Known Issues and Limitations in Cloudera Data Science Workbench 1.2.x

Risk of Data Loss During Cloudera Data Science Workbench (CDSW) Shutdown and Restart

Stopping Cloudera Data Science Workbench involves unmounting the NFS volumes that store CDSW project directories and then cleaning up a folder where the kubelet stores its temporary state. However, due to a race condition, this NFS unmount process can take too long or fail altogether. If this happens, CDSW projects that remain mounted will be deleted by the cleanup step.

Products affected: Cloudera Data Science Workbench

Users affected: This potentially affects all CDSW users.

Detected by: Nehmé Tohmé (Cloudera)

Severity (Low/Medium/High): High

Impact: If the NFS unmount fails during shutdown, data loss can occur. All CDSW project files might be deleted.

CVE: N/A

Immediate action required: If you are running any of the affected Cloudera Data Science Workbench versions, you must run the following script on the CDSW master node every time before you stop or restart Cloudera Data Science Workbench. Failure to do so can result in data loss.

This script should also be run before initiating a Cloudera Data Science Workbench upgrade. As always, we recommend creating a full backup prior to beginning an upgrade.

cdsw_protect_stop_restart.sh - Available for download at: cdsw_protect_stop_restart.sh.

#!/bin/bash

set -e

cat << EXPLANATION


This script is a workaround for Cloudera TSB-346. It protects your
CDSW projects from a rare race condition that can result in data loss.
Run this script before stopping the CDSW service, irrespective of whether
the stop precedes a restart, upgrade, or any other task.

Run this script only on the master node of your CDSW cluster.

You will be asked to specify a target folder on the master node where the
script will save a backup of all your project files. Make sure the target
folder has enough free space to accommodate all of your project files. To
determine how much space is required, run 'du -hs /var/lib/cdsw/current/projects'
on the CDSW master node.

This script will first back up your project files to the specified target
folder. It will then temporarily move your project files aside to protect
against the data loss condition. At that point, it is safe to stop the CDSW
service. After CDSW has stopped, the script will move the project files back
into place.

Note: This workaround is not required for CDSW 1.4.2 and higher.



EXPLANATION

read -p "Enter target folder for backups: " backup_target

echo "Backing up to $backup_target..."
rsync -azp /var/lib/cdsw/current/projects "$backup_target"

read -n 1 -p "Backup complete. Press enter when you are ready to stop CDSW: "

echo "Deleting all Kubernetes resources..."
kubectl delete configmaps,deployments,daemonsets,replicasets,services,ingress,secrets,persistentvolumes,persistentvolumeclaims,jobs --all
kubectl delete pods --all

echo "Temporarily saving project files to /var/lib/cdsw/current/projects_tmp..."
mkdir /var/lib/cdsw/current/projects_tmp
mv /var/lib/cdsw/current/projects/* /var/lib/cdsw/current/projects_tmp

echo -e "Please stop the CDSW service."

read -n 1 -p "Press enter when CDSW has stopped: "

echo "Moving projects back into place..."
mv /var/lib/cdsw/current/projects_tmp/* /var/lib/cdsw/current/projects
rm -rf /var/lib/cdsw/current/projects_tmp

echo -e "Done. You may now upgrade or start the CDSW service."
echo -e "When CDSW is running, if desired, you may delete the backup data at $backup_target"

Addressed in release/refresh/patch: This issue is fixed in Cloudera Data Science Workbench 1.4.2.

Note that you are required to run the workaround script above when you upgrade from an affected version to a release with the fix. This helps guard against data loss when the affected version needs to be shut down during the upgrade process.

For the latest update on this issue see the corresponding Knowledge article:

TSB 2018-346: Risk of Data Loss During Cloudera Data Science Workbench (CDSW) Shutdown and Restart

In version 1.2.2, the cdsw status command fails to perform all the required checks

Sending detailed logs to [/tmp/cdsw_status_abc.log] ...
CDSW Version: [1.2.2....]
OK: Application running as root check
OK: Sysctl params check
Failed to run CDSW Nodes Check.
Failed to run CDSW system pods check.
Failed to run CDSW application pods check.
Failed to run CDSW services check.
Failed to run CDSW config maps check.
Failed to run CDSW secrets check.
...

Affected Versions: Cloudera Data Science Workbench 1.2.2.

Fixed In: Cloudera Data Science Workbench 1.3.0. If you cannot upgrade, use the following workaround.

pip install backports.ssl_match_hostname==3.5.0.1 

Cloudera Bug: DSE-3070

Environment setup incomplete after upgrade from version 1.2.0 to 1.2.1 on CSD-based deployments

After upgrading from Cloudera Data Science Workbench 1.2.0 to 1.2.1 on a CSD-based deployment, CLI commands might not work as expected due to missing binaries in the environment. Note that this issue does not affect fresh installs.

Fixed In: Cloudera Data Science Workbench 1.2.2. If you cannot upgrade, use the following workaround.

Workaround: Deactivate and reactivate the Cloudera Data Science Workbench parcel in Cloudera Manager. To do so, go to the Cloudera Manager Admin Console. In the top navigation bar, click Hosts > Parcels.

Locate the current active CDSW parcel and click Deactivate. Once deactivation is complete, click Activate.

Cloudera Bug: DSE-2928

Output from CLI commands is inconsistent with Cloudera Manager CDSW service configuration

Clusters that have migrated from package-based to a CSD-based deployment might face issues with CLI commands showing output that is inconsistent with their settings in the Cloudera Manager CDSW service.

Affected Versions: Cloudera Data Science Workbench 1.2.0, 1.2.1

Fixed In: Cloudera Data Science Workbench 1.2.2. If you cannot upgrade, use the following workaround.

Workaround: Delete the /etc/cdsw/config directory on all the Cloudera Data Science Workbench gateway hosts.

Cloudera Bug: DSE-3021

Cloudera Data Science Workbench (1.4.x and lower) is not supported with Cloudera Manager 6.0.0 and CDH 6.0.0.

Cloudera Data Science Workbench will be supported with Cloudera Enterprise 6 in a future release.

CDH client configuration changes require a full Cloudera Data Science Workbench reset

Cloudera Data Science Workbench does not automatically detect configuration changes on the CDH cluster. Therefore, any changes made to CDH services, ranging from updates to service configuration properties to complete parcel upgrades, must be followed by a full reset of Cloudera Data Science Workbench.

Affected Versions: Cloudera Data Science Workbench 1.2.x, 1.3.x

Workaround: Run the following commands to ensure client configuration changes are picked up by Cloudera Data Science Workbench.

cdsw reset
cdsw init

Cloudera Bug: DSE-3350

With Spark 2.2 Release 1, a PySpark application can run only once per active Workbench session

With Spark 2.2 Release 1, you can run a PySpark application only once per active Workbench session. Subsequent runs of the application will fail with a getDelegationToken error.

Affected Versions: Cloudera Distribution of Apache Spark 2.2 Release 1

Fixed In: Cloudera Distribution of Apache Spark 2.2 Release 2. If you cannot upgrade, use one of the following workarounds.

• Use Cloudera Distribution of Apache Spark 2.1.

  or

• Launch a new PySpark session every time you want to run the application.

Cloudera Bug: CDH-58475

Certain CDS releases require additional configuration to run PySpark on Cloudera Data Science Workbench versions 1.3.x (and lower)

This version mismatch results in PySpark session/job failures on Cloudera Data Science Workbench.

TypeError: __init__() got an unexpected keyword argument 'auth_token'

TypeErrorTraceback (most recent call last)
in engine
----> 1 spark = SparkSession    .builder    .appName("PythonPi")    .getOrCreate()

Fixed Versions: Cloudera Data Science Workbench 1.4.x

Version 1.4 ships with engine 5 that includes a CDS-compatible version of py4j. Note that the upgrade itself will not automatically upgrade existing projects to engine 5. Once the upgrade to 1.4 is complete, project administrators/collaborators must manually configure their projects to use engine 5 as the base image (go to the project's Settings > Engine page).

Cloudera Bug: DSE-4046, DSE-4316

Only CUDA-enabled NVIDIA GPU hardware is supported

Cloudera Data Science Workbench only supports CUDA-enabled NVIDIA GPU cards.

Heterogeneous GPU hardware is not supported

You must use the same GPU hardware across a single Cloudera Data Science Workbench deployment.

GPUs are not detected after a machine reboot

This issue occurs because certain NVIDIA modules do not load automatically after a reboot.

Workaround: To work around this issue, use the following steps to manually load the required modules before Cloudera Data Science Workbench services start. The following commands load the nvidia.ko module, create the /dev/nvidiactl device, and create the list of devices at /dev/nvidia0. They will also create the /dev/nvidia-uvm and /dev/nvidia-uvm-tools devices, and assign execute privileges to /etc/rc.modules. Run these commands once on all the machines that have GPU hardware.

# Manually load the required NVIDIA modules
sudo cat >> /etc/rc.modules <<EOMSG
/usr/bin/nvidia-smi
/usr/bin/nvidia-modprobe -u -c=0
EOMSG

# Set execute permission for /etc/rc.modules 
sudo chmod +x /etc/rc.modules

Cloudera Bug: DSE-2847

NVIDIA driver directory mount is not set correctly

GPUs are not detected by Cloudera Data Science Workbench. This is because the mount for the NVIDIA driver directory is set incorrectly.

Affected Versions: Cloudera Data Science Workbench 1.2.1.

Fixed In: Cloudera Data Science Workbench 1.2.2. If you cannot upgrade, use the following workaround.

Workaround: To work around this, you will need to mount the directory specified by the NVIDIA_LIBRARY_PATH in your site administrator settings, and the project's environment.

/var/lib/nvidia-docker/volumes/nvidia_driver/381.22/

/var/lib/nvidia-docker/volumes/nvidia_driver/381.22/bin:$LD_LIBRARY_PATH

Cloudera Bug: DSE-2957

CUDA engines cannot access GPU libraries in sessions and jobs

The value for the LD_LIBRARY_PATH environmental variable is not propagated to CUDA engines. As a result, CUDA engines will not be able to access the libraries required to enable GPU usage in sessions and jobs.

Affected Versions: Cloudera Data Science Workbench 1.2.0.

Fixed In: Cloudera Data Science Workbench 1.2.1. If you cannot upgrade to Cloudera Data Science Workbench 1.2.1, use the following workaround.

LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/nvidia/lib:/usr/local/cuda/lib:$LD_LIBRARY_PATH

Cloudera Bug: DSE-2828

SSH access to Cloudera Data Science Workbench nodes must be disabled

The container runtime and application data storage is not fully secure from untrusted users who have SSH access to the gateway nodes. Therefore, SSH access to the gateway nodes for untrusted users should be disabled for security and resource utilization reasons.

TSB-328: Unauthenticated User Enumeration in Cloudera Data Science Workbench

Unauthenticated users can get a list of user accounts of Cloudera Data Science Workbench.

Affected Versions: Cloudera Data Science Workbench 1.4.0 (and lower)

Fixed Versions: Cloudera Data Science Workbench 1.4.2 (and higher)

Immediate action required: Upgrade to the latest version of Cloudera Data Science Workbench (1.4.2 or higher).

For more details, see the Security Bulletins - TSB-328.

Remote Command Execution and Information Disclosure in Cloudera Data Science Workbench

A configuration issue in Kubernetes used by Cloudera Data Science Workbench can allow remote command execution and privilege escalation in CDSW. A separate information permissions issue can cause the LDAP bind password to be exposed to authenticated CDSW users when LDAP bind search is enabled.

Affected Versions: Cloudera Data Science Workbench 1.3.0 (and lower)

Fixed Versions: Cloudera Data Science Workbench 1.3.1 (and higher)

For more details, see the Security Bulletins - TSB-313.

TLS/SSL

LDAP

Kerberos