Showing posts with label CentOS. Show all posts
Showing posts with label CentOS. Show all posts

Flume-Solr Integration

Integrate Flume with Solr. I have created a new sink. This sink is usually used with the regexAll decorators that perform light transformation of event data into attributes. This attributes are converted into solr document and commited in solr.

What is Solr
Solr is an open source enterprise search server based on Lucene. Solr is written in Java and runs as a standalone full-text search server within a servlet container such as Tomcat. Solr uses the Lucene Java search library at its core for full-text indexing and search, and has REST-like HTTP/XML and JSON APIs that make it easy to use from virtually any programming language.

What is Flume
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming
data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. The system is centrally managed and allows for intelligent dynamic management.

I have used flume-0.9.3 and apache-solr-3.1.0 for this POC.

RegexAllExtractor decorator prepare events that contain attributes ready to be written into an Solr. Implementing a RegexAllExtractor decorator is very simple.

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)

Installing Greenplum SNE / CE

After understanding basics of Greenplum, let’s Setup Greenplum Community edition (CE) or single node edition (SNE). In this Greenplum tutorial we will see how to install and Initialize Greenplum Database SNE. This section provides instructions on how to install the Greenplum Database SNE software and get your single-node Greenplum Database SNE system up and running. In the following Greenplum quick start we will use CentOS 5.X or above.
Before installing we have to change following OS configuration parameters:
Set the following parameters in the /etc/sysctl.conf file and reboot:
kernel.shmmax = 500000000
kernel.shmmni = 4096
kernel.shmall = 4000000000
kernel.sem = 250 64000 100 512
Set the following parameters in the /etc/security/limits.conf file:
* soft nofile 65536
* hard nofile 65536
* soft nproc 131072
* hard nproc 131072
Add the Greenplum database Admin account:
# useradd gpadmin
# passwd gpadmin
# New password: password
# Retype new password: password
You cannot run the Greenplum Database SNE server as root. While dealing with Greenplum use this newly created user account
Installing the Greenplum Database SNE / Community edition:
1. Download or copy the Greenplum Database SNE / CE from:
2. Unzip the installer file:
# unzip
3. Launch the installer using bash:
# /bin/bash greenplum-db-
4. The installer prompts you to provide an installation path. Press ENTER to accept the default install path (/usr/local/greenplum-db-, or enter new path
5. The installer installs the Greenplum Database SNE /CE software and creates a greenplum-db symbolic link one directory level above your version-specific Greenplum Database SNE
6. Change the ownership of your Greenplum Database SNE installation so that it is owned by the gpadmin
# chown -R gpadmin /usr/local/greenplum-db-
# chgrp -R gpadmin /usr/local/greenplum-db-
7. Preparing the Data Directory Locations
Every Greenplum Database SNE instance has a designated storage area on disk that is called the data directory location.
8. Create or choose a directory that will serve as your master data storage area
On this location user data is not stored, instead metadata (data about the data) is stored. Here global system catalog resides
# mkdir /gpmaster
# chown gpadmin /gpmaster
# chgrp gpadmin /gpmaster
9. Create or choose the directories that will serve as your segment storage areas:
This is the file system location where the database data is stored.
# mkdir /gpdata1
# chown gpadmin /gpdata1
# chgrp gpadmin /gpdata1
# mkdir /gpdata2
# chown gpadmin /gpdata2
# chgrp gpadmin /gpdata2
10. Configuring Greenplum Database SNE / CE Environment Variables:
$ vi .bashrc
Then add following entry
source /usr/local/greenplum-db/
now source it
$ source ~/.bashrc
11. Now let’s initialize Greenplum database:
Greenplum provides a utility called gpinitsystem which initializes a Greenplum Database system. After the Greenplum Database SNE system is initialized and started, you can then create and manage databases by connecting to the Greenplum master database process.
12. Log in to the system as the gpadmin user:
# su - gpadmin
13. Copy the single_hostlist example file from your Greenplum Database SNE installation to the current directory:
$ cp $GPHOME/docs/cli_help/single_hostlist_example/single_hostlist
14. Copy the gp_init_singlenode example file from your Greenplum Database SNE installation to the current directory:
$ cp $GPHOME/docs/cli_help/gp_init_singlenode_example/gp_init_singlenode
15. Edit the gp_init_singlenode file and enter your configuration settings, you can remain them default. Some default parameters in this file are:
declare -a DATA_DIRECTORY=(/gpdata1 /gpdata2)
16. Run the gpssh-exkeys utility to exchange ssh keys for the local host:
$ gpssh-exkeys -h
17. initialize Greenplum Database SNE:
$ gpinitsystem -c gp_init_singlenode
18. After the Greenplum Database SNE system is initialized and started, you can connect to the Greenplum master database process using the psql client program:
$ createdb mydb
$ psql mydb
19. Now export master data directory:
$ vi .bashrc
Then add following entry
export MASTER_DATA_DIRECTORY=/gpmaster/gpsne-1
now source it
$ source ~/.bashrc
20. Now you can perform any database operations using psql program (DDL, DML)
Uninstall Greenplum:
To uninstall run the following commands:
$ gpdeletesystem -d /gpmaster/gpsne-1
$ rm -rf /usr/local/greenplum-db-
$ rm /usr/local/greenplum-db
You can remove the environment variable and restore the default setting of OS parameters(Optional)

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.