Learn Apache Solr with Big Data and Cloud Computing

Learn Apache Solr with Big Data and Cloud Computing, available at $44.99, has an average rating of 3.15, with 55 lectures, based on 388 reviews, and has 2255 subscribers.

You will learn about Integrate Search functionality into any web or mobile app Understand Cloud Solve Search problem of big data You can build your own search engine This course is ideal for individuals who are Developers or Engineers or Data Scientists It is particularly useful for Developers or Engineers or Data Scientists.

Enroll now: Learn Apache Solr with Big Data and Cloud Computing

Summary

Title: Learn Apache Solr with Big Data and Cloud Computing

Price: $44.99

Average Rating: 3.15

Number of Lectures: 55

Number of Published Lectures: 55

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Integrate Search functionality into any web or mobile app

Understand Cloud

Solve Search problem of big data

You can build your own search engine

Who Should Attend

Developers

Engineers

Data Scientists

Target Audiences

Developers

Engineers

Data Scientists

Solr is the popular, blazing fast open source enterprise search platform from the Apache LuceneTMproject. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the worlds largest internet sites.

Solr is written in Java and runs as a standalone full-text search server within a servlet container such as Jetty. 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. Solrs powerful external configuration allows it to be tailored to almost any type of application without Java coding, and it has an extensive plugin architecture when more advanced customization is required.

Solr Features

Solr is a standalone enterprise search server with a REST-like API. You put documents in it (called indexing) via XML, JSON, CSV or binary over HTTP. You query it via HTTP GET and receive XML, JSON, CSV or binary results.

Advanced Full-Text Search Capabilities

Optimized for High Volume Web Traffic

Standards Based Open Interfaces – XML, JSON and HTTP

Comprehensive HTML Administration Interfaces

Server statistics exposed over JMX for monitoring

Linearly scalable, auto index replication, auto failover and recovery

Near Real-time indexing

Flexible and Adaptable with XML configuration

Extensible Plugin Architecture

Solr Uses the LuceneTM Search Library and Extends it!

A Real Data Schema, with Numeric Types, Dynamic Fields, Unique Keys

Powerful Extensions to the Lucene Query Language

Faceted Search and Filtering

Geospatial Search with support for multiple points per document and geo polygons

Advanced, Configurable Text Analysis

Highly Configurable and User Extensible Caching

Performance Optimizations

External Configuration via XML

An AJAX based administration interface

Monitorable Logging

Fast near real-time incremental indexing and index replication

Highly Scalable Distributed search with sharded index across multiple hosts

JSON, XML, CSV/delimited-text, and binary update formats

Easy ways to pull in data from databases and XML files from local disk and HTTP sources

Rich Document Parsing and Indexing (PDF, Word, HTML, etc) using Apache Tika

Apache UIMA integration for configurable metadata extraction

Multiple search indices

Detailed Features

Schema

Defines the field types and fields of documents

Can drive more intelligent processing

Declarative Lucene Analyzer specification

Dynamic Fields enables on-the-fly addition of new fields

CopyField functionality allows indexing a single field multiple ways, or combining multiple fields into a single searchable field

Explicit types eliminates the need for guessing types of fields

External file-based configuration of stopword lists, synonym lists, and protected word lists

Many additional text analysis components including word splitting, regex and sounds-like filters

Pluggable similarity model per field

Query

HTTP interface with configurable response formats (XML/XSLT, JSON, Python, Ruby, PHP, Velocity, CSV, binary)

Sort by any number of fields, and by complex functions of numeric fields

Advanced DisMax query parser for high relevancy results from user-entered queries

Highlighted context snippets

Faceted Searching based on unique field values, explicit queries, date ranges, numeric ranges or pivot

Multi-Select Faceting by tagging and selectively excluding filters

Spelling suggestions for user queries

More Like This suggestions for given document

Function Query – influence the score by user specified complex functions of numeric fields or query relevancy scores.

Range filter over Function Query results

Date Math – specify dates relative to NOW in queries and updates

Dynamic search results clustering using Carrot2

Numeric field statistics such as min, max, average, standard deviation

Combine queries derived from different syntaxes

Auto-suggest functionality for completing user queries

Allow configuration of top results for a query, overriding normal scoring and sorting

Simple join capability between two document types

Performance Optimizations

Core

Dynamically create and delete document collections without restarting

Pluggable query handlers and extensible XML data format

Pluggable user functions for Function Query

Customizable component based request handler with distributed search support

Document uniqueness enforcement based on unique key field

Duplicate document detection, including fuzzy near duplicates

Custom index processing chains, allowing document manipulation before indexing

User configurable commands triggered on index changes

Ability to control where docs with the sort field missing will be placed

Luke request handler for corpus information

Caching

Configurable Query Result, Filter, and Document cache instances

Pluggable Cache implementations, including a lock free, high concurrency implementation

Cache warming in background

When a new searcher is opened, configurable searches are run against it in order to warm it up to avoid slow first hits. During warming, the current searcher handles live requests.

Autowarming in background

The most recently accessed items in the caches of the current searcher are re-populated in the new searcher, enabling high cache hit rates across index/searcher changes.

Fast/small filter implementation

User level caching with autowarming support

SolrCloud

Centralized Apache ZooKeeper based configuration

Automated distributed indexing/sharding – send documents to any node and it will be forwarded to correct shard

Near Real-Time indexing with immediate push-based replication (also support for slower pull-based replication)

Transaction log ensures no updates are lost even if the documents are not yet indexed to disk

Automated query failover, index leader election and recovery in case of failure

No single point of failure

Admin Interface

Comprehensive statistics on cache utilization, updates, and queries

Interactive schema browser that includes index statistics

Replication monitoring

SolrCloud dashboard with graphical cluster node status

Full logging control

Text analysis debugger, showing result of every stage in an analyzer

Web Query Interface w/ debugging output

Parsed query output

Lucene explain() document score detailing

Explain score for documents outside of the requested range to debug why a given document wasnt ranked higher.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: “SOLR” Pronunciation

Chapter 2: Big Data Fundamentals

Lecture 1: What is Big Data

Lecture 2: What Big Data problems Apache Solr solves?

Chapter 3: Cloud Computing Fundamentals

Lecture 1: What is Cloud Computing?

Lecture 2: How does Solr fit into Cloud?

Chapter 4: Fundamentals of Solr

Lecture 1: Apache Solr Architecture

Lecture 2: Downloading and Installing Solr

Lecture 3: Solr basic Files

Lecture 4: Basic solr concepts

Lecture 5: Starting up Solr

Lecture 6: HTTP Requests and Responses with Solr

Lecture 7: Solr Admin UI

Chapter 5: Search Algorithms

Lecture 1: Inverted Index

Lecture 2: Forward Index

Chapter 6: Creating a Core

Lecture 1: Creating a Core via Admin Panel

Lecture 2: Understanding Structure of Schema.xml

Lecture 3: Define fieldType

Lecture 4: Define field

Lecture 5: Field properties

Lecture 6: copyfield

Lecture 7: dynamicfield

Lecture 8: unique fields

Lecture 9: docvalues vs fieldcache

Lecture 10: Analyzers, Tokenizers and Filters

Lecture 11: Character Filters

Chapter 7: Indexing Documents

Lecture 1: Adding documents

Lecture 2: Commit and Optimize

Lecture 3: Deleting Documents

Lecture 4: Updating document Values

Chapter 8: Querying Documents

Lecture 1: Search Fundamentals

Lecture 2: Filter, Fields, Debug and Time Allowed

Lecture 3: Understanding search components and request handlers in solrconfig.xml

Lecture 4: q Parameter in depth

Lecture 5: Range searching

Lecture 6: Function Queries

Lecture 7: Faceting

Lecture 8: Hignlighting

Lecture 9: Spell Checking

Lecture 10: Auto Suggester

Lecture 11: Morelikethis

Lecture 12: Result grouping

Lecture 13: Spatial search, terms component, stats component and query elevation component

Chapter 9: Modifying schema

Lecture 1: Modifying Schema.xml

Chapter 10: Miscellaneous

Lecture 1: Solr Logging

Lecture 2: Solr Security

Chapter 11: Clustering and Replication

Lecture 1: SolrCloud Concepts

Lecture 2: Clustering

Lecture 3: Replication

Chapter 12: ZooKeeper

Lecture 1: Understanding need of Zookeeper

Lecture 2: Setting up ZooKeeper

Lecture 3: Adding More Configs and Collections

Chapter 13: SolrCloud

Lecture 1: Setting Up Solr Cloud

Chapter 14: Final Thoughts

Lecture 1: Conclusion

Lecture 2: Exercise Files

Instructors

QScutter Tutorials
a place to learn technology

Rating Distribution

1 stars: 54 votes

2 stars: 49 votes

3 stars: 108 votes

4 stars: 96 votes

5 stars: 81 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!