Jupyterhub integrate with customized Kerberos authenticator

Since I didn’t spend much time on hadoop and spark maintaince, our first party would like to use jupyterhub/lab on hadoop, and their cluster was enhanced security with kerberos and hdfs encryption, so I have to modify jupyterhub/lab to adapt the cluster job submission.

The first party firstly wants to use the SSO login, and somedays they want to use linux pam users to login, so I wrote a custom authenticator to use linux pam login and use the keytab with linux username to authenticate from kerberos in jupyterhub. So the customers could submit their hive or spark jobs without kinit command.

Firstly, the keytab of each user has a fixed filename convention, such as , a principal on dmp-python1 host,  would like xianglei/dmp-python1@GC.COM, and the keytab file should be xianglei.py1.keytab.

And then, the login handler in jupyterhub is jupyterhub/handlers/login.py

change the login.py to login and request a username from customer’s SSO system.


class LoginHandler(BaseHandler):
    """Render the login page."""

    # commented for matrix ai
    args = dict()
    args['contenttype'] = 'application/json'
    args['app'] = 'dmp-jupyterhub'
    args['subkey'] = 'xxxxxxxxxxxxxxxxxxx'

    def _render(self, login_error=None, username=None):
        return self.render_template(
            next=url_escape(self.get_argument('next', default='')),
                {'next': self.get_argument('next', '')},

    async def get(self):
        modify to fit pg's customized oauth2 system, if this method publish with matrix ai, then comment all,
        and write code that only get username from matrixai login form.
        user = self.current_user

        if user:
            # set new login cookie
            # because single-user cookie may have been cleared or incorrect
            # if user exists, set a cookie and jump to next url
            self.redirect(self.get_next_url(user), permanent=False)
            # if user doesnt exists, jump to login page
            # below is original jupyterhub login, commented
            if self.authenticator.auto_login:
                auto_login_url = self.authenticator.login_url(self.hub.base_url)
                if auto_login_url == self.settings['login_url']:
                    # auto_login without a custom login handler
                    # means that auth info is already in the request
                    # (e.g. REMOTE_USER header)
                    user = await self.login_user()
                    if user is None:
                        # auto_login failed, just 403
                        raise web.HTTPError(403)
                    if self.get_argument('next', default=False):
                        auto_login_url = url_concat(
                            auto_login_url, {'next': self.get_next_url()}
            username = self.get_argument('username', default='')
            # below is cusstomized login get
            import json
            import requests
            access_token = self.get_cookie('access_token') # this is the oauth2 access_token
            self.log.info("access_token: " + access_token)
            token_type = self.get_argument('token_type', 'Bearer')
            if access_token != '':
                # use token to request sso address, to get ShortName ShortName is used for kerberos
                userinfo_url = 'https://xxxx.com/paas-ssofed/v3/token/userinfo'
                headers = {
                    'Content-Type': 'application/x-www-form-urlencoded',
                    'Ocp-Apim-Subscription-Key': self.args['subkey'],
                    'Auth-Type': 'ssofed',
                    'Authorization': token_type + ' ' + access_token
                body = {
                    'token': access_token
                resp = json.loads(requests.post(userinfo_url, headers=headers, data=body).text)
                user = resp['ShortName']
                data = dict()
                data['username'] = user
                data['password'] = ''
                # put ShortName into python dict, and call login_user method of jupyterhub
                user = await self.login_user(data)
                self.set_cookie('username', resp['ShortName'])

Do not change the post method of LoginHandler

And then, wrote a custom authenticator class, I named it as GCAuthenticator, it do nothing, just return the username from LoginHandler.And put the file into jupyterhub/garbage_customer/gcauthenticator.py

#!/usr/bin/env python

from tornado import gen
from jupyterhub.auth import Authenticator

class GCAuthenticator(Authenticator):
    1. generate hub config file with command
        jupyterhub --generate-config /path/to/store/jupyterhub_config.py

    2. edit config file, comment
    # c.JupyterHub.authenticator_class = 'jupyterhub.auth.PAMAuthenticator'
    and write a new line
    c.JupyterHub.authenticator_class = 'jupyterhub.garbage_customer.gcauthenticator.GCAuthenticator

    # 入口参数固定写法, 啥也不做, 直接返回用户名, 即为真.这里传递的data, 就是之前在login里面定义的data['username']和data['password']
    # 但是由于验证已经由甲方的sso做了, 所以我们用不到password, 但是格式还是要遵守的.
    # 其实按照这个思路, 自己把这个方法改写成mysql, postgres, 或者文本文件做用户验证, 其实也很简单.
    def authenticate(self, handler, data):
        user = data['username']
        return user


And then, the local kerberos authentication step.

When login successed,  jupyterhub will call spawner method in spawner.py to fork a sub process, the spawner method will call singleuserapp method to create a sub process of notebook and in system process, it will named as singleuser. But actually, the subprocess is created by spawner, so I should extend the spawner method, then I can use the kerberos authenticate and insert the environment variables, this step didn’t need to change spawner.py, only write a new file as a plugin. like gckrbspawner.py


# Save this file in your site-packages directory as krbspawner.py
# then in /etc/jupyterhub/config.py, set:
#    c.JupyterHub.spawner_class = 'garbage_customer.gckrbspawner.KerberosSpawner'

from jupyterhub.spawner import LocalProcessSpawner
from jupyterhub.utils import random_port
from subprocess import Popen,PIPE
from tornado import gen
import pipes


# KerberosSpawner扩展自spawner.py的localProcessSpawner类
class KerberosSpawner(LocalProcessSpawner):
    def start(self):
        if self.ip:
            self.user.server.ip = self.ip # 用户服务的ip为jupyterhub启动设置的ip
            self.user.server.ip = '' # 或者是
        self.user.server.port = random_port() # singleruser server, 也就是notebook子进程, 启动时使用随机端口
        self.log.info('Spawner ip: %s' % self.user.server.ip)
        self.log.info('Spawner port: %s' % self.user.server.port)
        cmd = []
        env = self.get_env() # 获取jupyterhub的环境变量
        # self.log.info(env)
        """ Get user uid and gid from linux"""
        uid_args = ['id', '-u', self.user.name] # 获取当前登录的用户名对应的linux uid
        uid = Popen(uid_args, stdin=PIPE, stdout=PIPE, stderr=PIPE)
        uid = uid.communicate()[0].decode().strip()
        gid_args = ['id', '-g', self.user.name] # 获取当前登录用户对应的 linux gid
        gid = Popen(gid_args, stdin=PIPE, stdout=PIPE, stderr=PIPE)
        gid = gid.communicate()[0].decode().strip()
        self.log.info('UID: ' + uid + ' GID: ' + gid)
        self.log.info('Authenticating: ' + self.user.name)


        self.log.info("Spawning %s", ' '.join(pipes.quote(s) for s in cmd))
        # 使用linux用户认证kerberos用户, 由于 jupyterhub默认使用 /home/username作为每个用户的文件夹, 所以我把用户认证需要的keytab放到每个/home/username下面
        # 例如 xianglei.wb1.keytab ,对应的linux用户就是xianglei, 对应的krb用户就是 xianglei/gc-dmp-workbench1@GC.COM.
        kinit = ['/usr/bin/kinit', '-kt',
                 '/home/%s/%s.wb1.keytab' % (self.user.name, self.user.name,),
                 '-c', '/tmp/krb5cc_%s' % (uid,),
                 '%s/gc-dmp-workbench1@%s' % (self.user.name, REALM)]
        self.log.info("KRB5 initializing with command %s", ' '.join(kinit))
        # 使用subprocess的Popen在spawner里面创建子进程去做krb认证
        Popen(kinit, preexec_fn=self.make_preexec_fn(self.user.name)).wait()

        popen_kwargs = dict(
            start_new_session=True,  # 不转发 signals
        popen_kwargs['env'] = env
        self.proc = Popen(cmd, **popen_kwargs)
        self.pid = self.proc.pid
        # 返回ip和端口号, 交还给jupyterhub server进行子进程服务注册
        return (self.user.server.ip, self.user.server.port)

And add a config into jupyterhub generated config file

c.JupyterHub.spawner_class = 'garbage_customer.gckrbspawner.KerberosSpawner'

In next topic, I will write how to auto refresh kerberos authentication.

jupyterlab and pyspark2 integration in 1 minute

As we use CDH 5.14.0 on our hadoop cluster, the highest spark version to be support is 2.1.3, so this blog is to record the procedure of how I install pyspark-2.1.3 and integrate it with jupyter-lab.

spark 2.1.3
CDH 5.14.0 – hive 1.1.0
Anaconda3 – python 3.6.8

  1. Add export to spark-env.sh
    export PYSPARK_PYTHON=/opt/anaconda3/bin/python
    export PYSPARK_DRIVER_PYTHON=/opt/anaconda3/bin/jupyter-lab
    export PYSPARK_DRIVER_PYTHON_OPTS='  --ip= --port=8890'
  2. install sparkmagic
    pip install sparkmagic
  3. Use conda or pip command to downgrade ipykernel to 4.9.0, cause ipykernel 5.x doesn’t support sparkmagic, it will throw a Future exception.
  4. /opt/spark-2.1.3/bin/pyspark –master yarn

If you need to run with backgrand , use nohup.

if nessasery, add a kernel json at /usr/share/jupyter/kernels/pyspark2 or /usr/local/share/jupyter/kernels/pyspark2, with the content as
"argv": [
"display_name": "Python3.6+PySpark2.1",
"language": "python",
"env": {
"PYSPARK_PYTHON": "/opt/anaconda3/bin/python",
"SPARK_HOME": "/opt/spark-2.1.3-bin-hadoop2.6",
"HADOOP_CONF_DIR": "/etc/hadoop/conf",
"HADOOP_CLIENT_OPTS": "-Xmx2147483648 -XX:MaxPermSize=512M -Djava.net.preferIPv4Stack=true",
"PYTHONPATH": "/opt/spark-2.1.3-bin-hadoop2.6/python/lib/py4j-0.10.7-src.zip:/opt/spark-2.1.3-bin-hadoop2.6/python/",
"PYTHONSTARTUP": "/opt/spark-2.1.3-bin-hadoop2.6/python/pyspark/shell.py",
"PYSPARK_SUBMIT_ARGS": " --jars /opt/spark-2.1.3-bin-hadoop2.6/jars/greenplum-spark_2.11-1.6.2.jar --master yarn --deploy-mode client --name JuPysparkHub pyspark-shell",
"JAVA_HOME": "/opt/jdk1.8.0_141"

Another problem, in pyspark, sqlContext cannot access remote hivemetastore and without any exceptions, when i run show databases in pyspark, it always return me default. And then i found out, in spark2’s jars dir, there was a hive-exec-1.1.0-cdh5.14.0.jar, delete this jar file, everythings ok.

Integrate pyspark and sklearn with distributed parallel running on YARN

Python is useful for data scientists, especially with pyspark, but it’s a big problem to sysadmins, they will install python 2.7+ and spark and numpy,scipy,sklearn,pandas on each node, well, because Cloudera said that. Wow, imaging this, You have a cluster with 1000+ nodes or even 5000+ nodes, although you are good at DevOPS tools such as puppet, fabric, this work still cost lot of time. Continue reading Integrate pyspark and sklearn with distributed parallel running on YARN

Spark read LZO file error in Zeppelin

Due to our dear stingy Party A  said they will add not any nodes to the cluster, so we must compress the data to reduce disk consumption. Actually  I like LZ4, it’s natively supported by hadoop, and the compress/decompress speed is good enough,  compress ratio is better than LZO. But, I must choose LZO finally, no reason.

Well, since we use Cloudera Manager to  install Hadoop and Spark, so it’s no error when read lzo file in command line, simply use as text file, Ex:

val data = sc.textFile("/user/dmp/miaozhen/ott/MZN_OTT_20170101131042_0000_ott.lzo")

But in zeppelin, it will told me: native-lzo library not available, WTF?

Well, Zeppelin is a self-run environment, it will read its configuration only, do not read any other configs, Ex: it will not try to read /etc/spark/conf/spark-defaults.conf . So I must wrote all spark config such as you wrote them in spark-deafults.conf.

In our cluster, the Zeppelin conf looks like this:

Troubleshooting on Zeppelin with keberized cluster

We’ve updated Zeppelin from 0.7.0 to 0.7.1, still work with kerberized hadoop cluster, we use some interpreters in zeppelin, not all. And I wanna write some troubleshooting records with this awesome webtool. BTW: I can write a webtool better than this 1000 times, such as phpHiveAdmin, basically I can see the map/reduce prograss bar Continue reading Troubleshooting on Zeppelin with keberized cluster

Use kerberized Hive in Zeppelin

We deployed Apache Zeppelin 0.7.0 for the Kerberos secured Hadoop cluster, and my dear colleague cannot use it correctly, so I have to find out why he can’t use anything in Zeppelin, except shell command.

I start with Kerberized Hive Continue reading Use kerberized Hive in Zeppelin