HBase major compaction per cronjob

Listen:

Sometimes I get asked how a admin can run a major compaction on a particular table at a time when the cluster isn't usually used.

This can be done per cron, or at. HBase shell needs a ruby script, which is very simple:

# cat m_compact.rb
major_compact 't1'
exit

A working shell script for cron, as example:

# cat daily_compact

#!/bin/bash
USER=hbase
PWD=`echo ~$USER`
TABLE=t1
# kerberos enabled
KEYTAB=/etc/hbase/conf/hbase.keytab
HOST=`hostname`
REALM=ALO.ALT
LOG=/var/log/daily_compact

# get a new ticket
sudo -u $USER kinit -k -t $KEYTAB $USER/$HOST@$REALM
# start compaction
sudo -u $USER hbase shell $PWD/m_compact.rb 2>&1 |tee -a $LOG

All messages will be redirected to /var/log/daily_compact:
11/15/13 06:49:26 WARN conf.Configuration: hadoop.native.lib is deprecated. Instead, use io.native.lib.available
12 row(s) in 0.7800 seconds

Comments

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...

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...

Run Llama3 (or any LLM / SLM) on Your MacBook in 2024

I'm gonna be real with you: the Cloud and SaaS / PaaS is great... until it isn't. When you're elbow-deep in doing something with the likes of ChatGPT or Gemini or whatever, the last thing you need is your AI assistant starts choking (It seems that upper network connection was reset) because 5G or the local WiFi crapped out or some server halfway across the world is having a meltdown(s). That's why I'm all about running large language models (LLMs) like Llama3 locally. Yep, right on your trusty MacBook. Sure, the cloud's got its perks, but here's why local is the way to go, especially for me: Privacy: When you're brainstorming the next big thing, you don't want your ideas floating around on some random server. Keeping your data local means it's yours , and that's a level of control I can get behind. Offline = Uninterrupted Flow: Whether you're on a plane, at a coffee shop with spotty wifi, or jus...

novatechflow

Search This Blog