Skip to main content

Hadoop and trusted MiTv5 Kerberos with Active Directory

Listen:

For actuality here a example how to enable an MiTv5 Kerberos <=> Active Directory trust just from scratch. Should work out of the box, just replace the realms:

HADOOP1.INTERNAL = local server (KDC)
ALO.LOCAL = local kerberos realm
AD.REMOTE = AD realm

with your servers. The KDC should be inside your hadoop network, the remote AD can be somewhere.

1. Install the bits

At the KDC server (CentOS, RHEL - other OS' should have nearly the same bits):
yum install krb5-server krb5-libs krb5-workstation -y

At the clients (hadoop nodes):
yum install krb5-libs krb5-workstation -y

Install Java's JCE policy (see Oracle documentation) on all hadoop nodes.

2. Configure your local KDC


/etc/krb5.conf

[libdefaults]
default_realm = ALO.LOCAL
dns_lookup_realm = false
dns_lookup_kdc = false
kdc_timesync = 1
ccache_type = 4
forwardable = true
proxiable = true
fcc-mit-ticketflags = true
max_life = 1d
max_renewable_life = 7d
renew_lifetime = 7d
default_tgs_enctypes = aes128-cts arcfour-hmac
default_tkt_enctypes = aes128-cts arcfour-hmac

[realms]
ALO.LOCAL = {
kdc = hadoop1.
internal:88
admin_server = hadoop1.internal:749
max_life = 1d
max_renewable_life = 7d
}
AD.REMOTE = {
kdc = ad.remote.internal:88
admin_server = ad.remote.internal:749
max_life = 1d
max_renewable_life = 7d
}

[domain_realm]
alo.local = ALO.LOCAL
.alo.local = ALO.LOCAL

ad.internal = AD.INTERNAL
.ad.internal = AD.INTERNAL

[logging]
kdc = FILE:/var/log/krb5kdc.log
admin_server = FILE:/var/log/kadmin.log
default = FILE:/var/log/krb5lib.log


/var/kerberos/krb5kdc/kdc.conf

[kdcdefaults]
kdc_ports = 88
kdc_tcp_ports = 88

[realms]
ALO.LOCAL = {
#master_key_type = aes256-cts
acl_file = /var/kerberos/krb5kdc/kadm5.acl
dict_file = /usr/share/dict/words
admin_keytab = /var/kerberos/krb5kdc/kadm5.keytab
supported_enctypes = aes256-cts:normal aes128-cts:normal des3-hmac-sha1:normal arcfour-hmac:normal des-hmac-sha1:normal des-cbc-md5:normal des-cbc-crc:normal
}
 
/var/kerberos/krb5kdc/kadm5.acl
*/admin@ALO.ALT *

Create the realm on your local KDC and start the services

kdb5_util create -s -r ALO.LOCAL
service kadmin restart
service krb5kdc restart
chkconfig kadmin on
chkconfig krb5kdc on

Create the admin principal

kadmin.local -q "addprinc root/admin" 

3. Create the MiTv5 trust in AD

Using the Windows - Power(!sic) - Shell
ksetup /addkdc ALO.LOCAL HADOOP1.INTERNAL
netdom trust ALO.LOCAL /DOMAIN: AD.REMOTE /add /realm /passwordt: passw0rd
ksetup /SetEncTypeAttr ALO.LOCAL RC4-HMAC-MD5 AES128-CTS-HMAC-SHA1-96 AES256-CTS-HMAC-SHA1-96 DES-CBC-CRC DES-CBC-MD5

=> On Windows 2003 this works, too:
ktpass /ALO.LOCAL /DOMAIN:AD.REMOTE /TrustEncryp aes128-cts arcfour-hmac

=> On Windows 2008 you have to add:
ksetup /SetEncTypeAttr ALO.LOCAL aes128-cts arcfour-hmac

4. Create the AD trust in MiTv5

kadmin.local: addprinc krbtgt/ALO.LOCAL@AD.REMOTE
password: passw0rd

5. Configure hadoop's mapping rules


core-site.xml

<property>
<name>hadoop.security.auth_to_local</name>
<value>RULE:[1:$1@$0](.*@\QAD.REMOTE\E$)s/@\QAD.REMOTE\E$//
RULE:[2:$1@$0](.*@\QAD.REMOTE\E$)s/@\QAD.REMOTE\E$//
DEFAULT</value>
</property>

Done. Now you should be able to get an ticket from your AD which let you work with your hadoop installation:

#> kinit alo.alt@AD.REMOTE
password:
#> klist
Ticket cache: FILE:/tmp/krb5cc_500
Default principal: alo.alt@AD.REMOTE

Comments

Post a Comment

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this inform

Hive query shows ERROR "too many counters"

A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s

AI's False Reality: Understanding Hallucination

Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unreprese