, defaults to "pmem-csi-operator"

2 Image versions depend on the Kubernetes release. The operator dynamically chooses suitable image versions. Users have to take care of that themselves when overriding the values.

3 In PMEM-CSI 0.9.0, "json" output is only available for the PMEM-CSI container. The sidecars are still producing plain text messages. This may change in the future. Also, the migration from formatted log messages (= printf style) to structured log messages (message plus key/value pairs) is not complete.

4 Pod level resource requirements (nodeResources and controllerResources) are deprecated in favor of per-container resource requirements (nodeDriverResources, nodeRegistrarResources, controllerDriverResources and provisionerResources).

WARNING: although all fields can be modified and changes will be propagated to the deployed driver, not all changes are safe. In particular, changing the deviceMode will not work when there are active volumes.

DeploymentStatus

A PMEM-CSI Deployment's status field is a DeploymentStatus object, which carries the detailed state of the driver deployment. It is comprised of deployment conditions, driver component status, and a phase field. The phase of a PMEM-CSI deployment is a high-level summary of where the the PmemCSIDployment is in its lifecycle.

The possible phase values and their meaning are as below:

Value Meaning
empty string A new deployment.
Running The operator has determined that the driver is usable1.
Failed For some reason, the PmemCSIDeployment failed and cannot be progressed. The failure reason is placed in the DeploymentStatus.Reason field.

1 This check has not been implemented yet. Instead, the deployment goes straight to Running after creating sub-resources.

Deployment Conditions

PMEM-CSI DeploymentStatus has an array of conditions through which the PMEM-CSI Deployment has or has not passed. Below are the possible condition types and their meanings:

Condition type Meaning
CertsReady Driver certificates/secrets are available.
CertsVerified Verified that the provided certificates are valid.
DriverDeployed All the componentes required for the PMEM-CSI deployment have been deployed.

Driver component status

PMEM-CSI DeploymentStatus has an array of components of type DriverStatus in which the operator records the brief driver components status. This is useful to know if all the driver instances of a deployment are ready. Below are the fields and their meanings of DriverStatus:

Field Meaning
component Represents the driver component type; one of Controller or Node.
status Represents the state of the component; one of Ready or NotReady. Component becomes Ready if all the instances of the driver component are running. Otherwise, NotReady.
reason A brief message that explains why the component is in this state.
lastUpdateTime Time at which the status updated.

Deployment Events

The PMEM-CSI operator posts events on the progress of a PmemCSIDeployment. If the deployment is in the Failed state, then one can look into the event(s) using kubectl describe on that deployment for the detailed failure reason.

Operator metrics data

PMEM-CSI operator exposes below metrics data about active PmemCSIDeployment custom resources and it's sub-object in addition to the metrics data provided by the controller-runtime:

Name Type Explanation
pmem_csi_deployment_reconcile counter Counter that gets incremented on each time a PmemCSIDeployment CR gone through a reconcile loop, labeled with the deployment name and uid.
pmem_csi_deployment_sub_resource_created_at gauge Timestamp at which a sub resource of the PmemCSIDeployment CR was created by the operator. Labeled by resource details ("name, "namespace", "group", "version", "kind", "uid", "ownedBy").
pmem_csi_deployment_sub_resource_updated_at gauge Timestamp at which a sub resource of the PmemCSIDeployment CR was updated by the operator. Labeled by resource details ("name, "namespace", "group", "version", "kind", "uid", "ownedBy").

Filing issues and contributing

Report a bug by filing a new issue.

Before making your first contribution, be sure to read the development documentation for guidance on code quality and branches.

Contribute by opening a pull request.

Learn about pull requests.

Reporting a Potential Security Vulnerability: If you have discovered potential security vulnerability in PMEM-CSI, please send an e-mail to [email protected]. For issues related to Intel Products, please visit Intel Security Center.

It is important to include the following details:

  • The projects and versions affected
  • Detailed description of the vulnerability
  • Information on known exploits

Vulnerability information is extremely sensitive. Please encrypt all security vulnerability reports using our PGP key.

A member of the Intel Product Security Team will review your e-mail and contact you to collaborate on resolving the issue. For more information on how Intel works to resolve security issues, see: vulnerability handling guidelines.