Skip to main content

Remove HDP and Ambari completely

Listen:
Its a bit hard to remove HDP and Ambari completely - so I share my removal script here. Works for me perfect, just adjust the HDFS directory. In my case it was /hadoop
#!/bin/bash
echo "==> Stop Ambari and Hue"
ambari-server stop && ambari-agent stop
/etc/init.d/hue stop
sleep 10
echo "==> Erase HDP and Ambari completely"
yum -y erase ambari-agent ambari-server ambari-log4j hadoop libconfuse nagios ganglia sqoop hcatalog\* hive\* hbase\* zookeeper\* oozie\* pig\* snappy\* hadoop-lzo\* knox\* hadoop\* storm\* hue\*
# remove configs
rm -rf /var/lib/ambari-*/keys /etc/hadoop/ /etc/hive /etc/hbase/ /etc/oozie/ /etc/zookeeper/ /etc/falcon/ /etc/ambari-* /etc/hue/
# remove ambaris default hdfs dir
rm -rf /hadoop
# remove the repos
echo "==> Remove HDP and Ambari Repo"
rm -rf /etc/yum.repos.d/HDP.repo /etc/yum.repos.d/ambari.repo
# delete all HDP related users
echo "==> Delete the user accounts"
userdel -f hdfs && userdel -f sqoop && userdel -f hue && userdel -f yarn && userdel -f hbase && userdel -f && hive userdel -f oozie && userdel -f hcat && userdel -f puppet && userdel -f storm && userdel -f ambari-qa && userdel -f ambari_qa && userdel -f tez && userdel -f flume && userdel -f hadoop_deploy && userdel -f hcatalog && userdel -f zookeeper && userdel -f falcon && userdel -f rrdcached
# remove the unwanted sockets
echo "==> remove the HDFS socket and logs"
rm -rf /var/run/hdfs-sockets
rm -rf /var/log/sqoop2 /var/log/hdfs* /var/log/hadoop-* /var/log/hbase* /var/log/hue* /var/log/nagios /var/log/oozie /var/log/storm /var/log/zookeeper /var/log/falcon /var/log/flume* /var/run/flume-ng/ /var/run/hadoop* /var/run/hbase/ /var/run/hue/ /var/run/nagios/ /var/run/oozie/ /var/run/solr/ /var/run/spark/ /var/run/sqoop2/ /var/run/storm/ /var/run/zookeeper/ 
/var/lib/oozie/
For CDH just follow the guidance here:
http://www.cloudera.com/content/cloudera-content/cloudera-docs/CM5/latest/Cloudera-Manager-Installation-Guide/cm5ig_uninstall_cm.html

And MapR here:


Comments

Popular posts from this blog

Beyond Ctrl+F - Use LLM's For PDF Analysis

PDFs are everywhere, seemingly indestructible, and present in our daily lives at all thinkable and unthinkable positions. We've all got mountains of them, and even companies shouting about "digital transformation" haven't managed to escape their clutches. Now, I'm a product guy, not a document management guru. But I started thinking: if PDFs are omnipresent in our existence, why not throw some cutting-edge AI at the problem? Maybe Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) could be the answer. Don't get me wrong, PDF search indexes like Solr exist, but they're basically glorified Ctrl+F. They point you to the right file, but don't actually help you understand what's in it. And sure, Microsoft Fabric's got some fancy PDF Q&A stuff, but it's a complex beast with a hefty price tag. That's why I decided to experiment with LLMs and RAG. My idea? An intelligent knowledge base built on top of our existing P...

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 in...

MySQL Scaling in 2024

When your MySQL database reaches its performance limits, vertical scaling through hardware upgrades provides a temporary solution. Long-term growth, though, requires a more comprehensive approach. This involves optimizing the database strategically and integrating complementary technologies. Caching The implementation of a caching layer, such as Memcached or Redis , can result in a notable reduction in the load and an increase ni performance at MySQL. In-memory stores cache data that is accessed frequently, enabling near-instantaneous responses and freeing the database for other tasks. For applications with heavy read traffic on relatively static data (e.g. product catalogues, user profiles), caching represents a low-effort, high-impact solution. Consider a online shop product catalogue with thousands of items. With each visit to the website, the application queries the database in order to retrieve product details. By using caching, the retrieved details can be stored in Memcached (a...