Cloudera Community

Community Articles

Find and share helpful community-sourced technical articles.
cancel
Turn on suggestions
Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.
Showing results for 
Show  only  | Search instead for 
Did you mean: 
Advanced Search

Running a producer in a kerberized HDP 3.1 Kafka 2.0.0 environment using kafka-python

Labels (1)
Labels:
avatar
author-rank JonathanSneep
Guru

Created on ‎03-25-2019 12:54 PM - edited ‎08-17-2019 02:34 PM

Short Description:

This article covers how to run a simple producer and consumer in kafka-python (1.4.5) on Kafka 2.0.0, within a kerberized HDP 3.1.0 cluster.

Article

Running a producer in a kerberized HDP 3.1 Kafka 2.0.0 environment using kafka-python

  • Covering pre-requisites
  • Validating the kerberos setup of kafka on HDP 3.1
  • Running a producer and consumer in kafka-python 1.4.5 on a kerberized kafka cluster

  • Pre-Requisites

    • You are running a kerberized HDP 3.1.0 cluster with Kafka 2.0.0 installed
    • You are running one of the following python versions: 2.7 , 3.4 , 3.5 , 3.6 , 3.7
    • Note that in the example from this article, I am using SASL_PLAINTEXT. You will need to modify some parameters if you are using SASL_SSL, as well as add some ssl producer/consumer parameters will need to be specified in your kafka-python code in addition.
    • Please also take note of the current kafka-python compatibility notes:

  • Steps

    We will first need to install the gssapi libraries for python in order to be able to use kafka-python with kerberos. We will also need to install kafka-python if you have not already done so. I will be using the latest kafka-python version currently available, which is version 1.4.5 at the time of writing this article.


pip install gssapi
pip install kafka-python


Make sure that Kerberos is correctly enabled for kafka on your cluster. We will want to verify that the kafka broker properties are set to listen on SASL_PLAINTEXT through ambari -> kafka -> configs:

107405-picture1s.png


Also verify that GSSAPI is listed in the sasl.enabled.mechanisms property:

107355-picture2s.png


Next, we obtain a Kerberos token before running our kafka-python producer.

[kafka@c2175-node4 ~]$ kinit -kt /etc/security/keytabs/kafka.service.keytab kafka/c2175-node4.hwx.com@HWX.COM
[kafka@c2175-node4 ~]$ klist Ticket cache: FILE:/tmp/krb5cc_1006 Default principal: kafka/c2175-node4.hwx.com@HWX.COM  
Valid starting       Expires              Service principal 
03/25/2019 11:59:26  03/26/2019 11:59:26  krbtgt/HWX.COM@HWX.COM


We will be running the following code from python:

from kafka import KafkaProducer
import logging
logging.basicConfig(level=logging.DEBUG)
producer = KafkaProducer(bootstrap_servers='c2175-node4.hwx.com:6667', api_version='0.10', security_protocol='SASL_PLAINTEXT', sasl_mechanism='GSSAPI', sasl_kerberos_service_name='kafka', connections_max_idle_ms=1200000, request_timeout_ms=1000000, api_version_auto_timeout_ms=1000000)

for item in range(10):
      producer.send('new_topic', b'Happy '+ str(item)+' Days!')


Let me explain some of the parameters in the above python block.
Also note that in the code example given I’ve enabled debug logging. This is of course not required in order to run the producer but it will help you in case you run into any errors.

  • boostrap_servers
    • List of kafka brokers (FQDN) and port (eg 6667)
  • Api_version
    • Kafka 2.0+ is backwards compatible, within kafka-python 1.4.5 we can’t set the api_version to 2,0,0 as this is not yet available from kafka-python but we can run other versions that are compatible on kafka-python version 1.4.5 with kafka 2.0.0. I used api version 0.10 for the examples given in this article.
  • Security_protocol
    • SASL_PLAINTEXT
      This would be SASL_SSL in case you’re enabling wire encryption on kafka at some stage.
  • Sasl_mechanism
    • GSSAPI
      For this to work, you need to install the GSSAPI libraries for python manually first.
      Plaintext for sasl_mechanism (not to be confused with SASL_plaintext!) WILL NOT WORK.
  • Sasl_kerberos_service_name
    • This defaults to ‘kafka’. If your kafka service is running under a different user, you would have to change this parameter accordingly.

Here is what running the kafka-python on a kerberized HDP 3.1 cluster looks like;

[kafka@c2175-node4 ~]$ klist
Ticket cache: FILE:/tmp/krb5cc_1006
Default principal: kafka/c2175-node4.hwx.com@HWX.COM

Valid starting        Expires              Service principal
03/25/2019 12:24:18   03/26/2019 12:24:18   krbtgt/HWX.COM@HWX.COM
[kafka@c2175-node4 ~]$ python
Python 2.7.5 (default, Apr 11 2018, 07:36:10)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-28)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from kafka import KafkaProducer
>>> import logging
>>> logging.basicConfig(level=logging.DEBUG)
>>> producer = KafkaProducer(bootstrap_servers='c2175-node4.hwx.com:6667', api_version='0.10', security_protocol='SASL_PLAINTEXT', sasl_mechanism='GSSAPI', sasl_kerberos_service_name='kafka', connections_max_idle_ms=1200000, request_timeout_ms=1000000)
DEBUG:kafka.producer.kafka:Starting the Kafka producer
WARNING:kafka.producer.kafka:use api_version=(0, 10) [tuple] -- "0.10" as str is deprecated
DEBUG:kafka.metrics.metrics:Added sensor with name connections-closed
DEBUG:kafka.metrics.metrics:Added sensor with name connections-created
DEBUG:kafka.metrics.metrics:Added sensor with name select-time
DEBUG:kafka.metrics.metrics:Added sensor with name io-time
DEBUG:kafka.metrics.metrics:Added sensor with name bufferpool-wait-time
DEBUG:kafka.metrics.metrics:Added sensor with name batch-size
DEBUG:kafka.metrics.metrics:Added sensor with name compression-rate
DEBUG:kafka.metrics.metrics:Added sensor with name queue-time
DEBUG:kafka.metrics.metrics:Added sensor with name produce-throttle-time
DEBUG:kafka.metrics.metrics:Added sensor with name records-per-request
DEBUG:kafka.metrics.metrics:Added sensor with name bytes
DEBUG:kafka.metrics.metrics:Added sensor with name record-retries
DEBUG:kafka.metrics.metrics:Added sensor with name errors
DEBUG:kafka.metrics.metrics:Added sensor with name record-size-max
DEBUG:kafka.producer.kafka:Kafka producer started
DEBUG:kafka.producer.sender:Starting Kafka producer I/O thread.
>>> DEBUG:kafka.client:Initializing connection to node bootstrap for metadata request
DEBUG:kafka.client:Initiating connection to node bootstrap at 172.25.38.138:6667
DEBUG:kafka.metrics.metrics:Added sensor with name bytes-sent-received
DEBUG:kafka.metrics.metrics:Added sensor with name bytes-sent
DEBUG:kafka.metrics.metrics:Added sensor with name bytes-received
DEBUG:kafka.metrics.metrics:Added sensor with name request-latency
DEBUG:kafka.metrics.metrics:Added sensor with name node-bootstrap.bytes-sent
DEBUG:kafka.metrics.metrics:Added sensor with name node-bootstrap.bytes-received
DEBUG:kafka.metrics.metrics:Added sensor with name node-bootstrap.latency
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <disconnected> [IPv4 None]>: creating new socket
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <disconnected> [IPv4 ('172.25.38.138', 6667)]>: setting socket option (6, 1, 1)
INFO:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <connecting> [IPv4 ('172.25.38.138', 6667)]>: connecting to 172.25.38.138:6667 [('172.25.38.138', 6667) IPv4]
DEBUG:kafka.client:Give up sending metadata request since no node is available
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <connecting> [IPv4 ('172.25.38.138', 6667)]>: established TCP connection
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <connecting> [IPv4 ('172.25.38.138', 6667)]>: initiating SASL authentication
DEBUG:kafka.protocol.parser:Sending request SaslHandShakeRequest_v0(mechanism='GSSAPI')
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <authenticating> [IPv4 ('172.25.38.138', 6667)]> Request 1: SaslHandShakeRequest_v0(mechanism='GSSAPI')
DEBUG:kafka.client:Give up sending metadata request since no node is available
DEBUG:kafka.protocol.parser:Received correlation id: 1
DEBUG:kafka.protocol.parser:Processing response SaslHandShakeResponse_v0
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <authenticating> [IPv4 ('172.25.38.138', 6667)]> Response 1 (1.20496749878 ms): SaslHandShakeResponse_v0(error_code=0, enabled_mechanisms=[u'GSSAPI'])
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <authenticating> [IPv4 ('172.25.38.138', 6667)]>: GSSAPI name: kafka/c2175-node4.hwx.com@HWX.COM
INFO:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <authenticating> [IPv4 ('172.25.38.138', 6667)]>: Authenticated as kafka/c2175-node4.hwx.com@HWX.COM via GSSAPI
INFO:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <authenticating> [IPv4 ('172.25.38.138', 6667)]>: Connection complete.
DEBUG:kafka.client:Node bootstrap connected
DEBUG:kafka.client:Give up sending metadata request since no node is available
DEBUG:kafka.client:Sending metadata request MetadataRequest_v1(topics=NULL) to node bootstrap
DEBUG:kafka.protocol.parser:Sending request MetadataRequest_v1(topics=NULL)
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <connected> [IPv4 ('172.25.38.138', 6667)]> Request 2: MetadataRequest_v1(topics=NULL)
DEBUG:kafka.protocol.parser:Received correlation id: 2
DEBUG:kafka.protocol.parser:Processing response MetadataResponse_v1
DEBUG:kafka.conn:<BrokerConnection node_id=bootstrap host=172.25.38.138:6667 <connected> [IPv4 ('172.25.38.138', 6667)]> Response 2 (4.12797927856 ms): MetadataResponse_v1(brokers=[(node_id=1001, host=u'c2175-node4.hwx.com', port=6667, rack=None)], controller_id=1001, topics=[(error_code=0, topic=u'test3', is_internal=False, partitions=[(error_code=0, partition=0, leader=1001, replicas=[1001], isr=[1001])]), (error_code=0, topic=u'__consumer_offsets', is_internal=True, partitions=[(error_code=0, partition=23, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=41, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=32, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=8, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=17, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=44, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=35, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=26, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=11, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=29, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=38, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=47, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=20, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=2, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=5, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=14, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=46, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=49, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=40, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=4, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=13, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=22, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=31, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=16, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=7, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=43, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=25, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=34, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=10, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=37, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=1, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=19, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=28, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=45, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=36, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=27, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=9, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=18, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=21, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=48, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=12, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=3, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=30, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=39, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=15, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=42, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=24, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=33, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=6, leader=1001, replicas=[1001], isr=[1001]), (error_code=0, partition=0, leader=1001, replicas=[1001], isr=[1001])]), (error_code=0, topic=u'ambari_kafka_service_check', is_internal=False, partitions=[(error_code=0, partition=0, leader=1001, replicas=[1001], isr=[1001])]), (error_code=0, topic=u'foobar', is_internal=False, partitions=[(error_code=0, partition=0, leader=1001, replicas=[1001], isr=[1001])]), (error_code=0, topic=u'new_topic', is_internal=False, partitions=[(error_code=0, partition=0, leader=1001, replicas=[1001], isr=[1001])])])
DEBUG:kafka.cluster:Updated cluster metadata to ClusterMetadata(brokers: 1, topics: 5, groups: 0)

>>> for item in range(10):
...       producer.send('new_topic', b'Happy '+ str(item)+' Days!')
...
DEBUG:kafka.producer.kafka:Sending (key=None value='Happy 0 Days!' headers=[]) to TopicPartition(topic='new_topic', partition=0)
DEBUG:kafka.producer.record_accumulator:Allocating a new 16384 byte message buffer for TopicPartition(topic='new_topic', partition=0)
DEBUG:kafka.producer.kafka:Waking up the sender since TopicPartition(topic='new_topic', partition=0) is either full or getting a new batch
<kafka.producer.future.FutureRecordMetadata object at 0x7f5fc2c99410>
DEBUG:kafka.producer.kafka:Sending (key=None value='Happy 1 Days!' headers=[]) to TopicPartition(topic='new_topic', partition=0)

Similarly, here is what a simple consumer in kafka-python 1.4.5 on an HDP 3.1 kerberized cluster looks like;

from kafka import KafkaConsumer
import logging
logging.basicConfig(level=logging.DEBUG)
consumer = KafkaConsumer('new_topic', bootstrap_servers='c2175-node4.hwx.com:6667', api_version='0.10', security_protocol='SASL_PLAINTEXT', sasl_mechanism='GSSAPI', sasl_kerberos_service_name='kafka', connections_max_idle_ms=1200000, request_timeout_ms=1000000)
for msg in consumer:
    print (msg)



===============================================

If you are running Centos 7.x and encountering the following during the install of gssapi:

Exception: Could not find main GSSAPI shared library.  Please try setting GSSAPI_MAIN_LIB yourself or setting ENABLE_SUPPORT_DETECTION to 'false'

This error is thrown because /bin/krb5-config is missing. To satisfy this requirement:

yum install -y krb5-devel


===============================================

Special thanks to Akshay Mankumbare


8,177 Views
Comments

Re: Running a producer in a kerberized HDP 3.1 Kafka 2.0.0 environment using kafka-python

avatar
author-rank rabah_houaoui-e
New Contributor

Created on ‎03-29-2019 06:26 PM

Hello,

When I try the following tutorial when I launch my KafkaProducer I got the following error : "AttributeError: 'BrokerConnection' object has no attribute '_sock'".

Re: Running a producer in a kerberized HDP 3.1 Kafka 2.0.0 environment using kafka-python

avatar
author-rank JonathanSneep
Guru

Created on ‎04-08-2019 12:53 PM

@Rabah Houaoui

Sounds like maybe an issue from the api_version used. Are you trying this out on HDP 3.1 or a different HDP or HDF version?
Could you share the full error stack?

Re: Running a producer in a kerberized HDP 3.1 Kafka 2.0.0 environment using kafka-python

avatar
author-rank PentaReddy
Expert Contributor

Created on ‎04-20-2020 03:07 PM

Hi All,

 

I am trying to connect from Local machine to kafka cluster(kerberized Cluster) through python. Can anyone help what are the properties to specify for the krb5.conf file and other properties.

 

your help is appreciated.

Announcements
What's New @ Cloudera
[RELEASED] Cloudera Streams Messaging - Kubernetes Operator ...
What's New @ Cloudera
[RELEASED] Cloudera Streaming Analytics 1.14 for Cloudera Pu...
What's New @ Cloudera
Cloudera Data Engineering 1.23: Access Spark from Your Favor...
What's New @ Cloudera
HBase REST server scaling support is Generally Available
What's New @ Cloudera
New CLI option in the update-database command
View More Announcements
Version history
Last update:
‎08-17-2019 02:34 PM
Updated by:
Guru
Contributors