openldap-2.3 on CentOS5 Tutorial

I had to get a dummy openldap setup that had “mail” as one of it’s attributes for the records. I specifically needed all the records to live in the root ou, meaning no Organizational Units, just the root, then all the records. Like this:

dn: cn=1,dc=example,dc=com
cn: 1
objectClass: top
objectClass: dkuser
mail: someemail1@somedomain1.com
mailHost: somesmtphostname1:25

dn: cn=2,dc=example,dc=com
cn: 2
objectClass: top
objectClass: dkuser
mail: someemail2@somedomain2.com
mailHost: somesmtphostname2:25

…. and so on.

It was hard to find a step by step instruction set. So, in this tutorial, I’ll give you command by command steps to install, configure and load openldap on a CentOS5 OS.

First, install the packages with Yum:

yum install openldap openldap-clients openldap-servers nss_ldap python-ldap

Next, set ldap to run at system startup time:

/sbin/chkconfig ldap on

Next, get your password for slapd.conf:

cd /etc/openldap/
/usr/sbin/slappasswd

…. it’ll prompt you for a new password, type it twice. All it does is spit out a password that you can copy paste into slapd. Looks like this:

New password:
Re-enter new password:
{SSHA}zskkuz1hd90SyXA4y+zN4AA0FBQorVEd

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Load Balancing Techniques

Load balancing is a term that describes a method to distribute incoming socket connections to different servers. It’s not distributed computing, where jobs are broken up into a series of sub-jobs, so each server does a fraction of the overall work. It’s not that at all. Rather, incoming socket connections are spread out to different servers. Each incoming connection will communicate with the node it was delegated to, and the entire interaction will occur there. Each node is not aware of the other nodes existence.

Why do you need load balancing?
Simple answer: Scalability and Redundancy.

Scalability

If your application becomes busy, resource limits, such as bandwidth, cpu, memory, disk space, disk I/O, and more may reach its limits. In order to remedy such problem, you have two options: scale up, or scale out. Load balancing is a scale out technique. Rather than increasing server resources, you add cost effective, commodity servers, creating a “cluster” of servers that perform the same task. Scaling out is more cost effective, because commodity level hardware provides the most bang for the buck. High end super computers come at a premium, and can be avoided in many cases.

Redundancy

Servers crash, this is the rule, not the exception. Your architecture should be devised in a way to reduce or eliminate single points of failure (SPOF). Load balancing a cluster of servers that perform the same role provides room for a server to be taken out manually for maintenance tasks, without taking down the system. You can also withstand a server crashing. This is called High Availability, or HA for short. Load balancing is a tactic that assists with High Availability, but is not High Availability by itself. To achieve high availability, you need automated monitoring that checks the status of the applications in your cluster, and automates taking servers out of rotation, in response to failure detected. These tools are often bundled into Load Balancing software and appliances, but sometimes need to be programmed independently.

How to perform load balancing?

There are 3 well known ways:

1. DNS based
2. Hardware based
3. Software based

READ FULL ARTICLE Load Balancing Techniques

Parallel Distributed Computing Example

You may have seen article, Hadoop Example, AccessLogCountByHourOfDay. This is a distributed computing solution, using Hadoop. The purpose of this article is to dive into the theory behind this.

To understand the power of distributed computing, we need to step back and understand the problem. First we’ll look at a command line java program that will process each http log file, one file at a time, one line at a time, until done. To speed up the job, we’ll then look at another approach: multi-threaded; we should be able to get the job done faster if we break the job up into a set of sub tasks and run them in parallel. Then, we’ll come to Hadoop, distributed computing. Same concept of breaking the job up into a set of sub tasks, but rather than running with one server, we’ll run on multiple servers in parallel.

At first you’d think that Hadoop would be the fastest, but in our basic example, you’ll see that Hadoop takes isn’t significantly faster. Why? The Hadoop overhead of scheduling the job and tracking the tasks is slowing us down. In order to see the power of Hadoop, we need much larger data sets. Think about our single server approach for a minute. As we ramp up the size and/or number of files to process, there is going to be a point where the server will hit resource limitations (cpu, ram, disk). If we have 4 threads making use of 4 cores of our CPU effectively, we may be able to do a job 4 times faster than single threaded. But, if we have a terabyte of data to process and it takes say 100 second per GB, it’s going to take 100,000 seconds to finish (that’s more than 1 day). With Hadoop, we can scale out horizontally. What if we had a 1000 node Hadoop cluster. Suddenly the overhead of scheduling the job and tracking the tasks is minuscule in comparison to the whole job. The whole job may complete in 100 seconds or less! We went from over a day to less than 2 minutes. Wow.

Please note: the single thread and multi-threaded examples in this article are not using the Map/Reduce algorithm. This is intentional. I’m trying to demonstrate the evolution of thought. When we think about how to solve the problem, the first thing that comes to mind is to walk through the files, one line at a time, and accumulate the result. Then, we realize we could split the job up into threads and gain some speed. The last evolution is is the Map/Reduce algorithm across a distributed computing platform.

Let’s dive in….
READ FULL ARTICLE Parallel Distributed Computing Example

Hadoop Example, AccessLogCountByHourOfDay

Inspired by an article written by Tom White, AWS author and developer:
“Running Hadoop MapReduce on Amazon EC2 and Amazon S3”

Instead of minute of the week, this one does by Hour Of The Day. I just find this more interesting than the minute of the week that’s most popular. The output is:
00\t
…
23\t

The main reason for writing this, however, is to provide a working example that will compile. I found a number of problems in the original post.
READ FULL ARTICLE Hadoop Example, AccessLogCountByHourOfDay

Inspiring MapReduce lectures by Google

Watched a set of 3 lectures run at Google, by Aaron Kimball, on MapReduce was inspiring to me. I feel like I have a much more solid grasp on MapReduce after watching these. I really liked how it started out with some basic functional programming and socket theory, then moved into how MapReduce builds on the basic principle of Map and Fold. Well worth the 3 hours!

http://www.youtube.com/watch?v=yjPBkvYh-ss

Thank you Google and Aaron Kimball!

hadoop-0.18.3 Could not create the Java virtual machine

Installed hadoop on a VM, and needed to set the java heap size, -Xmx1000m, lower than the default 1000 to get it to work.  I set the HADOOP_HEAPSIZE var in the conf/hadoop-env.sh dir to the lower value, but hadoop continued to spit out this error:

# hadoop -help
Could not create the Java virtual machine.
Exception in thread "main" java.lang.NoClassDefFoundError: Could_not_reserve_enough_space_for_object_heap
Caused by: java.lang.ClassNotFoundException: Could_not_reserve_enough_space_for_object_heap
        at java.net.URLClassLoader$1.run(URLClassLoader.java:200)
        at java.security.AccessController.doPrivileged(Native Method)
        at java.net.URLClassLoader.findClass(URLClassLoader.java:188)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:307)
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:301)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:252)
        at java.lang.ClassLoader.loadClassInternal(ClassLoader.java:320)
Could not find the main class: Could_not_reserve_enough_space_for_object_heap.  Program will exit.

Didn’t matter what I set the HADOOP_MAXHEAP to, the problem persisted. I never did find the answer online, so figured I do the world a favor today and make a note about how to fix it. Maybe I’ll save someone else the 2 hours it took me to figure this out!

THE SOLUTION:
READ FULL ARTICLE hadoop-0.18.3 Could not create the Java virtual machine

Holy Smokes, Hadoop works with S3 directly!

bin/hadoop fs -put /path/to/source s3://<s3id>:<s3secret>@<bucket>/path/to/destination

This is so cool. I’m guessing that I could also use S3 as my input or output directory for Map/Reduce jobs.
READ FULL ARTICLE Holy Smokes, Hadoop works with S3 directly!

Hadoop Streaming with PHP

I’ve started my journey with Hadoop, and the first thing I wanted to try was Streaming, so I could run the mapper and reducer methods with PHP programs.

The first thing I did was setup an alias:

alias stream='/usr/local/hadoop/bin/hadoop jar /usr/local/hadoop/contrib/streaming/hadoop-0.18.3-streaming.jar'

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