Showing posts with label Data Center. Show all posts
Showing posts with label Data Center. Show all posts

Hadoop Troubleshooting-001

Hadoop getting following error (java.lang.UnsupportedClassVersionError) when run Hadoop / HDFS Command

bash-3.00$ hadoop  dfs -ls /
DEPRECATED: Use of this script to execute hdfs command is deprecated.
Instead use the hdfs command for it.

Exception in thread "main" java.lang.UnsupportedClassVersionError: Bad version number in .class file
        at java.lang.ClassLoader.defineClass1(Native Method)
        at java.lang.ClassLoader.defineClass(
        at Method)
        at java.lang.ClassLoader.loadClass(
        at sun.misc.Launcher$AppClassLoader.loadClass(
        at java.lang.ClassLoader.loadClass(
        at java.lang.ClassLoader.loadClassInternal(

This is typical java error, which is caused when you run a class that is compiled with newer java version then you have currently in your system. If you are running Hadoop with older version (older than 1.6) you will get this error. Please check the version of java (java -version) and point Hadoop to correct java version.


In this tutorial we will understand the difference between Apche Solr and Elastic search, Before going to actual post lets understand what is Apache Solr and what is Elastic search

Apache Solr Introduction:
Solr is an open source enterprise search server based on Lucene. Developing a high performance, feature rich application that uses Lucene directly is difficult and it’s limited to Java applications. Solr solves this by exposing the wealth of power in Lucene via configuration files and HTTP parameters, while adding some features of its own. Configuration files, most notably for the index’s schema, which defines the fields and configuration of their text analysis.

Elastic Search Introduction: ElasticSearch is a distributed, RESTful, free/open source search server based on Apache Lucene. It is developed by Shay Banon[1] and is released under the terms of the Apache License. ElasticSearch is developed in Java.

To Read complete post please refer

Hbase-A Soft Introduction & Quickstart

After understanding whats is Hadoop, and after deploying hadoop , lets’ start understanding HBase. This tutorial explains basics of HBase, and its features. Here I tried to explain functionality HBase provides and a quick start about HBase, a Basic tutorial for beginners. You will get to know where to use HBase, in which situation HBase can be useful.

Apache HBase
Source: Apache
Understanding What is HBase
HBase is an open source, distributed, versioned, column-oriented, No-SQL / Non-relational database management system that runs on the top of Hadoop. It adds transactional capability to hadoop, allowing users to update data records. Hadoop is designed for batch processing of large dataset, but with HBase on the top of Hadoop we can process real time dataset.

Deploy Hadoop Cluster

Step by Step Tutorial to Deploy Hadoop Cluster (fully distributed mode):
To setup Hadoop in cluster (distributed cluster) requires multiple machines/nodes, one node will act as master and rest all will act as slaves.
If you want Hadoop quick introduction please click here.
If you want to setup hadoop in pseudo distributed mode please click here

In this tutorial:
  • I am using 3 nodes, 1 master 2 slaves
  • I am using Cloudera distribution for Apache hadoop CDH3U3 (you can use Apache hadoop (0.20.X) also)
  • I am deploying hadoop on ubuntu (you can use other OS (cent OS, Redhat, etc))

Install / Setup Hadoop on cluster

Install Hadoop on master:

1. Add entry of master and slaves in hosts file:
Edit hosts file and following add entries
$ sudo pico /etc/hosts
MASTER-IP    master
SLAVE01-IP   slave01
SLAVE02-IP   slave02
(In place of MASTER-IP, SLAVE01-IP, SLAVE02-IP put the value of corresponding IP)

Understanding Greenplum

This blog will guide you to understand Greenplum that includes what is Greenplum, its architecture, different segments, and its basics in details. In this Greenplum tutorial we will try to understand the capability and the architecture provided by Greenplum
What is Greenplum: Greenplum Database is a massively parallel processing (MPP) database server based on PostgreSQL open-source technology. MPP (also known as a shared nothing architecture) refers to systems with two or more processors which cooperate to carry out an operation - each processor with its own memory, operating system and disks.
Source: Greenplum
Greenplum Database Architecture: Greenplum Database utilizes a shared-nothing MPP (massively parallel processing) architecture. In this architecture, data is automatically partitioned across multiple 'segment' servers, and each 'segment' owns and manages a distinct portion of the overall data. All communication is via a network interconnect -- there is no disk-level sharing or contention to be concerned with (i.e. it is a 'shared-nothing' architecture). The segment servers are able to process every query in a fully parallel manner, use all disk connections simultaneously, and efficiently flow data between segments as query plans dictates
Source: Greenplum
Is Greenplum free: Greenplum is not free to setup for production or to setup cluster. But it has launched a community edition which is free but with pre specified guidelines.
What is Community edition: The EMC Greenplum Community Edition (CE) provides a powerful and comprehensive analytic environment enabling users to turn increasingly large amounts of data into useful insight. Developers, data scientists and other data professionals can experiment with real-world data, perform advanced analytics and most importantly - rapidly reveal insights from big data sets with ease
Parallel Query Optimizer: "Greenplum Database's parallel query optimizer is responsible for converting SQL or MapReduce into a physical execution plan." It does this using a cost-based optimization algorithm in which it evaluates a vast number of potential plans and selects the one that it believes will lead to the most efficient query execution.
Parallel Dataflow Engine:  At the heart of the Greenplum Database is the Parallel Dataflow Engine. This is where the real work of processing and analyzing data is done. Greenplum’s Parallel Dataflow Engine is highly optimized at executing both SQL and MapReduce, and does so in a massively parallel manner
 Source: Greenplum
Greenplum Database support (hardware requirements): Greenplum Database supported for production use on SUSE Linux Enterprise Server 10.2 (64-bit),  Red Hat Enterprise Linux 5.x (64-bit), CentOS Linux 5.x (64-bit) and Sun Solaris 10U5+ (64-bit). Greenplum Database 3.3 is supported on server hardware from a range of vendors including HP, Dell, Sun and IBM. Greenplum Database is supported for non-production (development and evaluation) use on Mac OSX 10.5, Red Hat Enterprise Linux 5.2 or higher (32-bit) and CentOS Linux 5.2 or higher (32-bit).
Greenplum Master Segment: "The master is the entry point to the Greenplum Database system. It is the database process that accepts client connections and processes the SQL commands issued by the users of the system". The master is where the global system catalog resides (the set of system tables that contain metadata about the Greenplum Database system itself), however the master does not contain any user data. Data resides only on the segments. The master does the work of authenticating client connections, processing the incoming SQL commands, distributing the work load between the segments, coordinating the results returned by each of the segments, and presenting the final results to the client program
Greenplum Primary Segments: In Greenplum Database, the segments are where the data is stored and where the majority of query processing takes place. User-defined tables and their indexes are distributed across the available number of segments in the Greenplum Database system, each segment containing a distinct portion of the data. Segment instances are the database server processes that serve segments. Users do not interact directly with the segments in a Greenplum Database system, but do so through the master.
Mirror Segment:Mirror segments allow database queries to fail over to a backup segment if the primary segment becomes unavailable. Mirror / secondary segment always resides on a different host than its primary
Source: Greenplum

Create an AMI

This blog will guide you through creating an Ubuntu AMI (Amazon Machine Image) from a launched Instance. In this tutorial we will create S3 backed AMI from running instance (Ubuntu). Before getting down to create an actual AMI let’s try to understand some basic terminologies:

Understand what AMI is: An Amazon Machine Image (AMI) is a special type of virtual appliance which is used to instantiate (create) a virtual machine within the Amazon Elastic Compute Cloud. It serves as the basic unit of deployment for services delivered using EC2. We can say that AMI is an image from which an instance can boot.

What is Amazon EC2: Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers
Create your own AMI, so that you can boot new custom instance which have all the required software preinstalled. Your AMI becomes basic unit of deployment; it will save your time of installing required software again and again.

Understanding What is Hadoop

What is Hadoop:
Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System and of MapReduce. HDFS is a highly fault-tolerant distributed file system and like Hadoop designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications that have large data sets (In the range of terabytes to zetabytes).