Enabling LDAP

Sparkling Water can use LDAP for user authentication. You need to have login.conf with the content similar to the one below:

ldaploginmodule {
    ai.h2o.org.eclipse.jetty.plus.jaas.spi.LdapLoginModule required
    debug="true"
    useLdaps="false"
    contextFactory="com.sun.jndi.ldap.LdapCtxFactory"
    hostname="ldap.h2o.ai"
    port="389"
    bindDn="cn=admin,dc=h2o,dc=ai"
    bindPassword="h2o"
    authenticationMethod="simple"
    forceBindingLogin="true"
    userBaseDn="ou=users,dc=h2o,dc=ai";
};

This configuration file needs to be modified for your specific LDAP configuration.

Generally, to enable LDAP you need to set the following environmental properties:

• spark.ext.h2o.ldap.login=true

• spark.ext.h2o.login.conf=ldap.conf

• spark.ext.h2o.user.name=username

where ldap.conf is the configuration file for the LDAP connection and username is a username of your LDAP account that will be used for authentication to the H2O-3 cluster.

Configuring LDAP in Scala

You can pass the required properties directly as Spark properties, such as:

./bin/sparkling-shell \
--conf spark.ext.h2o.ldap.login=true \
--conf spark.ext.h2o.login.conf=ldap.conf \
--conf spark.ext.h2o.user.name=username

And later, you can create H2OContext as:

import ai.h2o.sparkling._
conf = new H2OConf().setUserName("username").setPassword("password")
val hc = H2OContext.getOrCreate(conf)

Or, you can also use setters available on H2OConf as:

import ai.h2o.sparkling._
val conf = new H2OConf().setLoginConf("ldap.conf").setLdapLoginEnabled().setUserName("username").setPassword("password")
val hc = H2OContext.getOrCreate(conf)

Later when accessing Flow, you will be asked for the username and password of the user you specified in the configuration property spark.ext.h2o.user.name or via the method setUserName.

Configuring LDAP in Python (PySparkling)

You can pass the required properties directly as Spark properties, such as:

./bin/pysparkling \
--conf spark.ext.h2o.ldap.login=true \
--conf spark.ext.h2o.login.conf=ldap.conf \
--conf spark.ext.h2o.user.name=username

And later, you can create H2OContext as:

from pysparkling import *
conf = H2OConf().setUserName("username").setPassword("password")
hc = H2OContext.getOrCreate(conf)

Or, you can also use setters available on H2OConf as:

from pysparkling import *
conf = H2OConf().setLoginConf("ldap.conf").setLdapLoginEnabled().setUserName("username").setPassword("password")
hc = H2OContext.getOrCreate(conf)

You can see that in the case of PySparkling, you need to also specify the username and password as part of the H2OContext call. This is required because you want to have the Python client authenticated as well.

Later when accessing Flow, you will be asked for the username and password of the user you specified in the configuration property spark.ext.h2o.user.name or via the method setUserName.

Proxy only mode

In various deployment scenarios it might be required to make the Flow UI accessible only to the cluster owner, without specifying the password in the configuration beforehand. In those cases please see Proxy Only Authentication