SCALING BIG DATA WITH HADOOP AND SOLR PDF
Scaling Big Data with Hadoop and Solr. This book will provide users with a .. determines the type of file (that is, Word, Excel, or PDF) and extracts the content. Scaling Solr Performance Using Hadoop for Big. Data. Tarun Patel1, Dixa Patel2, Ravina Patel3, Siddharth Shah4. A D Patel for appropriate file in big data and scale the performance of. Solr using . cittadelmonte.info Scaling Big Data with . Chapter 3: Making Big Data Work for Hadoop and Solr. 45 determines the type of file (that is, Word, Excel, or PDF) and extracts the.
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Scaling Big Data with Hadoop and Solr. Second Edition. Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr. PDF | Together, Apache Hadoop and Apache Solr help organizations resolve the problem of information extraction from big data by providing. Scaling Big Data with Hadoop and Solr is a step-by-step guide that helps you build high performance enterprise search engines while scaling.
Second Edition Together, Apache Hadoop and Apache Solr help organizations resolve the problem of information extraction from big data by providing excellent distributed faceted search capabilities. This is a step-by-step guide that will teach you how to build a high performance enterprise search while scaling data with Hadoop and Solr in an effortless manner. Explore industry-based architectures by designing a big data enterprise search with their applicability and benefits Integrate Apache Solr with big data technologies such as Cassandra to enable better scalability and high availability for big data Optimize the performance of your big data search platform with scaling data Write MapReduce tasks to index your data Configure your Hadoop instance to handle real-world big data problems Work with Hadoop and Solr using real-world examples to benefit from their practical usage Use Apache Solr as a NoSQL database. This book is aimed at developers, designers, and architects who would like to build big data enterprise search solutions for their customers or organizations. This book will help you learn everything you need to know to build a distributed enterprise search platform as well as optimize this search to a greater extent, resulting in the maximum utilization of available resources.
It also talks about integration with Cassandra, Apache Blur, Storm, and search analytics. It covers different levels of optimization that you can perform on your Big Data search instance as the data keeps growing.
It discusses different performance improvement techniques that can be implemented by users for the purposes of deployment. Appendix, Use Cases for Big Data Search, discusses some of the most important business cases for high-level enterprise search architecture with Big Data and Solr. Processing Big Data Using Hadoop and MapReduce Continuous evolution in computer sciences has enabled the world to work in a faster, more reliable, and more efficient manner.
Many businesses have been transformed to utilize electronic media. They use information technologies to innovate the communication with their customers, partners, and suppliers. It has also given birth to new industries such as social media and e-commerce. This rapid increase in the amount of data has led to an "information explosion. Processing of these large and complex data using traditional systems and methods is a challenging task. Big Data is an umbrella term that encompasses the management and processing of such data.
Big data is usually associated with high-volume and heavily growing data with unpredictable content. The IT advisory firm Gartner defines big data using 3Vs high volume of data, high velocity of processing speed, and high variety of information.
IBM has added a fourth V high veracity to this definition to make sure that the data is accurate and helps you make your business decisions. While the potential benefits of big data are real and significant, there remain many challenges. So, organizations that deal with such a high volumes of data, must work on the following areas:. Big data poses a lot of challenges to the technologies in use today. Many organizations have started investing in these big data areas.
To handle the problem of storing and processing complex and large data, many software frameworks have been created to work on the big data problem. Among them, Apache Hadoop is one of the most widely used open source software frameworks for the storage and processing of big data. In this chapter, we are going to understand Apache Hadoop. We will be covering the following topics:. Apache Hadoop's ecosystem Apache Hadoop enables the distributed processing of large datasets across a commodity of clustered servers.
It is designed to scale up from a single server to thousands of commodity hardware machines, each offering partial computational units and data storage.
The Apache Hadoop system comes with the following primary components:. The Apache Hadoop distributed file system or HDFS provides a file system that can be used to store data in a replicated and distributed manner across various nodes, which are part of the Hadoop cluster. Apache Hadoop provides a distributed data processing framework for large datasets by using a simple programming model called MapReduce.
A programming task that takes a set of data key-value pair and converts it into another set of data, is called Map Task. The results of map tasks are combined into one or many Reduce Tasks. Overall, this approach towards computing tasks is called the MapReduce approach. The MapReduce programming paradigm forms the heart of the Apache Hadoop framework, and any application that is deployed on this framework must comply with MapReduce programming.
The following figure demonstrates how MapReduce can be used to sort input documents with the MapReduce approach:. MapReduce can also be used to transform data from a domain into the corresponding range.
We are going to look at these in more detail in the following chapters. Hadoop has been used in environments where data from various sources needs to be processed using large server farms.
Hadoop is capable of running its cluster of nodes on commodity hardware, and does not demand any high-end server configuration. With this, Hadoop also brings scalability that enables administrators to add and remove nodes dynamically.
Some of the most notable users of Hadoop are companies like Google in the past , Facebook, and Yahoo, who process petabytes of data every day, and produce rich analytics to the consumer in the shortest possible time.
All this is supported by a large community of users who consistently develop and enhance Hadoop every day. Apache Hadoop 2. The Apache Hadoop 1. X MapReduce framework used concepts of job tracker and task tracker. If you are using the older Hadoop versions, it is recommended to move to Hadoop 2.
This was released in Core components The following diagram demonstrates how the core components of Apache Hadoop work together to ensure distributed exaction of user jobs:. The Resource Manager RM in a Hadoop system is responsible for globally managing the resources of a cluster. Besides managing resources, it coordinates the allocation of resources on the cluster.
RM consists of Scheduler and ApplicationsManager. As the names suggest, Scheduler provides resource allocation, whereas ApplicationsManager is responsible for client interactions accepting jobs and identifying and assigning them to Application Masters. It interacts with RM to negotiate for resources.
The Node Manager NM is responsible for the management of all containers that run on a given node. It keeps a watch on resource usage CPU, memory, and so on , and reports the resource health consistently to the resource manager. The NameNode is the master node that performs coordination activities among data nodes, such as data replication across data nodes, naming system such as filenames, and the disk locations. NameNode stores the mapping of blocks on the Data Nodes.
Scaling Big Data with Hadoop and Solr Second Edition - Sample Chapter
In a Hadoop cluster, there can only be one single active NameNode. Earlier, NameNode, due to its functioning, was identified as the single point of failure in a Hadoop system. To compensate for this, the Hadoop framework introduced SecondaryNameNode, which constantly syncs with NameNode and can take over whenever NameNode is unavailable. DataNodes are nothing but slaves that are deployed on all the nodes in a Hadoop cluster.
DataNode is responsible for storing the application's data. Each uploaded data file in HDFS is split into multiple blocks, and these data blocks are stored on different data nodes. Each Hadoop file block is mapped to two files in the data node; one file is the file block data, while the other is checksum.
When Hadoop is started, each DataNode connects to NameNode informing it of its availability to serve the requests.
When the system is started, the namespace ID and software versions are verified by NameNode and DataNode sends the block report describing all the data blocks it holds for NameNode on startup. During runtime, each DataNode periodically sends a heartbeat signal to NameNode, confirming its availability. The default duration between two heartbeats is 3 seconds.
NameNode assumes the unavailability of DataNode if it does not receive a heartbeat in 10 minutes by default; in which case, NameNode replicates the data blocks of that DataNode to other DataNodes. When a client submits a job to Hadoop, the following activities take place: The AM, once booted, registers itself with the RM.
All the client communication with AM happens through RM. AM launches the container with help of NodeManager. A container that is responsible for executing a MapReduce task reports the progress status to the AM through an application-specific protocol.
On receiving any request for data access on HDFS, NameNode takes the responsibility of returning to the nearest location of DataNode from its repository. Understanding Hadoop's ecosystem Although Hadoop provides excellent storage capabilities along with the MapReduce programming framework, it is still a challenging task to transform conventional programming into a MapReduce type of paradigm, as MapReduce is a completely different programming paradigm.
The Hadoop ecosystem is designed to provide a set of rich applications and development framework. The following block diagram shows Apache Hadoop's ecosystem:.
Let us look at each of the blocks. HDFS is an append-only file system; it does not allow data modification. Apache HBase is a distributed, random-access, and column-oriented database.
However, it provides a command line-based interface, as well as a rich set of APIs to update the data. Apache Pig provides another abstraction layer on top of MapReduce. It's a platform for the analysis of very large datasets that runs on HDFS. It also provides an infrastructure layer, consisting of a compiler that produces sequences of MapReduce programs, along with a language layer consisting of the query language Pig Latin.
Pig was initially developed at Yahoo! Research to enable developers to create ad-hoc MapReduce jobs for Hadoop. Apache Hive provides data warehouse capabilities using big data. The Apache Hadoop framework is difficult to understand, and requires a different approach from traditional programming to write MapReduce-based programs.
With Hive, developers do not write MapReduce at all. Apache Hadoop nodes communicate with each other through Apache ZooKeeper. It forms a mandatory part of the Apache Hadoop ecosystem.
Apache ZooKeeper is responsible for maintaining co-ordination among various nodes. Besides coordinating among nodes, it also maintains configuration information and the group services to the distributed system.
Apache ZooKeeper can be used independent of Hadoop, unlike other components of the ecosystem. Due to its in-memory management of information, it offers distributed co-ordination at a high speed. Apache Mahout is an open source machine learning software library that can effectively empower Hadoop users with analytical capabilities, such as clustering and data mining, over a distributed Hadoop cluster.
Mahout is highly effective over large datasets; the algorithms provided by Mahout are highly optimized to run the MapReduce framework over HDFS.
Apache HCatalog provides metadata management services on top of Apache Hadoop. So, any users or scripts can run on Hadoop effectively without actually knowing where the data is physically stored on HDFS.
HCatalog provides DDL which stands for Data Definition Language commands with which the requested MapReduce, Pig, and Hive jobs can be queued for execution, and later monitored for progress as and when required. Apache Ambari provides a set of tools to monitor the Apache Hadoop cluster, hiding the complexities of the Hadoop framework. It offers features such as installation wizard, system alerts and metrics, provisioning and management of the Hadoop cluster, and job performances. Apache Oozie is a workflow scheduler used for Hadoop jobs.
It can be used with MapReduce as well as Pig scripts to run the jobs. Apache Chukwa is another monitoring application for distributed large systems. Apache Sqoop is a tool designed to load large datasets into Hadoop efficiently. Apache Flume provides a framework to populate Hadoop with data from non-conventional data sources. Typical usage of Apache Fume could be for log aggregation.
Apache Flume is a distributed data collection service that extracts data from the heterogeneous sources, aggregates the data, and stores it into the HDFS. Configuring Apache Hadoop Setting up a Hadoop cluster is a step-by-step process.
It is recommended to start with a single node setup and then extend it to the cluster mode. Apache Hadoop can be installed with three different types of setup:. Single node setup: In this mode, Hadoop can be set up on a single standalone machine. This mode is used by developers for evaluation, testing, basic development, and so on. Pseudo distributed setup: Apache Hadoop can be set up on a single machine with a distributed configuration. In this setup, Apache Hadoop can run with multiple Hadoop processes daemons on the same machine.
Using this mode, developers can do the testing for a distributed setup on a single machine. Fully distributed setup: In this mode, Apache Hadoop is set up on a cluster of nodes, in a fully distributed manner. Typically, production-level setups use this mode for actively using the Hadoop computing capabilities. In Linux, Apache Hadoop can be set up through the root user, which makes it globally available, or as a separate user, which makes it available to only that user Hadoop user , and the access can later be extended for other users.
It is better to use a separate user with limited privileges to ensure that the Hadoop runtime does not have any impact on the running system. Prerequisites Before setting up a Hadoop cluster, it is important to ensure that all prerequisites are addressed.
Hadoop runs on the following operating systems:. In the case of Windows, Microsoft Windows onwards are supported. Apache Hadoop version 2. The older versions of Hadoop have limited support through Cygwin. Java 1. Secure shell ssh is needed to run start, stop, status, or other such scripts across a cluster.
You may also consider using parallel-ssh more information is available at https: Apache Hadoop can be downloaded from http: You can choose to download the package or download the source, compile it on your OS, and then install it.
Using operating system package installer, install the Hadoop package. In the case of a cluster setup, this software should be installed on all the machines. Setting up ssh without passphrase Apache Hadoop uses ssh to run its scripts on different nodes, it is important to make this ssh login happen without any prompt for password. If you already have a key generated, then you can skip this step. To make ssh work without a password, run the following commands: For more information about differences between these two algorithms, visit http: Keep the default file for saving the key, and do not enter a passphrase.
This step will actually create an authorization key with ssh, bypassing the passphrase check as shown in the following screenshot:. Once this step is complete, you can ssh localhost to connect to your instance without password. File Name core-site. This file stores the entire configuration related to HDFS. So, properties like DFS site address, data directory, replication factors, and so on are covered in these files.
This file is responsible for handling the entire configuration related to the MapReduce framework.
This file is required for managing YARN-related configuration. This file is responsible for storing configuration related to the HttpFS server. This file contains information about user allocations and pooling information for the fair scheduler. It is currently under development.
This file is mainly used by the RM in Hadoop for setting up the scheduling parameters of job queues. All the environment variables are defined in this file; you can change any of the environments: This file contains the environment variables used by Hadoop while running MapReduce. In this file, you can modify the default properties of Hadoop.
This covers setting up different protocols for interaction, working directories, log management, security, buffers and blocks, temporary files, and so on. In this file, you can define the hostname for the masters and the slaves. The masters file lists all the masters, and the slaves file lists the slave nodes.
To run Hadoop in the cluster mode, you need to modify these files to point to the respective master and slaves on all nodes. You can define various log levels for your instance; this is helpful while developing or debugging Hadoop programs. You can define levels for logging. This book will help you learn everything you need to know to build a distributed enterprise search platform as well as optimize this search to a greater extent, resulting in the maximum utilization of available resources.
Starting with the basics of Apache Hadoop and Solr, the book covers advanced topics of optimizing search with some interesting real-world use cases and sample Java code. This is a step-by-step guide that will teach you how to build a high performance enterprise search while scaling data with Hadoop and Solr in an effortless manner. Hrishikesh Vijay Karambelkar is an innovator and an enterprise architect with 16 years of software design and development experience, specifically in the areas of big data, enterprise search, data analytics, text mining, and databases.
He is passionate about architecting new software implementations for the next generation of software solutions for various industries, including oil and gas, chemicals, manufacturing, utilities, healthcare, and government infrastructure.
In the past, he has authored three books for Packt Publishing: Sign up to our emails for regular updates, bespoke offers, exclusive discounts and great free content. Log in. My Account. Log in to your account.
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Scaling Big Data with Hadoop and Solr - Second Edition [Book]
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Scaling Big Data with Hadoop and Solr - Second Edition
Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr. Are you sure you want to claim this product using a token? Hrishikesh Vijay Karambelkar April Quick links: What do I get with a Packt subscription? What do I get with an eBook? What do I get with a Video? Frequently bought together. Learn more Add to cart. Apache Solr Search Patterns. Paperback pages. Book Description Together, Apache Hadoop and Apache Solr help organizations resolve the problem of information extraction from big data by providing excellent distributed faceted search capabilities.
Table of Contents Chapter 1: Chapter 2: Understanding Apache Solr. Chapter 3: Enabling Distributed Search using Apache Solr. Chapter 4: Chapter 5: Scaling Search Performance.
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