HDFS Setup

Driverless AI lets you explore HDFS data sources from within the Driverless AI application. This section provides instructions for configuring Driverless AI to work with HDFS.

Note: Depending on your Docker install version, use either the docker run --runtime=nvidia (>= Docker 19.03) or nvidia-docker (< Docker 19.03) command when starting the Driverless AI Docker image. Use docker version to check which version of Docker you are using.

Description of Configuration Attributes

  • hdfs_config_path (Required): The location the HDFS config folder path. This folder can contain multiple config files.

  • hdfs_auth_type (Required): Specifies the HDFS authentication. Available values are:

    • principal: Authenticate with HDFS with a principal user.

    • keytab: Authenticate with a keytab (recommended). If running DAI as a service, then the Kerberos keytab needs to be owned by the DAI user.

    • keytabimpersonation: Login with impersonation using a keytab.

    • noauth: No authentication needed.

  • key_tab_path: The path of the principal key tab file. This is required when hdfs_auth_type='principal'.

  • hdfs_app_principal_user: The Kerberos application principal user. This is required when hdfs_auth_type='keytab'.

  • hdfs_app_jvm_args: JVM args for HDFS distributions. Separate each argument with spaces.

    • -Djava.security.krb5.conf

    • -Dsun.security.krb5.debug

    • -Dlog4j.configuration

  • hdfs_app_classpath: The HDFS classpath.

  • hdfs_app_supported_schemes: The list of DFS schemas that is used to check whether a valid input to the connector has been established. For example:

    hdfs_app_supported_schemes = ['hdfs://', 'maprfs://', 'custom://']

    The following are the default values for this option. Additional schemas can be supported by adding values that are not selected by default to the list.

    • hdfs://

    • maprfs://

    • swift://

  • hdfs_max_files_listed: Specifies the maximum number of files that are viewable in the connector UI. Defaults to 100 files. To view more files, increase the default value.

  • hdfs_init_path: Specifies the starting HDFS path displayed in the UI of the HDFS browser.

  • enabled_file_systems: The file systems you want to enable. This must be configured in order for data connectors to function properly.

Example 1: Enable HDFS with No Authentication

This example enables the HDFS data connector and disables HDFS authentication. It does not pass any HDFS configuration file; however it configures Docker DNS by passing the name and IP of the HDFS name node. This lets you reference data stored in HDFS directly using name node address, for example: hdfs://name.node/datasets/iris.csv.

 nvidia-docker run \
   --pid=host \
   --init \
   --rm \
   --shm-size=2g --cap-add=SYS_NICE --ulimit nofile=131071:131071 --ulimit nproc=16384:16384 \
   --add-host name.node:172.16.2.186 \
   -e DRIVERLESS_AI_ENABLED_FILE_SYSTEMS="file,hdfs" \
   -e DRIVERLESS_AI_HDFS_AUTH_TYPE='noauth'  \
   -e DRIVERLESS_AI_PROCSY_PORT=8080 \
   -p 12345:12345 \
   -v /etc/passwd:/etc/passwd:ro \
   -v /etc/group:/etc/group:ro \
   -v /tmp/dtmp/:/tmp \
   -v /tmp/dlog/:/log \
   -v /tmp/dlicense/:/license \
   -v /tmp/ddata/:/data \
   -u $(id -u):$(id -g) \
   h2oai/dai-ubi8-x86_64:1.11.0-cuda11.8.0.xx

Example 2: Enable HDFS with Keytab-Based Authentication

Notes:

  • If using Kerberos Authentication, then the time on the Driverless AI server must be in sync with Kerberos server. If the time difference between clients and DCs are 5 minutes or higher, there will be Kerberos failures.

  • If running Driverless AI as a service, then the Kerberos keytab needs to be owned by the Driverless AI user; otherwise Driverless AI will not be able to read/access the Keytab and will result in a fallback to simple authentication and, hence, fail.

This example:

  • Places keytabs in the /tmp/dtmp folder on your machine and provides the file path as described below.

  • Configures the environment variable DRIVERLESS_AI_HDFS_APP_PRINCIPAL_USER to reference a user for whom the keytab was created (usually in the form of user@realm).

 nvidia-docker run \
     --pid=host \
     --init \
     --rm \
     --shm-size=2g --cap-add=SYS_NICE --ulimit nofile=131071:131071 --ulimit nproc=16384:16384 \
     -e DRIVERLESS_AI_ENABLED_FILE_SYSTEMS="file,hdfs" \
     -e DRIVERLESS_AI_HDFS_AUTH_TYPE='keytab'  \
     -e DRIVERLESS_AI_KEY_TAB_PATH='tmp/<<keytabname>>' \
     -e DRIVERLESS_AI_HDFS_APP_PRINCIPAL_USER='<<user@kerberosrealm>>' \
     -e DRIVERLESS_AI_PROCSY_PORT=8080 \
     -p 12345:12345 \
     -v /etc/passwd:/etc/passwd:ro \
     -v /etc/group:/etc/group:ro \
     -v /tmp/dtmp/:/tmp \
     -v /tmp/dlog/:/log \
     -v /tmp/dlicense/:/license \
     -v /tmp/ddata/:/data \
     -u $(id -u):$(id -g) \
     h2oai/dai-ubi8-x86_64:1.11.0-cuda11.8.0.xx

Example 3: Enable HDFS with Keytab-Based Impersonation

Notes:

  • If using Kerberos, be sure that the Driverless AI time is synched with the Kerberos server.

  • If running Driverless AI as a service, then the Kerberos keytab needs to be owned by the Driverless AI user.

  • Logins are case sensitive when keytab-based impersonation is configured.

The example:

  • Sets the authentication type to keytabimpersonation.

  • Places keytabs in the /tmp/dtmp folder on your machine and provides the file path as described below.

  • Configures the DRIVERLESS_AI_HDFS_APP_PRINCIPAL_USER variable, which references a user for whom the keytab was created (usually in the form of user@realm).

 nvidia-docker run \
     --pid=host \
     --init \
     --rm \
     --shm-size=2g --cap-add=SYS_NICE --ulimit nofile=131071:131071 --ulimit nproc=16384:16384 \
     -e DRIVERLESS_AI_ENABLED_FILE_SYSTEMS="file,hdfs" \
     -e DRIVERLESS_AI_HDFS_AUTH_TYPE='keytabimpersonation'  \
     -e DRIVERLESS_AI_KEY_TAB_PATH='/tmp/<<keytabname>>' \
     -e DRIVERLESS_AI_HDFS_APP_PRINCIPAL_USER='<<appuser@kerberosrealm>>' \
     -e DRIVERLESS_AI_PROCSY_PORT=8080 \
     -p 12345:12345 \
     -v /etc/passwd:/etc/passwd:ro \
     -v /etc/group:/etc/group:ro \
     -v /tmp/dlog/:/log \
     -v /tmp/dlicense/:/license \
     -v /tmp/ddata/:/data \
     -u $(id -u):$(id -g) \
     h2oai/dai-ubi8-x86_64:1.11.0-cuda11.8.0.xx

Specifying a Hadoop Platform

The following example shows how to build an H2O-3 Hadoop image and run Driverless AI. This example uses CDH 6.0. Change the H2O_TARGET to specify a different platform.

  1. Clone and then build H2O-3 for CDH 6.0.

git clone https://github.com/h2oai/h2o-3.git
cd h2o-3
./gradlew clean build -x test
export H2O_TARGET=cdh6.0
export BUILD_HADOOP=true
./gradlew clean build -x test

  1. Start H2O.

docker run -it --rm \
  -v `pwd`:`pwd` \
  -w `pwd` \
  --entrypoint bash \
  --network=host \
  -p 8020:8020  \
  docker.h2o.ai/cdh-6-w-hive \
  -c 'sudo -E startup.sh && \
  source /envs/h2o_env_python3.11/bin/activate && \
  hadoop jar h2o-hadoop-3/h2o-cdh6.0-assembly/build/libs/h2odriver.jar -libjars "$(cat /opt/hive-jars/hive-libjars)" -n 1 -mapperXmx 2g -baseport 54445 -notify h2o_one_node -ea -disown && \
  export CLOUD_IP=localhost && \
  export CLOUD_PORT=54445 && \
  make -f scripts/jenkins/Makefile.jenkins test-hadoop-smoke; \
  bash'

  1. Run the Driverless AI HDFS connector.

java -cp connectors/hdfs.jar ai.h2o.dai.connectors.HdfsConnector

  1. Verify the commands for ls and cp, for example.

{"coreSiteXmlPath": "/etc/hadoop/conf", "keyTabPath": "", authType: "noauth", "srcPath": "hdfs://localhost/user/jenkins/", "dstPath": "/tmp/xxx", "command": "cp", "user": "", "appUser": ""}