Elasticsearch is the solution to power intelligent search and develop ultra fast web applications. Feel the power of Elasticsearch.

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Introduction to

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What is Elasticsearch?
Elasticsearch is a search server based on Lucene.
It provides a distributed, multitenant-capable
full-text search engine with a RESTful web
interface and schema-free JSON
documents. Elasticsearch is developed in Java
and is released as open source under the terms
of the Apache License.

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What is multi-tenancy?
A cluster can host multiple indices which can be queried
independently or as a group. Index aliases allow you to add
indexes on the fly, while being transparent to your application.

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Elasticsearch Index
ElasticSearch is able to achieve fast search responses because,
instead of searching the text directly, it searches an index
instead. This is like retrieving pages in a book related to a
keyword by scanning the index at the back of a book, as opposed
to searching every word of every page of the book.
This type of index is called an inverted index, because it inverts a
page-centric data structure (page->words) to a keyword-centric
data structure (word->pages).
ElasticSearch uses Apache Lucene to create and manage this
inverted index.

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Mapping in Elasticsearch
• Mapping is the process of defining how a document should be mapped to
the Search Engine, including its searchable characteristics such as which
fields are searchable and if/how they are tokenized. In Elasticsearch, an
index may store documents of different "mapping types". Elasticsearch
allows one to associate multiple mapping definitions for each mapping
type.
• Explicit mapping is defined on an index/type level. By default, there isn’t a
need to define an explicit mapping, since one is automatically created and
registered when a new type or new field is introduced (with no
performance overhead) and have sensible defaults. Only when the
defaults need to be overridden must a mapping definition be provided.

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Distributed Nature of Elasticsearch
Elasticsearch tries hard to hide the complexity of distributed systems. Here
are some of the operations happening automatically under the hood:
• Partitioning your documents into different containers or shards, which can
be stored on a single node or on multiple nodes
• Balancing these shards across the nodes in your cluster to spread the
indexing and search load
• Duplicating each shard to provide redundant copies of your data, to
prevent data loss in case of hardware failure
• Routing requests from any node in the cluster to the nodes that hold the
data you’re interested in
• Seamlessly integrating new nodes as your cluster grows or redistributing
shards to recover from node loss

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Data flow examples
There are different design patterns for different use cases, but
each of them is focusing on how we would like to move the data
around.
• One index - sensible default if we are starting small.
• One index / user - if searches are user-centric, and it might be
extended with routing and aliasing.
• Time-based index - e.g. one index for each day, week or
month. It is easy to make 'last 3 months' alias, old indices can
be optimized, moved or deleted easily.

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How you can master Elasticsearch?
Become an expert in 2 days.
World class Core Elasticsearch corporate training by the
industry experts.
More Details

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Suggested Audience & Other Details
• Suggested Audience - Developers who would like to build
efficient, real-time search solutions.
• Duration – 2 Days
• Prerequisites
– Exposure to or interest in Elasticsearch, relational databases,
distributed systems, or information retrieval.
Syllabus

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For further info/assistance contact
training@springpeople.com
+91 80 656 79700
www.springpeople.com
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