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ElasticSearch – Using Python

Introduction

  • ElasticSearch is a search engine platform that is used to store data and perform various analyses.
  • It is based on the Apache Lucene Library.
  • It stores the data in the document form, which is a non-relational form, basically a complex data structure.
  • It is a distributed storage platform, that is having distributed clusters across the globe so that it can robust and easily accessible.
  • Indexing is done to optimize the data storage and analyses capabilities.
  • It is developed in JAVA, and is supported in many languages like JAVA, .NET, PYTHON, RUBY, C#, etc.
  • It is the most popular search engine database.

Schema-Less

  • Elasticsearch also has the ability to be schema-less.
  • Documents can be indexed without explicitly specifying how to handle each of the different fields that might occur in a document.
  • When dynamic mapping is enabled, Elasticsearch automatically detects and adds new fields to the index.
  • This default behavior makes it easy to index and explore your data.
  • Just start indexing documents and Elasticsearch will detect and map booleans, floating-point, and integer values, dates, and strings to the appropriate Elasticsearch data types.
  • Ultimately, however, you know more about your data and how you want to use it than Elasticsearch can.
  • You can define rules to control dynamic mapping and explicitly define mappings to take full control of how fields are stored and indexed.

Define Your Own Mapping

  • Distinguish between full-text string fields and exact value string fields.
  • Perform language-specific text analysis
  • Optimize fields for partial matching
  • Use custom date formats
  • Use data types such as geo_point and geo_shape that cannot be automatically detected
  • It’s often useful to index the same field in different ways for different purposes. For example, you might want to index a string field as both a text field for full-text search and as a keyword field for sorting or aggregating your data. Or, you might choose to use more than one language analyzer to process the contents of a string field that contains user input.

REST API’s

  • Elasticsearch provides a simple, coherent REST API for managing your cluster and indexing and searching your data.
  • For testing purposes, you can easily submit requests directly from the command line or through the Developer Console in Kibana.
  • From your applications, you can use the Elasticsearch client for your language of choice: Java, JavaScript, Go, .NET, PHP, Perl, Python, or Ruby.

Searching Your DataEdit

  • The Elasticsearch REST APIs support structured queries, full-text queries, and complex queries that combine the two.
  • Structured queries are similar to the types of queries you can construct in SQL.
  • For example, you could search the gender and age fields in your employee index and sort the matches by the hire_date field.
  • Full-text queries find all documents that match the query string and return them sorted by relevance—how good a match they are for your search terms.
  • In addition to searching for individual terms, you can perform phrase searches, similarity searches, and prefix searches, and get autocomplete suggestions.

Geo-Spatial Data

  • Elasticsearch indexes non-textual data in optimized data structures that support high-performance geo and numerical queries.

Coding – Demonstration

  • Create an index for your dataset.
  • Code For checking, deleting and fetching all the indexes.
  • Code for fetching, inserting, and querying dataset.
  • Code to read data from CSV.

References-

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